From 04fd860f0c979bb571f06f3c0a4b506f29b99113 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Wed, 28 Feb 2024 11:11:07 -0500 Subject: [PATCH 01/21] Add files via upload --- inst/pages/IntegratedLearner.qmd | 440 +++++++++++++++++++++++++++++++ 1 file changed, 440 insertions(+) create mode 100644 inst/pages/IntegratedLearner.qmd diff --git a/inst/pages/IntegratedLearner.qmd b/inst/pages/IntegratedLearner.qmd new file mode 100644 index 000000000..a7e3f21c9 --- /dev/null +++ b/inst/pages/IntegratedLearner.qmd @@ -0,0 +1,440 @@ +# Multi-omics Integration {#sec-multi-omics-integration} + +```{r setup, echo=FALSE, results="asis"} +library(rebook) +chapterPreamble() +``` + +## Multi-omics Integration + +In multiview data analysis, there are two main types of approaches: early fusion and late fusion. \underline{Early fusion} combines all datasets into a single representation, which then serves as input for a supervised learning model. In contrast, \underline{late fusion} builds individual models for each data view and combines their predictions using a second-level model as the final predictor. However, these traditional paradigms treat the data views in isolation and do not allow for interactions or dependencies between them. A more advanced method, called cooperative learning (Ding et al., 2022, \emph{PNAS}$^1$), which is also known as \underline{intermediate fusion}, combines the best of both worlds by encouraging predictions from different data views to align through an agreement parameter ($\rho$). + +## Multi-omics Prediction and Classification of Binary IBD Disease Status + +In this chapter, we showcase examples of various integration paradigms (early, late, and intermediate fusion) using the R packages `multiview`$^1$ and `SuperLearner`$^2$. We make use of the publicly available source code from the multi-omics integrated framework (`IntegratedLearner`) proposed by Mallick et al. (2024)$^3$. + +## Input data +We use the publicly available Inflammatory Bowel Diseases (IBD) data from the curatedMetagenomicData package (Lloyd-Price et al., 2019)$^4$, where we aim to predict IBD disease status based on both taxonomic (species abundances) and functional (pathway abundances) profiles. + +```{r message=FALSE, warning=FALSE} + +##################### +# Load example data # +##################### + +library(tidyverse) +library(curatedMetagenomicData) +library(curatedMetagenomicAnalyses) + +cond <- 'IBD' +se_relative <-curatedMetagenomicAnalyses::makeSEforCondition(cond, + dataType = "relative_abundance", + removestudies = c('HallAB_2017', 'IaniroG_2022', 'IjazUZ_2017', 'LiJ_2014', 'NielsenHB_2014', 'VilaAV_2018')) +se_pathway <-curatedMetagenomicAnalyses::makeSEforCondition(cond, + dataType = "pathway_abundance", + removestudies = c('HallAB_2017', 'IaniroG_2022', 'IjazUZ_2017', 'LiJ_2014', 'NielsenHB_2014', 'VilaAV_2018')) + +``` + +We will first prepare the input sample metadata and feature table for both relative abundance and pathway abundance data. + +```{r} + +########################## +# Create sample metadata # +########################## + +sample_metadata <- dplyr::select(as.data.frame(colData(se_relative)), c("study_name", "disease", "subject_id")) + +# Define response variable & sample id +sample_metadata$Y <- ifelse(sample_metadata$disease == "IBD", 1, 0) +sample_metadata<- sample_metadata %>% dplyr::select(subject_id, Y)%>% rename(subjectID = subject_id) + +########################### +# Create Species Features # +########################### + +feature_species <- as.data.frame(assay(se_relative)) +rownames(feature_species) <- sub('.*s__', '', rownames(feature_species)) + +########################### +# Create Pathway Features # +########################### + +feature_pwys <- as.data.frame(assay(se_pathway)) +feature_pwys <- rownames_to_column(feature_pwys, "ID") +feature_pwys <- feature_pwys %>% + filter(!grepl("\\|", ID)) %>% + filter(!ID %in% c('UNMAPPED', 'UNINTEGRATED')) %>% + column_to_rownames('ID') %>% + as.data.frame() + +################################ +# Combine Species and Pathways # +################################ + +feature_table <- bind_rows(feature_species, feature_pwys) + +######################################################## +# Check row names of feature_table and sample_metadata # +######################################################## + +all(rownames(feature_table) == rownames(sample_metadata)) + +``` + +We will then create a metadata table for the features. This table captures high-level information related to the features (e.g., which layer they belong to). + +```{r} + +###################################### +# Create metadata table for features # +###################################### + +rowID <- rep(c('Species', 'Pathways'), c(nrow(feature_species), nrow(feature_pwys))) +feature_metadata <- cbind.data.frame(featureID = rownames(feature_table), featureType = rowID) +rownames(feature_metadata) <- feature_metadata$featureID + +################ +# Sanity check # +################ + +all(rownames(feature_table) == rownames(feature_metadata)) # TRUE +all(colnames(feature_table) == rownames(sample_metadata)) # TRUE + +``` + +Further data pre-processing is necessary to handle near-zero-variance features in this dataset. Ultimately, 483 features are retained, consisting of 360 pathways and 123 species. A combination of variance and prevalence filtering is applied to the feature table, while related metadata is cleaned to ensure the retention of matching samples. + +```{r} + +library(caret) + +################# +# feature_table # +################# + +feature_table_t <- as.data.frame(t(feature_table)) +abd_threshold = 0 +prev_threshold = 0.1 +nzv <- nearZeroVar(feature_table_t) +features_var_filtered <- feature_table_t[, -nzv] +features_var_filtered<-as.data.frame(features_var_filtered) +features_filtered <- features_var_filtered[, colSums(features_var_filtered > abd_threshold) > nrow(features_var_filtered) * prev_threshold] +feature_table <- as.data.frame(t(features_filtered)) + +#################### +# feature_metadata # +#################### + +feature_metadata <- feature_metadata[rownames(feature_table), ] +table(feature_metadata$featureType) + +###################### +# Sanity check again # +###################### + +all(rownames(feature_table) == rownames(feature_metadata)) # TRUE +all(colnames(feature_table) == rownames(sample_metadata)) # TRUE + +``` + +Following the preprocessing, we conduct additional input data preparation, which includes configuring parameters for sample splitting and 5-fold cross-validation. It's important to note that this dataset contains repeated measures (i.e., multiple samples per subject). To address this, we will conduct 5-fold cross-validation at the subject level. The source code is derived from the `IntegratedLearner` R package$^3$. + +```{r} +# Set parameters and extract subject IDs for sample splitting +seed <- 1 +set.seed(seed) +subjectID <- unique(sample_metadata$subjectID) + +################################## +# Trigger 5-fold CV (Outer Loop) # +################################## + +subjectCvFoldsIN <- caret::createFolds(1:length(subjectID), k = 5, returnTrain=TRUE) + +######################################### +# Curate subject-level samples per fold # +######################################### + +obsIndexIn <- vector("list", 5) +for(k in 1:length(obsIndexIn)){ + x <- which(!sample_metadata$subjectID %in% subjectID[subjectCvFoldsIN[[k]]]) + obsIndexIn[[k]] <- x +} +names(obsIndexIn) <- sapply(1:5, function(x) paste(c("fold", x), collapse = '')) + +############################### +# Set up data for SL training # +############################### + +cvControl = list(V = 5, shuffle = FALSE, validRows = obsIndexIn) + +################################################# +# Stacked generalization input data preparation # +################################################# + +feature_metadata$featureType <- as.factor(feature_metadata$featureType) +name_layers <- with(droplevels(feature_metadata), list(levels = levels(featureType)), nlevels = nlevels(featureType))$levels +SL_fit_predictions <- vector("list", length(name_layers)) +SL_fit_layers <- vector("list", length(name_layers)) +names(SL_fit_layers) <- name_layers +names(SL_fit_predictions) <- name_layers +X_train_layers <- vector("list", length(name_layers)) +names(X_train_layers) <- name_layers +layer_wise_predictions_train <- vector("list", length(name_layers)) +names(layer_wise_predictions_train) <- name_layers + +``` + +Next, we run the late fusion algorithm described in Mallick et al., 2024$^3$. To this end, we subset the data for each feature type and conduct the analysis on each layer for the first-stage learning to predict the outcome. In other words, we fit a machine learning algorithm per layer (called a 'base learner'), utilizing the SuperLearner R package. + +```{r} + +######################################################## +# Subset data per omics and run each individual layers # +######################################################## + +for (i in seq_along(name_layers)){ + + # Prepare single-omic input data + include_list <- feature_metadata %>% dplyr::filter(featureType == name_layers[i]) + t_dat_slice <- feature_table[rownames(feature_table) %in% include_list$featureID, ] + dat_slice <- as.data.frame(t(t_dat_slice)) + Y = sample_metadata$Y + X = dat_slice + X_train_layers[[i]] <- X + + # Run user-specified base learner + library(SuperLearner) + + SL_fit_layers[[i]] <- SuperLearner::SuperLearner(Y = Y, + X = X, + cvControl = cvControl, + SL.library = c('SL.randomForest'), + family = binomial()) + + # Append the corresponding y and X to the results + SL_fit_layers[[i]]$Y <- sample_metadata['Y'] + SL_fit_layers[[i]]$X <- X + + # Remove redundant data frames and collect pre-stack predictions + rm(t_dat_slice) + rm(dat_slice) + rm(X) + SL_fit_predictions[[i]] <- SL_fit_layers[[i]]$Z + + # Re-fit to entire dataset for final predictions + layer_wise_predictions_train[[i]] <- SL_fit_layers[[i]]$SL.predict + +} + +``` + +The next step of the analysis is to combine the layer-wise cross-validated predictions with a meta-model (meta_learner) to generate final predictions based on all available data points for the stacked model. Here, we will use `glmnet` as the meta-learner. However, other choices are also possible. + +```{r} +############################## +# Prepare stacked input data # +############################## + +combo <- as.data.frame(do.call(cbind, SL_fit_predictions)) +names(combo) <- name_layers + +############################### +# Set aside final predictions # +############################### + +combo_final <- as.data.frame(do.call(cbind, layer_wise_predictions_train)) +names(combo_final) <- name_layers + +#################### +# Stack all models # +#################### + +# Run user-specified meta learner +SL_fit_stacked<-SuperLearner::SuperLearner(Y = Y, + X = combo, + cvControl = cvControl, + verbose = TRUE, + SL.library = 'SL.glmnet', + family = binomial()) + +# Extract the fit object from superlearner +model_stacked <- SL_fit_stacked$fitLibrary[[1]]$object +stacked_prediction_train <- predict.SuperLearner(SL_fit_stacked, newdata = combo_final)$pred + +# Append the corresponding y and X to the results +SL_fit_stacked$Y <- sample_metadata['Y'] +SL_fit_stacked$X <- combo + +``` + +In contrast to late fusion, in early fusion, we will simply supply a combined representation of the data and we will use the `random forest` method for building an integrated prediction model. + +```{r} + +##################################### +# EARLY FUSION (CONCATENATED MODEL) # +##################################### + +fulldat<-as.data.frame(t(feature_table)) + +# Early Fusion using Random Forest +SL_fit_concat <- SuperLearner::SuperLearner(Y = Y, + X = fulldat, + cvControl = cvControl, + SL.library = 'SL.randomForest', + family = binomial()) + +# Extract the fit object from SuperLearner +model_concat <- SL_fit_concat$fitLibrary[[1]]$object + +# Append the corresponding y and X to the results +SL_fit_concat$Y <- sample_metadata['Y'] +SL_fit_concat$X <- fulldat + +``` + +Finally, we consider the intermediate fusion approach, which combines ideas from both late fusion and early fusion by integrating the usual squared-error loss of predictions with an "agreement" (fusion) penalty ($\rho$) so that the predictions from different data views agree. + +The intermediate fusion adjusts the degree of fusion in an adaptive manner, where the test set prediction error is estimated with a cross-validation method. By varying the weight of the fusion penalty - hyperparameter $\rho$, we obtain the early and late fusion approaches as special cases. If $\rho = 0$, we have a simple form of early fusion; if $\rho = 1$, we obtain a simple form of late fusion. For $0 < \rho < 1$, we obtain the intermediate fusion. + +Here, as examples, we will prepare the input data, fit the multiview model, and run the cross-validation in this section. + +```{r} +############################## +# Prepare data for multiview # +############################## + +# Separate omics layers +feature_metadata$featureType <- as.factor(feature_metadata$featureType) +name_layers <- with(droplevels(feature_metadata), list(levels = levels(featureType)), nlevels = nlevels(featureType))$levels + +# Define a list +dataList <- vector("list", length = length(name_layers)) +names(dataList) <- name_layers +table(feature_metadata$featureType) +dataList[[1]] <- t(feature_table[1:123, ]) +dataList[[2]] <- t(feature_table[124:nrow(feature_table), ]) + +# Extract y and X's +dataList <- lapply(dataList, as.matrix) +dataList <- lapply(dataList, scale) + +######################## +# Run cross-validation # +######################## + +set.seed(1234) +library(multiview) +library(glmnet) + +cvfit <- cv.multiview(dataList, Y, family = binomial(), alpha = 0.5) +DD <- as.data.frame(as.matrix(coef_ordered(cvfit, s="lambda.min", alpha = 0.5))) +DD$standardized_coef <- as.numeric(DD$standardized_coef) +DD$coef <- as.numeric(DD$coef) + +``` + +In this section, we visualize the prediction performance by gathering all predictions and extracting the data necessary for ROC plotting, corresponding to each integration paradigm. + +```{r} +######################################################### +# Gather all predictions and ROC curve data preparation # +######################################################### + +coop_pred <- predict(cvfit, newx = dataList, s = "lambda.min", alpha = 0.5, type = "response") +yhat.train <- cbind(combo, stacked_prediction_train, SL_fit_concat$Z, coop_pred) +colnames(yhat.train) <- c(colnames(combo), "Late Fusion", "Early Fusion", "Intermediate Fusion") + +# Extract ROC plot data +list.ROC <- vector("list", length = ncol(yhat.train)) +names(list.ROC) <- colnames(yhat.train) + +# Loop over layers +for(k in 1:length(list.ROC)){ + preds <- yhat.train[ ,k] + pred <- ROCR::prediction(preds, Y) + AUC <- round(ROCR::performance(pred, "auc")@y.values[[1]], 2) + perf <- ROCR::performance(pred, "sens", "spec") + list.ROC[[k]] <- data.frame(sensitivity = methods::slot(perf, "y.values")[[1]], + specificity = 1 - methods::slot(perf, "x.values")[[1]], + AUC = AUC, + layer = names(list.ROC)[k]) + } + +# Combine +ROC_table <- do.call('rbind', list.ROC) + +``` + +Based on the ROC plot described below, we observe that the AUC is 0.65 when considering only the pathway abundance data in the model, and 0.64 for the model including only the species abundance data. The AUC increases to 0.67 when using the early fusion model and reaches 1 with the late fusion model. In contrast, the intermediate fusion model achieves an AUC of 0.99. Overall, most integrated classifiers outperform individual layers in distinguishing between IBD and non-IBD controls. + + +```{r} +# Prepare data for plotting +plot_data <- ROC_table +plot_data$displayItem <- paste(plot_data$layer, " AUC = ", plot_data$AUC, sep="") +plot_data$displayItem <- factor(plot_data$displayItem, + levels = unique(plot_data$displayItem)) + +# ROC curves +p <- ggplot(plot_data, + aes(x = specificity, + y = sensitivity, + group = displayItem)) + + geom_line(aes(x = specificity,y = sensitivity,color = displayItem)) + + theme(legend.position = "bottom", + legend.background=element_blank(), + legend.box.background=element_rect(colour = "black")) + + theme_bw() + + xlab("False Positive Rate") + + ylab("True Positive Rate") + + theme(legend.position = "right", legend.direction = "vertical") + + labs(color = '') + +# Print +print(p) + +``` + +Finally, we visualize the results for the top 20 features for each layer based on the intermediate fusion. + +```{r} +# Only plot UP TO TOP 20 PER LAYER +DD <- DD %>% + group_by(view) %>% + top_n(n = 20, wt = abs(standardized_coef)) + +# Visualization +library(forcats) + +p <- DD %>% + mutate(view_col = forcats::fct_reorder(view_col, standardized_coef)) %>% + ggplot(aes(x = view_col, y = standardized_coef, fill = view, width = 0.75)) + + geom_bar(stat = "identity", show.legend = FALSE, width = 1) + + coord_flip() + + facet_wrap(~ view, scales = 'free_y', nrow = 2) + + ylab('Standardized LFC (2 years vs. baseline)') + + xlab('') + + ggtitle('IBD-associated multi-omics features') + + theme_bw() + theme(strip.background = element_blank(), + panel.grid.major = element_line(colour = "grey80"), + panel.border = element_blank(), + axis.ticks = element_line(size = 0), + panel.grid.minor.y = element_blank(), + panel.grid.major.y = element_blank()) +p + +``` +Based on the bar plot, we observe that \emph{Proteobacteria_bacterium_CAG.139} is the species most positively associated, while \emph{Gemmiger_formicilis} is the most negatively associated species. Additionally, L-lysine biosynthesis VI is the pathway most positively associated, and PYRIDNUSCYN-PWY: NAD biosynthesis I (from aspartate) is the pathway most positively associated, respectively. + +## Reference +1. Ding DY, Li S, Narasimhan B, Tibshirani R. Cooperative learning for multiview analysis. Proc Natl Acad Sci U S A. 2022;119(38):e2202113119. doi:10.1073/pnas.2202113119. +2. van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007;6:Article25. doi:10.2202/1544-6115.1309. +3. Mallick H, Porwal A, Saha S, Basak P, Svetnik V, Paul E. An integrated Bayesian framework for multi-omics prediction and classification. Stat Med. Published online December 26, 2023. doi:10.1002/sim.9953. +4. Lloyd-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. 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zy}Ti;yTO)*b@iqH_EA=UEsHX|U{r_T!U|CO(<}WSc2g`65#jeK+5ES!