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sensitivity.Rmd
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---
title: "Untitled"
author: "Ameek Bhalla"
date: "10/31/2021"
output: powerpoint_presentation
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```
## Agenda
## Life History
## Genetic models of different diseases
[] rebuild Berg and Geschwind figure
## Open science
[] Science ambassador certificate
## Agenda Item #1
## The problem of missing heritability
## Controvery around solutions to missing heritability
## Limitations of previous studies
## PhD schematic
[] Write out model equation
## Analysis pipeline
## Analysitcal challenges
## How to treat sex
[] sensitivity analysis for effect of sex
## Quantile normalization -- within or across
[] simulation for effect of normalization
## Can we estimate random effects
[] semnsitivity analysis of random effects
## My soltuions have general applicability
[] how much did the overall power increase
## PhD results
## Summary side
## Domian specific knowledge
## Match between Sage ethos and mine
This is an R Markdown presentation. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.
## Slide with Bullets
- Bullet 1
- Bullet 2
- Bullet 3
## Slide with R Output
```{r cars, echo = TRUE}
summary(cars)
```
## Slide with Plot
```{r pressure}
plot(pressure)
```
#####
```{r setup results = "hide"}
## Set default chunk options
knitr::opts_chunk$set(results = "hide",
tidy = TRUE)
## Load Packages
library(affy) # Bioconductor package for analysis of older Affymetrix arrays
#library(affyio)
#library(affyPLM)
#library(annaffy)
#library(annotate)
#library(arrayQualityMetrics)
library(BiocManager)
library(drosophila2.db) # annotation package for the model organism
library(drosophila2cdf)
library(formatR)
library(gcrma)
library(htmltools)
library(knitr)
library(limma)
#library(markdown)
library(rmarkdown)
#library(simpleaffy)
library(tidyverse)
```
Without eBayes
```{r without eBayes}
top_1_geno_0$t_old <- fit2_model_1$coef[,4]/fit2_model_1$stdev.unscaled[,4]/fit2_model_1$sigma
top_1_geno_0$P.Value_old <- 2 * pt(-abs(top_1_geno_0$t_old), df = fit2_model_1$df.residual)
top_1_geno_0 %>% filter(abs(t_old) >= 1.96) %>% count()
top_1_geno_0 %>% filter(abs(t) >= 1.96) %>% count()
top_1_geno_0 %>% filter(P.Value_old <= 0.05) %>% count()
top_1_geno_0 %>% filter(P.Value <= 0.05) %>% count()
# number of DE genes is reduced after eBayes!
```
#####
both sexes, no blocking, quantile normalized across
```{r genotype_cleanup}
#retreive remaining rownames after excluding F-1, three F0 genotypes, and males
mini <- which(
pData(eset)$generation != "F-1" & #using filter() instead of subset() does not preserve rownames
# pData(eset)$generation != "F0 + F1" &
pData(eset)$genotype != "cyo/+; dilp3/+" &
pData(eset)$genotype != "rpr/+; ser/+" &
pData(eset)$genotype != "rpr/+; dilp3/+"
# pData(eset)$genotype != "rpr/cyo; dilp3/ser" &
# pData(eset)$sex != "female" &
# pData(eset)$sex != "male"
)
#use the rownames object `mini` to perform the actual subsetting
eset_mini <- eset[ , mini] #ExpressionSet objects cannot be subset with filter() or subset()
remove(mini)
pData(eset_mini)$sex <- pData(eset_mini)$sex %>% droplevels()
pData(eset_mini)$generation <- pData(eset_mini)$generation %>% droplevels()
pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% droplevels()
pData(eset_mini)$day <- pData(eset_mini)$day %>% droplevels()
#convert the `generation` column to a character vector to allow downstream processing
pData(eset_mini)$generation <- pData(eset_mini)$generation %>% as.character()
# #identify the rows that contain the string "dilp3", signifying the F0 generation
# generation_F0 <- pData(eset_mini)$genotype %>%
# str_which("dilp3")
#
# #change the `generation` of the rows identified above to "F0"
# pData(eset_mini)[generation_F0, "generation"] <- "F0"
#split the `generation` "F0 + F1" into F0 and F1
pData(eset_mini) <- pData(eset_mini) %>%
mutate(generation =
case_when(genotype == "rpr/cyo; dilp3/ser" ~ "F0",
genotype == "cyo/+; ser/+" ~ "F1",
