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limmaDGE.R
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limmaDGE.R
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# library(DESeq2)
# library(ggplot2)
# library(pheatmap)
# library(vsn)
# library(devtools)
# library(rafalib)
# library(BiocStyle)
# library(rmarkdown)
# library(geneplotter)
# library(plyr)
# library(LSD)
# library(gplots)
# library(RColorBrewer)
# library(stringr)
# library(topGO)
# library(genefilter)
# library(biomaRt)
# library(dplyr)
# library(EDASeq)
# library(fdrtool)
# library("sva")
library(limma)
library(edgeR)
library(qvalue)
library(tidyverse)
library(dplyr)
library(sva)
# library(gee)
# library(geepack)
# options(contrasts=c("contr.sum","contr.poly"))
# library(clusterProfiler)
# library(RColorBrewer)
# setwd("/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level-redo-07222020/00-amygdala")
counts_data = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/01-lncRNAKB-gene-level-counts/amygdala.gene.featurecount.txt")
# setwd("/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level-redo-07222020/00-sACC")
# counts_data = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/01-lncRNAKB-gene-level-counts/sacc.gene.featurecount.txt")
counts_data
phenodata = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/00-phenotype/amygdala_subjects_pheno.txt")
# phenodata = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/00-phenotype/sacc_subjects_pheno.txt")
phenodata
group1 = "Bipolar"
group2 = "Control"
feature = "gene"
# bregion = "sacc"
bregion = "amy"
phenodata = filter(phenodata, dx == group1 | dx == group2)
phenodata
counts_data = select(counts_data, c("gene",phenodata$SampleID))
counts_data
annot = read_csv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level/annot_format_v7.csv")
annot
## filtering
#Keep lncRNAs with least 1 counts/reads in 90% of replicates/samples
counts_data_lncRNA = counts_data %>% semi_join(filter(annot, gene_type == "lncRNA"), by = c("gene" = "lnckbID"))
counts_data_lncRNA
counts_data_lncRNA = counts_data_lncRNA %>% filter(rowSums(counts_data_lncRNA>1) >= as.integer(0.90*(dim(counts_data_lncRNA)[2]-1)))
counts_data_lncRNA
#Keep protein coding genes with least 5 counts/reads in 90% of replicates/samples
counts_data_pc = counts_data %>% semi_join(filter(annot, gene_type == "protein_coding"), by = c("gene" = "lnckbID"))
counts_data_pc
counts_data_pc = counts_data_pc %>% filter(rowSums(counts_data_pc>5) >= as.integer(0.90*(dim(counts_data_pc)[2]-1)))
counts_data_pc
################################################################################################################
#
# Features were then filtered if they did not exceed the following mean expression thresholds:
# 0.25 RPKM for genes
# 0.30 RPKM for exons
# 0.35 RP10M for junctions
# 0.40 TPM for transcripts
# as determined with the expression_cutoff() function from the jaffelab R package (33).
# Cutoffs were applied based on the samples from the amygdala and subgenual anterior cingulate cortex.
#
################################################################################################################
# pdf('test_expression_cutoff.pdf', width = 12)
# expression_cutoff(y2)
# dev.off()
################################################################################################################
#
#
# Graph of different threshold vs. number of lncRNA and protein coding <genes> compared to flagship count matrix
#
#
################################################################################################################
# load("/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/andrew_rda/eQTL_expressed_rse_amygdala.rda")
load("/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/andrew_rda/eQTL_expressed_rse_sacc.rda")
genes_passed_threshold = as_tibble(rownames(rse_gene))
genes_passed_threshold
gencodev25annotation = read_tsv(file="/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/00-leafcutter/02-step4-run-fastQTL/01-annotate-sQTL-results-pz/gencode.v25.basic.annotation.sorted.bed", col_names=FALSE)
gencodev25annotation
genes_passed_threshold_annotated_gencodev25 = gencodev25annotation %>% semi_join(genes_passed_threshold, by = c("X5" = "value"))
count_of_types_of_genes = data.frame(table(genes_passed_threshold_annotated$X6))
colnames(count_of_types_of_genes) = c("gene_type","count")
count_of_types_of_genes
################################################################################################################
#
#
# PCA plot by brain region lncRNAKB GTEx v7
# All lncRNA
#
#
################################################################################################################
subjecttobrainregions = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/GTEx_brain_samples_by_region.txt")
subjecttobrainregions
# pca = readRDS(file="/data/NHLBI_BCB/Fayaz/21-GTEx-data/12-gene-expression-boxplots-lncRNAKB-v6/log2_TPM_lncRNAsonly_lncRNAKBv6_pca.rds")
pca = readRDS(file="/data/NHLBI_BCB/Fayaz/21-GTEx-data/12-gene-expression-boxplots-lncRNAKB-v6/log2_TPM_pcgssonly_lncRNAKBv6_pca.rds")
pcs <- rownames_to_column(data.frame(pca$x), var = "subjectID") %>% as_tibble()
pcs
pcswbrainregion = pcs %>% inner_join(subjecttobrainregions, by = c("subjectID" = "SubjectID"))
pcswbrainregion
subjecttobrainregions = subjecttobrainregions %>% semi_join(pcswbrainregion, by = c("SubjectID" = "subjectID"))
subjecttobrainregions
library(randomcoloR)
n = length(levels(as.factor(subjecttobrainregions$Brain_Region)))
col = distinctColorPalette(k=n, runTsne=T)
BrainRegionColor=col[unclass(as.factor(subjecttobrainregions$Brain_Region))]
BrainRegionColor[1:10]
brainregions = unique(subjecttobrainregions$Brain_Region)
brainregions
samples_each_brainregion=c("SRR1078879",
"SRR1073755",
"SRR1075898",
"SRR1070986",
"SRR1071289",
"SRR1088389",
"SRR1072367",
"SRR1081741",
"SRR1069188",
"SRR1074164",
"SRR1072178",
"SRR1073143",
"SRR1077687"
)
# pcswtissue %>%
# ggplot(aes(x=PC1,y=PC2)) +
# geom_point(aes(colour=Tissuecolor), alpha=0.20) +
# guides(colour=guide_legend(override.aes = list(alpha=1, size=1))) +
# scale_color_discrete(name="Tissue", labels=tissues) +
# theme(legend.text=element_text(size=7)) +
# labs(x=spc1, y=spc2, title="Principal Components_PC1.vs.PC2, lncRNAs, lncRNAKB, GTEx, v7") +
# ggrepel::geom_text_repel(data = pcswtissue %>% filter(subjectID %in% samples_each_tissue), aes(label=Tissue), colour="black", size=2.5)
# dev.off()
eigs <- pca$sdev^2
e1=eigs[1] / sum(eigs) * 100
e2=eigs[2] / sum(eigs) * 100
e3=eigs[3] / sum(eigs) * 100
e4=eigs[4] / sum(eigs) * 100
e5=eigs[5] / sum(eigs) * 100
spc1=sprintf('PC1 (%% %.1f)',e1)
spc2=sprintf('PC2 (%% %.1f)',e2)
spc3=sprintf('PC3 (%% %.1f)',e3)
spc4=sprintf('PC4 (%% %.1f)',e4)
spc5=sprintf('PC5 (%% %.1f)',e5)
pcswbrainregion %>%
ggplot(aes(x=PC1,y=PC2)) +
geom_point(aes(colour=BrainRegionColor), alpha=0.9) +
theme(legend.position="none") +
labs(x=spc1, y=spc2, title="Principal Components_PC1.vs.PC2, protein coding, lncRNAKB, GTEx, v7, brain") +
ggrepel::geom_text_repel(data = pcswbrainregion %>% filter(subjectID %in% samples_each_brainregion), aes(label=Brain_Region), colour="black", size=3)
dev.off()
counts_data = bind_rows(counts_data_lncRNA, counts_data_pc)
counts_data
################################################################################################################
#
#
# How many lncRNA in GENCODE vs. Other annotations survive cut-offs
#
#
#
################################################################################################################
counts_data_lncRNA_annot = counts_data %>% inner_join(filter(annot, gene_type == "lncRNA"), by = c("gene" = "lnckbID"))
counts_data_lncRNA_annot
data.frame(table(counts_data_lncRNA_annot$source))
counts_data = data.frame(counts_data[,2:dim(counts_data)[2]], row.names=counts_data$gene)
counts_data[1:10,1:10]
dge = DGEList(counts = counts_data)
dge = calcNormFactors(dge, method="TMM")
################################################################################################################
# covariates used by AJ
# modSep = model.matrix(~Dx + AgeDeath + Sex + snpPC1 + snpPC2 + snpPC3 + mitoRate + rRNA_rate + totalAssignedGene + RIN + ERCCsumLogErr, data=colData(rse_gene))
