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GenerateCheck_TrainingTestData.Rmd
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---
title: "Generate and check the RNA-seq expression levels and phenotypes in the training and test datasets"
author: "Anni Liu"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r}
## RStudio keyboard shortcut
# Jump to line: Alt+Shift+G (Windows) or Option+Shift+Cmd+G (Mac)
# Cursor at the beginning of a command line: Ctrl+A
# Cursor at the end of a command line: Ctrl+E
# Clear all the code from your console: Ctrl+L
# Create a pipe operator %>%: Ctrl+Shift+M (Windows) or Cmd+Shift+M (Mac)
# Create an assignment operator <-: Alt+- (Windows) or Option+-(Mac)
# Knit a document (knitr): Ctrl+Shift+K (Windows) or Cmd+Shift+K (Mac)
# Comment or uncomment current selection: Ctrl+Shift+C (Windows) or Cmd+Shift+C (Mac)
```
# Save and load images
```{r save.image, eval=FALSE}
image.date <- format(Sys.Date(), "%Y%b%d")
save.image(file = paste0("../ApplicationData/derived/", image.date, "_checkdata_image_ALiu.RData"))
```
```{r load.image}
load("../ApplicationData/derived/2023Jan23_checkdata_image_ALiu.RData")
```
# Attach libraries and functions
```{r attach.lib.func}
suppressPackageStartupMessages(easypackages::libraries("microbenchmark", "fst", "parallel"))
```
# Load original data
```{r read.data}
load("../ApplicationData/derived/rownames.RData")
dat.expr <- read_fst(path = "../ApplicationData/derived/dat.expr.Subcutaneous.RDS")
rownames(dat.expr) <- dat.expr.rownames
dat.pheno <- read_fst(path = "../ApplicationData/derived/dat.pheno.Subcutaneous.RDS")
rownames(dat.pheno) <- dat.pheno.rownames
sapply(c("dat.expr", "dat.pheno"), function(x) get(x) |> class())
# dat.expr dat.pheno
# "data.frame" "data.frame"
dim(dat.expr) # 24660 genes and 404 subjects
dim(dat.pheno) # 404 subjects and 13 phenotypes
dat.pheno[1:5, ]
dat.expr[1:5, 1:5]
cat(paste0("The column names of gene expression data", ifelse(all(colnames(dat.expr) == rownames(dat.pheno)), " MATCH ", " NOT MATCH"), "the row names of phenotype data."))
```
# Preprocess expression data
```{r filter.gene}
# Pre-filter the genes
# Here we perform the median absolute deviation with the threshold of 0.8 to select genes that are most likely to distinguish the samples
dat.expr.new <- dat.expr[apply(dat.expr, 1, function(x) mad(x) > 0.8), ] # We have 24455 genes
```
# One function of checking the training and test data
```{r data.func}
data_func <- function(seed.num = 999) {
set.seed(seed.num)
date.analysis <- format(Sys.Date(), "%Y%b%d")
test.ind <- sample(1:nrow(dat.pheno), ceiling(0.5*nrow(dat.pheno)))
dat.pheno.train <- dat.pheno[-test.ind, ]
dat.pheno.test <- dat.pheno[test.ind, ]
dat.expr.train <- dat.expr.new[, rownames(dat.pheno.train)]
dat.expr.test <- dat.expr.new[, rownames(dat.pheno.test)]
saveRDS(dat.pheno.train, file = paste0("../ApplicationData/derived/RandomSeed/dat.pheno.train", seed.num, ".RDS"))
saveRDS(dat.pheno.test, file = paste0("../ApplicationData/derived/RandomSeed/dat.pheno.test", seed.num, ".RDS"))
saveRDS(dat.expr.train, file = paste0("../ApplicationData/derived/RandomSeed/dat.expr.train", seed.num, ".RDS"))
saveRDS(dat.expr.test, file = paste0("../ApplicationData/derived/RandomSeed/dat.expr.test", seed.num, ".RDS"))
cat("~~~Start for one random seed~~~")
cat(paste0("The column names of training gene expression data", ifelse(all(colnames(dat.expr.train) == rownames(dat.pheno.train)), " MATCH ", "NOT MATCH"), "the row names of training phenotype data."))
cat("\n")
cat(paste0("The column names of test gene expression data", ifelse(all(colnames(dat.expr.test) == rownames(dat.pheno.test)), " MATCH ", "NOT MATCH"), "the row names of test phenotype data."))