genotype == "w1118" ~ "F1",
generation == "F2" ~ "F2", generation == "F3" ~ "F3"),
genotype =
case_when(genotype == "rpr/cyo; dilp3/ser" ~ "test",
genotype == "cyo/+; ser/+" ~ "test",
genotype == "w1118" ~ "control",
genotype == "test" ~ "test",
genotype == "control" ~ "control")
)
#convert the `generation` column back to a factor vecor
pData(eset_mini)$generation <- pData(eset_mini)$generation %>% as.factor()
#convert the `genotype` column to a character vector to allow downstream processing
pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% as.character()
#replace the `genotype` string `w1118` with the string `control`
# pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% str_replace("w1118", "control")
#replace the other `genotype` strings not matching the string 'control' using the lookahead regex (?!__)
# pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% str_replace("^(?!control)(.*)$", "test")
#convert the `genotype` column back to a factor vecor
pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% as.factor()
```
```{r no_blocking}
# Create single variable "group"
group <- with(pData(eset_mini),
paste(generation,
genotype,
# sex,
# day,
sep = "."))
pData(eset_mini)$group <- as.factor(group)
remove(group)
# Create design matrix with group-means parametrization (no intercept)
design_1 <- model.matrix(~ 0
# + day
+ group, data = pData(eset_mini))
colnames(design_1) <- levels(pData(eset_mini)$group)
colnames(design_1)
# Count the number of samples modeled by each coefficient
colSums(design_1)
# #fit the linear model fit_model_1 without blocking
fit_model_1 <- lmFit(eset_mini, design_1)
cm_1_genotype_simple <- makeContrasts(
gen_1_geno = (F1.test - F1.control),
gen_2_geno = (F2.test - F2.control),
gen_3_geno = (F3.test - F3.control),
gen_0_geno = (F0.test - F1.control),
levels = design_1)
cm_1_genotype_simple
#filter out probes with an average expression level below 3
keep <- fit_model_1$Amean > 0 #`Amean` stores the average log expression values
fit2_model_1 <- fit_model_1[keep, ]
nrow(fit2_model_1)
#fit the contrast matrix
fit2_model_1 <- contrasts.fit(fit2_model_1, cm_1_genotype_simple)
#set trend = TRUE since the data do not show a uniform variance across all expression intensities (this trend in the variance at different mean intensities is discovered with `plotSA(fit_model)` )
fit2_model_1 <- eBayes(fit2_model_1,
trend = TRUE, #accommodates a mean-variance trend;
robust = TRUE #increases power for some kinds of studies
)
#Results of all contrasts simultaneously
# results_1_geno <- decideTests(fit2_model_1)
# summary(results_1_geno)
#
# # #Venn diagram showing numbers of genes significant in all contrasts, simultaneously
# vennDiagram(results_1_geno)
#A list of top genes differentially expressed in a contarst can be obtained by specifying its coef
# topTable(fit2_model_1, coef=c(1:3), adjust="BH", number = 30)
top_1_geno_1 <- topTable(fit2_model_1,
coef=c(1),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_2 <- topTable(fit2_model_1,
coef=c(2),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_3 <- topTable(fit2_model_1,
coef=c(3),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 <- topTable(fit2_model_1,
coef = c(4),
adjust = "BH", number = nrow(fit2_model_1), sort.by = "none")
# hist(top_1_geno_3$t, breaks = seq(from = -2, to = 2, by = 0.1))
top_1_geno_0 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_0 %>% filter(adj.P.Val <= 0.05) %>% count()
# 2139
```
2139
#####
both sexes, blocking, quantile normalized across
```{r blocking}
# Create single variable "group"
group <- with(pData(eset_mini),
paste(generation,
genotype,
# sex,
# day,
sep = "."))
pData(eset_mini)$group <- as.factor(group)
remove(group)
# Create design matrix with group-means parametrization (no intercept)
design_1 <- model.matrix(~ 0
# + day
+ group, data = pData(eset_mini))
colnames(design_1) <- levels(pData(eset_mini)$group)
colnames(design_1)
# Count the number of samples modeled by each coefficient
colSums(design_1)
group2 <- with(pData(eset_mini),
paste(replicate,
day,
sep = "."))