# age at death
# sex
# mitochondrial rate
# ribosomal RNA rate
# total gene assignment (EXCLUDED)
# RIN
# ERCC spike-in rates
# the top 3 principal components of the genotype data (calculated using ~100,000 LD-independent SNPs) (EXCLUDED)
# X quality surrogate variables (qSVs).
library(SummarizedExperiment)
library(stringr)
# load("/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/andrew_rda/eQTL_expressed_rse_amygdala.rda")
load("/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/andrew_rda/eQTL_expressed_rse_sacc.rda")
all_covariates = as_tibble(colData(rse_gene))
all_covariates
covariates = select(all_covariates, c("PrimaryDx", "AgeDeath", "Sex", "mitoRate", "rRNA_rate", "RIN", "ERCCsumLogErr", "RNum", "BrNum"))
covariates
covariates = mutate(covariates, PrimaryDx = ifelse(PrimaryDx == "Other","Bipolar",as.character(PrimaryDx)))
covariates
covariates = covariates %>% semi_join(phenodata, by = c("RNum" = "SampleID"))
covariates
covariates = mutate(covariates, Dx = ifelse(PrimaryDx == "Bipolar",1,0))
covariates
load("/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/00-leafcutter/03-step5-run-differential-introl-excision-analysis/qSV_mat.rda")
qSV_mat = as_tibble(qSV_mat, rownames="SampleID")
qSV_mat
qSV_mat = qSV_mat %>% filter(str_detect(SampleID, "_sACC"))
# qSV_mat = qSV_mat %>% filter(str_detect(SampleID, "_Amygdala"))
qSV_mat
qSV_mat = qSV_mat %>% mutate(SampleID = str_replace_all(qSV_mat$SampleID, c("_Amygdala"="", "_sACC"="")))
qSV_mat
covariates = covariates %>% inner_join(qSV_mat, by = c("BrNum" = "SampleID"))
covariates
covariates = arrange(covariates, PrimaryDx)
covariates
# load("/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/00-leafcutter/03-step5-run-differential-introl-excision-analysis/qSV_mat.rda")
# rownames(qSV_mat) = str_replace_all(rownames(qSV_mat), c("_Amygdala"="", "_sACC"=""))
# covs = merge(x=covariates, y=qSV_mat, by="row.names", all.x=T)
# load("/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/andrew_rda/zandiHyde_bipolar_Genotypes_n511.rda")
# snpPCs = mds[,1:3]
# covs2 = merge(x=covs, y=snpPCs, by.x="Row.names", by.y="row.names", all.x=T)
# covs4 = data.frame(covs2[,2:dim(covs2)[2]], row.names=covs2$Row.names)
# amygdala_subjects = read.table(file="/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/00-leafcutter/03-step5-run-differential-introl-excision-analysis/amy_transposed_sex-bp-5geno-pc_covs_07182019.txt", header=F, row.names=1)
# covs5 = covs4[rownames(amygdala_subjects),]
# covs5[,"Sex"] = str_replace_all(covs5[,"Sex"] , c("M"="1", "F"="0"))
# covs5[,"Dx"] = str_replace_all(covs5[,"Dx"] , c("Bipolar"="1", "Control"="0", "Other"="0"))
# write.table(covs5, file="/data/NHLBI_BCB/Fayaz/31-leafcutter/lieber/00-leafcutter/03-step5-run-differential-introl-excision-analysis/amy_transposed_covs_11212019.txt", quote=F, col.names=F)
# mod1 = model.matrix(~Dx, data=covariates)
# mod0 = cbind(mod1[,1])
# svseq = svaseq(as.matrix(counts_data),mod1,mod0,n.sv=5)$sv
# pdf(paste("bipseq_lncRNAKB_svaplot_plot_all_genes_pairs_plot_5svs_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
# pairs(svseq[,1:5], main=paste("scatter plot matrix of Surrogate Variable_5SVs_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=21, bg=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex.main=0.75)
# dev.off()
#
################################################################################################################
# design = model.matrix(~Dx + AgeDeath + Sex + mitoRate + rRNA_rate + RIN + ERCCsumLogErr + svseq[,1:5], data=covariates)
design = model.matrix(~Dx + AgeDeath + Sex + mitoRate + rRNA_rate + RIN + ERCCsumLogErr + PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9 + PC10 + PC11 + PC12 + PC13 + PC14 + PC15 + PC16 + PC17 + PC18, data=covariates)
v = voom(dge, design) #Transform count data to log2-counts per million (logCPM+0.5), estimate the mean-variance relationship and use this to compute appropriate observation-level weights. The data are then ready for linear modelling.