@$*0Q>tOx& zxBJ(@`Zu;<{XhLWSicU|%9;3Ov3_oX|H+yBa)tlBU;A~iejTip)AB3E`tJkrU%~r7 m8Q!0V-M Date: Wed, 28 Feb 2024 11:22:37 -0500 Subject: [PATCH 03/21] Add files via upload --- inst/pages/MMUPHin_meta_analysis.qmd | 418 +++++++++++++++++++++++++++ 1 file changed, 418 insertions(+) create mode 100644 inst/pages/MMUPHin_meta_analysis.qmd diff --git a/inst/pages/MMUPHin_meta_analysis.qmd b/inst/pages/MMUPHin_meta_analysis.qmd new file mode 100644 index 000000000..4081e1bb1 --- /dev/null +++ b/inst/pages/MMUPHin_meta_analysis.qmd @@ -0,0 +1,418 @@ +# Meta-analyses {#sec-meta-analyses} + +```{r setup, echo=FALSE, results="asis"} +library(rebook) +chapterPreamble() +``` + +Meta-analysis is a statistical analysis that combines the results of multiple scientific studies by appropriately weighting the individual study-specific effect sizes$^{1, 2, 3}$. A common meta-analytic combination strategy is to take a weighted average of the study-specific summary measures. In fixed-effects meta-analysis, the weights are based on the assumption that there is a single true parameter underlying all the studies, while in random-effects meta-analysis, the weights are based on a model where the true parameter varies across studies according to a probability distribution. + +Meta-analysis is particularly useful when individual studies are small, and a joint analysis of these studies can enhance the precision of effect size estimation and improve statistical power. Especially in microbiome studies, individual effect sizes from each study tend to be small$^{4, 5, 6}$. Only through meta-analysis can these small effects reach statistical significance. Therefore, by jointly analyzing these studies, we amplify the collective voice of the data, offering a more comprehensive understanding of the effects under investigation. + +In this chapter, our focus is on using the R/Bioconductor package [MMUPHin]$^6$ (Meta-Analysis Methods with a Uniform Pipeline for Heterogeneity in microbiome studies) to perform the meta-analysis of microbiome data. We utilize publicly available cancer microbiome datasets from the R/Bioconductor package [curatedMetagenomicData]$^7$ + +## Meta-analysis of cancer microbiome studies using relative abundance data + +Recent work has indicated a possible involvement of the gut microbiome in influencing the efficacy of immune checkpoint inhibitor (ICI) treatment strategies for PD-1/PD-L1 (Programmed Cell Death Protein 1/Programmed Cell Death Ligand 1) targeting checkpoint inhibitors$^{8, 9, 10, 11, 12}$. Several investigations into the microbiome have provided clinical data suggesting that the gut microbiome modulates the response to inhibitors of the PD-1/PD-L1 axis. However, published cancer microbiome studies face challenges, such as limited sample sizes, inconsistent data analysis methods, and a lack of functionally relevant consensus signatures. + +Elucidation of the mechanisms by which the gut microbiome alters the function of the immune system to enable or promote cancer development may reveal novel pathways to explore in cancer therapy. Consequently, a high-quality re-analysis of public cancer microbiome data through a systematic meta-analysis approach could provide valuable insights into the microbiome's role in cancer development and progression in a rapid and cost-effective manner. + +Using advanced melanoma as a model, extensively studied in the context of PD-1/PD-L1 immune checkpoint inhibitors, our aim is to identify functional biomarkers positively and negatively associated with ICI response by analyzing both taxonomic and functional profiles with ICI response. + +```{r load_curatedMetagenomicData} +library(curatedMetagenomicData) +library(curatedMetagenomicAnalyses) +``` +In the following section, we provide detailed examples of how to perform batch effect correction and meta-analytic differential abundance testing on publicly available cancer microbiome data from multiple studies. We use the R package MMUPHin for these tasks. MMUPHin is a recently developed R/Bioconductor package designed for meta-analysis of microbiome taxonomic and functional profiles. It is agnostic to the data type and leverages another R/Bioconductor package, MaAsLin2$^8$, as a backend to conduct the meta-analysis. MaAsLin2 is particularly designed to be applicable to various microbial community data types (taxonomy or functional profiles) and environments (human or otherwise). It is modular, including implementations of alternative normalization/transformation schemes and statistical models (e.g., for amplicon vs. shotgun metagenomic profiles). Leveraging this flexible framework under the hood, MMUPHin performs normalization, batch effect correction, and meta-analysis using the default random effects meta-regression. + +Here, we will first perform the batch effect correction analysis. We use both the relative abundance data and the pathway abundance data from 5 public datasets: Frankel et al. (2017)$^9$, LeeK et al. (2022)$^10$, Matson et al. (2018)$^11$, Peters et al. (2019)$^12$, and Wind et al. (2020)$^13$. All these datasets are available from the curatedMetagenomicData package, with a total of 285 subjects included. + +```{r load-pkg-data} +# Load example data +cond <- 'melanoma' +se_relative <-curatedMetagenomicAnalyses::makeSEforCondition(cond, removestudies = "HMP_2019_ibdmdb", dataType = "relative_abundance") +se_pathway <-curatedMetagenomicAnalyses::makeSEforCondition(cond, removestudies = "HMP_2019_ibdmdb", dataType = "pathway_abundance") + +``` +First, let's look into the relative abundance data. + +### Performing batch (study) effect adjustment with adjust_batch for relative abundance {#sec-batch-effect-adjustment} + +In this analysis, we are using two input files. The first input file is the relative abundance data file, which consists of a feature-by-sample matrix. The second input is the metadata associated with the relative abundance file, which includes the study names and combines overall response rate (ORR), progression-free survival at 12 months (PFS12), as well as the response to anti-PD-1 (anti_PD_1) as the response variable. By the end of this step, we obtain a batch-adjusted relative abundance matrix. + +```{r message=FALSE, warning=FALSE} +# Load library +library(dplyr) +library(MMUPHin) + +# Prepare the meta data +data_meta <- select(as.data.frame(colData(se_relative)), c("study_name", "ORR", "PFS12", "anti_PD_1")) + +# Define response variable +data_meta$resvar <- NA +data_meta$anti_PD_1[data_meta$anti_PD_1 == "responder"] <- "yes" +data_meta$anti_PD_1[data_meta$anti_PD_1 == "non_responder"] <- "no" +data_meta$resvar[!is.na(data_meta$anti_PD_1)] <- data_meta$anti_PD_1[!is.na(data_meta$anti_PD_1)] +data_meta$resvar[!is.na(data_meta$PFS12)] <- data_meta$PFS12[!is.na(data_meta$PFS12)] +data_meta$resvar[!is.na(data_meta$ORR)] <- data_meta$ORR[!is.na(data_meta$ORR)] + +# Filter individuals without response variable +data_meta <- data_meta[!is.na(data_meta$resvar), c("study_name", "resvar")] + +# Convert the "study_name" to factor variable +data_meta$study_name <- as.factor(data_meta$study_name) + +# Prepare the abundance data +# Transpose the abundance matrix and change the value of abundance data to proportion unit +data_abd <- assay(se_relative) +data_abd <- data_abd/100 + +# Match the individuals in the data_abd +data_abd <- data_abd[, colnames(data_abd) %in% rownames(data_meta)] + +# Change the rownames names for variables of interest +rownames(data_abd) <- sub('.*s__', '', rownames(data_abd)) + +# Use adjust_batch to correct for differences in the five studies, while controlling for the effect of cases versus control (variable resvar in data_meta). +fit_adjust_batch <- adjust_batch(feature_abd = data_abd, + batch = "study_name", + covariates = "resvar", + data = data_meta, + control = list(verbose = FALSE)) + +data_abd_adj <- fit_adjust_batch$feature_abd_adj + +``` +![](https://github.com/microbiome/OMA/blob/master/inst/pages/images/adjust_batch_diagnostic_relative_abundance) + +The first figure shows the relationship between the original batch mean parameters (Gamma) and the shrunk (regularized) batch mean parameters (Gamma (shrinked)). The points represent the 5 different study groups within the dataset. The var_batch points represent the variance within each batch, with different colors indicating different levels or categories of variance. The scatter plot on the right represents the change in mean abundance between the original and adjusted ones, displaying an obvious decrease after the batch effect adjustment analysis. + +### Evaluation for the batch effect adjustment {#sec-batch-adjustment-evaluation} + +After obtaining the adjusted abundance matrix, we can evaluate the improvement of the batch effect adjustment. Here, the total variation from study difference will be assessed through the PERMANOVA$^14$ test. + +```{r message=FALSE, warning=FALSE} +# Load library +library(vegan) + +D_before <- vegdist(t(data_abd)) +D_after <- vegdist(t(data_abd_adj)) + +set.seed(1) +fit_adonis_before <- adonis(D_before ~ study_name, data = data_meta) +fit_adonis_after <- adonis(D_after ~ study_name, data = data_meta) + +print(fit_adonis_before$aov.tab) +print(fit_adonis_after$aov.tab) + +``` +Based on the results, we can see that the study differences can explain a total of 11.922% ($R^2$) of the variability in microbial abundance profiles before study effect adjustment, whereas after adjustment this was reduced to 2.806% ($R^2$). + +Let's visualize the results of the PERMANOVA test. +```{r} +# Load library +library(ggplot2) +library(cowplot) + +# Ordination +# Before +R2_before <- round(fit_adonis_before$aov.tab[1, 5]*100, 1) +pcoa_before <- cmdscale(D_before, eig = TRUE) +ord_before <- as.data.frame(pcoa_before$points) +percent_var_before <- round(pcoa_before$eig / sum(pcoa_before$eig) * 100, 1)[1:2] +before_labels <- c(paste('Axis 1 (', percent_var_before[1], '%)', sep = ''), + paste('Axis 2 (', percent_var_before[2], '%)', sep = '')) + +before_phrase <- paste('Before (PERMANOVA R2 = ', R2_before, '%)', sep = '') +colnames(ord_before) <- c('PC1', 'PC2') +ord_before$Study <- data_meta$study_name + +# After +R2_after <- round(fit_adonis_after$aov.tab[1, 5]*100, 1) +pcoa_after <- cmdscale(D_after, eig = TRUE) +ord_after <- as.data.frame(pcoa_after$points) +percent_var_after <- round(pcoa_after$eig / sum(pcoa_after$eig) * 100, 1)[1:2] +after_labels <- c(paste('Axis 1 (', percent_var_after[1], '%)', sep = ''), + paste('Axis 2 (', percent_var_after[2], '%)', sep = '')) + +after_phrase <- paste('After (PERMANOVA R2 = ', R2_after, '%)', sep = '') +colnames(ord_after) <- c('PC1', 'PC2') +ord_after$Study <- data_meta$study_name + +# Ordination Plot +p_before <- ggplot(ord_before, aes(x = PC1, y = PC2, color = Study)) + + geom_point(size = 4) + + theme_bw() + + xlab(before_labels[1]) + + ylab(before_labels[2]) + + ggtitle(before_phrase) + + theme(plot.title = element_text(hjust = 0.5)) + + theme(legend.position ="none") + +p_after <- ggplot(ord_after, aes(x = PC1, y = PC2, color = Study)) + + geom_point(size = 4) + + theme_bw() + + xlab(after_labels[1]) + + ylab(after_labels[2]) + + ggtitle(after_phrase) + + theme(plot.title = element_text(hjust = 0.5)) + + theme(legend.position ="none") + +p <- plot_grid(p_before, p_after, ncol = 2) +p + +``` + +### Meta-analytical differential abundance testing with lm_meta {#sec-differential-abundance-testing} + +Here, we illustrate the details of the meta-analytical differential abundance testing. We have several choices for the analysis methods. First, we apply linear models (LM) for individual study-specific analyses with various transformations. + +```{r message=FALSE, warning=FALSE} +transform <- c("NONE", "AST", "LOGIT", "LOG") +num <- NULL + +for (i in 1:length(transform)) { + fit_lm_meta <- lm_meta(feature_abd = data_abd_adj, + batch = "study_name", + exposure = "resvar", + data = data_meta, + control = list(analysis_method = 'LM', transform = transform[i])) + + num[i] <- sum(fit_lm_meta$meta_fits$qval.fdr < 0.05, na.rm = T) +} + +print(num) + +``` + +Since none of the linear models can generate significant results, we next apply the Tweedie model (using the "CPLM" analysis method in `MaAsLin2`, also implemented in the R package `Tweedieverse`$^15$. + +```{r message=FALSE, warning=FALSE} +fit_lm_meta <- lm_meta(feature_abd = data_abd_adj, + batch = "study_name", + exposure = "resvar", + data = data_meta, + control = list(analysis_method = 'CPLM', transform = 'NONE')) + +meta_fits <- fit_lm_meta$meta_fits + +# Load library +library(ggplot2) + +meta_fits %>% + filter(qval.fdr < 0.05) %>% + arrange(coef) %>% + mutate(feature = factor(feature, levels = feature)) %>% + ggplot(aes(y = coef, x = feature)) + + geom_bar(stat = "identity") + + coord_flip() + +``` + +Based on the results, we see that there are 22 significant features in total, among which the \emph{kluyvera ascorbata} has the strongest positive effect, while the \emph{pseudomonas putida} species has the strongest negative effect. + +## Meta-analysis for pathway abundance data {#sec-meta-analysis-pathway} +In this section, we repeat all the analysis mentioned above using the pathway relative abundance data. We first prepare the input data for the analysis. + +```{r message=FALSE, warning=FALSE} +# Load library +library(dplyr) +library(MMUPHin) + +# Prepare the meta data +data_meta <- select(as.data.frame(colData(se_pathway)), c("study_name", "ORR", "PFS12", "anti_PD_1")) + +# Define response variable +data_meta$resvar <- NA +data_meta$anti_PD_1[data_meta$anti_PD_1 == "responder"] <- "yes" +data_meta$anti_PD_1[data_meta$anti_PD_1 == "non_responder"] <- "no" +data_meta$resvar[!is.na(data_meta$anti_PD_1)] <- data_meta$anti_PD_1[!is.na(data_meta$anti_PD_1)] +data_meta$resvar[!is.na(data_meta$PFS12)] <- data_meta$PFS12[!is.na(data_meta$PFS12)] +data_meta$resvar[!is.na(data_meta$ORR)] <- data_meta$ORR[!is.na(data_meta$ORR)] + +# Filter individuals without response variable +data_meta <- data_meta[!is.na(data_meta$resvar), c("study_name", "resvar")] + +# Convert the "study_name" to factor variable +data_meta$study_name <- as.factor(data_meta$study_name) + +# Prepare the abundance data +# Transpose the abundance matrix and change the value of abundance data to proportion unit +data_abd <- assay(se_pathway) +data_abd <- data_abd/100 + +# Filter out stratified features +data_abd<-data_abd[!grepl("\\|", rownames(data_abd)),] +data_abd<-as.data.frame(data_abd) +data_abd<-data_abd[-c(1:2), ] + +# Match the individuals in the data_abd +data_abd <- data_abd[, colnames(data_abd) %in% rownames(data_meta)] + +# Use adjust_batch to correct for differences in the five studies, while controlling for the effect of cases versus control (variable resvar in data_meta). +fit_adjust_batch <- adjust_batch(feature_abd = data_abd, + batch = "study_name", + covariates = "resvar", + data = data_meta, + control = list(verbose = FALSE)) + +data_abd_adj <- fit_adjust_batch$feature_abd_adj + +``` +![](https://github.com/microbiome/OMA/tree/master/inst/pages/images/adjust_batch_diagnostic_pathway_abundance) + + +Next, we will evaluate the improvement of the batch effect adjustment and applying the PERMANOVA test. +```{r message=FALSE, warning=FALSE} +# Load library +library(vegan) + +D_before <- vegdist(t(data_abd)) +D_after <- vegdist(t(data_abd_adj)) + +set.seed(1) +fit_adonis_before <- adonis(D_before ~ study_name, data = data_meta) +fit_adonis_after <- adonis(D_after ~ study_name, data = data_meta) + +print(fit_adonis_before$aov.tab) +print(fit_adonis_after$aov.tab) + +``` +Based on the results, wesee that the study differences can explain a total of 15.508% ($R^2$) of the variability in microbial pathway profiles before study effect adjustment, whereas after adjustment this was reduced to 5.037% ($R^2$). + +Let's also visualize the results of the PERMANOVA test. +```{r} +# Load library +library(ggplot2) +library(cowplot) + +# Ordination +# Before +R2_before <- round(fit_adonis_before$aov.tab[1, 5]*100, 1) +pcoa_before <- cmdscale(D_before, eig = TRUE) +ord_before <- as.data.frame(pcoa_before$points) +percent_var_before <- round(pcoa_before$eig / sum(pcoa_before$eig) * 100, 1)[1:2] +before_labels <- c(paste('Axis 1 (', percent_var_before[1], '%)', sep = ''), + paste('Axis 2 (', percent_var_before[2], '%)', sep = '')) + +before_phrase <- paste('Before (PERMANOVA R2 = ', R2_before, '%)', sep = '') +colnames(ord_before) <- c('PC1', 'PC2') +ord_before$Study <- data_meta$study_name + +# After +R2_after <- round(fit_adonis_after$aov.tab[1, 5]*100, 1) +pcoa_after <- cmdscale(D_after, eig = TRUE) +ord_after <- as.data.frame(pcoa_after$points) +percent_var_after <- round(pcoa_after$eig / sum(pcoa_after$eig) * 100, 1)[1:2] +after_labels <- c(paste('Axis 1 (', percent_var_after[1], '%)', sep = ''), + paste('Axis 2 (', percent_var_after[2], '%)', sep = '')) + +after_phrase <- paste('After (PERMANOVA R2 = ', R2_after, '%)', sep = '') +colnames(ord_after) <- c('PC1', 'PC2') +ord_after$Study <- data_meta$study_name + +# Ordination Plot +p_before <- ggplot(ord_before, aes(x = PC1, y = PC2, color = Study)) + + geom_point(size = 4) + + theme_bw() + + xlab(before_labels[1]) + + ylab(before_labels[2]) + + ggtitle(before_phrase) + + theme(plot.title = element_text(hjust = 0.5)) + + theme(legend.position ="none") + +p_after <- ggplot(ord_after, aes(x = PC1, y = PC2, color = Study)) + + geom_point(size = 4) + + theme_bw() + + xlab(after_labels[1]) + + ylab(after_labels[2]) + + ggtitle(after_phrase) + + theme(plot.title = element_text(hjust = 0.5)) + + theme(legend.position ="none") + +p <- plot_grid(p_before, p_after, ncol = 2) +p + +``` + +As before, we have several choices on the analysis methods. However, since we don't have significance at the FDR level, we use the unadjusted p-values to showcase the ``top hits''. + +```{r message=FALSE, warning=FALSE} +transform <- c("NONE", "AST", "LOGIT", "LOG") +num <- NULL + +for (i in 1:length(transform)) { + fit_lm_meta <- lm_meta(feature_abd = data_abd_adj, + batch = "study_name", + exposure = "resvar", + data = data_meta, + control = list(analysis_method = 'LM', transform = transform[i])) + + num[i] <- sum(fit_lm_meta$meta_fits$pval < 0.05, na.rm = T) +} + +print(num) + +``` + +```{r} +fit_lm_meta2 <- lm_meta(feature_abd = data_abd_adj, + batch = "study_name", + exposure = "resvar", + data = data_meta, + control = list(analysis_method = 'CPLM', transform = 'NONE')) + +num <- sum(fit_lm_meta2$meta_fits$pval < 0.05, na.rm = T) +print(num) + +``` +Here is the summary table for the number of significant pathways using various meta-analytic differential analysis methods and transformations: + +| Analysis Method | Transform Parameter | Number of Significant Pathways +| :----------- | :----------- | :----------- +| LM | NONE | 11 +| LM | AST | 10 +| LM | LOGIT | 9 +| LM | LOG | 11 +| CPLM | NONE | 15 + +Let's than visualize the results using the "CPLM" analysis method, which identified largest number of significant pathways. + +```{r} +# Load library +library(ggplot2) + +# Extract the results +meta_fits2 <- fit_lm_meta2$meta_fits +# meta_fits3 <- meta_fits2[meta_fits2$pval < 0.05 & !is.na(meta_fits2$pval), ] + +# Create the figure +meta_fits2 %>% + filter(pval < 0.05) %>% + arrange(coef) %>% + mutate(feature = factor(feature, levels = feature)) %>% + ggplot(aes(y = coef, x = feature)) + + theme(axis.text.y = element_text(size = 10)) + + geom_bar(stat = "identity") + + coord_flip() + +``` + +Based on the results, we observe 15 significant features in total. Among these, the queuosine biosynthesis bacteroides group exhibits the strongest negative effects. Additionally, L-lysine degradation XI is the most positively significant pathway, while xylose degradation IV is the most negatively significant pathway. + +## Reference +1. Young LH. Strengths and Limitations of Meta-Analysis. Korean J Med. 2019;94(5):391-395. doi: https://doi.org/10.3904/kjm.2019.94.5.391. +2. Finckh A, Tramèr MR. Primer: strengths and weaknesses of meta-analysis. Nat Clin Pract Rheumatol. 2008;4(3):146-152. doi:10.1038/ncprheum0732. +3. Israel H, Richter RR. A guide to understanding meta-analysis. J Orthop Sports Phys Ther. 2011;41(7):496-504. doi:10.2519/jospt.2011.3333. +4. Hoffman JIE. Meta-analysis. Basic Biostatistics for Medical and Biomedical Practitioners. 2nd ed. Academic Press; 2019:621-629. doi:10.1016/B978-0-12-817084-7.00036-X. +5. Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14(Suppl 1):29-37. +6. Ma S, Shungin D, Mallick H, et al. Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin. Genome Biol. 2022;23(1):208. Published 2022 Oct 3. doi:10.1186/s13059-022-02753-4. +7. Pasolli E, Schiffer L, Manghi P, et al. “Accessible, curated metagenomic data through ExperimentHub.” Nat. Methods. 2017;14(11):1023–1024. doi:10.1038/nmeth.4468. +8. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable Association Discovery in Population-scale Meta-omics Studies. PLoS Computational Biology. 2021:17(11):e1009442. +9. Frankel AE, Coughlin LA, Kim J, et al. Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma Patients. Neoplasia. 2017;19(10):848-855. doi:10.1016/j.neo.2017.08.004. +10. Lee KA, Thomas AM, Bolte LA, et al. Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma. Nat Med. 2022;28(3):535-544. doi:10.1038/s41591-022-01695-5. +11. Matson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science. 2018;359(6371):104-108. doi:10.1126/science.aao3290. +12. Peters BA, Wilson M, Moran U, et al. Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients. Genome Med. 2019;11(1):61. Published 2019 Oct 9. doi:10.1186/s13073-019-0672-4. +13. Wind TT, Gacesa R, Vich Vila A, et al. Gut microbial species and metabolic pathways associated with response to treatment with immune checkpoint inhibitors in metastatic melanoma. Melanoma Res. 2020;30(3):235-246. doi:10.1097/CMR.0000000000000656. +14. Tang ZZ, Chen G, Alekseyenko AV. PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances. Bioinformatics. 