pData(eset_mini)$group2 <- as.factor(group2)
#estimate the correlation between measurements made on the same repliactes:
corfit_1 <- duplicateCorrelation(eset_mini, design_1, block = pData(eset_mini)$group2)
corfit_1$consensus
# corfit_1$consensus <- by_sex$both[[1]]$consensus.correlation
# corfit_1$cor <- by_sex$both[[1]]$cor
# corfit_1$atanh.correlations <- by_sex$both[[1]]$atanh.correlations
#fit the linear model fit_model_1 with blocking
fit_model_1 <- lmFit(eset_mini, design_1,
block = pData(eset_mini)$group2,
correlation = corfit_1$consensus)
cm_1_genotype_simple <- makeContrasts(
gen_1_geno = (F1.test - F1.control),
gen_2_geno = (F2.test - F2.control),
gen_3_geno = (F3.test - F3.control),
gen_0_geno = (F0.test - F1.control),
levels = design_1)
cm_1_genotype_simple
#filter out probes with an average expression level below 3
keep <- fit_model_1$Amean > 0 #`Amean` stores the average log expression values
fit2_model_1 <- fit_model_1[keep, ]
nrow(fit2_model_1)
#fit the contrast matrix
fit2_model_1 <- contrasts.fit(fit2_model_1, cm_1_genotype_simple)
#set trend = TRUE since the data do not show a uniform variance across all expression intensities (this trend in the variance at different mean intensities is discovered with `plotSA(fit_model)` )
fit2_model_1 <- eBayes(fit2_model_1,
trend = TRUE, #accommodates a mean-variance trend;
robust = TRUE #increases power for some kinds of studies
)
#A list of top genes differentially expressed in a contarst can be obtained by specifying its coef
# topTable(fit2_model_1, coef=c(1:3), adjust="BH", number = 30)
top_1_geno_1 <- topTable(fit2_model_1,
coef=c(1),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_2 <- topTable(fit2_model_1,
coef=c(2),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_3 <- topTable(fit2_model_1,
coef=c(3),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 <- topTable(fit2_model_1,
coef = c(4),
adjust = "BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_0 %>% filter(adj.P.Val <= 0.05) %>% count()
top_1_geno_1 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_1 %>% filter(adj.P.Val <= 0.05) %>% count()
top_1_geno_2 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_2 %>% filter(adj.P.Val <= 0.05) %>% count()
top_1_geno_3 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_3 %>% filter(adj.P.Val <= 0.05) %>% count()
# 4159
```
4159
#####
only females, no blocking, quantile normalized across
```{r genotype_cleanup}
#retreive remaining rownames after excluding F-1, three F0 genotypes, and males
mini <- which(
pData(eset)$generation != "F-1" & #using filter() instead of subset() does not preserve rownames
# pData(eset)$generation != "F0 + F1" &
pData(eset)$genotype != "cyo/+; dilp3/+" &
pData(eset)$genotype != "rpr/+; ser/+" &
pData(eset)$genotype != "rpr/+; dilp3/+" &
# pData(eset)$genotype != "rpr/cyo; dilp3/ser" &
# pData(eset)$sex != "female" &
pData(eset)$sex != "male"
)
#use the rownames object `mini` to perform the actual subsetting
eset_mini <- eset[ , mini] #ExpressionSet objects cannot be subset with filter() or subset()
remove(mini)
pData(eset_mini)$sex <- pData(eset_mini)$sex %>% droplevels()
pData(eset_mini)$generation <- pData(eset_mini)$generation %>% droplevels()
pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% droplevels()
pData(eset_mini)$day <- pData(eset_mini)$day %>% droplevels()
#convert the `generation` column to a character vector to allow downstream processing
pData(eset_mini)$generation <- pData(eset_mini)$generation %>% as.character()
# #identify the rows that contain the string "dilp3", signifying the F0 generation
# generation_F0 <- pData(eset_mini)$genotype %>%
# str_which("dilp3")
#
# #change the `generation` of the rows identified above to "F0"
# pData(eset_mini)[generation_F0, "generation"] <- "F0"
#split the `generation` "F0 + F1" into F0 and F1