fit = lmFit(v, design)
fit = eBayes(fit)
log2FC = fit$coefficients[, 2]
pvalue = fit$p.value[, 2]
coefs = fit$coefficients[, 2]
qvalue = qvalue(pvalue)$qvalues
gene_res = data.frame(log2FC, pvalue, qvalue)
gene_res = rownames_to_column(gene_res, var = "gene") %>% as_tibble()
gene_res
gene_res = gene_res %>% inner_join(annot, by = c("gene" = "lnckbID"))
gene_res
gene_res = arrange(gene_res, qvalue)
gene_res
write.csv(gene_res, paste("bipseq_lncRNAKB","_",feature,"_",bregion,"_",group1,"_",group2,".csv", sep=""), row.names=FALSE)
counts_data_normalized = as.data.frame(v$E) %>% as_tibble()
counts_data_normalized = rownames_to_column(as.data.frame(v$E), var = "gene") %>% as_tibble()
counts_data_normalized
# counts_data_lncRNA = counts_data %>% semi_join(filter(annot, gene_type == "lncRNA"), by = c("gene" = "lnckbID"))
# pca plot - using all genes (only filtered above for low counts)
pca = prcomp(t(counts_data_normalized %>% select(-one_of("gene"))), scale=F)
eigs <- pca$sdev^2
e1=eigs[1] / sum(eigs) * 100
e2=eigs[2] / sum(eigs) * 100
e3=eigs[3] / sum(eigs) * 100
e4=eigs[4] / sum(eigs) * 100
e5=eigs[5] / sum(eigs) * 100
spc1=sprintf('PC1 (%% %.1f)',e1)
spc2=sprintf('PC2 (%% %.1f)',e2)
spc3=sprintf('PC3 (%% %.1f)',e3)
spc4=sprintf('PC4 (%% %.1f)',e4)
spc5=sprintf('PC5 (%% %.1f)',e5)
pdf(paste("bipseq_lncRNAKB_pca_plot_all_genes_pairs_plot_first_five_PCs_TMM_CPM_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
pairs(pca$x[,1:5], main=paste("scatter plot matrix of Principal Components_first_five_PCs_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=21, bg=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex.main=0.75)
dev.off()
##########################################################################################
#
# countfiltered >> log2CPM0.5 >> TMM >> lncRNA >> PCA
#
##########################################################################################
pca = prcomp(t(counts_data_normalized %>% semi_join(filter(gene_res, gene_type=="lncRNA"), by = c("gene" = "gene")) %>% select(-one_of("gene"))), scale=F)
eigs <- pca$sdev^2
e1=eigs[1] / sum(eigs) * 100
e2=eigs[2] / sum(eigs) * 100
e3=eigs[3] / sum(eigs) * 100
e4=eigs[4] / sum(eigs) * 100
e5=eigs[5] / sum(eigs) * 100
spc1=sprintf('PC1 (%% %.1f)',e1)
spc2=sprintf('PC2 (%% %.1f)',e2)
spc3=sprintf('PC3 (%% %.1f)',e3)
spc4=sprintf('PC4 (%% %.1f)',e4)
spc5=sprintf('PC5 (%% %.1f)',e5)
pdf(paste("bipseq_lncRNAKB_pca_plot_lncRNA_first_five_PCs_log2CPM0.5_TMM",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
pairs(pca$x[,1:5], main=paste("scatter plot matrix of Principal Components_first_five_PCs_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=21, bg=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex.main=0.75)
dev.off()
pdf(paste("bipseq_lncRNAKB_pca_plot_lncRNA_PC1_PC2_log2CPM0.5_TMM_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
# Add extra space to right of plot area; change clipping to figure
par(mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
plot(pca$x[,1], pca$x[,2], main=paste("scatter plot matrix of Principal Components_PC1.vs.PC2_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=16, col=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex=1.20, cex.main=0.80, xlab=spc1, ylab=spc2)
# text(pca$x[,1], pca$x[,2], labels=paste(rownames(phenodata),phenodata$dx,sep="_"), pos=3, cex=0.30)
legend("topright", inset=c(-0.23,0), legend=c(group1,group2), pch=c(16,16), col=rev(levels(as.factor(c('#e41a1c','#377eb8')[as.factor(phenodata$dx)]))), title="Group", cex=0.60)
dev.off()
##########################################################################################
#
# countfiltered >> log2CPM0.5 >> TMM >> lncRNA >> qvalue<0.05 >> PCA
#
##########################################################################################
pca = prcomp(t(counts_data_normalized %>% semi_join(filter(gene_res, qvalue < 0.05, gene_type=="lncRNA"), by = c("gene" = "gene")) %>% select(-one_of("gene"))), scale=F)
eigs <- pca$sdev^2
e1=eigs[1] / sum(eigs) * 100
e2=eigs[2] / sum(eigs) * 100
e3=eigs[3] / sum(eigs) * 100
e4=eigs[4] / sum(eigs) * 100
e5=eigs[5] / sum(eigs) * 100
spc1=sprintf('PC1 (%% %.1f)',e1)
spc2=sprintf('PC2 (%% %.1f)',e2)
spc3=sprintf('PC3 (%% %.1f)',e3)
spc4=sprintf('PC4 (%% %.1f)',e4)
spc5=sprintf('PC5 (%% %.1f)',e5)
pdf(paste("bipseq_lncRNAKB_pca_plot_lncRNA_qvalue0.05_first_five_PCs_log2CPM0.5_TMM",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
pairs(pca$x[,1:5], main=paste("scatter plot matrix of Principal Components_first_five_PCs_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=21, bg=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex.main=0.75)
dev.off()
pdf(paste("bipseq_lncRNAKB_pca_plot_lncRNA_qvalue0.05_PC1_PC2_log2CPM0.5_TMM_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
# Add extra space to right of plot area; change clipping to figure
par(mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
plot(pca$x[,1], pca$x[,2], main=paste("scatter plot matrix of Principal Components_PC1.vs.PC2_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=16, col=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex=1.20, cex.main=0.80, xlab=spc1, ylab=spc2)
# text(pca$x[,1], pca$x[,2], labels=paste(rownames(phenodata),phenodata$dx,sep="_"), pos=3, cex=0.30)
legend("topright", inset=c(-0.23,0), legend=c(group1,group2), pch=c(16,16), col=rev(levels(as.factor(c('#e41a1c','#377eb8')[as.factor(phenodata$dx)]))), title="Group", cex=0.60)
dev.off()
# pca plot - using results with q-value < 0.05 or any other subset based on significant DE for e.g. fc, log2fc
pca = prcomp(t(counts_data_normalized %>% semi_join(filter(gene_res, log2FC < -0.50 | log2FC > 0.50, qvalue < 0.05), by = c("gene" = "gene")) %>% select(-one_of("gene"))), scale=F)
eigs <- pca$sdev^2
e1=eigs[1] / sum(eigs) * 100
e2=eigs[2] / sum(eigs) * 100
e3=eigs[3] / sum(eigs) * 100
e4=eigs[4] / sum(eigs) * 100
e5=eigs[5] / sum(eigs) * 100
spc1=sprintf('PC1 (%% %.1f)',e1)