2016;32(17):2618-2625. doi:10.1093/bioinformatics/btw311. +15. Mallick, H, Chatterjee, S, Chowdhury, S, Chatterjee, S, Rahnavard, A, Hicks, SC. Differential expression of single-cell RNA-seq data using Tweedie models. Statistics in Medicine. 2022; 41( 18): 3492- 3510. doi:10.1002/sim.9430. \ No newline at end of file From 6e24f7482a29366e73e555d35aa2328704608545 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Thu, 21 Mar 2024 11:01:05 -0400 Subject: [PATCH 04/21] Add files via upload --- inst/GSEA.qmd | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 inst/GSEA.qmd diff --git a/inst/GSEA.qmd b/inst/GSEA.qmd new file mode 100644 index 000000000..cc37289b7 --- /dev/null +++ b/inst/GSEA.qmd @@ -0,0 +1,29 @@ +--- +title: "GSEA" +format: html +editor: visual +author: Yihan Liu, Himel Mallick +--- + +# Gene set Enrichment Analysis + +## Quarto + +Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see . + +## Running Code + +When you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this: + +```{r} +1 + 1 +``` + +You can add options to executable code like this + +```{r} +#| echo: false +2 * 2 +``` + +The `echo: false` option disables the printing of code (only output is displayed). From 38448eb5ca8c749d4af676b015c57cd39faa5e60 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Thu, 21 Mar 2024 11:06:15 -0400 Subject: [PATCH 05/21] Add files via upload --- inst/pages/images/GSEA.qmd | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 inst/pages/images/GSEA.qmd diff --git a/inst/pages/images/GSEA.qmd b/inst/pages/images/GSEA.qmd new file mode 100644 index 000000000..89b062b95 --- /dev/null +++ b/inst/pages/images/GSEA.qmd @@ -0,0 +1,34 @@ +--- +title: "GSEA" +format: html +editor: visual +author: Yihan Liu, Himel Mallick +--- + +# Gene set Enrichment Analysis {#sec-GSEA} + +```{r setup, echo=FALSE, results="asis"} +library(rebook) +chapterPreamble() +``` + +## Quarto + +Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see . + +## Running Code + +When you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this: + +```{r} +1 + 1 +``` + +You can add options to executable code like this + +```{r} +#| echo: false +2 * 2 +``` + +The `echo: false` option disables the printing of code (only output is displayed). From fa73146fcfd7041e42279bd26fccd5f64cd7f85b Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Thu, 21 Mar 2024 11:09:18 -0400 Subject: [PATCH 06/21] Delete inst/GSEA.qmd --- inst/GSEA.qmd | 29 ----------------------------- 1 file changed, 29 deletions(-) delete mode 100644 inst/GSEA.qmd diff --git a/inst/GSEA.qmd b/inst/GSEA.qmd deleted file mode 100644 index cc37289b7..000000000 --- a/inst/GSEA.qmd +++ /dev/null @@ -1,29 +0,0 @@ ---- -title: "GSEA" -format: html -editor: visual -author: Yihan Liu, Himel Mallick ---- - -# Gene set Enrichment Analysis - -## Quarto - -Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see . - -## Running Code - -When you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this: - -```{r} -1 + 1 -``` - -You can add options to executable code like this - -```{r} -#| echo: false -2 * 2 -``` - -The `echo: false` option disables the printing of code (only output is displayed). From 7b4d5d65143edf526c23695a76ee62e45d556c05 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Tue, 26 Mar 2024 05:37:00 -0400 Subject: [PATCH 07/21] Update IntegratedLearner.qmd --- inst/pages/IntegratedLearner.qmd | 150 ++++++++++++++++++++++--------- 1 file changed, 108 insertions(+), 42 deletions(-) diff --git a/inst/pages/IntegratedLearner.qmd b/inst/pages/IntegratedLearner.qmd index a7e3f21c9..9a56d289d 100644 --- a/inst/pages/IntegratedLearner.qmd +++ b/inst/pages/IntegratedLearner.qmd @@ -7,16 +7,34 @@ chapterPreamble() ## Multi-omics Integration -In multiview data analysis, there are two main types of approaches: early fusion and late fusion. \underline{Early fusion} combines all datasets into a single representation, which then serves as input for a supervised learning model. In contrast, \underline{late fusion} builds individual models for each data view and combines their predictions using a second-level model as the final predictor. However, these traditional paradigms treat the data views in isolation and do not allow for interactions or dependencies between them. A more advanced method, called cooperative learning (Ding et al., 2022, \emph{PNAS}$^1$), which is also known as \underline{intermediate fusion}, combines the best of both worlds by encouraging predictions from different data views to align through an agreement parameter ($\rho$). +In multiview data analysis, there are two main types of approaches: early fusion +and late fusion. \underline{Early fusion} combines all datasets into a single +representation, which then serves as input for a supervised learning model. In +contrast, \underline{late fusion} builds individual models for each data view +and combines their predictions using a second-level model as the final +predictor. However, these traditional paradigms treat the data views in +isolation and do not allow for interactions or dependencies between them. A more +advanced method, called cooperative learning (Ding et al., 2022, +\emph{PNAS}$^1$), which is also known as \underline{intermediate fusion}, +combines the best of both worlds by encouraging predictions from different data +views to align through an agreement parameter ($\rho$). ## Multi-omics Prediction and Classification of Binary IBD Disease Status -In this chapter, we showcase examples of various integration paradigms (early, late, and intermediate fusion) using the R packages `multiview`$^1$ and `SuperLearner`$^2$. We make use of the publicly available source code from the multi-omics integrated framework (`IntegratedLearner`) proposed by Mallick et al. (2024)$^3$. +In this chapter, we showcase examples of various integration paradigms (early, +late, and intermediate fusion) using the R packages `multiview`$^1$ and +`SuperLearner`$^2$. We make use of the publicly available source code from the +multi-omics integrated framework (`IntegratedLearner`) proposed by Mallick et +al. (2024)$^3$. ## Input data -We use the publicly available Inflammatory Bowel Diseases (IBD) data from the curatedMetagenomicData package (Lloyd-Price et al., 2019)$^4$, where we aim to predict IBD disease status based on both taxonomic (species abundances) and functional (pathway abundances) profiles. -```{r message=FALSE, warning=FALSE} +We use the publicly available Inflammatory Bowel Diseases (IBD) data from the +curatedMetagenomicData package (Lloyd-Price et al., 2019)$^4$, where we aim to +predict IBD disease status based on both taxonomic (species abundances) and +functional (pathway abundances) profiles. + +```{r message=FALSE, warning=FALSE load-pkg-data} ##################### # Load example data # @@ -24,31 +42,38 @@ We use the publicly available Inflammatory Bowel Diseases (IBD) data from the cu library(tidyverse) library(curatedMetagenomicData) -library(curatedMetagenomicAnalyses) cond <- 'IBD' -se_relative <-curatedMetagenomicAnalyses::makeSEforCondition(cond, - dataType = "relative_abundance", - removestudies = c('HallAB_2017', 'IaniroG_2022', 'IjazUZ_2017', 'LiJ_2014', 'NielsenHB_2014', 'VilaAV_2018')) -se_pathway <-curatedMetagenomicAnalyses::makeSEforCondition(cond, - dataType = "pathway_abundance", - removestudies = c('HallAB_2017', 'IaniroG_2022', 'IjazUZ_2017', 'LiJ_2014', 'NielsenHB_2014', 'VilaAV_2018')) +se_relative <- makeSEforCondition( + cond, + dataType = "relative_abundance", + removestudies = c('HallAB_2017', 'IaniroG_2022', 'IjazUZ_2017', 'LiJ_2014', 'NielsenHB_2014', 'VilaAV_2018')) + + +?curatedMetagenomicData + + + +se_pathway <- makeSEforCondition( + cond, + dataType = "pathway_abundance", + removestudies = c('HallAB_2017', 'IaniroG_2022', 'IjazUZ_2017', 'LiJ_2014', 'NielsenHB_2014', 'VilaAV_2018')) ``` We will first prepare the input sample metadata and feature table for both relative abundance and pathway abundance data. -```{r} +```{r prepare-metadata-feature} ########################## # Create sample metadata # ########################## -sample_metadata <- dplyr::select(as.data.frame(colData(se_relative)), c("study_name", "disease", "subject_id")) +sample_metadata <- select(as.data.frame(colData(se_relative)), c("study_name", "disease", "subject_id")) # Define response variable & sample id sample_metadata$Y <- ifelse(sample_metadata$disease == "IBD", 1, 0) -sample_metadata<- sample_metadata %>% dplyr::select(subject_id, Y)%>% rename(subjectID = subject_id) +sample_metadata<- sample_metadata %>% dplyr::select(subject_id, Y) %>% rename(subjectID = subject_id) ########################### # Create Species Features # @@ -85,7 +110,7 @@ all(rownames(feature_table) == rownames(sample_metadata)) We will then create a metadata table for the features. This table captures high-level information related to the features (e.g., which layer they belong to). -```{r} +```{r create-metadata-table} ###################################### # Create metadata table for features # @@ -106,7 +131,7 @@ all(colnames(feature_table) == rownames(sample_metadata)) # TRUE Further data pre-processing is necessary to handle near-zero-variance features in this dataset. Ultimately, 483 features are retained, consisting of 360 pathways and 123 species. A combination of variance and prevalence filtering is applied to the feature table, while related metadata is cleaned to ensure the retention of matching samples. -```{r} +```{r create-feature-table} library(caret) @@ -139,9 +164,14 @@ all(colnames(feature_table) == rownames(sample_metadata)) # TRUE ``` -Following the preprocessing, we conduct additional input data preparation, which includes configuring parameters for sample splitting and 5-fold cross-validation. It's important to note that this dataset contains repeated measures (i.e., multiple samples per subject). To address this, we will conduct 5-fold cross-validation at the subject level. The source code is derived from the `IntegratedLearner` R package$^3$. +Following the preprocessing, we conduct additional input data preparation, +which includes configuring parameters for sample splitting and 5-fold +cross-validation. It's important to note that this dataset contains repeated +measures (i.e., multiple samples per subject). To address this, we will conduct +5-fold cross-validation at the subject level. The source code is derived from +the `IntegratedLearner` R package$^3$. -```{r} +```{r additional-data-preprocessing} # Set parameters and extract subject IDs for sample splitting seed <- 1 set.seed(seed) @@ -187,9 +217,13 @@ names(layer_wise_predictions_train) <- name_layers ``` -Next, we run the late fusion algorithm described in Mallick et al., 2024$^3$. To this end, we subset the data for each feature type and conduct the analysis on each layer for the first-stage learning to predict the outcome. In other words, we fit a machine learning algorithm per layer (called a 'base learner'), utilizing the SuperLearner R package. +Next, we run the late fusion algorithm described in Mallick et al., 2024$^3$. +To this end, we subset the data for each feature type and conduct the analysis +on each layer for the first-stage learning to predict the outcome. In other +words, we fit a machine learning algorithm per layer (called a 'base learner'), +utilizing the SuperLearner R package. -```{r} +```{r late-fusion} ######################################################## # Subset data per omics and run each individual layers # @@ -231,9 +265,12 @@ for (i in seq_along(name_layers)){ ``` -The next step of the analysis is to combine the layer-wise cross-validated predictions with a meta-model (meta_learner) to generate final predictions based on all available data points for the stacked model. Here, we will use `glmnet` as the meta-learner. However, other choices are also possible. +The next step of the analysis is to combine the layer-wise cross-validated +predictions with a meta-model (meta_learner) to generate final predictions +based on all available data points for the stacked model. Here, we will use +`glmnet` as the meta-learner. However, other choices are also possible. -```{r} +```{r layer-wise-cross-validated-prediction} ############################## # Prepare stacked input data # ############################## @@ -270,9 +307,11 @@ SL_fit_stacked$X <- combo ``` -In contrast to late fusion, in early fusion, we will simply supply a combined representation of the data and we will use the `random forest` method for building an integrated prediction model. +In contrast to late fusion, in early fusion, we will simply supply a combined +representation of the data and we will use the `random forest` method for +building an integrated prediction model. -```{r} +```{r early-fusion} ##################################### # EARLY FUSION (CONCATENATED MODEL) # @@ -296,13 +335,22 @@ SL_fit_concat$X <- fulldat ``` -Finally, we consider the intermediate fusion approach, which combines ideas from both late fusion and early fusion by integrating the usual squared-error loss of predictions with an "agreement" (fusion) penalty ($\rho$) so that the predictions from different data views agree. +Finally, we consider the intermediate fusion approach, which combines ideas +from both late fusion and early fusion by integrating the usual squared-error +loss of predictions with an "agreement" (fusion) penalty ($\rho$) so that the +predictions from different data views agree. -The intermediate fusion adjusts the degree of fusion in an adaptive manner, where the test set prediction error is estimated with a cross-validation method. By varying the weight of the fusion penalty - hyperparameter $\rho$, we obtain the early and late fusion approaches as special cases. If $\rho = 0$, we have a simple form of early fusion; if $\rho = 1$, we obtain a simple form of late fusion. For $0 < \rho < 1$, we obtain the intermediate fusion. +The intermediate fusion adjusts the degree of fusion in an adaptive manner, +where the test set prediction error is estimated with a cross-validation method. +By varying the weight of the fusion penalty - hyperparameter $\rho$, we obtain +the early and late fusion approaches as special cases. If $\rho = 0$, we have a +simple form of early fusion; if $\rho = 1$, we obtain a simple form of late +fusion. For $0 < \rho < 1$, we obtain the intermediate fusion. -Here, as examples, we will prepare the input data, fit the multiview model, and run the cross-validation in this section. +Here, as examples, we will prepare the input data, fit the multiview model, and +run the cross-validation in this section. -```{r} +```{r prepare-input-data} ############################## # Prepare data for multiview # ############################## @@ -337,9 +385,11 @@ DD$coef <- as.numeric(DD$coef) ``` -In this section, we visualize the prediction performance by gathering all predictions and extracting the data necessary for ROC plotting, corresponding to each integration paradigm. +In this section, we visualize the prediction performance by gathering all +predictions and extracting the data necessary for ROC plotting, corresponding +to each integration paradigm. -```{r} +```{r visualization1} ######################################################### # Gather all predictions and ROC curve data preparation # ######################################################### @@ -369,10 +419,15 @@ ROC_table <- do.call('rbind', list.ROC) ``` -Based on the ROC plot described below, we observe that the AUC is 0.65 when considering only the pathway abundance data in the model, and 0.64 for the model including only the species abundance data. The AUC increases to 0.67 when using the early fusion model and reaches 1 with the late fusion model. In contrast, the intermediate fusion model achieves an AUC of 0.99. Overall, most integrated classifiers outperform individual layers in distinguishing between IBD and non-IBD controls. - +Based on the ROC plot described below, we observe that the AUC is 0.65 when +considering only the pathway abundance data in the model, and 0.64 for the model +including only the species abundance data. The AUC increases to 0.67 when using +the early fusion model and reaches 1 with the late fusion model. In contrast, +the intermediate fusion model achieves an AUC of 0.99. Overall, most integrated +classifiers outperform individual layers in distinguishing between IBD and +non-IBD controls. -```{r} +```{r visualization2} # Prepare data for plotting plot_data <- ROC_table plot_data$displayItem <- paste(plot_data$layer, " AUC = ", plot_data$AUC, sep="") @@ -399,9 +454,10 @@ print(p) ``` -Finally, we visualize the results for the top 20 features for each layer based on the intermediate fusion. +Finally, we visualize the results for the top 20 features for each layer based +on the intermediate fusion. -```{r} +```{r visualization3} # Only plot UP TO TOP 20 PER LAYER DD <- DD %>% group_by(view) %>% @@ -428,13 +484,23 @@ p <- DD %>% p ``` -Based on the bar plot, we observe that \emph{Proteobacteria_bacterium_CAG.139} is the species most positively associated, while \emph{Gemmiger_formicilis} is the most negatively associated species. Additionally, L-lysine biosynthesis VI is the pathway most positively associated, and PYRIDNUSCYN-PWY: NAD biosynthesis I (from aspartate) is the pathway most positively associated, respectively. - -## Reference -1. Ding DY, Li S, Narasimhan B, Tibshirani R. Cooperative learning for multiview analysis. Proc Natl Acad Sci U S A. 2022;119(38):e2202113119. doi:10.1073/pnas.2202113119. -2. van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007;6:Article25. doi:10.2202/1544-6115.1309. -3. Mallick H, Porwal A, Saha S, Basak P, Svetnik V, Paul E. An integrated Bayesian framework for multi-omics prediction and classification. Stat Med. Published online December 26, 2023. doi:10.1002/sim.9953. -4. Lloyd-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569(7758):655-662. doi:10.1038/s41586-019-1237-9. +Based on the bar plot, we observe that \emph{Proteobacteria_bacterium_CAG.139} +is the species most positively associated, while \emph{Gemmiger_formicilis} is +the most negatively associated species. Additionally, L-lysine biosynthesis VI +is the pathway most positively associated, and PYRIDNUSCYN-PWY: NAD biosynthesis +I (from aspartate) is the pathway most positively associated, respectively. +## Reference +1. Ding DY, Li S, Narasimhan B, Tibshirani R. Cooperative learning for +multiview analysis. Proc Natl Acad Sci U S A. 2022;119(38):e2202113119. +doi:10.1073/pnas.2202113119. +2. van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol +Biol. 2007;6:Article25. doi:10.2202/1544-6115.1309. +3. Mallick H, Porwal A, Saha S, Basak P, Svetnik V, Paul E. An integrated +Bayesian framework for multi-omics prediction and classification. Stat Med. +Published online December 26, 2023. doi:10.1002/sim.9953. +4. Lloyd-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut +microbial ecosystem in inflammatory bowel diseases. Nature. 