pData(eset_mini) <- pData(eset_mini) %>%
mutate(generation =
case_when(genotype == "rpr/cyo; dilp3/ser" ~ "F0",
genotype == "cyo/+; ser/+" ~ "F1",
genotype == "w1118" ~ "F1",
generation == "F2" ~ "F2", generation == "F3" ~ "F3"),
genotype =
case_when(genotype == "rpr/cyo; dilp3/ser" ~ "test",
genotype == "cyo/+; ser/+" ~ "test",
genotype == "w1118" ~ "control",
genotype == "test" ~ "test",
genotype == "control" ~ "control")
)
#convert the `generation` column back to a factor vecor
pData(eset_mini)$generation <- pData(eset_mini)$generation %>% as.factor()
#convert the `genotype` column to a character vector to allow downstream processing
pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% as.character()
#replace the `genotype` string `w1118` with the string `control`
# pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% str_replace("w1118", "control")
#replace the other `genotype` strings not matching the string 'control' using the lookahead regex (?!__)
# pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% str_replace("^(?!control)(.*)$", "test")
#convert the `genotype` column back to a factor vecor
pData(eset_mini)$genotype <- pData(eset_mini)$genotype %>% as.factor()
```
```{r no_blocking}
# Create single variable "group"
group <- with(pData(eset_mini),
paste(generation,
genotype,
# sex,
# day,
sep = "."))
pData(eset_mini)$group <- as.factor(group)
remove(group)
# Create design matrix with group-means parametrization (no intercept)
design_1 <- model.matrix(~ 0
# + day
+ group, data = pData(eset_mini))
colnames(design_1) <- levels(pData(eset_mini)$group)
colnames(design_1)
# Count the number of samples modeled by each coefficient
colSums(design_1)
# #fit the linear model fit_model_1 without blocking
fit_model_1 <- lmFit(eset_mini, design_1)
cm_1_genotype_simple <- makeContrasts(
gen_1_geno = (F1.test - F1.control),
gen_2_geno = (F2.test - F2.control),
gen_3_geno = (F3.test - F3.control),
gen_0_geno = (F0.test - F1.control),
levels = design_1)
cm_1_genotype_simple
#filter out probes with an average expression level below 3
keep <- fit_model_1$Amean > 0 #`Amean` stores the average log expression values
fit2_model_1 <- fit_model_1[keep, ]
nrow(fit2_model_1)
#fit the contrast matrix
fit2_model_1 <- contrasts.fit(fit2_model_1, cm_1_genotype_simple)
#set trend = TRUE since the data do not show a uniform variance across all expression intensities (this trend in the variance at different mean intensities is discovered with `plotSA(fit_model)` )
fit2_model_1 <- eBayes(fit2_model_1,
trend = TRUE, #accommodates a mean-variance trend;
robust = TRUE #increases power for some kinds of studies
)
#Results of all contrasts simultaneously
# results_1_geno <- decideTests(fit2_model_1)
# summary(results_1_geno)
#
# # #Venn diagram showing numbers of genes significant in all contrasts, simultaneously
# vennDiagram(results_1_geno)
#A list of top genes differentially expressed in a contarst can be obtained by specifying its coef
# topTable(fit2_model_1, coef=c(1:3), adjust="BH", number = 30)
top_1_geno_1 <- topTable(fit2_model_1,
coef=c(1),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_2 <- topTable(fit2_model_1,
coef=c(2),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_3 <- topTable(fit2_model_1,
coef=c(3),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 <- topTable(fit2_model_1,
coef = c(4),
adjust = "BH", number = nrow(fit2_model_1), sort.by = "none")
# hist(top_1_geno_3$t, breaks = seq(from = -2, to = 2, by = 0.1))
top_1_geno_0 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_0 %>% filter(adj.P.Val <= 0.05) %>% count()
# 1664
```
1664
#####
only females, blocking, quantile normalized across
```{r blocking}
# Create single variable "group"
group <- with(pData(eset_mini),
paste(generation,
genotype,
# sex,
# day,
sep = "."))