spc2=sprintf('PC2 (%% %.1f)',e2)
spc3=sprintf('PC3 (%% %.1f)',e3)
spc4=sprintf('PC4 (%% %.1f)',e4)
spc5=sprintf('PC5 (%% %.1f)',e5)
pdf(paste("bipseq_lncRNAKB_pca_plot_genes_q_less_than_0.05_foldchange_1_pairs_plot_first_five_PCs_TMM_CPM_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
pairs(pca$x[,1:5], main=paste("scatter plot matrix of Principal Components_first_five_PCs_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=21, bg=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex.main=0.75)
dev.off()
# pca plot - using results with q-value < 0.05 or any other subset based on significant DE for e.g. fc, log2fc
pca = prcomp(t(counts_data_normalized %>% semi_join(filter(gene_res, log2FC < -0.50 | log2FC > 0.50, qvalue < 0.05, gene_type=="protein_coding"), by = c("gene" = "gene")) %>% select(-one_of("gene"))), scale=F)
eigs <- pca$sdev^2
e1=eigs[1] / sum(eigs) * 100
e2=eigs[2] / sum(eigs) * 100
e3=eigs[3] / sum(eigs) * 100
e4=eigs[4] / sum(eigs) * 100
e5=eigs[5] / sum(eigs) * 100
spc1=sprintf('PC1 (%% %.1f)',e1)
spc2=sprintf('PC2 (%% %.1f)',e2)
spc3=sprintf('PC3 (%% %.1f)',e3)
spc4=sprintf('PC4 (%% %.1f)',e4)
spc5=sprintf('PC5 (%% %.1f)',e5)
pdf(paste("bipseq_lncRNAKB_pca_plot_proteincoding_genes_q_less_than_0.05_foldchange_1_pairs_plot_first_five_PCs_TMM_CPM_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
pairs(pca$x[,1:5], main=paste("scatter plot matrix of Principal Components_first_five_PCs_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=21, bg=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex.main=0.75)
dev.off()
# pca plot - using results with q-value < 0.05 or any other subset based on significant DE for e.g. fc, log2fc
pca = prcomp(t(counts_data_normalized %>% semi_join(filter(gene_res, log2FC < -0.50 | log2FC > 0.50, qvalue < 0.05, gene_type=="lncRNA"), by = c("gene" = "gene")) %>% select(-one_of("gene"))), scale=F)
eigs <- pca$sdev^2
e1=eigs[1] / sum(eigs) * 100
e2=eigs[2] / sum(eigs) * 100
e3=eigs[3] / sum(eigs) * 100
e4=eigs[4] / sum(eigs) * 100
e5=eigs[5] / sum(eigs) * 100
spc1=sprintf('PC1 (%% %.1f)',e1)
spc2=sprintf('PC2 (%% %.1f)',e2)
spc3=sprintf('PC3 (%% %.1f)',e3)
spc4=sprintf('PC4 (%% %.1f)',e4)
spc5=sprintf('PC5 (%% %.1f)',e5)
pdf(paste("bipseq_lncRNAKB_pca_plot_lncRNA_genes_q_less_than_0.05_foldchange_1_pairs_plot_first_five_PCs_TMM_CPM_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
pairs(pca$x[,1:5], main=paste("scatter plot matrix of Principal Components_first_five_PCs_",feature,"_",bregion,"_",group1,"_",group2, sep=""), pch=21, bg=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], cex.main=0.75)
dev.off()
# pdf(paste("pca_plot_genes_q_less_than_0.05_pc1_vs_pc2_cpm_TMM_CPM_",feature,"_",group1,"_",group2,".pdf", sep=""))
# Add extra space to right of plot area; change clipping to figure
# par(mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)
# plot(pca$x[,1], pca$x[,2], main=paste("scatter plot matrix of Principal Components_PC1.vs.PC2_",feature,"_",group1,"_",group2, sep=""), pch=16, col=c('blue','orange')[as.factor(phenodata$dx)], cex=1.20, cex.main=0.80, xlab=spc1, ylab=spc2)
# text(pca$x[,1], pca$x[,2], labels=paste(rownames(phenodata),phenodata$dx,sep="_"), pos=3, cex=0.30)
# legend("topright", inset=c(-0.23,0), legend=c(group1,group2), pch=c(16,16), col=levels(as.factor(c('#e41a1c','#377eb8')[as.factor(phenodata$dx)])), title="Group", cex=0.60)
# dev.off()
## Plotting abundance distribution, after applying filter, normalization
pdf(file=paste("bipseq_lncRNAKB_log2_TMM_CPM_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
boxplot(counts_data_normalized %>% select(-one_of("gene")) %>% rename_all(funs(str_replace(.,covariates$RNum,as.character(covariates$Dx)))), col=c('#e41a1c','#377eb8')[as.factor(phenodata$dx)], las=2, ylab='log2(cpm(counts)+0.5)', main=paste("cpm:",feature,"_",bregion,"_",group1,"_",group2), xlab="sample", cex.main=0.80, cex.axis=0.50)
dev.off()
## Plotting volcano plot, Differential Expression Analysis-qval
q.trans = -1*log(gene_res$qvalue, base = 10)
logfold = gene_res$log2FC
pdf(file=paste("bipseq_lncRNAKB_volcano_qval_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
plot(logfold,q.trans,type="n",ylab="-1*log10(q-value)",xlab="log2(fold change)",main=paste("cpm:",feature,"_",bregion,"_",group1,"_",group2), cex.main=0.80, xlim=range(logfold), ylim=range(q.trans))
points(logfold,q.trans,col="black",cex=0.65)
points(logfold[(q.trans>1.3&logfold>0.50)],q.trans[(q.trans>1.3&logfold>0.50)],col="red",pch=16,cex=0.65)
points(logfold[(q.trans>1.3&logfold<(-0.50))],q.trans[(q.trans>1.3&logfold<(-0.50))],col="green",pch=16,cex=0.65)
# text(logfold[(q.trans>1.3&logfold>1.0)],q.trans[(q.trans>1.3&logfold>1.0)],labels=as.character(data$genesymbol[(q.trans>1.3&logfold>1.0)]), pos=3, cex=0.40)
# text(logfold[(q.trans>1.3&logfold<(-1.0))],q.trans[(q.trans>1.3&logfold<(-1.0))],labels=as.character(data$genesymbol[(q.trans>1.3&logfold<(-1.0))]), pos=3, cex=0.40)
abline(h=1.3)
abline(v=-0.50)
abline(v=0.50)
dev.off()
gene_res = gene_res %>%
mutate(
gene_type_significant = case_when(
qvalue < 0.05 & gene_type == "lncRNA" ~ "lncRNA",
qvalue < 0.05 & gene_type == "protein_coding" ~ "protein-coding",
TRUE ~ "not_significant"
)
)
ggplot(gene_res, aes(log2FC, -log10(qvalue))) + geom_point(aes(col=gene_type_significant)) + scale_color_manual(values=c("green","lightblue","red")) +
coord_cartesian(xlim = c(-1.0, 1.0), expand = TRUE) +
geom_hline(aes(yintercept = 1.3), color="black", linetype="dashed", size=0.5)
ggsave(file=paste("bipseq_lncRNAKB_volcano_plot_qvalue",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
dev.off()
## Plotting volcano plot, Differential Expression Analysis-pval
p.trans = -1*log(gene_res$pvalue, base = 10)
logfold = gene_res$log2FC
pdf(file=paste("bipseq_lncRNAKB_volcano_pval_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
plot(logfold,p.trans,type="n",ylab="-1*log10(p-value)",xlab="log2(fold change)",main=paste("LimmaVoom:",feature,"_",bregion,"_",group1,"_",group2), cex.main=0.80, xlim=c(-1.0,1.0), ylim=range(p.trans))