2 +019;569(7758):655-662. doi:10.1038/s41586-019-1237-9. From 93252df95f3af6242a41bc5da1238b50217b0214 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Tue, 26 Mar 2024 05:38:56 -0400 Subject: [PATCH 08/21] Update MMUPHin_meta_analysis.qmd --- inst/pages/MMUPHin_meta_analysis.qmd | 224 ++++++++++++++++++++------- 1 file changed, 171 insertions(+), 53 deletions(-) diff --git a/inst/pages/MMUPHin_meta_analysis.qmd b/inst/pages/MMUPHin_meta_analysis.qmd index 4081e1bb1..739e08f6c 100644 --- a/inst/pages/MMUPHin_meta_analysis.qmd +++ b/inst/pages/MMUPHin_meta_analysis.qmd @@ -5,27 +5,79 @@ library(rebook) chapterPreamble() ``` -Meta-analysis is a statistical analysis that combines the results of multiple scientific studies by appropriately weighting the individual study-specific effect sizes$^{1, 2, 3}$. A common meta-analytic combination strategy is to take a weighted average of the study-specific summary measures. In fixed-effects meta-analysis, the weights are based on the assumption that there is a single true parameter underlying all the studies, while in random-effects meta-analysis, the weights are based on a model where the true parameter varies across studies according to a probability distribution. - -Meta-analysis is particularly useful when individual studies are small, and a joint analysis of these studies can enhance the precision of effect size estimation and improve statistical power. Especially in microbiome studies, individual effect sizes from each study tend to be small$^{4, 5, 6}$. Only through meta-analysis can these small effects reach statistical significance. Therefore, by jointly analyzing these studies, we amplify the collective voice of the data, offering a more comprehensive understanding of the effects under investigation. - -In this chapter, our focus is on using the R/Bioconductor package [MMUPHin]$^6$ (Meta-Analysis Methods with a Uniform Pipeline for Heterogeneity in microbiome studies) to perform the meta-analysis of microbiome data. We utilize publicly available cancer microbiome datasets from the R/Bioconductor package [curatedMetagenomicData]$^7$ +Meta-analysis is a statistical analysis that combines the results of multiple +scientific studies by appropriately weighting the individual study-specific +effect sizes$^{1, 2, 3}$. A common meta-analytic combination strategy is to +take a weighted average of the study-specific summary measures. In fixed-effects +meta-analysis, the weights are based on the assumption that there is a single +true parameter underlying all the studies, while in random-effects +meta-analysis, the weights are based on a model where the true parameter varies +across studies according to a probability distribution. + +Meta-analysis is particularly useful when individual studies are small, and a +joint analysis of these studies can enhance the precision of effect size +estimation and improve statistical power. Especially in microbiome studies, +individual effect sizes from each study tend to be small$^{4, 5, 6}$. Only +through meta-analysis can these small effects reach statistical significance. +Therefore, by jointly analyzing these studies, we amplify the collective voice +of the data, offering a more comprehensive understanding of the effects under +investigation. + +In this chapter, our focus is on using the R/Bioconductor package [MMUPHin]$^6$ +(Meta-Analysis Methods with a Uniform Pipeline for Heterogeneity in microbiome +studies) to perform the meta-analysis of microbiome data. We utilize publicly +available cancer microbiome datasets from the R/Bioconductor package +[curatedMetagenomicData]$^7$ ## Meta-analysis of cancer microbiome studies using relative abundance data -Recent work has indicated a possible involvement of the gut microbiome in influencing the efficacy of immune checkpoint inhibitor (ICI) treatment strategies for PD-1/PD-L1 (Programmed Cell Death Protein 1/Programmed Cell Death Ligand 1) targeting checkpoint inhibitors$^{8, 9, 10, 11, 12}$. Several investigations into the microbiome have provided clinical data suggesting that the gut microbiome modulates the response to inhibitors of the PD-1/PD-L1 axis. However, published cancer microbiome studies face challenges, such as limited sample sizes, inconsistent data analysis methods, and a lack of functionally relevant consensus signatures. - -Elucidation of the mechanisms by which the gut microbiome alters the function of the immune system to enable or promote cancer development may reveal novel pathways to explore in cancer therapy. Consequently, a high-quality re-analysis of public cancer microbiome data through a systematic meta-analysis approach could provide valuable insights into the microbiome's role in cancer development and progression in a rapid and cost-effective manner. - -Using advanced melanoma as a model, extensively studied in the context of PD-1/PD-L1 immune checkpoint inhibitors, our aim is to identify functional biomarkers positively and negatively associated with ICI response by analyzing both taxonomic and functional profiles with ICI response. +Recent work has indicated a possible involvement of the gut microbiome in +influencing the efficacy of immune checkpoint inhibitor (ICI) treatment +strategies for PD-1/PD-L1 (Programmed Cell Death Protein 1/Programmed Cell Death +Ligand 1) targeting checkpoint inhibitors$^{8, 9, 10, 11, 12}$. Several +investigations into the microbiome have provided clinical data suggesting that +the gut microbiome modulates the response to inhibitors of the PD-1/PD-L1 axis. +However, published cancer microbiome studies face challenges, such as limited +sample sizes, inconsistent data analysis methods, and a lack of functionally +relevant consensus signatures. + +Elucidation of the mechanisms by which the gut microbiome alters the function of +the immune system to enable or promote cancer development may reveal novel +pathways to explore in cancer therapy. Consequently, a high-quality re-analysis +of public cancer microbiome data through a systematic meta-analysis approach +could provide valuable insights into the microbiome's role in cancer development +and progression in a rapid and cost-effective manner. + +Using advanced melanoma as a model, extensively studied in the context of +PD-1/PD-L1 immune checkpoint inhibitors, our aim is to identify functional +biomarkers positively and negatively associated with ICI response by analyzing +both taxonomic and functional profiles with ICI response. ```{r load_curatedMetagenomicData} library(curatedMetagenomicData) library(curatedMetagenomicAnalyses) ``` -In the following section, we provide detailed examples of how to perform batch effect correction and meta-analytic differential abundance testing on publicly available cancer microbiome data from multiple studies. We use the R package MMUPHin for these tasks. MMUPHin is a recently developed R/Bioconductor package designed for meta-analysis of microbiome taxonomic and functional profiles. It is agnostic to the data type and leverages another R/Bioconductor package, MaAsLin2$^8$, as a backend to conduct the meta-analysis. MaAsLin2 is particularly designed to be applicable to various microbial community data types (taxonomy or functional profiles) and environments (human or otherwise). It is modular, including implementations of alternative normalization/transformation schemes and statistical models (e.g., for amplicon vs. shotgun metagenomic profiles). Leveraging this flexible framework under the hood, MMUPHin performs normalization, batch effect correction, and meta-analysis using the default random effects meta-regression. - -Here, we will first perform the batch effect correction analysis. We use both the relative abundance data and the pathway abundance data from 5 public datasets: Frankel et al. (2017)$^9$, LeeK et al. (2022)$^10$, Matson et al. (2018)$^11$, Peters et al. (2019)$^12$, and Wind et al. (2020)$^13$. All these datasets are available from the curatedMetagenomicData package, with a total of 285 subjects included. +In the following section, we provide detailed examples of how to perform batch +effect correction and meta-analytic differential abundance testing on publicly +available cancer microbiome data from multiple studies. We use the R package +MMUPHin for these tasks. MMUPHin is a recently developed R/Bioconductor package +designed for meta-analysis of microbiome taxonomic and functional profiles. It +is agnostic to the data type and leverages another R/Bioconductor package, +MaAsLin2$^8$, as a backend to conduct the meta-analysis. MaAsLin2 is +particularly designed to be applicable to various microbial community data types +(taxonomy or functional profiles) and environments (human or otherwise). It is +modular, including implementations of alternative normalization/transformation +schemes and statistical models (e.g., for amplicon vs. shotgun metagenomic +profiles). Leveraging this flexible framework under the hood, MMUPHin performs +normalization, batch effect correction, and meta-analysis using the default +random effects meta-regression. + +Here, we will first perform the batch effect correction analysis. We use both +the relative abundance data and the pathway abundance data from 5 public +datasets: Frankel et al. (2017)$^9$, LeeK et al. (2022)$^10$, Matson et al. +(2018)$^11$, Peters et al. (2019)$^12$, and Wind et al. (2020)$^13$. All these +datasets are available from the curatedMetagenomicData package, with a total of +285 subjects included. ```{r load-pkg-data} # Load example data @@ -38,9 +90,15 @@ First, let's look into the relative abundance data. ### Performing batch (study) effect adjustment with adjust_batch for relative abundance {#sec-batch-effect-adjustment} -In this analysis, we are using two input files. The first input file is the relative abundance data file, which consists of a feature-by-sample matrix. The second input is the metadata associated with the relative abundance file, which includes the study names and combines overall response rate (ORR), progression-free survival at 12 months (PFS12), as well as the response to anti-PD-1 (anti_PD_1) as the response variable. By the end of this step, we obtain a batch-adjusted relative abundance matrix. +In this analysis, we are using two input files. The first input file is the +relative abundance data file, which consists of a feature-by-sample matrix. The +second input is the metadata associated with the relative abundance file, which +includes the study names and combines overall response rate (ORR), +progression-free survival at 12 months (PFS12), as well as the response to +anti-PD-1 (anti_PD_1) as the response variable. By the end of this step, we +obtain a batch-adjusted relative abundance matrix. -```{r message=FALSE, warning=FALSE} +```{r message=FALSE, warning=FALSE prepare-input-data} # Load library library(dplyr) library(MMUPHin) @@ -85,13 +143,22 @@ data_abd_adj <- fit_adjust_batch$feature_abd_adj ``` ![](https://github.com/microbiome/OMA/blob/master/inst/pages/images/adjust_batch_diagnostic_relative_abundance) -The first figure shows the relationship between the original batch mean parameters (Gamma) and the shrunk (regularized) batch mean parameters (Gamma (shrinked)). The points represent the 5 different study groups within the dataset. The var_batch points represent the variance within each batch, with different colors indicating different levels or categories of variance. The scatter plot on the right represents the change in mean abundance between the original and adjusted ones, displaying an obvious decrease after the batch effect adjustment analysis. +The first figure shows the relationship between the original batch mean +parameters (Gamma) and the shrunk (regularized) batch mean parameters (Gamma +(shrinked)). The points represent the 5 different study groups within the +dataset. The var_batch points represent the variance within each batch, with +different colors indicating different levels or categories of variance. The +scatter plot on the right represents the change in mean abundance between the +original and adjusted ones, displaying an obvious decrease after the batch +effect adjustment analysis. ### Evaluation for the batch effect adjustment {#sec-batch-adjustment-evaluation} -After obtaining the adjusted abundance matrix, we can evaluate the improvement of the batch effect adjustment. Here, the total variation from study difference will be assessed through the PERMANOVA$^14$ test. +After obtaining the adjusted abundance matrix, we can evaluate the improvement +of the batch effect adjustment. Here, the total variation from study difference +will be assessed through the PERMANOVA$^14$ test. -```{r message=FALSE, warning=FALSE} +```{r message=FALSE, warning=FALSE batch-effect-evaluation} # Load library library(vegan) @@ -106,10 +173,13 @@ print(fit_adonis_before$aov.tab) print(fit_adonis_after$aov.tab) ``` -Based on the results, we can see that the study differences can explain a total of 11.922% ($R^2$) of the variability in microbial abundance profiles before study effect adjustment, whereas after adjustment this was reduced to 2.806% ($R^2$). +Based on the results, we can see that the study differences can explain a total +of 11.922% ($R^2$) of the variability in microbial abundance profiles before +study effect adjustment, whereas after adjustment this was reduced to 2.806% +($R^2$). Let's visualize the results of the PERMANOVA test. -```{r} +```{r visualization} # Load library library(ggplot2) library(cowplot) @@ -165,9 +235,12 @@ p ### Meta-analytical differential abundance testing with lm_meta {#sec-differential-abundance-testing} -Here, we illustrate the details of the meta-analytical differential abundance testing. We have several choices for the analysis methods. First, we apply linear models (LM) for individual study-specific analyses with various transformations. +Here, we illustrate the details of the meta-analytical differential abundance +testing. We have several choices for the analysis methods. First, we apply +linear models (LM) for individual study-specific analyses with various +transformations. -```{r message=FALSE, warning=FALSE} +```{r message=FALSE, warning=FALSE model-selection1} transform <- c("NONE", "AST", "LOGIT", "LOG") num <- NULL @@ -185,9 +258,11 @@ print(num) ``` -Since none of the linear models can generate significant results, we next apply the Tweedie model (using the "CPLM" analysis method in `MaAsLin2`, also implemented in the R package `Tweedieverse`$^15$. +Since none of the linear models can generate significant results, we next apply +the Tweedie model (using the "CPLM" analysis method in `MaAsLin2`, also +implemented in the R package `Tweedieverse`$^15$. -```{r message=FALSE, warning=FALSE} +```{r message=FALSE, warning=FALSE model-selection2} fit_lm_meta <- lm_meta(feature_abd = data_abd_adj, batch = "study_name", exposure = "resvar", @@ -209,12 +284,15 @@ meta_fits %>% ``` -Based on the results, we see that there are 22 significant features in total, among which the \emph{kluyvera ascorbata} has the strongest positive effect, while the \emph{pseudomonas putida} species has the strongest negative effect. +Based on the results, we see that there are 22 significant features in total, +among which the \emph{kluyvera ascorbata} has the strongest positive effect, +while the \emph{pseudomonas putida} species has the strongest negative effect. ## Meta-analysis for pathway abundance data {#sec-meta-analysis-pathway} -In this section, we repeat all the analysis mentioned above using the pathway relative abundance data. We first prepare the input data for the analysis. +In this section, we repeat all the analysis mentioned above using the pathway +relative abundance data. We first prepare the input data for the analysis. -```{r message=FALSE, warning=FALSE} +```{r message=FALSE, warning=FALSE prepare-input-data-pathway-abundance} # Load library library(dplyr) library(MMUPHin) @@ -262,8 +340,9 @@ data_abd_adj <- fit_adjust_batch$feature_abd_adj ![](https://github.com/microbiome/OMA/tree/master/inst/pages/images/adjust_batch_diagnostic_pathway_abundance) -Next, we will evaluate the improvement of the batch effect adjustment and applying the PERMANOVA test. -```{r message=FALSE, warning=FALSE} +Next, we will evaluate the improvement of the batch effect adjustment and +applying the PERMANOVA test. +```{r message=FALSE, warning=FALSE batch-effect-evaluation-pathway-abundance} # Load library library(vegan) @@ -278,10 +357,12 @@ print(fit_adonis_before$aov.tab) print(fit_adonis_after$aov.tab) ``` -Based on the results, wesee that the study differences can explain a total of 15.508% ($R^2$) of the variability in microbial pathway profiles before study effect adjustment, whereas after adjustment this was reduced to 5.037% ($R^2$). +Based on the results, wesee that the study differences can explain a total of +15.508% ($R^2$) of the variability in microbial pathway profiles before study +effect adjustment, whereas after adjustment this was reduced to 5.037% ($R^2$). Let's also visualize the results of the PERMANOVA test. -```{r} +```{r visualization-pathway-abundance} # Load library library(ggplot2) library(cowplot) @@ -335,9 +416,11 @@ p ``` -As before, we have several choices on the analysis methods. However, since we don't have significance at the FDR level, we use the unadjusted p-values to showcase the ``top hits''. +As before, we have several choices on the analysis methods. However, since we +don't have significance at the FDR level, we use the unadjusted p-values to +showcase the ``top hits''. -```{r message=FALSE, warning=FALSE} +```{r message=FALSE, warning=FALSE pathway-abundance-model-selection1} transform <- c("NONE", "AST", "LOGIT", "LOG") num <- NULL @@ -355,7 +438,7 @@ print(num) ``` -```{r} +```{r pathway-abundance-model-selection2} fit_lm_meta2 <- lm_meta(feature_abd = data_abd_adj, batch = "study_name", exposure = "resvar", @@ -366,7 +449,8 @@ num <- sum(fit_lm_meta2$meta_fits$pval < 0.05, na.rm = T) print(num) ``` -Here is the summary table for the number of significant pathways using various meta-analytic differential analysis methods and transformations: +Here is the summary table for the number of significant pathways using various +meta-analytic differential analysis methods and transformations: | Analysis Method | Transform Parameter | Number of Significant Pathways | :----------- | :----------- | :----------- @@ -376,9 +460,10 @@ Here is the summary table for the number of significant pathways using various m | LM | LOG | 11 | CPLM | NONE | 15 -Let's than visualize the results using the "CPLM" analysis method, which identified largest number of significant pathways. +Let's than visualize the results using the "CPLM" analysis method, which +identified largest number of significant pathways. -```{r} +```{rvisualization-pathway-abundance} # Load library library(ggplot2) @@ -398,21 +483,54 @@ meta_fits2 %>% ``` -Based on the results, we observe 15 significant features in total. Among these, the queuosine biosynthesis bacteroides group exhibits the strongest negative effects. Additionally, L-lysine degradation XI is the most positively significant pathway, while xylose degradation IV is the most negatively significant pathway. +Based on the results, we observe 15 significant features in total. Among these, +the queuosine biosynthesis bacteroides group exhibits the strongest negative +effects. Additionally, L-lysine degradation XI is the most positively +significant pathway, while xylose degradation IV is the most negatively +significant pathway. ## Reference -1. Young LH. Strengths and Limitations of Meta-Analysis. Korean J Med. 2019;94(5):391-395. doi: https://doi.org/10.3904/kjm.2019.94.5.391. -2. Finckh A, Tramèr MR. Primer: strengths and weaknesses of meta-analysis. Nat Clin Pract Rheumatol. 2008;4(3):146-152. doi:10.1038/ncprheum0732. -3. Israel H, Richter RR. A guide to understanding meta-analysis. J Orthop Sports Phys Ther. 2011;41(7):496-504. doi:10.2519/jospt.2011.3333. -4. Hoffman JIE. Meta-analysis. Basic Biostatistics for Medical and Biomedical Practitioners. 2nd ed. Academic Press; 2019:621-629. doi:10.1016/B978-0-12-817084-7.00036-X. -5. Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14(Suppl 1):29-37. -6. Ma S, Shungin D, Mallick H, et al. Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin. Genome Biol. 2022;23(1):208. Published 2022 Oct 3. doi:10.1186/s13059-022-02753-4. -7. Pasolli E, Schiffer L, Manghi P, et al. “Accessible, curated metagenomic data through ExperimentHub.” Nat. Methods. 2017;14(11):1023–1024. doi:10.1038/nmeth.4468. -8. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable Association Discovery in Population-scale Meta-omics Studies. PLoS Computational Biology. 2021:17(11):e1009442. -9. Frankel AE, Coughlin LA, Kim J, et al. Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma Patients. Neoplasia. 2017;19(10):848-855. doi:10.1016/j.neo.2017.08.004. -10. Lee KA, Thomas AM, Bolte LA, et al. Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma. Nat Med. 2022;28(3):535-544. doi:10.1038/s41591-022-01695-5. -11. Matson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science. 2018;359(6371):104-108. doi:10.1126/science.aao3290. -12. Peters BA, Wilson M, Moran U, et al. Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients. Genome Med. 2019;11(1):61. Published 2019 Oct 9. doi:10.1186/s13073-019-0672-4. -13. Wind TT, Gacesa R, Vich Vila A, et al. Gut microbial species and metabolic pathways associated with response to treatment with immune checkpoint inhibitors in metastatic melanoma. Melanoma Res. 2020;30(3):235-246. doi:10.1097/CMR.0000000000000656. -14. Tang ZZ, Chen G, Alekseyenko AV. PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances. Bioinformatics. 2016;32(17):2618-2625. doi:10.1093/bioinformatics/btw311. -15. Mallick, H, Chatterjee, S, Chowdhury, S, Chatterjee, S, Rahnavard, A, Hicks, SC. Differential expression of single-cell RNA-seq data using Tweedie models. Statistics in Medicine. 2022; 41( 18): 3492- 3510. doi:10.1002/sim.9430. \ No newline at end of file +1. Young LH. Strengths and Limitations of Meta-Analysis. Korean J Med. +2019;94(5):391-395. doi: https://doi.org/10.3904/kjm.2019.94.5.391. +2. Finckh A, Tramèr MR. Primer: strengths and weaknesses of meta-analysis. +Nat Clin Pract Rheumatol. 2008;4(3):146-152. doi:10.1038/ncprheum0732. +3. Israel H, Richter RR. A guide to understanding meta-analysis. J Orthop Sports +Phys Ther. 2011;41(7):496-504. doi:10.2519/jospt.2011.3333. +4. Hoffman JIE. Meta-analysis. Basic Biostatistics for Medical and Biomedical +Practitioners. 2nd ed. Academic Press; 2019:621-629. +doi:10.1016/B978-0-12-817084-7.00036-X. +5. Haidich AB. Meta-analysis in medical research. Hippokratia. +2010;14(Suppl 1):29-37. +6. Ma S, Shungin D, Mallick H, et al. Population structure discovery in +meta-analyzed microbial communities and inflammatory bowel disease using +MMUPHin. Genome Biol. 2022;23(1):208. Published 2022 Oct 3. +doi:10.1186/s13059-022-02753-4. +7. Pasolli E, Schiffer L, Manghi P, et al. “Accessible, curated metagenomic data +through ExperimentHub.” Nat. Methods. 2017;14(11):1023–1024. +doi:10.1038/nmeth.4468. +8. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable Association Discovery +in Population-scale Meta-omics Studies. PLoS Computational Biology. +2021:17(11):e1009442. +9. Frankel AE, Coughlin LA, Kim J, et al. Metagenomic Shotgun Sequencing and +Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and +Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma +Patients. Neoplasia. 2017;19(10):848-855. doi:10.1016/j.neo.2017.08.004. +10. Lee KA, Thomas AM, Bolte LA, et al. Cross-cohort gut microbiome associations +with immune checkpoint inhibitor response in advanced melanoma. Nat Med. +2022;28(3):535-544. doi:10.1038/s41591-022-01695-5. +11. Matson V, Fessler J, Bao R, et al. The commensal microbiome is associated +with anti-PD-1 efficacy in metastatic melanoma patients. Science. +2018;359(6371):104-108. doi:10.1126/science.aao3290. +12. Peters BA, Wilson M, Moran U, et al. Relating the gut metagenome and +metatranscriptome to immunotherapy responses in melanoma patients. Genome Med. +2019;11(1):61. Published 2019 Oct 9. doi:10.1186/s13073-019-0672-4. +13. Wind TT, Gacesa R, Vich Vila A, et al. Gut microbial species and metabolic +pathways associated with response to treatment with immune checkpoint inhibitors +in metastatic melanoma. Melanoma Res. 2020;30(3):235-246. +doi:10.1097/CMR.0000000000000656. +14. Tang ZZ, Chen G, Alekseyenko AV. PERMANOVA-S: association test for microbial +community composition that accommodates confounders and multiple distances. +Bioinformatics. 2016;32(17):2618-2625. doi:10.1093/bioinformatics/btw311. +15. Mallick, H, Chatterjee, S, Chowdhury, S, Chatterjee, S, Rahnavard, A, Hicks, +SC. Differential expression of single-cell RNA-seq data using Tweedie models. +Statistics in Medicine. 2022; 41( 18): 3492- 3510. doi:10.1002/sim.9430. From be305d001ce77a95e7a1c554c52470885b67c489 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Tue, 26 Mar 2024 05:54:14 -0400 Subject: [PATCH 09/21] Update DESCRIPTION --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2285ccd41..683373376 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -80,7 +80,8 @@ Suggests: igraph, corpcor, circlize, - BiocBook + BiocBook, + SuperLearner Remotes: github::microbiome/miaTime VignetteBuilder: knitr From c485f9837095a120c3b6a82b888eb8e5ddd7b0db Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Mon, 1 Apr 2024 12:23:05 -0400 Subject: [PATCH 10/21] Update MMUPHin_meta_analysis.qmd --- inst/pages/MMUPHin_meta_analysis.qmd | 53 ++++++++++++++++++++-------- 1 file changed, 38 insertions(+), 15 deletions(-) diff --git a/inst/pages/MMUPHin_meta_analysis.qmd b/inst/pages/MMUPHin_meta_analysis.qmd index 739e08f6c..591b0d639 100644 --- a/inst/pages/MMUPHin_meta_analysis.qmd +++ b/inst/pages/MMUPHin_meta_analysis.qmd @@ -3,6 +3,7 @@ ```{r setup, echo=FALSE, results="asis"} library(rebook) chapterPreamble() + ``` Meta-analysis is a statistical analysis that combines the results of multiple @@ -55,7 +56,7 @@ both taxonomic and functional profiles with ICI response. ```{r load_curatedMetagenomicData} library(curatedMetagenomicData) -library(curatedMetagenomicAnalyses) + ``` In the following section, we provide detailed examples of how to perform batch effect correction and meta-analytic differential abundance testing on publicly @@ -80,10 +81,23 @@ datasets are available from the curatedMetagenomicData package, with a total of 285 subjects included. ```{r load-pkg-data} -# Load example data -cond <- 'melanoma' -se_relative <-curatedMetagenomicAnalyses::makeSEforCondition(cond, removestudies = "HMP_2019_ibdmdb", dataType = "relative_abundance") -se_pathway <-curatedMetagenomicAnalyses::makeSEforCondition(cond, removestudies = "HMP_2019_ibdmdb", dataType = "pathway_abundance") + +###################### +# Load melanoma data # +###################### + +library(curatedMetagenomicData) +library(dplyr) + +se_relative <- sampleMetadata |> + filter(study_name %in% c("FrankelAE_2017", "GopalakrishnanV_2018", "LeeKA_2022", + "MatsonV_2018", "PetersBA_2019", "WindTT_2020")) |> + returnSamples("relative_abundance", rownames = "short") + +se_pathway <- sampleMetadata |> + filter(study_name %in% c("FrankelAE_2017", "GopalakrishnanV_2018", "LeeKA_2022", + "MatsonV_2018", "PetersBA_2019", "WindTT_2020")) |> + returnSamples("pathway_abundance", rownames = "short") ``` First, let's look into the relative abundance data. @@ -98,12 +112,14 @@ progression-free survival at 12 months (PFS12), as well as the response to anti-PD-1 (anti_PD_1) as the response variable. By the end of this step, we obtain a batch-adjusted relative abundance matrix. -```{r message=FALSE, warning=FALSE prepare-input-data} -# Load library +```{r prepare-input-data} + library(dplyr) library(MMUPHin) -# Prepare the meta data +########################## +# Create sample metadata # +########################## data_meta <- select(as.data.frame(colData(se_relative)), c("study_name", "ORR", "PFS12", "anti_PD_1")) # Define response variable @@ -120,10 +136,12 @@ data_meta <- data_meta[!is.na(data_meta$resvar), c("study_name", "resvar")] # Convert the "study_name" to factor variable data_meta$study_name <- as.factor(data_meta$study_name) -# Prepare the abundance data +########################### +# Create Species Features # +########################### # Transpose the abundance matrix and change the value of abundance data to proportion unit data_abd <- assay(se_relative) -data_abd <- data_abd/100 +data_abd <- data_abd / 100 # Match the individuals in the data_abd data_abd <- data_abd[, colnames(data_abd) %in% rownames(data_meta)] @@ -152,14 +170,14 @@ scatter plot on the right represents the change in mean abundance between the original and adjusted ones, displaying an obvious decrease after the batch effect adjustment analysis. -### Evaluation for the batch effect adjustment {#sec-batch-adjustment-evaluation} +### Evaluation for batch effect adjustment {#sec-batch-adjustment-evaluation} After obtaining the adjusted abundance matrix, we can evaluate the improvement of the batch effect adjustment. Here, the total variation from study difference will be assessed through the PERMANOVA$^14$ test. ```{r message=FALSE, warning=FALSE batch-effect-evaluation} -# Load library + library(vegan) D_before <- vegdist(t(data_abd)) @@ -180,11 +198,13 @@ study effect adjustment, whereas after adjustment this was reduced to 2.806% Let's visualize the results of the PERMANOVA test. ```{r visualization} -# Load library + library(ggplot2) library(cowplot) -# Ordination +############## +# Ordination # +############## # Before R2_before <- round(fit_adonis_before$aov.tab[1, 5]*100, 1) pcoa_before <- cmdscale(D_before, eig = TRUE) @@ -240,7 +260,10 @@ testing. We have several choices for the analysis methods. First, we apply linear models (LM) for individual study-specific analyses with various transformations. -```{r message=FALSE, warning=FALSE model-selection1} +```{r model-selection1} + +library(Maaslin2) + transform <- c("NONE", "AST", "LOGIT", "LOG") num <- NULL From 4c2a41f2117dbaa829c9d9ae3bc537a05d46bf76 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Mon, 1 Apr 2024 13:32:54 -0400 Subject: [PATCH 11/21] Update IntegratedLearner.qmd --- inst/pages/IntegratedLearner.qmd | 86 ++++++++++++++++---------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/inst/pages/IntegratedLearner.qmd b/inst/pages/IntegratedLearner.qmd index 9a56d289d..57b689230 100644 --- a/inst/pages/IntegratedLearner.qmd +++ b/inst/pages/IntegratedLearner.qmd @@ -36,28 +36,21 @@ functional (pathway abundances) profiles. ```{r message=FALSE, warning=FALSE load-pkg-data} -##################### -# Load example data # -##################### +################## +# Load iHMP data # +################## -library(tidyverse) library(curatedMetagenomicData) +library(dplyr) +library(tidyverse) -cond <- 'IBD' -se_relative <- makeSEforCondition( - cond, - dataType = "relative_abundance", - removestudies = c('HallAB_2017', 'IaniroG_2022', 'IjazUZ_2017', 'LiJ_2014', 'NielsenHB_2014', 'VilaAV_2018')) - - -?curatedMetagenomicData - - +se_relative <- sampleMetadata |> + filter(study_name == "HMP_2019_ibdmdb") |> + returnSamples("relative_abundance", rownames = "short") -se_pathway <- makeSEforCondition( - cond, - dataType = "pathway_abundance", - removestudies = c('HallAB_2017', 'IaniroG_2022', 'IjazUZ_2017', 'LiJ_2014', 'NielsenHB_2014', 'VilaAV_2018')) +se_pathway <- sampleMetadata |> + filter(study_name == "HMP_2019_ibdmdb") |> + returnSamples("pathway_abundance", rownames = "short") ``` @@ -73,7 +66,7 @@ sample_metadata <- select(as.data.frame(colData(se_relative)), c("study_name", " # Define response variable & sample id sample_metadata$Y <- ifelse(sample_metadata$disease == "IBD", 1, 0) -sample_metadata<- sample_metadata %>% dplyr::select(subject_id, Y) %>% rename(subjectID = subject_id) +sample_metadata <- sample_metadata %>% select(subject_id, Y) %>% rename(subjectID = subject_id) ########################### # Create Species Features # @@ -172,6 +165,7 @@ measures (i.e., multiple samples per subject). To address this, we will conduct the `IntegratedLearner` R package$^3$. ```{r additional-data-preprocessing} + # Set parameters and extract subject IDs for sample splitting seed <- 1 set.seed(seed) @@ -181,7 +175,7 @@ subjectID <- unique(sample_metadata$subjectID) # Trigger 5-fold CV (Outer Loop) # ################################## -subjectCvFoldsIN <- caret::createFolds(1:length(subjectID), k = 5, returnTrain=TRUE) +subjectCvFoldsIN <- createFolds(1:length(subjectID), k = 5, returnTrain=TRUE) ######################################### # Curate subject-level samples per fold # @@ -232,7 +226,7 @@ utilizing the SuperLearner R package. for (i in seq_along(name_layers)){ # Prepare single-omic input data - include_list <- feature_metadata %>% dplyr::filter(featureType == name_layers[i]) + include_list <- feature_metadata %>% filter(featureType == name_layers[i]) t_dat_slice <- feature_table[rownames(feature_table) %in% include_list$featureID, ] dat_slice <- as.data.frame(t(t_dat_slice)) Y = sample_metadata$Y @@ -242,11 +236,11 @@ for (i in seq_along(name_layers)){ # Run user-specified base learner library(SuperLearner) - SL_fit_layers[[i]] <- SuperLearner::SuperLearner(Y = Y, - X = X, - cvControl = cvControl, - SL.library = c('SL.randomForest'), - family = binomial()) + SL_fit_layers[[i]] <- SuperLearner(Y = Y, + X = X, + cvControl = cvControl, + SL.library = c('SL.randomForest'), + family = binomial()) # Append the corresponding y and X to the results SL_fit_layers[[i]]$Y <- sample_metadata['Y'] @@ -271,6 +265,7 @@ based on all available data points for the stacked model. Here, we will use `glmnet` as the meta-learner. However, other choices are also possible. ```{r layer-wise-cross-validated-prediction} + ############################## # Prepare stacked input data # ############################## @@ -290,12 +285,12 @@ names(combo_final) <- name_layers #################### # Run user-specified meta learner -SL_fit_stacked<-SuperLearner::SuperLearner(Y = Y, - X = combo, - cvControl = cvControl, - verbose = TRUE, - SL.library = 'SL.glmnet', - family = binomial()) +SL_fit_stacked <- SuperLearner(Y = Y, + X = combo, + cvControl = cvControl, + verbose = TRUE, + SL.library = 'SL.glmnet', + family = binomial()) # Extract the fit object from superlearner model_stacked <- SL_fit_stacked$fitLibrary[[1]]$object @@ -320,11 +315,11 @@ building an integrated prediction model. fulldat<-as.data.frame(t(feature_table)) # Early Fusion using Random Forest -SL_fit_concat <- SuperLearner::SuperLearner(Y = Y, - X = fulldat, - cvControl = cvControl, - SL.library = 'SL.randomForest', - family = binomial()) +SL_fit_concat <- SuperLearner(Y = Y, + X = fulldat, + cvControl = cvControl, + SL.library = 'SL.randomForest', + family = binomial()) # Extract the fit object from SuperLearner model_concat <- SL_fit_concat$fitLibrary[[1]]$object @@ -351,6 +346,7 @@ Here, as examples, we will prepare the input data, fit the multiview model, and run the cross-validation in this section. ```{r prepare-input-data} + ############################## # Prepare data for multiview # ############################## @@ -403,13 +399,15 @@ list.ROC <- vector("list", length = ncol(yhat.train)) names(list.ROC) <- colnames(yhat.train) # Loop over layers +library(ROCR) + for(k in 1:length(list.ROC)){ preds <- yhat.train[ ,k] - pred <- ROCR::prediction(preds, Y) - AUC <- round(ROCR::performance(pred, "auc")@y.values[[1]], 2) - perf <- ROCR::performance(pred, "sens", "spec") - list.ROC[[k]] <- data.frame(sensitivity = methods::slot(perf, "y.values")[[1]], - specificity = 1 - methods::slot(perf, "x.values")[[1]], + pred <- prediction(preds, Y) + AUC <- round(performance(pred, "auc")@y.values[[1]], 2) + perf <- performance(pred, "sens", "spec") + list.ROC[[k]] <- data.frame(sensitivity = slot(perf, "y.values")[[1]], + specificity = 1 - slot(perf, "x.values")[[1]], AUC = AUC, layer = names(list.ROC)[k]) } @@ -428,6 +426,7 @@ classifiers outperform individual layers in distinguishing between IBD and non-IBD controls. ```{r visualization2} + # Prepare data for plotting plot_data <- ROC_table plot_data$displayItem <- paste(plot_data$layer, " AUC = ", plot_data$AUC, sep="") @@ -458,6 +457,7 @@ Finally, we visualize the results for the top 20 features for each layer based on the intermediate fusion. ```{r visualization3} + # Only plot UP TO TOP 20 PER LAYER DD <- DD %>% group_by(view) %>% @@ -467,7 +467,7 @@ DD <- DD %>% library(forcats) p <- DD %>% - mutate(view_col = forcats::fct_reorder(view_col, standardized_coef)) %>% + mutate(view_col = fct_reorder(view_col, standardized_coef)) %>% ggplot(aes(x = view_col, y = standardized_coef, fill = view, width = 0.75)) + geom_bar(stat = "identity", show.legend = FALSE, width = 1) + coord_flip() + From 972c40d2cb725b4d4345075a180e1be76368a136 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Tue, 2 Apr 2024 13:04:18 -0400 Subject: [PATCH 12/21] Delete inst/pages/images/GSEA.qmd --- inst/pages/images/GSEA.qmd | 34 ---------------------------------- 1 file changed, 34 deletions(-) delete mode 100644 inst/pages/images/GSEA.qmd diff --git a/inst/pages/images/GSEA.qmd b/inst/pages/images/GSEA.qmd deleted file mode 100644 index 89b062b95..000000000 --- a/inst/pages/images/GSEA.qmd +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: "GSEA" -format: html -editor: visual -author: Yihan Liu, Himel Mallick ---- - -# Gene set Enrichment Analysis {#sec-GSEA} - -```{r setup, echo=FALSE, results="asis"} -library(rebook) -chapterPreamble() -``` - -## Quarto - -Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see . - -## Running Code - -When you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this: - -```{r} -1 + 1 -``` - -You can add options to executable code like this - -```{r} -#| echo: false -2 * 2 -``` - -The `echo: false` option disables the printing of code (only output is displayed). From 69a7365c05a158498bd95f08abbd5545b5501e2a Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Tue, 2 Apr 2024 13:06:21 -0400 Subject: [PATCH 13/21] Add files via upload --- inst/pages/MSEA.qmd | 574 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 574 insertions(+) create mode 100644 inst/pages/MSEA.qmd diff --git a/inst/pages/MSEA.qmd b/inst/pages/MSEA.qmd new file mode 100644 index 000000000..8a23b274d --- /dev/null +++ b/inst/pages/MSEA.qmd @@ -0,0 +1,574 @@ +--- +title: "MSEA" +format: html +editor: visual +author: Yihan Liu, Himel Mallick +--- + +# **Microbe** Set **Enrichment Analysis** (MSEA) {#sec-MSEA} + +```{r setup, echo=FALSE, results="asis"} +library(rebook) +chapterPreamble() + +``` + +Similar to gene set enrichment analyses for genes$^1$, an obvious next step +following differential abundance analysis in microbiome studies is to conduct +enrichment analysis for microbe sets, known as microbe set enrichment analysis +(MSEA)$^2$. Similar to GSEA, the primary goal of MSEA is to detect the modest +but coordinated changes in pre-specified sets of related microbial features. +Such a set might include all the microbes in a specific pathway or microbial +genes that have been shown to be co-regulated based on previously published +studies. Like GSEA, MSEA aggregates the per-feature statistics across microbes +within a microbe set. This corresponds to the hypothesis that many relevant +phenotype differences are manifested by small but consistent changes in a set of +features. + +The goal of the MSEA approach is to determine if the members of S (microbe set) +are randomly distributed throughout the ranked list of features (L) or primarily +found at the top or bottom. We will use the R package `gsEasy` to conduct the +GSEA test described by Subramanian et al., 2005$^1$ + +## Input data for MSEA using microbiome abundance data + +In this chapter, we will use the publicly available Inflammatory Bowel Diseases +(IBD) microbiome data from the integrative Human Microbiome Project (iHMP) +available from the `curatedMetagenomicData` package (Lloyd-Price et al., +2019$^3$). We aim to conduct MSEA analysis based on both taxonomic profiles +(species relative abundances) and functional profiles (pathway relative +abundances). + +## Performing the MSEA analysis with species relative abundance data + +We will first prepare the input feature table and sample metadata for +differential abundance analysis using `MaAsLin2`$^4$. The ranked feature list from +the differential abundance analysis serves as an input for the MSEA. + +```{r load-pkg-data} + +################## +# Load iHMP data # +################## + +library(curatedMetagenomicData) +library(dplyr) + +se_relative <- sampleMetadata |> + filter(study_name == "HMP_2019_ibdmdb") |> + returnSamples("relative_abundance", rownames = "short") + +########################## +# Create sample metadata # +########################## + +sample_metadata <- + colData(se_relative) %>% + as.data.frame() %>% filter(visit_number == 1) %>% + .[, c("age", "disease", "antibiotics_current_use")] + +################# +# Set reference # +################# + +sample_metadata$disease <- as.factor(sample_metadata$disease) +sample_metadata$disease <- relevel(sample_metadata$disease, 'healthy') + +########################### +# Create species features # +########################### + +feature_species_t <- as.data.frame(assay(se_relative)) +rownames(feature_species_t) <- sub('.