pData(eset_mini)$group <- as.factor(group)
remove(group)
# Create design matrix with group-means parametrization (no intercept)
design_1 <- model.matrix(~ 0
# + day
+ group, data = pData(eset_mini))
colnames(design_1) <- levels(pData(eset_mini)$group)
colnames(design_1)
# Count the number of samples modeled by each coefficient
colSums(design_1)
group2 <- with(pData(eset_mini),
paste(replicate,
day,
sep = "."))
pData(eset_mini)$group2 <- as.factor(group2)
#estimate the correlation between measurements made on the same repliactes:
corfit_1 <- duplicateCorrelation(eset_mini, design_1, block = pData(eset_mini)$group2)
corfit_1$consensus
#fit the linear model fit_model_1 with blocking
fit_model_1 <- lmFit(eset_mini, design_1,
block = pData(eset_mini)$group2,
correlation = corfit_1$consensus)
cm_1_genotype_simple <- makeContrasts(
gen_1_geno = (F1.test - F1.control),
gen_2_geno = (F2.test - F2.control),
gen_3_geno = (F3.test - F3.control),
gen_0_geno = (F0.test - F1.control),
levels = design_1)
cm_1_genotype_simple
#filter out probes with an average expression level below 3
keep <- fit_model_1$Amean > 0 #`Amean` stores the average log expression values
fit2_model_1 <- fit_model_1[keep, ]
nrow(fit2_model_1)
#fit the contrast matrix
fit2_model_1 <- contrasts.fit(fit2_model_1, cm_1_genotype_simple)
#set trend = TRUE since the data do not show a uniform variance across all expression intensities (this trend in the variance at different mean intensities is discovered with `plotSA(fit_model)` )
fit2_model_1 <- eBayes(fit2_model_1,
trend = TRUE, #accommodates a mean-variance trend;
robust = TRUE #increases power for some kinds of studies
)
#A list of top genes differentially expressed in a contarst can be obtained by specifying its coef
# topTable(fit2_model_1, coef=c(1:3), adjust="BH", number = 30)
top_1_geno_1 <- topTable(fit2_model_1,
coef=c(1),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_2 <- topTable(fit2_model_1,
coef=c(2),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_3 <- topTable(fit2_model_1,
coef=c(3),
adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 <- topTable(fit2_model_1,
coef = c(4),
adjust = "BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_0 %>% filter(adj.P.Val <= 0.05) %>% count()
# 3120
```
3120
#####
both sexes, no blocking, quantile normalized within
```{r qnormed_within}
#import_CEL
raw_data <- ReadAffy(celfile.path = "./CEL Files")
#import_anno
sample_data <- read_csv(
"sample_data.csv",
col_types = cols(
age = col_integer(),
day = col_factor(levels = c("1", "2", "3", "4", "5", "6")),
generation = col_factor(levels = c("F-1", "F0 + F1", "F2", "F3")),
module = col_factor(levels = c("1", "2", "3", "4")),
number = col_integer(),
replicate = col_factor(levels = c("L3", "L4", "L5", "L7", "L8", "L9", "L10")),
sex = col_factor(levels = c("female", "male")),
stored = col_date(format = "%m/%d/%Y"),
wash = col_factor(levels = c("1", "2", "3", "4")),
genotype = col_factor(levels = c(
"control", "test", "w1118", "rpr/cyo; dilp3/ser", "cyo/+; ser/+", "rpr/+; dilp3/+", "cyo/+; dilp3/+", "rpr/+; ser/+"))))
#assign_anno
phenoData(raw_data) <- AnnotatedDataFrame(sample_data)
#rename_sample
sampleNames(raw_data) <- sample_data$label
# keep only generation F0 females
keep <- which(pData(raw_data)$generation == "F0 + F1"
& (pData(raw_data)$genotype == "rpr/cyo; dilp3/ser" | pData(raw_data)$genotype == "w1118")
)
raw_data <- raw_data[ , keep] #ExpressionSet objects cannot be subset with filter() or filter()
# drop empty levels
pData(raw_data)$generation <- pData(raw_data)$generation %>% droplevels()
# subsetting an AffyBatch object removes row names so they need be reassigned
sampleNames(raw_data) <- pData(raw_data)$label
# cleanup factor levels
pData(raw_data)$generation <- pData(raw_data) %>% transmute(
generation = case_when( # convert F-1 and the "F0" in "F0 + F1" into F0
genotype == "rpr/cyo; dilp3/ser" ~ "F0",
TRUE ~ "F1") %>% as_factor()) %>% pluck(1)
pData(raw_data)$genotype <- pData(raw_data) %>% transmute(
genotype = case_when( # convert all non-transgenic lineages into w1118 genotype
genotype == "rpr/cyo; dilp3/ser" ~ "test",
TRUE ~ "control") %>% as_factor()) %>% pluck(1)
sampleNames(raw_data) <- pData(raw_data)$label
#check_object
validObject(raw_data)
#gcrma
eset <- gcrma(raw_data)
```
```{r no_blocking}
# Create single variable "group"
group <- with(pData(eset),
paste(generation,
genotype,
# sex,
# day,
sep = "."))