points(logfold,p.trans,col="black",cex=0.65)
points(logfold[(p.trans>1.3)], p.trans[(p.trans>1.3)],col="lightblue",pch=16,cex=0.65)
# points(logfold[(p.trans>1.3&logfold>1.0)],p.trans[(p.trans>1.3&logfold>1.0)],col="red",pch=16,cex=0.65)
# points(logfold[(p.trans>1.3&logfold<(-1.0))],p.trans[(p.trans>1.3&logfold<(-1.0))],col="green",pch=16,cex=0.65)
# text(logfold[(p.trans>1.3&logfold>1.0)],p.trans[(p.trans>1.3&logfold>1.0)],labels=as.character(data$genesymbol[(p.trans>1.3&logfold>1.0)]), pos=3, cex=0.40)
# text(logfold[(p.trans>1.3&logfold<(-1.0))],p.trans[(p.trans>1.3&logfold<(-1.0))],labels=as.character(data$genesymbol[(p.trans>1.3&logfold<(-1.0))]), pos=3, cex=0.40)
abline(h=1.3)
# abline(v=-1.0)
# abline(v=1.0)
dev.off()
pdf(file=paste("bipseq_lncRNAKB_qval_distribution_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
hist(gene_res$qvalue, col="grey", border="white", xlab="qval", ylab="", main=paste("cpm:",feature,"_",bregion,"_",group1,"_",group2))
dev.off()
pdf(file=paste("bipseq_lncRNAKB_pval_distribution_",feature,"_",bregion,"_",group1,"_",group2,".pdf", sep=""))
hist(gene_res$pvalue, col="grey", border="white", xlab="pval", ylab="", main=paste("cpm:",feature,"_",bregion,"_",group1,"_",group2))
dev.off()
gene_res = read_csv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level/00-sacc/bipseq_lncRNAKB_gene_sacc_Bipolar_Control.csv")
# /data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level/00-amygdala/bipseq_lncRNAKB_gene_amy_Bipolar_Control.csv
# /data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level/00-sacc/bipseq_lncRNAKB_gene_sacc_Bipolar_Control.csv
filter(gene_res, log2FC < -0.50 | log2FC > 0.50, qvalue < 0.05)
#######################################################################################################################
#
#
#
#######################################################################################################################
gene_res_amy = read_csv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level-redo-07222020/00-amygdala/bipseq_lncRNAKB_gene_amy_Bipolar_Control.csv")
gene_res_amy
gene_res_amy_sig = filter(gene_res_amy, qvalue < 0.05, gene_type=="lncRNA")
gene_res_amy_sig = filter(gene_res_amy, qvalue < 0.05)
# gene_res_amy_sig = filter(gene_res_amy, log2FC < -0.50 | log2FC > 0.50, qvalue < 0.05)
gene_res_amy_sig
gene_res_sacc = read_csv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level-redo-07222020/00-sACC/bipseq_lncRNAKB_gene_sacc_Bipolar_Control.csv")
gene_res_sacc
gene_res_sacc_sig = filter(gene_res_sacc, qvalue < 0.05, gene_type=="lncRNA")
gene_res_sacc_sig = filter(gene_res_sacc, qvalue < 0.05)
# gene_res_sacc_sig = filter(gene_res_sacc, log2FC < -0.50 | log2FC > 0.50, qvalue < 0.05)
gene_res_sacc_sig
gene_res_sig_common_amy_sacc = gene_res_amy_sig %>% inner_join(gene_res_sacc_sig, by = c("gene" = "gene"))
gene_res_sig_common_amy_sacc
gene_res_amy_uniq = anti_join(gene_res_amy_sig, gene_res_sacc_sig, by = c("gene" = "gene"))
gene_res_amy_uniq
gene_res_sacc_uniq = anti_join(gene_res_sacc_sig, gene_res_amy_sig, by = c("gene" = "gene"))
gene_res_sacc_uniq
#######################################################################################################################
###############################################################################################################################
#
# Plot Expression by bin vs. significance -log10(pvalue)
#
###############################################################################################################################
dge = DGEList(counts = counts_data)
dge = calcNormFactors(dge, method="TMM")
counts_data_cpm = cpm(dge, normalized.lib.sizes=TRUE)
counts_data_cpm = as_tibble(counts_data_cpm, rownames="gene")
counts_data_cpm
df = counts_data_cpm %>% select(!("gene")) %>% mutate(mean_exp_tissues = rowMeans(.))
summary(df$mean_exp_tissues)
df
df = df %>% add_column(counts_data_cpm[,'gene'], .before = 1) %>% select(gene,mean_exp_tissues)
df
gene_res_p0.05 = filter(gene_res, pvalue < 0.05) %>% select(gene,pvalue,gene_type)
gene_res_p0.05 = as_tibble(data.frame(gene=gene_res_p0.05$gene, pvalue=gene_res_p0.05$pvalue, log10pvalue=-1*log10(gene_res_p0.05$pvalue), gene_type=gene_res_p0.05$gene_type))
gene_res_p0.05
df1 = gene_res_p0.05 %>% inner_join(df, by = "gene")
df1
df = df1
df
# df = df %>% filter(gene_type == "lncRNA")
df = df %>% filter(gene_type == "protein_coding")
df
# df = as_tibble(mean_expression_across_all_tissue_tau_pem)
# ggplot(data = df, mapping = aes(x=mean_exp_tissues)) +
# geom_histogram(aes(y=..density..),fill="bisque",color="white",alpha=0.7,binwidth=0.05) +
# geom_density() +
# geom_rug() +
# labs(x='mean expression per tissue') +
# theme_minimal()
# mutate(dat, dist = ifelse(speed == 4, dist * 100, dist)
# set up cut-off values
breaks = c(seq(from=0, to=0.1, by = 0.01), seq(from=0.2, to=0.4, by=0.1), seq(from=0.5, to=15, by=0.50), seq(from=20, to=1000, by=100), seq(from=1000, to=12000, by=1000), max(df$mean_exp_tissues))
# specify interval/bin labels
tags <- c("[0.0-0.01)","[0.01-0.02)","[0.02-0.03)","[0.03-0.04)","[0.04-0.05)","[0.05-0.06)","[0.06-0.07)","[0.07-0.08)","[0.08-0.09)","[0.09-0.1)","[0.1-0.2)","[0.2-0.3)","[0.3-0.4)","[0.4-0.5)","[0.5-1.0)","[1.0-1.5)","[1.5-2.0)","[2.0-2.5)","[2.5-3.0)","[3.0-3.5)","[3.5-4.0)","[4.0-4.5)","[4.5-5.0)","[5.0-5.5)","[5.5-6.0)","[6.0-6.5)", "[6.5-7.0)","[7.0-7.5)","[7.5-8.0)","[8.0-8.5)","[8.5-9.0)","[9.0-9.5)","[9.5-10.0)","[10.0-10.5)","[10.5-11.0)","[11.0-11.5)","[11.5-12.0)","[12.0-12.5)","[12.5- 13.0)","[13.0-13.5)","[13.5-14.0)","[14.0-14.5)","[14.5-15.0)","[15.0-20.0)","[20.0-120.0)", "[120.0-220.0)", "[220.0-320.0)", "[320.0-420.0)", "[420.0-520.0)", "[520.0-620.0)", "[620.0-720.0)", "[720.0-820.0)", "[820.0-920.0)","[920.0-1000.0)","[1000-2000)","[2000-3000)","[3000-4000)","[4000-5000)","[5000-6000)","[6000-7000)","[7000-8000)","[8000-9000)","[9000-10000)","[10000-11000)","[11000-12000)","[>12000]")