cat("~~~End for one random seed~~~")
cat("\n")
}
```
# Ten trials of generating the training and test datasets
```{r}
seed.num.vec <- c(8809678, 98907, 233, 556, 7890, 120, 2390, 778, 666, 99999)
for (i in 1:10) {data_func(seed.num = seed.num.vec[i])}
```
# Check the datasets under the seeds 120, 233, and 556
```{r}
dat.expr.train120 <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.train120.RDS")
dat.expr.test120 <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.test120.RDS")
dat.pheno.train120 <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.train120.RDS")
dat.pheno.test120 <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.test120.RDS")
dat.expr.train233 <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.train233.RDS")
dat.expr.test233 <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.test233.RDS")
dat.pheno.train233 <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.train233.RDS")
dat.pheno.test233 <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.test233.RDS")
dat.expr.train556 <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.train556.RDS")
dat.expr.test556 <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.test556.RDS")
dat.pheno.train556 <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.train556.RDS")
dat.pheno.test556 <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.test556.RDS")
```
## Check the sample names
```{r}
length(colnames(dat.expr.train120)) # 202 samples
intersect(colnames(dat.expr.train120), colnames(dat.expr.test120)) # Intersection of sample names between the training and the test set under the seed 120
length(intersect(colnames(dat.expr.train120), colnames(dat.expr.train233))) # 95 common samples
length(intersect(colnames(dat.expr.train120), colnames(dat.expr.train556))) # 101 common samples
length(intersect(colnames(dat.expr.train233), colnames(dat.expr.train556))) # 102 common samples
```
## Check the gene expression distributions across samples - heatmap of the count matrix
```{r}
# http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#heatmap-of-the-count-matrix
library(pheatmap)
# Select the top 500 genes with the high mean counts across samples
select.500 <- order(rowMeans(dat.expr.train120), decreasing = T)[1:500]
pheatmap(dat.expr.train120[select.500, ],
show_rownames = F,
show_colnames = F,
cluster_rows = F,
cluster_cols = F)
select.500 <- order(rowMeans(dat.expr.train233), decreasing = T)[1:500]
pheatmap(dat.expr.train233[select.500, ],
show_rownames = F,
show_colnames = F,
cluster_rows = F,
cluster_cols = F)
select.500 <- order(rowMeans(dat.expr.train556), decreasing = T)[1:500]
pheatmap(dat.expr.train556[select.500, ],
show_rownames = F,
show_colnames = F,
cluster_rows = F,
cluster_cols = F)
```
## Check the gene expression distributions between two BMI levels - heatmap of the count matrix
```{r}
select.500 <- order(rowMeans(dat.expr.train120), decreasing = T)[1:500]
col.order <- c(rownames(dat.pheno.train120[dat.pheno.train120$BMI == "low", ]),
rownames(dat.pheno.train120[dat.pheno.train120$BMI == "high", ]))
bmi <- dat.pheno.train120["BMI"]
pheatmap(dat.expr.train120[select.500, col.order],
show_rownames = F,
show_colnames = F,
cluster_rows = F,
cluster_cols = F,
annotation_col = bmi)
select.500 <- order(rowMeans(dat.expr.train233), decreasing = T)[1:500]
col.order <- c(rownames(dat.pheno.train233[dat.pheno.train233$BMI == "low", ]),
rownames(dat.pheno.train233[dat.pheno.train233$BMI == "high", ]))
bmi <- dat.pheno.train233["BMI"]
pheatmap(dat.expr.train233[select.500, col.order],
show_rownames = F,
show_colnames = F,
cluster_rows = F,
cluster_cols = F,
annotation_col = bmi)
select.500 <- order(rowMeans(dat.expr.train556), decreasing = T)[1:500]
col.order <- c(rownames(dat.pheno.train556[dat.pheno.train556$BMI == "low", ]),
rownames(dat.pheno.train556[dat.pheno.train556$BMI == "high", ]))
bmi <- dat.pheno.train556["BMI"]
pheatmap(dat.expr.train556[select.500, col.order],
show_rownames = F,
show_colnames = F,
cluster_rows = F,
cluster_cols = F,
annotation_col = bmi)
```
# Extract eFDR/q-value of the top ranked 2000 genes
## Seed: 2390
```{r}
dat.expr.train <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.train2390.RDS")
dat.expr.test <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.test2390.RDS")
dat.pheno.train <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.train2390.RDS")
dat.pheno.test <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.test2390.RDS")
```
```{r}
var.pool <- c("BMI", "AGE", "SEX", "MHABNWBC")
var.pool.add <- c(var.pool, "BMIxSEX")
##------BMAseq------
##################################################################
## BMAseq multivariate analysis ##
##################################################################
output.multi.int1.train <- BMAseq.multi.postprob.norm(
dat.expr.counts = dat.expr.train,