pData(eset)$group <- as.factor(group)
remove(group)
# Create design matrix with group-means parametrization (no intercept)
design_1 <- model.matrix(~ 0
# + day
+ group, data = pData(eset))
colnames(design_1) <- levels(pData(eset)$group)
colnames(design_1)
# Count the number of samples modeled by each coefficient
colSums(design_1)
# #fit the linear model fit_model_1 without blocking
fit_model_1 <- lmFit(eset, design_1)
cm_1_genotype_simple <- makeContrasts(
# gen_1_geno = (F1.test - F1.control),
# gen_2_geno = (F2.test - F2.control),
# gen_3_geno = (F3.test - F3.control),
gen_0_geno = (F0.test - F1.control),
levels = design_1)
cm_1_genotype_simple
#filter out probes with an average expression level below 3
keep <- fit_model_1$Amean > 0 #`Amean` stores the average log expression values
fit2_model_1 <- fit_model_1[keep, ]
nrow(fit2_model_1)
#fit the contrast matrix
fit2_model_1 <- contrasts.fit(fit2_model_1, cm_1_genotype_simple)
#set trend = TRUE since the data do not show a uniform variance across all expression intensities (this trend in the variance at different mean intensities is discovered with `plotSA(fit_model)` )
fit2_model_1 <- eBayes(fit2_model_1,
trend = TRUE, #accommodates a mean-variance trend;
robust = TRUE #increases power for some kinds of studies
)
#Results of all contrasts simultaneously
# results_1_geno <- decideTests(fit2_model_1)
# summary(results_1_geno)
#
# # #Venn diagram showing numbers of genes significant in all contrasts, simultaneously
# vennDiagram(results_1_geno)
#A list of top genes differentially expressed in a contarst can be obtained by specifying its coef
# topTable(fit2_model_1, coef=c(1:3), adjust="BH", number = 30)
# top_1_geno_1 <- topTable(fit2_model_1,
# coef=c(1),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
#
# top_1_geno_2 <- topTable(fit2_model_1,
# coef=c(2),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
#
# top_1_geno_3 <- topTable(fit2_model_1,
# coef=c(3),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 <- topTable(fit2_model_1,
coef = c(1),
adjust = "BH", number = nrow(fit2_model_1), sort.by = "none")
# hist(top_1_geno_3$t, breaks = seq(from = -2, to = 2, by = 0.1))
top_1_geno_0 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_0 %>% filter(adj.P.Val <= 0.05) %>% count()
```
680
#####
both sexes, blocking, quantile normalized within
```{r blocking}
# Create single variable "group"
group <- with(pData(eset),
paste(generation,
genotype,
# sex,
# day,
sep = "."))
pData(eset)$group <- as.factor(group)
remove(group)
# Create design matrix with group-means parametrization (no intercept)
design_1 <- model.matrix(~ 0
# + day
+ group, data = pData(eset))
colnames(design_1) <- levels(pData(eset)$group)
colnames(design_1)
# Count the number of samples modeled by each coefficient
colSums(design_1)
group2 <- with(pData(eset),
paste(replicate,
day,
sep = "."))