*s__', '', rownames(feature_species_t)) + +############################## +# Subset to baseline samples # +############################## + +feature_species <- as.data.frame(t(feature_species_t)) +feature_species <- feature_species[rownames(sample_metadata),] +feature_species <- feature_species / 100 +rm(feature_species_t); rm(se_relative) + +``` + +In the next step, we will use `MaAsLin2` to fit a multivariable regression +model for testing the association between microbial species abundance versus IBD +diagnosis. The analysis method we use here is "LM", which is the default setting. +We also adjust for age and antibiotic usage, following the original study. + +```{r fit-model-Maaslin2} + +library(Maaslin2) + +fit_data = Maaslin2(input_data = feature_species, + input_metadata = sample_metadata, + normalization = "NONE", + output = "output_species", + fixed_effects = c("disease", "age", "antibiotics_current_use")) + +``` + +Unlike gene expression studies, we do not have well-defined signatures or +modules for microbiome data. Here, we will construct data-driven modules using +weighted gene co-expression network analysis (WGCNA)${^5^,^6}$. By working on +the residuals, we want to ensure that the effect of disease and other covariates +has been removed. Following the WGCNA tutorial, we will first check whether +there are outliers in our data. + +```{r extract-residuals} + +library(WGCNA) + +datExpr <- as.data.frame(t(fit_data$residuals)) +gsg = goodSamplesGenes(datExpr, verbose = 3) +gsg$allOK + +``` + +If the last statement returns TRUE, no outliers are identified. If not, we need +to remove the outliers from the data. + +```{r check-outliers1} + +if (!gsg$allOK) +{if (sum(!gsg$goodGenes) > 0) + printFlush(paste("Removing genes:", paste(names(datExpr)[!gsg$goodGenes], collapse = ", "))); + if (sum(!gsg$goodSamples) > 0) + printFlush(paste("Removing samples:", paste(rownames(datExpr)[!gsg$goodSamples], collapse =", "))) + datExpr = datExpr[gsg$goodSamples, gsg$goodGenes] +} + +``` + +After removing the outliers, we need to choose a suitable soft threshold +parameter for creating the modules as part of the WGCNA algorithm. This power +value must produce a graph similar to a scale-free network. We can use the mean +connectivity graphic for the selection of this power parameter. + +```{r choose-soft-threshold} + +# Choose a set of soft threshold parameters +powers = c(c(1:20), seq(from = 22, to=30, by=2)) +sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5, dataIsExpr = T, RsquaredCut = 0.30) + +``` + +In this step, we will conduct a one-step module detection based on the selected +soft threshold parameter selected above. + +```{r module-detection} + +power = sft$powerEstimate +net = blockwiseModules(datExpr, + power = power, + corFnc="bicor", + corOptions=list(maxPOutliers=0.1), + networkType ="unsigned", + maxBlockSize = ncol(datExpr), + minModuleSize = 3, + TOMType = "unsigned", + reassignThreshold = 0, + mergeCutHeight = 0, + verbose = 3) + +#################### +# How many modules # +#################### + +ncol(net$MEs) +table(net$colors) + +``` + +The WGCNA algorithm produced 14 modules which we can visualize as follows. + +```{r visualization} + +########################## +# Plot module dendrogram # +########################## + +eigenGenes <- net$MEs +MEDiss = 1-cor(eigenGenes) +METree = hclust(as.dist(MEDiss), method = "average") +plot(METree, main = "Clustering of module eigengenes", xlab = "", sub = "") + +``` + +Next, we calculate hub genes for the modules and create the mapping files to +proceed with the MSEA. + +```{r create-mapping-files} + +########################################### +# Re-calculate modules and find hub genes # +########################################### + +moduleColors <- net$colors +MEs0 = moduleEigengenes(datExpr, moduleColors)$eigengenes +modules_data = orderMEs(MEs0) + +####################### +# Create mapping file # +####################### + +library(tidyverse) + +feature_by_modules <- as.data.frame(net$colors) +feature_by_modules <- rownames_to_column(feature_by_modules) +colnames(feature_by_modules) <- c('Feature', 'Module') +features_mapping <- feature_by_modules +features_mapping$Module <- paste('ME', features_mapping$Module, sep = '') + +``` + +Finally, we will run the MSEA analysis on the modules we constructed using +WGCNA. Here, we first create a wrapper for the MSEA analysis using the `gsEasy` +package. + +```{r MSEA-wrapper} + +library(reshape2) +library(gsEasy) + +################ +# MSEA Wrapper # +################ + +run_MSEA <- function(microbeSet, # A list + ranked_features, # Ranked list of featured + filter.count = 3, + seed = 1234, + fdr.correction = 'BH') { + + + ################### + # Filter out sets # + ################## + + microbeSet0 <- microbeSet + cond <- sapply(microbeSet0, function(x) length(x) > filter.count) + microbeSet <- microbeSet0[cond] + lengthmicrobeSet <- as.data.frame(reshape2::melt(lapply(microbeSet, function(x) length(x)))) + colnames(lengthmicrobeSet) <- c('Freq','Set') + + ################ + # Classic MSEA # + ################ + + set.seed(seed) + enrichment <- as.data.frame(sapply(microbeSet, function(set) gset(S = set, r = ranked_features))) + colnames(enrichment)<-'ES' + enrichment <- rownames_to_column(enrichment, 'Set') + enrichment <- merge(enrichment, lengthmicrobeSet, 'Set') + enrichment$qval <- p.adjust(enrichment$ES, fdr.correction) + + ########## + # Return # + ########## + + return(enrichment) + +} + +``` + +Before running the MSEA, we also need to rank the differential analysis results +from `MaAsLin2`. We use the `topGo` package to create a list of microbe sets +from the mapping. + +```{r MSEA-processing} + +################### +# Rank DA results # +################### + +results <- fit_data$results %>% filter(metadata == 'disease') +results$qval <- p.adjust(results$pval, 'BH') +results <- results[order(results$qval, decreasing = FALSE), ] + +################### +# MSEA Processing # +################### + +library(topGO) +module_map <- features_mapping +mod.gs <- tapply(module_map$Module, module_map$Feature, as.character) +microbeSet <- inverseList(mod.gs) +microbeSet + +``` + +We are now ready to run the MSEA analysis. We run $100,000$ permutations to +calculate the enrichment scores. + +```{r calculate-enrichment-scores} + +MSEA <- run_MSEA(microbeSet, results$feature) +MSEA <- MSEA[, c('Set', 'Freq', 'ES', setdiff(names(MSEA), c('Set', 'Freq', 'ES')))] +colnames(MSEA) <- c('ID', 'Size', 'pval', 'qval') +MSEA$ID <- paste(MSEA$ID, ' (', MSEA$Size, ')', sep = '') + +``` + +We can plot the enrichment scores to visualize the MSEA results. + +```{r visualzation} + +p <- MSEA %>% + arrange(-pval) %>% + mutate(ID = factor(ID, levels = ID)) %>% + ggplot(aes(y = -log10(pval), x = ID)) + + geom_bar(stat = "identity", fill = 'cornflowerblue') + theme_bw() + + coord_flip() + + ggtitle('Statistically significant modules associated with disease') + + xlab('') + + ylab('MSEA enrichment score') + +print(p) + +``` + +Based on the MSEA results, we obtain 13 enriched modules of microbial species. +We can also examine the members of the top enriched modules. + +## Print the most significant modules for relative abundance data + +```{r module-print} + +microbeSet[["MEpurple"]] +microbeSet[["MEbrown"]] + +``` + +## Performing the MSEA analysis with pathway relative abundance data + +Next, we repeat the MSEA with the pathway relative abundance data from the iHMP +project and follow the same steps as before. + +```{r input-data} + +########################## +# Load HMP2 pathway data # +########################## + +se_pathway <- sampleMetadata |> + filter(study_name == "HMP_2019_ibdmdb") |> + returnSamples("pathway_abundance", rownames = "short") + +########################## +# Create sample metadata # +########################## + +sample_metadata <- + colData(se_pathway) %>% + as.data.frame() %>% filter(visit_number == 1) %>% + select("age", "disease", "antibiotics_current_use") + +# Set reference +sample_metadata$disease <- as.factor(sample_metadata$disease) +sample_metadata$disease <- relevel(sample_metadata$disease, 'healthy') + +########################### +# Create Pathway Features # +########################### + +feature_pwys_t <- as.data.frame(assay(se_pathway)) +feature_pwys_t <- rownames_to_column(feature_pwys_t, "ID") +feature_pwys_t <- feature_pwys_t %>% + filter(!grepl("\\|", ID)) %>% + filter(!ID %in% c('UNMAPPED', 'UNINTEGRATED')) %>% + column_to_rownames('ID') %>% + as.data.frame() + +############################## +# Subset to baseline samples # +############################## + +feature_pwys <- as.data.frame(t(feature_pwys_t)) +feature_pwys <- feature_pwys[rownames(sample_metadata),] +feature_pwys <- feature_pwys / 100 +rm(feature_pwys_t); rm(se_pathway) + +``` + +As before, we first run a `MaAsLin2` analysis using default settings and +construct the modules using residuals from the `MaAsLin2` models. + +```{r MSEA-preparation} + +fit_data = Maaslin2(input_data = feature_pwys, + input_metadata = sample_metadata, + normalization = "NONE", + output = "output_pwys", + fixed_effects = c("disease", "age", "antibiotics_current_use")) + +########################## +# Extract the residuals # +########################## + +datExpr <- as.data.frame(t(fit_data$residuals)) + +######################## +# Create WGCNA modules # +######################## + +gsg = goodSamplesGenes(datExpr, verbose = 3) +gsg$allOK + +if (!gsg$allOK) +{if (sum(!gsg$goodGenes) > 0) + printFlush(paste("Removing genes:", paste(names(datExpr)[!gsg$goodGenes], collapse = ", "))); + if (sum(!gsg$goodSamples) > 0) + printFlush(paste("Removing samples:", paste(rownames(datExpr)[!gsg$goodSamples], collapse =", "))) + datExpr = datExpr[gsg$goodSamples, gsg$goodGenes] +} + +gsg = goodSamplesGenes(datExpr, verbose = 3) +gsg$allOK # TRUE + +################################### +# Choose soft threshold parameter # +################################### + +powers = c(c(1:20), seq(from = 22, to=30, by=2)) +sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5, dataIsExpr = T, RsquaredCut = 0.30) + +############################## +# One-step module detection # +############################## + +power = sft$powerEstimate +net = blockwiseModules(datExpr, + power = power, + corFnc = "bicor", + corOptions = list(maxPOutliers = 0.1), + networkType ="unsigned", + maxBlockSize = ncol(datExpr), + minModuleSize = 3, + TOMType = "unsigned", + reassignThreshold = 0, + mergeCutHeight = 0, + verbose = 3) + +#################### +# How many modules # +#################### + +ncol(net$MEs) +table(net$colors) + +########################## +# Plot module dendrogram # +########################## + +eigenGenes <- net$MEs +MEDiss = 1-cor(eigenGenes) +METree = hclust(as.dist(MEDiss), method = "average") +plot(METree, main = "Clustering of module eigengenes", xlab = "", sub = "") + +########################################### +# Re-calculate modules and find hub genes # +########################################### + +moduleColors <- net$colors +MEs0 = moduleEigengenes(datExpr, moduleColors)$eigengenes +modules_data = orderMEs(MEs0) + +####################### +# Create mapping file # +####################### + +feature_by_modules <- as.data.frame(net$colors) +feature_by_modules <- rownames_to_column(feature_by_modules) +colnames(feature_by_modules) <- c('Feature', 'Module') +features_mapping <- feature_by_modules +features_mapping$Module <- paste('ME', features_mapping$Module, sep = '') + +``` + +Similarly, we perform the MSEA as before using the modules from the WGCNA analysis on the +pathways. + +```{r perform-MSEA-and-visualization} + +################### +# Rank DA results # +################### + +results <- fit_data$results %>% filter(metadata=='disease') +results$qval <- p.adjust(results$pval, 'BH') +sum(results$qval < 0.05) +results <- results[order(results$qval, decreasing = FALSE),] + +################### +# MSEA Processing # +################## + +module_map <- features_mapping +mod.gs <- tapply(module_map$Module, module_map$Feature, as.character) +microbeSet <- inverseList(mod.gs) +microbeSet + +############ +# Run MSEA # +############ + +MSEA <- run_MSEA(microbeSet, results$feature) +MSEA <- MSEA[, c('Set', 'Freq', 'ES', setdiff(names(MSEA), c('Set', 'Freq', 'ES')))] +colnames(MSEA) <- c('ID', 'Size', 'pval', 'qval') +MSEA$ID <- paste(MSEA$ID, ' (', MSEA$Size, ')', sep = '') + +######## +# Plot # +######## + +p <- MSEA %>% + arrange(-pval) %>% + mutate(ID = factor(ID, levels = ID)) %>% + ggplot(aes(y = -log10(pval), x = ID)) + + geom_bar(stat = "identity", fill = 'cornflowerblue') + theme_bw() + + coord_flip() + + ggtitle('Statistically significant modules associated with disease') + + xlab('') + + ylab('MSEA enrichment score') + +print(p) + +``` + +Based on the MSEA results, we obtain 4 enriched modules of microbial pathways. +We can similarly examine the members of the top enriched modules. + +## Print the most significant modules for pathway abundance data + +```{r significant-results} + +# Print the most significant modules +microbeSet[["MEpurple"]] +microbeSet[["MEbrown"]] + +``` + +## Reference + +1. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a +knowledge-based approach for interpreting genome-wide expression profiles. Proc +Natl Acad Sci U S A. 2005;102(43):15545-15550. doi:10.1073/pnas.0506580102. +2. Kou, Y., Xu, X., Zhu, Z. *et al.* Microbe-set enrichment analysis +facilitates functional interpretation of microbiome profiling data. *Sci Rep* +**10**, 21466 (2020). https://doi.org/10.1038/s41598-020-78511-y. +3. Lloyd-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut +microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569(7758):655- +662. doi:10.1038/s41586-019-1237-9. +4. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable association discovery +in population-scale meta-omics studies. PLoS Comput Biol. 2021;17(11):e1009442. +Published 2021 Nov 16. doi:10.1371/journal.pcbi.1009442. +5. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network +analysis. BMC Bioinformatics. 2008;9:559. Published 2008 Dec 29. doi:10.1186/ +1471-2105-9-559. +6. Geistlinger L, Mirzayi C, Zohra F, et al. BugSigDB captures patterns of +differential abundance across a broad range of host-associated microbial +signatures. Nat Biotechnol. Published online September 11, 2023. doi:10.1038/ +s41587-023-01872-y. \ No newline at end of file From 56acf653d7d10dac06bbf82c4f45f0504c412404 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Tue, 9 Apr 2024 13:22:25 -0400 Subject: [PATCH 14/21] Update 90_acknowledgments.qmd --- inst/pages/90_acknowledgments.qmd | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/inst/pages/90_acknowledgments.qmd b/inst/pages/90_acknowledgments.qmd index 7c832ba95..a55b5406e 100644 --- a/inst/pages/90_acknowledgments.qmd +++ b/inst/pages/90_acknowledgments.qmd @@ -87,6 +87,10 @@ key authors/contributors include: - *Jacques Serizay* converted the _OMA_ book to the `BiocBook` format. This allows the _OMA_ book to be built and distributed by Bioconductor. +- *Himel Mallick, PhD, FASA*, principal investigator and tenure-track faculty at Cornell University’s Department of Population Health Sciences and an adjunct faculty of Statistics and Data Science at Bowers College of Computing and Information Science. He contributed to the chapters on meta-analyses, microbe set enrichment analysis (MSEA) and multi-omics prediction and classification. + +- *Yihan Liu*, assisted Dr. Mallick in writing the chapters on meta-anlayses, MSEA and multi-omics prediction and classification. + ### Acknowledgments {-} This work would not have been possible without the countless From 55d985a5e748b74c8438bced2906c91b52d46abd Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Mon, 15 Apr 2024 04:31:18 -0400 Subject: [PATCH 15/21] Update DESCRIPTION --- DESCRIPTION | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 683373376..72ca59944 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -81,7 +81,18 @@ Suggests: corpcor, circlize, BiocBook, - SuperLearner + SuperLearner, + WGCNA, + reshape2, + gsEasy, + topGO, + caret, + multiview, + glmnet, + forcats, + ROCR, + MMUPHin, + cowplot Remotes: github::microbiome/miaTime VignetteBuilder: knitr From 844dd32c28fb866f416f6eb2de9d5be8a4a6efdd Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Mon, 15 Apr 2024 08:32:13 -0400 Subject: [PATCH 16/21] Update bibliography.bib --- inst/assets/bibliography.bib | 283 +++++++++++++++++++++++++++++++++++ 1 file changed, 283 insertions(+) diff --git a/inst/assets/bibliography.bib b/inst/assets/bibliography.bib index d3bc47578..117828d90 100644 --- a/inst/assets/bibliography.bib +++ b/inst/assets/bibliography.bib @@ -1832,3 +1832,286 @@ @article{yaverouglu2014revealing volume = {4}, year = {2014} } + + + + + + + + + +@article{Lee2019, + author = {Lee YH}, + title = {Strengths and Limitations of Meta-Analysis}, + journal = {The Korean Journal of Internal Medicine}, + volume = {94}, + number = {5}, + pages = {391-395}, + url = {https://www.ekjm.org/journal/view.php?number=25636}, + year = {2019} +} + + +@article{Finckh2008, + author = {Finckh A}, 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ecosystem in inflammatory bowel diseases}, + journal = {Nature}, + volume = {569}, + number = {7758}, + pages = {655-662}, + url = {}, + year = {2019} +} + +2005;102(43):. doi:10.1073/pnas.0506580102. +@article{Subramanian2005, + author = {Subramanian A}, + title = {Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles}, + journal = {Proceedings of the National Academy of Sciences}, + volume = {102}, + number = {43}, + pages = {15545-15550}, + url = {https://www.pnas.org/doi/10.1073/pnas.0506580102?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed}, + year = {2005} +} + + +@article{Kou2020, + author = {Kou Y}, + title = {Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data}, + journal = {Scientific Reports}, + volume = {10}, + number = {1}, + pages = {21466}, + url = {https://www.nature.com/articles/s41598-020-78511-y}, + year = {2020} +} + + +@article{Langfelder2008, + author = {Langfelder}, + title = {WGCNA: an R package for weighted correlation network analysis}, + journal = {BMC Bioinformatics}, + volume = {9}, + number = {}, + pages = {559}, + url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559}, + year = {2008} +} + + +@article{Geistlinger2023, + author = {Geistlinger L}, + title = {BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures}, + journal = {Nature Biotechnology}, + volume = {}, + number = {}, + pages = {}, + url = {https://doi.org/10.1038/s41587-023-01872-y}, + year = {2023} +} From 235fe8d522f753e35f8ef83fd653b73be1fe8a52 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Mon, 15 Apr 2024 08:32:38 -0400 Subject: [PATCH 17/21] Update bibliography.bib --- inst/assets/bibliography.