# bucketing values into bins
group_tags <- cut(df$mean_exp_tissues,
breaks=breaks,
include.lowest=TRUE,
right=FALSE,
labels=tags)
# inspect bins
summary(group_tags)
vgroup <- df %>%
mutate(tag = case_when(
mean_exp_tissues < 0.01 ~ tags[1],
mean_exp_tissues >= 0.01 & mean_exp_tissues < 0.02 ~ tags[2],
mean_exp_tissues >= 0.02 & mean_exp_tissues < 0.03 ~ tags[3],
mean_exp_tissues >= 0.03 & mean_exp_tissues < 0.04 ~ tags[4],
mean_exp_tissues >= 0.04 & mean_exp_tissues < 0.05 ~ tags[5],
mean_exp_tissues >= 0.05 & mean_exp_tissues < 0.06 ~ tags[6],
mean_exp_tissues >= 0.06 & mean_exp_tissues < 0.07 ~ tags[7],
mean_exp_tissues >= 0.07 & mean_exp_tissues < 0.08 ~ tags[8],
mean_exp_tissues >= 0.08 & mean_exp_tissues < 0.09 ~ tags[9],
mean_exp_tissues >= 0.09 & mean_exp_tissues < 0.1 ~ tags[10],
mean_exp_tissues >= 0.1 & mean_exp_tissues < 0.2 ~ tags[11],
mean_exp_tissues >= 0.2 & mean_exp_tissues < 0.3 ~ tags[12],
mean_exp_tissues >= 0.3 & mean_exp_tissues < 0.4 ~ tags[13],
mean_exp_tissues >= 0.4 & mean_exp_tissues < 0.5 ~ tags[14],
mean_exp_tissues >= 0.5 & mean_exp_tissues < 1.0 ~ tags[15],
mean_exp_tissues >= 1.0 & mean_exp_tissues < 1.5 ~ tags[16],
mean_exp_tissues >= 1.5 & mean_exp_tissues < 2.0 ~ tags[17],
mean_exp_tissues >= 2.0 & mean_exp_tissues < 2.5 ~ tags[18],
mean_exp_tissues >= 2.5 & mean_exp_tissues < 3.0 ~ tags[19],
mean_exp_tissues >= 3.0 & mean_exp_tissues < 3.5 ~ tags[20],
mean_exp_tissues >= 3.5 & mean_exp_tissues < 4.0 ~ tags[21],
mean_exp_tissues >= 4.0 & mean_exp_tissues < 4.5 ~ tags[22],
mean_exp_tissues >= 4.5 & mean_exp_tissues < 5.0 ~ tags[23],
mean_exp_tissues >= 5.0 & mean_exp_tissues < 5.5 ~ tags[24],
mean_exp_tissues >= 5.5 & mean_exp_tissues < 6.0 ~ tags[25],
mean_exp_tissues >= 6.0 & mean_exp_tissues < 6.5 ~ tags[26],
mean_exp_tissues >= 6.5 & mean_exp_tissues < 7.0 ~ tags[27],
mean_exp_tissues >= 7.0 & mean_exp_tissues < 7.5 ~ tags[28],
mean_exp_tissues >= 7.5 & mean_exp_tissues < 8.0 ~ tags[29],
mean_exp_tissues >= 8.0 & mean_exp_tissues < 8.5 ~ tags[30],
mean_exp_tissues >= 8.5 & mean_exp_tissues < 9.0 ~ tags[31],
mean_exp_tissues >= 9.0 & mean_exp_tissues < 9.5 ~ tags[32],
mean_exp_tissues >= 9.5 & mean_exp_tissues < 10.0 ~ tags[33],
mean_exp_tissues >= 10.0 & mean_exp_tissues < 10.5 ~ tags[34],
mean_exp_tissues >= 10.5 & mean_exp_tissues < 11.0 ~ tags[35],
mean_exp_tissues >= 11.0 & mean_exp_tissues < 11.5 ~ tags[36],
mean_exp_tissues >= 11.5 & mean_exp_tissues < 12.0 ~ tags[37],
mean_exp_tissues >= 12.0 & mean_exp_tissues < 12.5 ~ tags[38],
mean_exp_tissues >= 12.5 & mean_exp_tissues < 13.0 ~ tags[39],
mean_exp_tissues >= 13.0 & mean_exp_tissues < 13.5 ~ tags[40],
mean_exp_tissues >= 13.5 & mean_exp_tissues < 14.0 ~ tags[41],
mean_exp_tissues >= 14.0 & mean_exp_tissues < 14.5 ~ tags[42],
mean_exp_tissues >= 14.5 & mean_exp_tissues < 15.0 ~ tags[43],
mean_exp_tissues >= 15.0 & mean_exp_tissues < 20.0 ~ tags[44],
mean_exp_tissues >= 20.0 & mean_exp_tissues < 120.0 ~ tags[45],
mean_exp_tissues >= 120.0 & mean_exp_tissues < 220.0 ~ tags[46],
mean_exp_tissues >= 220.0 & mean_exp_tissues < 320.0 ~ tags[47],
mean_exp_tissues >= 320.0 & mean_exp_tissues < 420.0 ~ tags[48],
mean_exp_tissues >= 420.0 & mean_exp_tissues < 520.0 ~ tags[49],
mean_exp_tissues >= 520.0 & mean_exp_tissues < 620.0 ~ tags[50],
mean_exp_tissues >= 620.0 & mean_exp_tissues < 720.0 ~ tags[51],
mean_exp_tissues >= 720.0 & mean_exp_tissues < 820.0 ~ tags[52],
mean_exp_tissues >= 820.0 & mean_exp_tissues < 920.0 ~ tags[53],
mean_exp_tissues >= 920.0 & mean_exp_tissues < 1000.0 ~ tags[54],
mean_exp_tissues >= 1000 & mean_exp_tissues < 2000 ~ tags[55],
mean_exp_tissues >= 2000 & mean_exp_tissues < 3000 ~ tags[56],
mean_exp_tissues >= 3000 & mean_exp_tissues < 4000 ~ tags[57],
mean_exp_tissues >= 4000 & mean_exp_tissues < 5000 ~ tags[58],
mean_exp_tissues >= 5000 & mean_exp_tissues < 6000 ~ tags[59],
mean_exp_tissues >= 6000 & mean_exp_tissues < 7000 ~ tags[60],
mean_exp_tissues >= 7000 & mean_exp_tissues < 8000 ~ tags[61],
mean_exp_tissues >= 8000 & mean_exp_tissues < 9000 ~ tags[62],
mean_exp_tissues >= 9000 & mean_exp_tissues < 10000 ~ tags[63],
mean_exp_tissues >= 10000 & mean_exp_tissues < 11000 ~ tags[64],
mean_exp_tissues >= 11000 & mean_exp_tissues < 12000 ~ tags[65],
))
summary(vgroup)
vgroup$tag <- factor(vgroup$tag,
levels = tags,
ordered = FALSE)
summary(vgroup$tag)
ggplot(data = vgroup, mapping = aes(x=tag,y=log10pvalue)) +
geom_jitter(aes(color='blue'),alpha=0.2) +
geom_boxplot(fill="bisque",color="black",alpha=0.3) +
labs(title="Plot of -log10pvalue by mean CPM expression across all samples-lncRNA", x='mean CPM expression across all samples') +
guides(color=FALSE) +
theme_minimal() +
theme(axis.text.x = element_text(face="bold",size=5, angle=90)) +
scale_y_continuous(breaks=seq(from=0,to=10, by=1))
dev.off()
#######################################################################################################################################
#
#
#
# WGCNA - CEMiTools
#
#
#######################################################################################################################################
library("CEMiTool")
library("AnnotationDbi")
library("org.Hs.eg.db")
# setwd("/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/03-WGCNA-analysis-CEMiTools/00-amygdala")
# counts_data = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/01-lncRNAKB-gene-level-counts/amygdala.gene.featurecount.txt")
setwd("/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/03-WGCNA-analysis-CEMiTools/00-sACC")
counts_data = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/01-lncRNAKB-gene-level-counts/sacc.gene.featurecount.txt")
counts_data
# phenodata = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/00-phenotype/amygdala_subjects_pheno.txt")
phenodata = read_tsv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/00-phenotype/sacc_subjects_pheno.txt")
phenodata
group1 = "Bipolar"