pData(eset)$group2 <- as.factor(group2)
#estimate the correlation between measurements made on the same repliactes:
corfit_1 <- duplicateCorrelation(eset, design_1, block = pData(eset)$group2)
corfit_1$consensus
#fit the linear model fit_model_1 with blocking
fit_model_1 <- lmFit(eset, design_1,
block = pData(eset)$group2,
correlation = corfit_1$consensus)
cm_1_genotype_simple <- makeContrasts(
# gen_1_geno = (F1.test - F1.control),
# gen_2_geno = (F2.test - F2.control),
# gen_3_geno = (F3.test - F3.control),
gen_0_geno = (F0.test - F1.control),
levels = design_1)
cm_1_genotype_simple
#filter out probes with an average expression level below 3
keep <- fit_model_1$Amean > 0 #`Amean` stores the average log expression values
fit2_model_1 <- fit_model_1[keep, ]
nrow(fit2_model_1)
#fit the contrast matrix
fit2_model_1 <- contrasts.fit(fit2_model_1, cm_1_genotype_simple)
#set trend = TRUE since the data do not show a uniform variance across all expression intensities (this trend in the variance at different mean intensities is discovered with `plotSA(fit_model)` )
fit2_model_1 <- eBayes(fit2_model_1,
trend = TRUE, #accommodates a mean-variance trend;
robust = TRUE #increases power for some kinds of studies
)
#A list of top genes differentially expressed in a contarst can be obtained by specifying its coef
# topTable(fit2_model_1, coef=c(1:3), adjust="BH", number = 30)
# top_1_geno_1 <- topTable(fit2_model_1,
# coef=c(1),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
#
# top_1_geno_2 <- topTable(fit2_model_1,
# coef=c(2),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
#
# top_1_geno_3 <- topTable(fit2_model_1,
# coef=c(3),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 <- topTable(fit2_model_1,
coef = c(1),
adjust = "BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_0 %>% filter(adj.P.Val <= 0.05) %>% count()
# 3120
```
1551
#####
only females, no blocking, quantile normalized within
```{r qnormed_within}
#import_CEL
raw_data <- ReadAffy(celfile.path = "./CEL Files")
#import_anno
sample_data <- read_csv(
"sample_data.csv",
col_types = cols(
age = col_integer(),
day = col_factor(levels = c("1", "2", "3", "4", "5", "6")),
generation = col_factor(levels = c("F-1", "F0 + F1", "F2", "F3")),
module = col_factor(levels = c("1", "2", "3", "4")),
number = col_integer(),
replicate = col_factor(levels = c("L3", "L4", "L5", "L7", "L8", "L9", "L10")),
sex = col_factor(levels = c("female", "male")),
stored = col_date(format = "%m/%d/%Y"),
wash = col_factor(levels = c("1", "2", "3", "4")),
genotype = col_factor(levels = c(
"control", "test", "w1118", "rpr/cyo; dilp3/ser", "cyo/+; ser/+", "rpr/+; dilp3/+", "cyo/+; dilp3/+", "rpr/+; ser/+"))))
#assign_anno
phenoData(raw_data) <- AnnotatedDataFrame(sample_data)
#rename_sample
sampleNames(raw_data) <- sample_data$label
# keep only generation F0 females
keep <- which(pData(raw_data)$sex == "female" & pData(raw_data)$generation == "F0 + F1"
& (pData(raw_data)$genotype == "rpr/cyo; dilp3/ser" | pData(raw_data)$genotype == "w1118")
)
raw_data <- raw_data[ , keep] #ExpressionSet objects cannot be subset with filter() or filter()
# drop empty levels
pData(raw_data)$generation <- pData(raw_data)$generation %>% droplevels()
# subsetting an AffyBatch object removes row names so they need be reassigned
sampleNames(raw_data) <- pData(raw_data)$label
# cleanup factor levels
pData(raw_data)$generation <- pData(raw_data) %>% transmute(
generation = case_when( # convert F-1 and the "F0" in "F0 + F1" into F0
genotype == "rpr/cyo; dilp3/ser" ~ "F0",
TRUE ~ "F1") %>% as_factor()) %>% pluck(1)
pData(raw_data)$genotype <- pData(raw_data) %>% transmute(
genotype = case_when( # convert all non-transgenic lineages into w1118 genotype
genotype == "rpr/cyo; dilp3/ser" ~ "test",
TRUE ~ "control") %>% as_factor()) %>% pluck(1)
sampleNames(raw_data) <- pData(raw_data)$label
#check_object
validObject(raw_data)
#gcrma
eset <- gcrma(raw_data)
```
```{r no_blocking}
# Create single variable "group"
group <- with(pData(eset),
paste(generation,
genotype,
# sex,
# day,
sep = "."))