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inst/assets/bibliography.bib b/inst/assets/bibliography.bib index 117828d90..22b0672f9 100644 --- a/inst/assets/bibliography.bib +++ b/inst/assets/bibliography.bib @@ -2068,7 +2068,7 @@ @article{Lloyd-Price2019 year = {2019} } -2005;102(43):. doi:10.1073/pnas.0506580102. + @article{Subramanian2005, author = {Subramanian A}, title = {Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles}, From c6d0926e6e9b6eaf015fa55f44fbf0013a41bee3 Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Sat, 20 Apr 2024 12:38:12 -0400 Subject: [PATCH 18/21] Update MMUPHin_meta_analysis.qmd --- inst/pages/MMUPHin_meta_analysis.qmd | 362 +++++++++++++-------------- 1 file changed, 169 insertions(+), 193 deletions(-) diff --git a/inst/pages/MMUPHin_meta_analysis.qmd b/inst/pages/MMUPHin_meta_analysis.qmd index 591b0d639..93d1b3431 100644 --- a/inst/pages/MMUPHin_meta_analysis.qmd +++ b/inst/pages/MMUPHin_meta_analysis.qmd @@ -1,3 +1,9 @@ +--- +editor: + markdown: + wrap: 72 +--- + # Meta-analyses {#sec-meta-analyses} ```{r setup, echo=FALSE, results="asis"} @@ -6,79 +12,88 @@ chapterPreamble() ``` -Meta-analysis is a statistical analysis that combines the results of multiple -scientific studies by appropriately weighting the individual study-specific -effect sizes$^{1, 2, 3}$. A common meta-analytic combination strategy is to -take a weighted average of the study-specific summary measures. In fixed-effects -meta-analysis, the weights are based on the assumption that there is a single -true parameter underlying all the studies, while in random-effects -meta-analysis, the weights are based on a model where the true parameter varies -across studies according to a probability distribution. - -Meta-analysis is particularly useful when individual studies are small, and a -joint analysis of these studies can enhance the precision of effect size -estimation and improve statistical power. Especially in microbiome studies, -individual effect sizes from each study tend to be small$^{4, 5, 6}$. Only -through meta-analysis can these small effects reach statistical significance. -Therefore, by jointly analyzing these studies, we amplify the collective voice -of the data, offering a more comprehensive understanding of the effects under +Meta-analysis is a statistical analysis that combines the results of +multiple scientific studies by appropriately weighting the individual +study-specific effect sizes [@Lee2019, @Finckh2008, @Israel2011]. A +common meta-analytic combination strategy is to take a weighted average +of the study-specific summary measures. In fixed-effects meta-analysis, +the weights are based on the assumption that there is a single true +parameter underlying all the studies, while in random-effects +meta-analysis, the weights are based on a model where the true parameter +varies across studies according to a probability distribution. + +Meta-analysis is particularly useful when individual studies are small, +and a joint analysis of these studies can enhance the precision of +effect size estimation and improve statistical power. Especially in +microbiome studies, individual effect sizes from each study tend to be +small [@Hoffman2019, @Haidich2010, @Ma2022]. Only through meta-analysis +can these small effects reach statistical significance. Therefore, by +jointly analyzing these studies, we amplify the collective voice of the +data, offering a more comprehensive understanding of the effects under investigation. -In this chapter, our focus is on using the R/Bioconductor package [MMUPHin]$^6$ -(Meta-Analysis Methods with a Uniform Pipeline for Heterogeneity in microbiome -studies) to perform the meta-analysis of microbiome data. We utilize publicly -available cancer microbiome datasets from the R/Bioconductor package -[curatedMetagenomicData]$^7$ +In this chapter, our focus is on using the R/Bioconductor package +\[MMUPHin\] (Meta-Analysis Methods with a Uniform Pipeline for +Heterogeneity in microbiome studies) [@Ma2022] to perform the +meta-analysis of microbiome data. We utilize publicly available cancer +microbiome datasets from the R/Bioconductor package +\[curatedMetagenomicData\] [@Pasolli2017]. ## Meta-analysis of cancer microbiome studies using relative abundance data -Recent work has indicated a possible involvement of the gut microbiome in -influencing the efficacy of immune checkpoint inhibitor (ICI) treatment -strategies for PD-1/PD-L1 (Programmed Cell Death Protein 1/Programmed Cell Death -Ligand 1) targeting checkpoint inhibitors$^{8, 9, 10, 11, 12}$. Several -investigations into the microbiome have provided clinical data suggesting that -the gut microbiome modulates the response to inhibitors of the PD-1/PD-L1 axis. -However, published cancer microbiome studies face challenges, such as limited -sample sizes, inconsistent data analysis methods, and a lack of functionally -relevant consensus signatures. - -Elucidation of the mechanisms by which the gut microbiome alters the function of -the immune system to enable or promote cancer development may reveal novel -pathways to explore in cancer therapy. Consequently, a high-quality re-analysis -of public cancer microbiome data through a systematic meta-analysis approach -could provide valuable insights into the microbiome's role in cancer development -and progression in a rapid and cost-effective manner. - -Using advanced melanoma as a model, extensively studied in the context of -PD-1/PD-L1 immune checkpoint inhibitors, our aim is to identify functional -biomarkers positively and negatively associated with ICI response by analyzing -both taxonomic and functional profiles with ICI response. +Recent work has indicated a possible involvement of the gut microbiome +in influencing the efficacy of immune checkpoint inhibitor (ICI) +treatment strategies for PD-1/PD-L1 (Programmed Cell Death Protein +1/Programmed Cell Death Ligand 1) targeting checkpoint inhibitors +[@Frankel2017, @Lee2022, @Matson2018, @Peters2019]. Several +investigations into the microbiome have provided clinical data +suggesting that the gut microbiome modulates the response to inhibitors +of the PD-1/PD-L1 axis. However, published cancer microbiome studies +face challenges, such as limited sample sizes, inconsistent data +analysis methods, and a lack of functionally relevant consensus +signatures. + +Elucidation of the mechanisms by which the gut microbiome alters the +function of the immune system to enable or promote cancer development +may reveal novel pathways to explore in cancer therapy. Consequently, a +high-quality re-analysis of public cancer microbiome data through a +systematic meta-analysis approach could provide valuable insights into +the microbiome's role in cancer development and progression in a rapid +and cost-effective manner. + +Using advanced melanoma as a model, extensively studied in the context +of PD-1/PD-L1 immune checkpoint inhibitors, our aim is to identify +functional biomarkers positively and negatively associated with ICI +response by analyzing both taxonomic and functional profiles with ICI +response. ```{r load_curatedMetagenomicData} library(curatedMetagenomicData) ``` -In the following section, we provide detailed examples of how to perform batch -effect correction and meta-analytic differential abundance testing on publicly -available cancer microbiome data from multiple studies. We use the R package -MMUPHin for these tasks. MMUPHin is a recently developed R/Bioconductor package -designed for meta-analysis of microbiome taxonomic and functional profiles. It -is agnostic to the data type and leverages another R/Bioconductor package, -MaAsLin2$^8$, as a backend to conduct the meta-analysis. MaAsLin2 is -particularly designed to be applicable to various microbial community data types -(taxonomy or functional profiles) and environments (human or otherwise). It is -modular, including implementations of alternative normalization/transformation -schemes and statistical models (e.g., for amplicon vs. shotgun metagenomic -profiles). Leveraging this flexible framework under the hood, MMUPHin performs -normalization, batch effect correction, and meta-analysis using the default -random effects meta-regression. - -Here, we will first perform the batch effect correction analysis. We use both -the relative abundance data and the pathway abundance data from 5 public -datasets: Frankel et al. (2017)$^9$, LeeK et al. (2022)$^10$, Matson et al. -(2018)$^11$, Peters et al. (2019)$^12$, and Wind et al. (2020)$^13$. All these -datasets are available from the curatedMetagenomicData package, with a total of -285 subjects included. + +In the following section, we provide detailed examples of how to perform +batch (study) effect correction and meta-analytic differential abundance +testing on publicly available cancer microbiome data from multiple +studies. We use the R package MMUPHin for these tasks. MMUPHin is a +recently developed R/Bioconductor package designed for meta-analysis of +microbiome taxonomic and functional profiles. It is agnostic to the data +type and leverages another R/Bioconductor package, MaAsLin2 +[@Mallick2021], as a backend to conduct the meta-analysis. MaAsLin2 is +particularly designed to be applicable to various microbial community +data types (taxonomy or functional profiles) and environments (human or +otherwise). It is modular, including implementations of alternative +normalization/transformation schemes and statistical models (e.g., +amplicon vs. shotgun metagenomic profiles). Leveraging this flexible +framework under the hood, MMUPHin performs normalization, study (batch) +effect correction, and meta-analysis using the default random effects +meta-regression. + +Here, we will first perform the batch effect correction analysis. We use +both the relative abundance data and the pathway abundance data from 5 +public datasets: @Frankel2017, @Lee2022, @Matson2018, @Peters2019, and +@Wind2020. All these datasets are available from the +curatedMetagenomicData package, with a total of 285 subjects included. ```{r load-pkg-data} @@ -86,7 +101,6 @@ datasets are available from the curatedMetagenomicData package, with a total of # Load melanoma data # ###################### -library(curatedMetagenomicData) library(dplyr) se_relative <- sampleMetadata |> @@ -100,21 +114,20 @@ se_pathway <- sampleMetadata |> returnSamples("pathway_abundance", rownames = "short") ``` + First, let's look into the relative abundance data. ### Performing batch (study) effect adjustment with adjust_batch for relative abundance {#sec-batch-effect-adjustment} -In this analysis, we are using two input files. The first input file is the -relative abundance data file, which consists of a feature-by-sample matrix. The -second input is the metadata associated with the relative abundance file, which -includes the study names and combines overall response rate (ORR), -progression-free survival at 12 months (PFS12), as well as the response to -anti-PD-1 (anti_PD_1) as the response variable. By the end of this step, we -obtain a batch-adjusted relative abundance matrix. +In this analysis, we are using two input files. The first input file is +the relative abundance data file, which consists of a feature-by-sample +matrix. The second input is the metadata associated with the relative +abundance file, which includes the study names and overall response rate +(ORR) the response variable along with other metadata. By the end of +this step, we obtain a batch-adjusted relative abundance matrix. ```{r prepare-input-data} -library(dplyr) library(MMUPHin) ########################## @@ -159,24 +172,27 @@ fit_adjust_batch <- adjust_batch(feature_abd = data_abd, data_abd_adj <- fit_adjust_batch$feature_abd_adj ``` + ![](https://github.com/microbiome/OMA/blob/master/inst/pages/images/adjust_batch_diagnostic_relative_abundance) -The first figure shows the relationship between the original batch mean -parameters (Gamma) and the shrunk (regularized) batch mean parameters (Gamma -(shrinked)). The points represent the 5 different study groups within the -dataset. The var_batch points represent the variance within each batch, with -different colors indicating different levels or categories of variance. The -scatter plot on the right represents the change in mean abundance between the -original and adjusted ones, displaying an obvious decrease after the batch -effect adjustment analysis. +The first figure shows the relationship between the original batch mean +parameters (Gamma) and the shrunk (regularized) batch mean parameters +(Gamma (shrunken)). The points represent the 5 different study groups +within the dataset. The var_batch points represent the variance within +each batch, with different colors indicating different levels or +categories of variance. The scatter plot on the right represents the +change in mean abundance between the original and adjusted ones, +displaying an obvious decrease after the batch effect adjustment +analysis. ### Evaluation for batch effect adjustment {#sec-batch-adjustment-evaluation} -After obtaining the adjusted abundance matrix, we can evaluate the improvement -of the batch effect adjustment. Here, the total variation from study difference -will be assessed through the PERMANOVA$^14$ test. +After obtaining the adjusted abundance matrix, we can evaluate the +improvement of the batch effect adjustment. Here, the total variation +from study difference will be assessed through the PERMANOVA test +[@Tang2016]. -```{r message=FALSE, warning=FALSE batch-effect-evaluation} +```{r batch-effect-evaluation} library(vegan) @@ -191,12 +207,14 @@ print(fit_adonis_before$aov.tab) print(fit_adonis_after$aov.tab) ``` -Based on the results, we can see that the study differences can explain a total -of 11.922% ($R^2$) of the variability in microbial abundance profiles before -study effect adjustment, whereas after adjustment this was reduced to 2.806% -($R^2$). -Let's visualize the results of the PERMANOVA test. +Based on the results, we can see that the study differences can explain +a total of 11.922% ($R^2$) of the variability in microbial abundance +profiles before study effect adjustment, whereas after adjustment this +was reduced to 2.806% ($R^2$). + +Let's visualize the results of the PERMANOVA test. + ```{r visualization} library(ggplot2) @@ -255,10 +273,10 @@ p ### Meta-analytical differential abundance testing with lm_meta {#sec-differential-abundance-testing} -Here, we illustrate the details of the meta-analytical differential abundance -testing. We have several choices for the analysis methods. First, we apply -linear models (LM) for individual study-specific analyses with various -transformations. +Here, we illustrate the details of the meta-analytical differential +abundance testing. We have several choices for the analysis methods. +First, we apply linear models (LM) for individual study-specific +analyses with various transformations. ```{r model-selection1} @@ -281,11 +299,12 @@ print(num) ``` -Since none of the linear models can generate significant results, we next apply -the Tweedie model (using the "CPLM" analysis method in `MaAsLin2`, also -implemented in the R package `Tweedieverse`$^15$. +Since none of the linear models are able to generate significant +results, we next apply the Tweedie model (using the "CPLM" analysis +method in `MaAsLin2`, also implemented in the R package `Tweedieverse` +[@Mallick2022]. -```{r message=FALSE, warning=FALSE model-selection2} +```{r model-selection2} fit_lm_meta <- lm_meta(feature_abd = data_abd_adj, batch = "study_name", exposure = "resvar", @@ -294,9 +313,6 @@ fit_lm_meta <- lm_meta(feature_abd = data_abd_adj, meta_fits <- fit_lm_meta$meta_fits -# Load library -library(ggplot2) - meta_fits %>% filter(qval.fdr < 0.05) %>% arrange(coef) %>% @@ -307,18 +323,18 @@ meta_fits %>% ``` -Based on the results, we see that there are 22 significant features in total, -among which the \emph{kluyvera ascorbata} has the strongest positive effect, -while the \emph{pseudomonas putida} species has the strongest negative effect. +Based on the results, we see that there are 22 significant features in +total, among which the \emph{kluyvera ascorbata} has the strongest +positive effect, while the \emph{pseudomonas putida} species has the +strongest negative effect. ## Meta-analysis for pathway abundance data {#sec-meta-analysis-pathway} -In this section, we repeat all the analysis mentioned above using the pathway -relative abundance data. We first prepare the input data for the analysis. -```{r message=FALSE, warning=FALSE prepare-input-data-pathway-abundance} -# Load library -library(dplyr) -library(MMUPHin) +In this section, we repeat all the analysis mentioned above using the +pathway relative abundance data. We first prepare the input data for the +analysis. + +```{r prepare-input-data-pathway-abundance} # Prepare the meta data data_meta <- select(as.data.frame(colData(se_pathway)), c("study_name", "ORR", "PFS12", "anti_PD_1")) @@ -360,14 +376,13 @@ fit_adjust_batch <- adjust_batch(feature_abd = data_abd, data_abd_adj <- fit_adjust_batch$feature_abd_adj ``` + ![](https://github.com/microbiome/OMA/tree/master/inst/pages/images/adjust_batch_diagnostic_pathway_abundance) +Next, we will evaluate the improvement of the batch effect adjustment +and apply the PERMANOVA test. -Next, we will evaluate the improvement of the batch effect adjustment and -applying the PERMANOVA test. -```{r message=FALSE, warning=FALSE batch-effect-evaluation-pathway-abundance} -# Load library -library(vegan) +```{r batch-effect-evaluation-pathway-abundance} D_before <- vegdist(t(data_abd)) D_after <- vegdist(t(data_abd_adj)) @@ -380,15 +395,15 @@ print(fit_adonis_before$aov.tab) print(fit_adonis_after$aov.tab) ``` -Based on the results, wesee that the study differences can explain a total of -15.508% ($R^2$) of the variability in microbial pathway profiles before study -effect adjustment, whereas after adjustment this was reduced to 5.037% ($R^2$). -Let's also visualize the results of the PERMANOVA test. +Based on the results, we see that the study differences can explain a +total of 15.508% ($R^2$) of the variability in microbial pathway +profiles before study effect adjustment, whereas after adjustment this +was reduced to 5.037% ($R^2$). + +Let's also visualize the results of the PERMANOVA test. + ```{r visualization-pathway-abundance} -# Load library -library(ggplot2) -library(cowplot) # Ordination # Before @@ -439,11 +454,11 @@ p ``` -As before, we have several choices on the analysis methods. However, since we -don't have significance at the FDR level, we use the unadjusted p-values to -showcase the ``top hits''. +As before, we have several choices on the analysis methods. However, +since we don't have significance at the FDR level, we use the unadjusted +p-values to showcase the \`\`top hits''. -```{r message=FALSE, warning=FALSE pathway-abundance-model-selection1} +```{r pathway-abundance-model-selection1} transform <- c("NONE", "AST", "LOGIT", "LOG") num <- NULL @@ -472,23 +487,30 @@ num <- sum(fit_lm_meta2$meta_fits$pval < 0.05, na.rm = T) print(num) ``` -Here is the summary table for the number of significant pathways using various -meta-analytic differential analysis methods and transformations: - -| Analysis Method | Transform Parameter | Number of Significant Pathways -| :----------- | :----------- | :----------- -| LM | NONE | 11 -| LM | AST | 10 -| LM | LOGIT | 9 -| LM | LOG | 11 -| CPLM | NONE | 15 - -Let's than visualize the results using the "CPLM" analysis method, which -identified largest number of significant pathways. - -```{rvisualization-pathway-abundance} -# Load library -library(ggplot2) + +Here is the summary table for the number of significant pathways using +various meta-analytic differential analysis methods and transformations: + +```{r create-summary-table} + +library(knitr) + +analysis_data <- data.frame( + Analysis_Method = c("LM", "LM", "LM", "LM", "CPLM"), + Transform_Parameter = c("NONE", "AST", "LOGIT", "LOG", "NONE"), + Number_Of_Significant_Pathways = c(11, 10, 9, 11, 15) +) + +names(analysis_data) <- c("Analysis Method ", " Transformation ", " Number of Significant Pathways") + +kable(analysis_data, caption = "Analysis Results", align = c('l','l','r')) + +``` + +Finally, let's visualize the results using the "CPLM" analysis method, +which identified largest number of significant pathways. + +```{r visualization-pathway-abundance} # Extract the results meta_fits2 <- fit_lm_meta2$meta_fits @@ -506,54 +528,8 @@ meta_fits2 %>% ``` -Based on the results, we observe 15 significant features in total. Among these, -the queuosine biosynthesis bacteroides group exhibits the strongest negative -effects. Additionally, L-lysine degradation XI is the most positively -significant pathway, while xylose degradation IV is the most negatively -significant pathway. - -## Reference -1. Young LH. Strengths and Limitations of Meta-Analysis. Korean J Med. -2019;94(5):391-395. doi: https://doi.org/10.3904/kjm.2019.94.5.391. -2. Finckh A, Tramèr MR. Primer: strengths and weaknesses of meta-analysis. -Nat Clin Pract Rheumatol. 2008;4(3):146-152. doi:10.1038/ncprheum0732. -3. Israel H, Richter RR. A guide to understanding meta-analysis. J Orthop Sports -Phys Ther. 2011;41(7):496-504. doi:10.2519/jospt.2011.3333. -4. Hoffman JIE. Meta-analysis. Basic Biostatistics for Medical and Biomedical -Practitioners. 2nd ed. Academic Press; 2019:621-629. -doi:10.1016/B978-0-12-817084-7.00036-X. -5. Haidich AB. Meta-analysis in medical research. Hippokratia. -2010;14(Suppl 1):29-37. -6. Ma S, Shungin D, Mallick H, et al. Population structure discovery in -meta-analyzed microbial communities and inflammatory bowel disease using -MMUPHin. Genome Biol. 2022;23(1):208. Published 2022 Oct 3. -doi:10.1186/s13059-022-02753-4. -7. Pasolli E, Schiffer L, Manghi P, et al. “Accessible, curated metagenomic data -through ExperimentHub.” Nat. Methods. 