group2 = "Control"
feature = "gene"
bregion = "sacc"
# bregion = "amy"
phenodata = filter(phenodata, dx == group1 | dx == group2)
phenodata
counts_data = dplyr::select(counts_data, c("gene",phenodata$SampleID))
counts_data
annot = read_csv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level/annot_format_v7.csv")
annot
counts_data_unfiltered = counts_data
######################################################################################################################
#
# Calculate TPM
# total lncRNAs vs diff. expressed lncRNAs Within vs. outside
# Test enrichment of differentially lncRNAs within modules
# Axis1 - Differentially expressed, not expressed
# Axis2 - in module, out module
#
# Wilcoxon rank sum test to test the enrichment of eigengenes with case-control status
#
######################################################################################################################
counts_data = counts_data %>% inner_join(annot, by = c("gene" = "lnckbID"))
counts_data
gene_lenghts_kb = (counts_data$end - counts_data$start)/1000
counts_data = data.frame(counts_data[,2:269], row.names=counts_data$gene) #change column number to (# of samples)+1
counts_data_scaled_by_gene_length = sapply(1:nrow(counts_data), function(i) counts_data[i,]/gene_lenghts_kb[i])
counts_data_scaled_by_gene_length_df = data.frame(matrix(unlist(counts_data_scaled_by_gene_length), nrow=dim(counts_data)[2], byrow=F), stringsAsFactors=FALSE)
counts_data_scaled_by_gene_length_df = t(counts_data_scaled_by_gene_length_df)
dim(counts_data_scaled_by_gene_length_df)
lib_sizes = colSums(counts_data_scaled_by_gene_length_df)
scaling_factor = lib_sizes/1000000
counts_data_scaled_by_gene_length_and_scaling_factor_df = sapply(1:ncol(counts_data_scaled_by_gene_length_df), function(i) counts_data_scaled_by_gene_length_df[,i]/scaling_factor[i])
counts_data_scaled_by_gene_length_and_scaling_factor_df = data.frame(counts_data_scaled_by_gene_length_and_scaling_factor_df, row.names=rownames(counts_data))
colnames(counts_data_scaled_by_gene_length_and_scaling_factor_df) = colnames(counts_data)
dim(counts_data_scaled_by_gene_length_and_scaling_factor_df)
tpm = counts_data_scaled_by_gene_length_and_scaling_factor_df
tpm = as_tibble(tpm, rownames="gene")
write.table(tpm, file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/01-lncRNAKB-gene-level-counts/sacc.gene.featurecount.TPM.txt")
######################################################################################################################
## filtering-level1 by TPM
#Keep genes with TPM>0.50 in at least 20% of samples
# tpm = read.table(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/01-lncRNAKB-gene-level-counts/amygdala.gene.featurecount.TPM.txt", header=T)
tpm = read.table(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/01-lncRNAKB-gene-level-counts/sacc.gene.featurecount.TPM.txt", header=T)
tpm[1:10,1:10]
tpm = as_tibble(tpm)
tpm
tpm_filtered = tpm %>% filter(rowSums(tpm>0.50) >= as.integer(0.20*(dim(tpm)[2]-1)))
tpm_filtered
## filtering-level2 by raw counts
#Keep lncRNAs with least 1 counts/reads in 90% of replicates/samples
counts_data = counts_data %>% semi_join(tpm_filtered, by="gene")
counts_data
counts_data_lncRNA = counts_data %>% semi_join(filter(annot, gene_type == "lncRNA"), by = c("gene" = "lnckbID"))
counts_data_lncRNA
counts_data_lncRNA = counts_data_lncRNA %>% filter(rowSums(counts_data_lncRNA>1) >= as.integer(0.90*(dim(counts_data_lncRNA)[2]-1)))
counts_data_lncRNA
#Keep protein coding genes with least 5 counts/reads in 90% of replicates/samples
counts_data_pc = counts_data %>% semi_join(filter(annot, gene_type == "protein_coding"), by = c("gene" = "lnckbID"))
counts_data_pc
counts_data_pc = counts_data_pc %>% filter(rowSums(counts_data_pc>5) >= as.integer(0.90*(dim(counts_data_pc)[2]-1)))
counts_data_pc
counts_data = bind_rows(counts_data_lncRNA, counts_data_pc)
counts_data
df = data.frame(counts_data)
df1 = data.frame(df[,2:dim(df)[2]], row.names=df$gene)
counts_data_cpm = as_tibble(cpm(df1), rownames="gene")
counts_data_cpm
## filtering-level3 by CPM
#Keep lncRNAs with least 1 counts/reads in 90% of replicates/samples
counts_data_cpm_lncRNA = counts_data_cpm %>% semi_join(filter(annot, gene_type == "lncRNA"), by = c("gene" = "lnckbID"))
counts_data_cpm_lncRNA
counts_data_cpm_lncRNA = counts_data_cpm_lncRNA %>% filter(rowSums(counts_data_cpm_lncRNA>1) >= as.integer(0.90*(dim(counts_data_cpm_lncRNA)[2]-1)))
counts_data_cpm_lncRNA
#Keep protein coding genes with least 5 counts/reads in 90% of replicates/samples
counts_data_cpm_pc = counts_data_cpm %>% semi_join(filter(annot, gene_type == "protein_coding"), by = c("gene" = "lnckbID"))
counts_data_cpm_pc
counts_data_cpm_pc = counts_data_cpm_pc %>% filter(rowSums(counts_data_cpm_pc>2) >= as.integer(0.90*(dim(counts_data_cpm_pc)[2]-1)))
counts_data_cpm_pc
counts_data_cpm_filt = bind_rows(counts_data_cpm_lncRNA, counts_data_cpm_pc)
counts_data_cpm_filt
# annotv2 = read_csv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/03-WGCNA-analysis-CEMiTools/annot_format_v3.csv")
counts_data_cpm_filt = counts_data_cpm_filt %>% inner_join(annot, by = c("gene" = "lnckbID"))
counts_data_cpm_filt
counts_data_cpm_filt_expr = dplyr::select(counts_data_cpm_filt, -c("gene", "lnckb", "chr","start","end","source","gene_type","info","chr_st_en","Gene_stable_ID","Gene_name","Gene_type","NCBI_gene_formerly_Entrezgene_ID","HGNC_ID"))
counts_data_cpm_filt_expr
df = data.frame(counts_data_cpm_filt_expr)
df1 = data.frame(df[,1:dim(df)[2]-1], row.names=make.unique(df$idents))
counts_data_cpm_filt_df = df1
dim(counts_data_cpm_filt_df)
counts_data_cpm_filt_df[1:10,1:10]
######################################################################################################################
#
#
#
######################################################################################################################
lncRNA = counts_data_cpm_filt %>% filter(gene_type == "lncRNA")