pData(eset)$group <- as.factor(group)
remove(group)
# Create design matrix with group-means parametrization (no intercept)
design_1 <- model.matrix(~ 0
# + day
+ group, data = pData(eset))
colnames(design_1) <- levels(pData(eset)$group)
colnames(design_1)
# Count the number of samples modeled by each coefficient
colSums(design_1)
# #fit the linear model fit_model_1 without blocking
fit_model_1 <- lmFit(eset, design_1)
cm_1_genotype_simple <- makeContrasts(
# gen_1_geno = (F1.test - F1.control),
# gen_2_geno = (F2.test - F2.control),
# gen_3_geno = (F3.test - F3.control),
gen_0_geno = (F0.test - F1.control),
levels = design_1)
cm_1_genotype_simple
#filter out probes with an average expression level below 3
keep <- fit_model_1$Amean > 0 #`Amean` stores the average log expression values
fit2_model_1 <- fit_model_1[keep, ]
nrow(fit2_model_1)
#fit the contrast matrix
fit2_model_1 <- contrasts.fit(fit2_model_1, cm_1_genotype_simple)
#set trend = TRUE since the data do not show a uniform variance across all expression intensities (this trend in the variance at different mean intensities is discovered with `plotSA(fit_model)` )
fit2_model_1 <- eBayes(fit2_model_1,
trend = TRUE, #accommodates a mean-variance trend;
robust = TRUE #increases power for some kinds of studies
)
#Results of all contrasts simultaneously
# results_1_geno <- decideTests(fit2_model_1)
# summary(results_1_geno)
#
# # #Venn diagram showing numbers of genes significant in all contrasts, simultaneously
# vennDiagram(results_1_geno)
#A list of top genes differentially expressed in a contarst can be obtained by specifying its coef
# topTable(fit2_model_1, coef=c(1:3), adjust="BH", number = 30)
# top_1_geno_1 <- topTable(fit2_model_1,
# coef=c(1),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
#
# top_1_geno_2 <- topTable(fit2_model_1,
# coef=c(2),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
#
# top_1_geno_3 <- topTable(fit2_model_1,
# coef=c(3),
# adjust="BH", number = nrow(fit2_model_1), sort.by = "none")
top_1_geno_0 <- topTable(fit2_model_1,
coef = c(1),
adjust = "BH", number = nrow(fit2_model_1), sort.by = "none")
# hist(top_1_geno_3$t, breaks = seq(from = -2, to = 2, by = 0.1))
top_1_geno_0 %>% filter(P.Value <= 0.05) %>% count()
top_1_geno_0 %>% filter(adj.P.Val <= 0.05) %>% count()
```
456
#####
only females, blocking, quantile normalized within
```{r blocking}
# Create single variable "group"
group <- with(pData(eset),
paste(generation,
genotype,
# sex,
# day,
sep = "."))
pData(eset)$group <- as.factor(group)
remove(group)
# Create design matrix with group-means parametrization (no intercept)
design_1 <- model.matrix(~ 0
# + day
+ group, data = pData(eset))
colnames(design_1) <- levels(pData(eset)$group)
colnames(design_1)
# Count the number of samples modeled by each coefficient
colSums(design_1)
group2 <- with(pData(eset),
paste(replicate,
day,
sep = "."))
pData(eset)$group2 <- as.factor(group2)
#estimate the correlation between measurements made on the same repliactes:
corfit_1 <- duplicateCorrelation(eset, design_1, block = pData(eset)$group2)
corfit_1$consensus
#fit the linear model fit_model_1 with blocking
fit_model_1 <- lmFit(eset, design_1,
block = pData(eset)$group2,
correlation = corfit_1$consensus)
cm_1_genotype_simple <- makeContrasts(
# gen_1_geno = (F1.test - F1.control),