2017;14(11):1023–1024. -doi:10.1038/nmeth.4468. -8. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable Association Discovery -in Population-scale Meta-omics Studies. PLoS Computational Biology. -2021:17(11):e1009442. -9. Frankel AE, Coughlin LA, Kim J, et al. Metagenomic Shotgun Sequencing and -Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and -Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma -Patients. Neoplasia. 2017;19(10):848-855. doi:10.1016/j.neo.2017.08.004. -10. Lee KA, Thomas AM, Bolte LA, et al. Cross-cohort gut microbiome associations -with immune checkpoint inhibitor response in advanced melanoma. Nat Med. -2022;28(3):535-544. doi:10.1038/s41591-022-01695-5. -11. Matson V, Fessler J, Bao R, et al. The commensal microbiome is associated -with anti-PD-1 efficacy in metastatic melanoma patients. Science. -2018;359(6371):104-108. doi:10.1126/science.aao3290. -12. Peters BA, Wilson M, Moran U, et al. Relating the gut metagenome and -metatranscriptome to immunotherapy responses in melanoma patients. Genome Med. -2019;11(1):61. Published 2019 Oct 9. doi:10.1186/s13073-019-0672-4. -13. Wind TT, Gacesa R, Vich Vila A, et al. Gut microbial species and metabolic -pathways associated with response to treatment with immune checkpoint inhibitors -in metastatic melanoma. Melanoma Res. 2020;30(3):235-246. -doi:10.1097/CMR.0000000000000656. -14. Tang ZZ, Chen G, Alekseyenko AV. PERMANOVA-S: association test for microbial -community composition that accommodates confounders and multiple distances. -Bioinformatics. 2016;32(17):2618-2625. doi:10.1093/bioinformatics/btw311. -15. Mallick, H, Chatterjee, S, Chowdhury, S, Chatterjee, S, Rahnavard, A, Hicks, -SC. Differential expression of single-cell RNA-seq data using Tweedie models. -Statistics in Medicine. 2022; 41( 18): 3492- 3510. doi:10.1002/sim.9430. +Based on the results, we observe 15 significant features in total. Among +these, queuosine biosynthesis exhibits the strongest negative effects. +Additionally, L-lysine degradation XI is the most positively significant +pathway, while xylose degradation IV is the most negatively significant +pathway. From a9dde8f6c0be6c19e45cb2e16600c4d61b00fb5d Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Sat, 20 Apr 2024 12:41:15 -0400 Subject: [PATCH 19/21] Update MSEA.qmd --- inst/pages/MSEA.qmd | 97 ++++++++++++++++----------------------------- 1 file changed, 35 insertions(+), 62 deletions(-) diff --git a/inst/pages/MSEA.qmd b/inst/pages/MSEA.qmd index 8a23b274d..802101ab3 100644 --- a/inst/pages/MSEA.qmd +++ b/inst/pages/MSEA.qmd @@ -1,10 +1,3 @@ ---- -title: "MSEA" -format: html -editor: visual -author: Yihan Liu, Himel Mallick ---- - # **Microbe** Set **Enrichment Analysis** (MSEA) {#sec-MSEA} ```{r setup, echo=FALSE, results="asis"} @@ -13,37 +6,37 @@ chapterPreamble() ``` -Similar to gene set enrichment analyses for genes$^1$, an obvious next step -following differential abundance analysis in microbiome studies is to conduct -enrichment analysis for microbe sets, known as microbe set enrichment analysis -(MSEA)$^2$. Similar to GSEA, the primary goal of MSEA is to detect the modest -but coordinated changes in pre-specified sets of related microbial features. -Such a set might include all the microbes in a specific pathway or microbial -genes that have been shown to be co-regulated based on previously published -studies. Like GSEA, MSEA aggregates the per-feature statistics across microbes -within a microbe set. This corresponds to the hypothesis that many relevant -phenotype differences are manifested by small but consistent changes in a set of -features. +Similar to gene set enrichment analyses for genes [@Subramanian2005], an obvious +next step following differential abundance analysis in microbiome studies is to +conduct enrichment analysis for microbe sets, known as microbe set enrichment +analysis (MSEA) [@Kou2020]. Similar to GSEA, the primary goal of MSEA is to +detect the modest but coordinated changes in pre-specified sets of related +microbial features. Such a set might include all the microbes in a specific +pathway or microbial genes that have been shown to be co-regulated based on +previously published studies. Like GSEA, MSEA aggregates the per-feature +statistics across microbes within a microbe set. This corresponds to the +hypothesis that many relevant phenotype differences are manifested by small but +consistent changes in a set of features. The goal of the MSEA approach is to determine if the members of S (microbe set) are randomly distributed throughout the ranked list of features (L) or primarily found at the top or bottom. We will use the R package `gsEasy` to conduct the -GSEA test described by Subramanian et al., 2005$^1$ +MSEA test described by @Subramanian2005. -## Input data for MSEA using microbiome abundance data +## Input data for MSEA using species relative abundance data In this chapter, we will use the publicly available Inflammatory Bowel Diseases (IBD) microbiome data from the integrative Human Microbiome Project (iHMP) -available from the `curatedMetagenomicData` package (Lloyd-Price et al., -2019$^3$). We aim to conduct MSEA analysis based on both taxonomic profiles -(species relative abundances) and functional profiles (pathway relative -abundances). +available from the `curatedMetagenomicData` package [@Lloyd-Price2019]. We aim +to conduct MSEA analysis based on both taxonomic profiles (species relative +abundances) and functional profiles (pathway relative abundances). ## Performing the MSEA analysis with species relative abundance data We will first prepare the input feature table and sample metadata for -differential abundance analysis using `MaAsLin2`$^4$. The ranked feature list from -the differential abundance analysis serves as an input for the MSEA. +differential abundance analysis using `MaAsLin2` [@Mallick2021]. The ranked +feature list from the differential abundance analysis serves as an input for the +MSEA. ```{r load-pkg-data} @@ -92,10 +85,11 @@ rm(feature_species_t); rm(se_relative) ``` -In the next step, we will use `MaAsLin2` to fit a multivariable regression -model for testing the association between microbial species abundance versus IBD -diagnosis. The analysis method we use here is "LM", which is the default setting. -We also adjust for age and antibiotic usage, following the original study. +In the next step, we will use `MaAsLin2` to fit a multivariable regression model +for testing the association between microbial species abundance versus IBD +diagnosis. The analysis method we use here is "LM", which is the default +setting. We also adjust for age and antibiotic usage, following the original +study. ```{r fit-model-Maaslin2} @@ -111,10 +105,11 @@ fit_data = Maaslin2(input_data = feature_species, Unlike gene expression studies, we do not have well-defined signatures or modules for microbiome data. Here, we will construct data-driven modules using -weighted gene co-expression network analysis (WGCNA)${^5^,^6}$. By working on -the residuals, we want to ensure that the effect of disease and other covariates -has been removed. Following the WGCNA tutorial, we will first check whether -there are outliers in our data. +weighted gene co-expression network analysis (WGCNA) [@Langfelder2008, +@Geistlinger2023]. We aim to ensure that the effect of disease and other +covariates has been removed by working on the residuals. Following the WGCNA +tutorial, our first step will be to check whether there are any outliers in our +data. ```{r extract-residuals} @@ -150,7 +145,7 @@ connectivity graphic for the selection of this power parameter. # Choose a set of soft threshold parameters powers = c(c(1:20), seq(from = 22, to=30, by=2)) -sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5, dataIsExpr = T, RsquaredCut = 0.30) +sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5, dataIsExpr = TRUE, RsquaredCut = 0.30) ``` @@ -276,7 +271,7 @@ run_MSEA <- function(microbeSet, # A list Before running the MSEA, we also need to rank the differential analysis results from `MaAsLin2`. We use the `topGo` package to create a list of microbe sets -from the mapping. +from the mapping file created above. ```{r MSEA-processing} @@ -301,7 +296,7 @@ microbeSet ``` We are now ready to run the MSEA analysis. We run $100,000$ permutations to -calculate the enrichment scores. +calculate the enrichment scores. ```{r calculate-enrichment-scores} @@ -333,7 +328,7 @@ print(p) Based on the MSEA results, we obtain 13 enriched modules of microbial species. We can also examine the members of the top enriched modules. -## Print the most significant modules for relative abundance data +## Print the most significant modules for species relative abundance data ```{r module-print} @@ -433,7 +428,7 @@ gsg$allOK # TRUE ################################### powers = c(c(1:20), seq(from = 22, to=30, by=2)) -sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5, dataIsExpr = T, RsquaredCut = 0.30) +sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5, dataIsExpr = TRUE, RsquaredCut = 0.30) ############################## # One-step module detection # @@ -488,7 +483,7 @@ features_mapping$Module <- paste('ME', features_mapping$Module, sep = '') ``` -Similarly, we perform the MSEA as before using the modules from the WGCNA analysis on the +We perform the MSEA as before using the modules from the WGCNA analysis on the pathways. ```{r perform-MSEA-and-visualization} @@ -550,25 +545,3 @@ microbeSet[["MEpurple"]] microbeSet[["MEbrown"]] ``` - -## Reference - -1. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a -knowledge-based approach for interpreting genome-wide expression profiles. Proc -Natl Acad Sci U S A. 2005;102(43):15545-15550. doi:10.1073/pnas.0506580102. -2. Kou, Y., Xu, X., Zhu, Z. *et al.* Microbe-set enrichment analysis -facilitates functional interpretation of microbiome profiling data. *Sci Rep* -**10**, 21466 (2020). https://doi.org/10.1038/s41598-020-78511-y. -3. Lloyd-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut -microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569(7758):655- -662. doi:10.1038/s41586-019-1237-9. -4. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable association discovery -in population-scale meta-omics studies. PLoS Comput Biol. 2021;17(11):e1009442. -Published 2021 Nov 16. doi:10.1371/journal.pcbi.1009442. -5. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network -analysis. BMC Bioinformatics. 2008;9:559. Published 2008 Dec 29. doi:10.1186/ -1471-2105-9-559. -6. Geistlinger L, Mirzayi C, Zohra F, et al. BugSigDB captures patterns of -differential abundance across a broad range of host-associated microbial -signatures. Nat Biotechnol. Published online September 11, 2023. doi:10.1038/ -s41587-023-01872-y. \ No newline at end of file From 1e1006ff6871a465192dec8e653a8903c0674a5a Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Sat, 20 Apr 2024 12:41:41 -0400 Subject: [PATCH 20/21] Update MMUPHin_meta_analysis.qmd --- inst/pages/MMUPHin_meta_analysis.qmd | 6 ------ 1 file changed, 6 deletions(-) diff --git a/inst/pages/MMUPHin_meta_analysis.qmd b/inst/pages/MMUPHin_meta_analysis.qmd index 93d1b3431..153e352ec 100644 --- a/inst/pages/MMUPHin_meta_analysis.qmd +++ b/inst/pages/MMUPHin_meta_analysis.qmd @@ -1,9 +1,3 @@ ---- -editor: - markdown: - wrap: 72 ---- - # Meta-analyses {#sec-meta-analyses} ```{r setup, echo=FALSE, results="asis"} From 88a637a7b5e70137cad3c085809f81c5fd8fb94d Mon Sep 17 00:00:00 2001 From: YihanLiu4023 <149611140+YihanLiu4023@users.noreply.github.com> Date: Sat, 20 Apr 2024 12:47:21 -0400 Subject: [PATCH 21/21] Update IntegratedLearner.qmd --- inst/pages/IntegratedLearner.qmd | 77 ++++++++++++++++---------------- 1 file changed, 38 insertions(+), 39 deletions(-) diff --git a/inst/pages/IntegratedLearner.qmd b/inst/pages/IntegratedLearner.qmd index 57b689230..059324e6b 100644 --- a/inst/pages/IntegratedLearner.qmd +++ b/inst/pages/IntegratedLearner.qmd @@ -14,27 +14,27 @@ contrast, \underline{late fusion} builds individual models for each data view and combines their predictions using a second-level model as the final predictor. However, these traditional paradigms treat the data views in isolation and do not allow for interactions or dependencies between them. A more -advanced method, called cooperative learning (Ding et al., 2022, -\emph{PNAS}$^1$), which is also known as \underline{intermediate fusion}, -combines the best of both worlds by encouraging predictions from different data -views to align through an agreement parameter ($\rho$). +advanced method, called cooperative learning [@Ding2022], which is also known as +\underline{intermediate fusion}, combines the best of both worlds by encouraging +predictions from different data views to align through an agreement parameter +($\rho$). ## Multi-omics Prediction and Classification of Binary IBD Disease Status In this chapter, we showcase examples of various integration paradigms (early, -late, and intermediate fusion) using the R packages `multiview`$^1$ and -`SuperLearner`$^2$. We make use of the publicly available source code from the -multi-omics integrated framework (`IntegratedLearner`) proposed by Mallick et -al. (2024)$^3$. +late, and intermediate fusion) using the R packages `multiview` [[@Ding2022]] +and `SuperLearner` [@Van]. We make use of the publicly available source code +from the multi-omics integrated framework (`IntegratedLearner`) proposed by +@Mallick2023. ## Input data We use the publicly available Inflammatory Bowel Diseases (IBD) data from the -curatedMetagenomicData package (Lloyd-Price et al., 2019)$^4$, where we aim to +curatedMetagenomicData package [@Lloyd-Price2019], where we aim to predict IBD disease status based on both taxonomic (species abundances) and functional (pathway abundances) profiles. -```{r message=FALSE, warning=FALSE load-pkg-data} +```{r load-pkg-data} ################## # Load iHMP data # @@ -54,7 +54,8 @@ se_pathway <- sampleMetadata |> ``` -We will first prepare the input sample metadata and feature table for both relative abundance and pathway abundance data. +We will first prepare the input sample metadata and feature table for both +relative abundance and pathway abundance data. ```{r prepare-metadata-feature} @@ -97,11 +98,13 @@ feature_table <- bind_rows(feature_species, feature_pwys) # Check row names of feature_table and sample_metadata # ######################################################## -all(rownames(feature_table) == rownames(sample_metadata)) +all(colnames(feature_table) == rownames(sample_metadata)) ``` -We will then create a metadata table for the features. This table captures high-level information related to the features (e.g., which layer they belong to). +We will then create a metadata table for the features. This table captures +high-level information related to the features (e.g., which layer they belong +to). ```{r create-metadata-table} @@ -122,22 +125,26 @@ all(colnames(feature_table) == rownames(sample_metadata)) # TRUE ``` -Further data pre-processing is necessary to handle near-zero-variance features in this dataset. Ultimately, 483 features are retained, consisting of 360 pathways and 123 species. A combination of variance and prevalence filtering is applied to the feature table, while related metadata is cleaned to ensure the retention of matching samples. +Further data pre-processing is necessary to handle near-zero-variance features +in this dataset. Ultimately, 483 features are retained, consisting of 360 +pathways and 123 species. A combination of variance and prevalence filtering +is applied to the feature table, while related metadata is cleaned to ensure the +retention of matching samples. ```{r create-feature-table} -library(caret) - ################# # feature_table # ################# +library(caret) + feature_table_t <- as.data.frame(t(feature_table)) abd_threshold = 0 prev_threshold = 0.1 nzv <- nearZeroVar(feature_table_t) features_var_filtered <- feature_table_t[, -nzv] -features_var_filtered<-as.data.frame(features_var_filtered) +features_var_filtered <- as.data.frame(features_var_filtered) features_filtered <- features_var_filtered[, colSums(features_var_filtered > abd_threshold) > nrow(features_var_filtered) * prev_threshold] feature_table <- as.data.frame(t(features_filtered)) @@ -162,10 +169,12 @@ which includes configuring parameters for sample splitting and 5-fold cross-validation. It's important to note that this dataset contains repeated measures (i.e., multiple samples per subject). To address this, we will conduct 5-fold cross-validation at the subject level. The source code is derived from -the `IntegratedLearner` R package$^3$. +the `IntegratedLearner` R package [@Mallick2023]. ```{r additional-data-preprocessing} +library(caret) + # Set parameters and extract subject IDs for sample splitting seed <- 1 set.seed(seed) @@ -211,7 +220,7 @@ names(layer_wise_predictions_train) <- name_layers ``` -Next, we run the late fusion algorithm described in Mallick et al., 2024$^3$. +Next, we run the late fusion algorithm described in @Mallick2023. To this end, we subset the data for each feature type and conduct the analysis on each layer for the first-stage learning to predict the outcome. In other words, we fit a machine learning algorithm per layer (called a 'base learner'), @@ -359,8 +368,8 @@ name_layers <- with(droplevels(feature_metadata), list(levels = levels(featureTy dataList <- vector("list", length = length(name_layers)) names(dataList) <- name_layers table(feature_metadata$featureType) -dataList[[1]] <- t(feature_table[1:123, ]) -dataList[[2]] <- t(feature_table[124:nrow(feature_table), ]) +dataList[[1]] <- t(feature_table[124:nrow(feature_table), ]) +dataList[[2]] <- t(feature_table[1:123, ]) # Extract y and X's dataList <- lapply(dataList, as.matrix) @@ -419,7 +428,7 @@ ROC_table <- do.call('rbind', list.ROC) Based on the ROC plot described below, we observe that the AUC is 0.65 when considering only the pathway abundance data in the model, and 0.64 for the model -including only the species abundance data. The AUC increases to 0.67 when using +including only the species abundance data. The AUC increases to 0.68 when using the early fusion model and reaches 1 with the late fusion model. In contrast, the intermediate fusion model achieves an AUC of 0.99. Overall, most integrated classifiers outperform individual layers in distinguishing between IBD and @@ -456,13 +465,15 @@ print(p) Finally, we visualize the results for the top 20 features for each layer based on the intermediate fusion. -```{r visualization3} - -# Only plot UP TO TOP 20 PER LAYER +```{r visualization3, fig.width = 6, fig.height = 3} +#### Change the pathway +# Only plot tp to 20 per layer DD <- DD %>% group_by(view) %>% top_n(n = 20, wt = abs(standardized_coef)) +DD$view_col <- gsub(":.*", "", DD$view_col) + # Visualization library(forcats) @@ -472,7 +483,7 @@ p <- DD %>% geom_bar(stat = "identity", show.legend = FALSE, width = 1) + coord_flip() + facet_wrap(~ view, scales = 'free_y', nrow = 2) + - ylab('Standardized LFC (2 years vs. baseline)') + + ylab('Standardized LFC') + xlab('') + ggtitle('IBD-associated multi-omics features') + theme_bw() + theme(strip.background = element_blank(), @@ -491,16 +502,4 @@ the most negatively associated species. Additionally, L-lysine biosynthesis VI is the pathway most positively associated, and PYRIDNUSCYN-PWY: NAD biosynthesis I (from aspartate) is the pathway most positively associated, respectively. -## Reference - -1. Ding DY, Li S, Narasimhan B, Tibshirani R. Cooperative learning for -multiview analysis. Proc Natl Acad Sci U S A. 2022;119(38):e2202113119. -doi:10.1073/pnas.2202113119. -2. van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol -Biol. 2007;6:Article25. doi:10.2202/1544-6115.1309. -3. Mallick H, Porwal A, Saha S, Basak P, Svetnik V, Paul E. An integrated -Bayesian framework for multi-omics prediction and classification. Stat Med. -Published online December 26, 2023. doi:10.1002/sim.9953. -4. Lloyd-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut -microbial ecosystem in inflammatory bowel diseases. Nature. 2 -019;569(7758):655-662. doi:10.1038/s41586-019-1237-9. +