lncRNA = distinct(lncRNA %>% dplyr::select(idents))
lncRNA
pc = counts_data_cpm_filt %>% filter(gene_type == "protein_coding")
pc = distinct(pc %>% dplyr::select(idents))
pc
gene_res = read_csv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level-redo-07222020/00-sACC/bipseq_lncRNAKB_gene_sacc_Bipolar_Control.csv")
gene_res
# gene_res = read_csv(file="/data/NHLBI_BCB/Fayaz/34-lncRNAKB-BP-JHU/02-differential-expression-analysis/00-gene-level-redo-07222020/00-amygdala/bipseq_lncRNAKB_gene_amy_Bipolar_Control.csv")
# gene_res
gene_res = gene_res %>% semi_join(counts_data_cpm_filt, by = "gene")
gene_res
gene_res = gene_res %>% inner_join(annot, by = c("gene" = "lnckbID"))
gene_res
####################################################################
#
# pvalue < 0.05
#
####################################################################
lncRNA_p0.05 = filter(gene_res, pvalue < 0.05) %>% filter(gene_type.y == "lncRNA") %>% dplyr::select(idents)
lncRNA_p0.05
lncRNA_pgreater0.05 = filter(gene_res, pvalue > 0.05) %>% filter(gene_type.y == "lncRNA") %>% dplyr::select(idents)
lncRNA_pgreater0.05
lncRNA_p0.05_up = filter(gene_res, pvalue < 0.05) %>% filter(gene_type.y == "lncRNA") %>% filter(log2FC > 0) %>% dplyr::select(idents)
lncRNA_p0.05_up
lncRNA_p0.05_down = filter(gene_res, pvalue < 0.05) %>% filter(gene_type.y == "lncRNA") %>% filter(log2FC < 0) %>% dplyr::select(idents)
lncRNA_p0.05_down
pc_p0.05 = filter(gene_res, pvalue < 0.05) %>% filter(gene_type.y == "protein_coding") %>% dplyr::select(idents)
pc_p0.05
pc_pgreater0.05 = filter(gene_res, pvalue > 0.05) %>% filter(gene_type.y == "protein_coding") %>% dplyr::select(idents)
pc_pgreater0.05
pc_p0.05_up = filter(gene_res, pvalue < 0.05) %>% filter(gene_type.y == "protein_coding") %>% filter(log2FC > 0) %>% dplyr::select(idents)
pc_p0.05_up
pc_p0.05_down = filter(gene_res, pvalue < 0.05) %>% filter(gene_type.y == "protein_coding") %>% filter(log2FC < 0) %>% dplyr::select(idents)
pc_p0.05_down
####################################################################
#
# qvalue < 0.05
#
####################################################################
lncRNA_q0.05 = filter(gene_res, qvalue < 0.05) %>% filter(gene_type.y == "lncRNA") %>% dplyr::select(idents)
lncRNA_q0.05
lncRNA_qgreater0.05 = filter(gene_res, qvalue > 0.05) %>% filter(gene_type.y == "lncRNA") %>% dplyr::select(idents)
lncRNA_qgreater0.05
lncRNA_q0.05_up = filter(gene_res, qvalue < 0.05) %>% filter(gene_type.y == "lncRNA") %>% filter(log2FC > 0) %>% dplyr::select(idents)
lncRNA_q0.05_up
lncRNA_q0.05_down = filter(gene_res, qvalue < 0.05) %>% filter(gene_type.y == "lncRNA") %>% filter(log2FC < 0) %>% dplyr::select(idents)
lncRNA_q0.05_down
pc_q0.05 = filter(gene_res, qvalue < 0.05) %>% filter(gene_type.y == "protein_coding") %>% dplyr::select(idents)
pc_q0.05
pc_q0.05_up = filter(gene_res, qvalue < 0.05) %>% filter(gene_type.y == "protein_coding") %>% filter(log2FC > 0) %>% dplyr::select(idents)
pc_q0.05_up
pc_q0.05_down = filter(gene_res, qvalue < 0.05) %>% filter(gene_type.y == "protein_coding") %>% filter(log2FC < 0) %>% dplyr::select(idents)
pc_q0.05_down
#1. lncRNA
#2. lncRNA_p0.05
#3. lncRNA_pgreater0.05
#4. lncRNA_upreg
#5. lncRNA_downreg
#6. protein_coding
#7. protein_coding_p0.05
#8. protein_coding_upreg
#9. protein_coding_downreg
#10. lncRNA_q0.05
#11. lncRNA_qgreater0.05
#12. lncRNA_upreg
#13. lncRNA_downreg
#14. protein_coding_q0.05
#15. protein_coding_upreg
#16. protein_coding_downreg
######################################################################################################################
df = data.frame(phenodata)
colnames(df) = c("SampleName","Class")
phenodata = df
phenodata[1:10,]
gmt.cemitool = read_gmt("/data/NHLBI_BCB/Larochelle_Andre/05_Notch_Hypoxia_RNAseq/08-WGCNA-Cemitools/c5.all.v7.0.symbols.gmt")
cemres = cemitool(expr=counts_data_cpm_filt_df,
annot=phenodata,
gmt=gmt.cemitool,
verbose=T,
cor_method="pearson",
apply_vst=F,
filter=F,
ora_pval=0.05,
network_type="unsigned",
diss_thresh=0.90)
cem <- mod_gsea(cemres)
cem <- plot_gsea(cemres)
show_plot(cem, "gsea")
dev.off()
eigengene = mod_summary(cemres, method="eigengene")
eigengenedf = data.frame(eigengene[,2:dim(eigengene)[2]], row.names=eigengene$modules)
eigengenedf_tr = t(eigengenedf)
eig = as_tibble(eigengenedf_tr, rownames = "subjectID")
# eig = eig %>% dplyr::select(-c("Not.Correlated"))
eig
###
# What to do if there are binary traits? Examples of binary traits:Sex (male/female) Disease status (healthy/diseased)
# Pearsonand Spearmancorrelation (function cor): will work
# Biweightmidcorrelation(function bicor) needs modification- restrict number of values that will be treated as outliers - turn off robust treatment for the binary variable
# bicor(moduleEigengenes, datTraits, use=“p",robustY=FALSE, maxPOutliers=0.1)
phenodata = as_tibble(phenodata)
phenodata = phenodata %>%
mutate(
dx = case_when(
Class == "Bipolar" ~ 1,
Class == "Control" ~ 0,
TRUE ~ NA_real_
)
)
phenodata
x <- correlate(x = eig, y = phenodata %>% dplyr::select(c("dx"))) %>% # Create correlation data frame (cor_df)
rearrange() %>% # rearrange by correlations
shave() # Shave off the upper triangle for a clean result
#>
fashion(x)
rplot(x)
correlations = data.frame(cor(x = data.frame(eig), y = data.frame(phenodata %>% dplyr::select(c("dx")))))
correlations = as_tibble(data.frame(modules = rownames(correlations), corr = correlations$dx))
correlations
correlations = correlations %>%
mutate(
corrd = case_when(
corr > 0 ~ "positive",
corr < 0 ~ "negative",
TRUE ~ "other"
)
)
correlations
# correlations <- correlations %>% group_by(corrd)
# correlations <- correlations %>% arrange(desc(corr))
# correlations
p<-ggplot(data=correlations, aes(x=reorder(modules,corr), y=corr, fill=corrd)) +
geom_bar(stat="identity") +
xlab("Modules") + ylab("Pearsons Correlation with Phenotype (Bipolar/Control)") +
theme(axis.text.x = element_text(size = 7.0, angle = 90))
p
dev.off()