dat.pheno = dat.pheno.train,
var.pool = var.pool,
max.nvar = 4,
interaction = "BMI&SEX",
cut.BF = 1)
output.multi.int2.train <- BMAseq.multi.DEG(
dat.pheno = output.multi.int1.train$dat.pheno.new,
model.space = output.multi.int1.train$model.space,
post.modelprob = output.multi.int1.train$post.modelprob,
var.pool = var.pool,
interact = T,
cut.FDR = 0.05)
output.multi.int1.test <- BMAseq.multi.postprob.norm(
dat.expr.counts = dat.expr.test,
dat.pheno = dat.pheno.test,
var.pool = var.pool,
max.nvar = 4,
interaction = "BMI&SEX",
cut.BF = 1)
output.multi.int2.test <- BMAseq.multi.DEG(
dat.pheno = output.multi.int1.test$dat.pheno.new,
model.space = output.multi.int1.test$model.space,
post.modelprob = output.multi.int1.test$post.modelprob,
var.pool = var.pool,
interact = T,
cut.FDR = 0.05)
BMAseq.eFDR.Main.2000.train <- mclapply(1:length(var.pool),
function(i) output.multi.int2.train$eFDR.Main[, i][order(output.multi.int2.train$eFDR.Main[, i])[1:2000]],
mc.cores = 4)
BMAseq.eFDR.Main.2000.test <- mclapply(1:length(var.pool),
function(i) output.multi.int2.test$eFDR.Main[, i][order(output.multi.int2.test$eFDR.Main[, i])[1:2000]],
mc.cores = 4)
names(BMAseq.eFDR.Main.2000.train) = names(BMAseq.eFDR.Main.2000.test) = var.pool
BMAseq.eFDR.Interaction.2000.train <- mclapply(1:length(var.pool),
function(i) output.multi.int2.train$eFDR.Interaction[, i][order(output.multi.int2.train$eFDR.Interaction[, i])[1:2000]],
mc.cores = 4)
BMAseq.eFDR.Interaction.2000.test <- mclapply(1:length(var.pool),
function(i) output.multi.int2.test$eFDR.Interaction[, i][order(output.multi.int2.test$eFDR.Interaction[, i])[1:2000]],
mc.cores = 4)
names(BMAseq.eFDR.Interaction.2000.train) = names(BMAseq.eFDR.Interaction.2000.test) = var.pool
save(dat.expr.train, dat.expr.test, dat.pheno.train, dat.pheno.test, var.pool, var.pool.add, output.multi.int1.train, output.multi.int2.train, output.multi.int1.test, output.multi.int2.test, BMAseq.eFDR.Main.2000.train, BMAseq.eFDR.Main.2000.test, BMAseq.eFDR.Interaction.2000.train, BMAseq.eFDR.Interaction.2000.test, file = "../ApplicationData/derived/RandomSeed/BMAseqMultiInt2390.RData")
##------voom + limma------
##################################################################
## voom + limma multivariate analysis ##
##################################################################
design.train <- model.matrix(~BMI + AGE + SEX + MHABNWBC + BMI*SEX, data = dat.pheno.train)
design.test <- model.matrix(~BMI + AGE + SEX + MHABNWBC + BMI*SEX, data = dat.pheno.test)
voom.train <- voom(counts = dat.expr.train,
design = design.train,
lib.size = colSums(dat.expr.train)*calcNormFactors(dat.expr.train))
voom.fit.train <- lmFit(voom.train[["E"]],
design = design.train,
weights = voom.train[["weights"]])
voom.eFDR.train <- mclapply(1:length(var.pool.add),
function(i) {
t <- voom.fit.train[["coefficients"]][, i + 1]/voom.fit.train[["stdev.unscaled"]][, i+1]/voom.fit.train[["sigma"]]
p <- 2*pt(-abs(t), df = voom.fit.train[["df.residual"]])
return(qvalue(p)) },
mc.cores = 4L)
voom.eFDR.2000.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- voom.eFDR.train[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(voom.eFDR.train) = names(voom.eFDR.2000.train) = var.pool.add
voom.test <- voom(counts = dat.expr.test,
design = design.test,
lib.size = colSums(dat.expr.test)*calcNormFactors(dat.expr.test))
voom.fit.test <- lmFit(voom.test[["E"]],
design = design.test,
weights = voom.test[["weights"]])
voom.eFDR.test <- mclapply(1:length(var.pool.add),
function(i) {
t <- voom.fit.test[["coefficients"]][, i + 1]/voom.fit.test[["stdev.unscaled"]][, i+1]/voom.fit.test[["sigma"]]
p <- 2*pt(-abs(t), df = voom.fit.test[["df.residual"]])
return(qvalue(p)) },
mc.cores = 4L)
voom.eFDR.2000.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- voom.eFDR.test[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(voom.eFDR.test) = names(voom.eFDR.2000.test) = var.pool.add
save(dat.expr.train, dat.expr.test, dat.pheno.train, dat.pheno.test, var.pool, var.pool.add, design.train, design.test, voom.train, voom.fit.train, voom.eFDR.train, voom.eFDR.2000.train, voom.test, voom.fit.test, voom.eFDR.test, voom.eFDR.2000.test, file = "../ApplicationData/derived/RandomSeed/VoomLimmaMultiInt2390.RData")
##------voom + limma + eBayes------
#################################################################
## voom + limma + eBayes multivariate analysis ##
#################################################################
voom.train <- voom(counts = dat.expr.train,
design = design.train,
lib.size = colSums(dat.expr.train)*calcNormFactors(dat.expr.train))
eBayes.fit.train <- lmFit(voom.train[["E"]],
design = design.train,
weights = voom.train[["weights"]]) %>%
eBayes()
eBayes.eFDR.train <- mclapply(1:length(var.pool.add),
function(i) eBayes.fit.train[["p.value"]][, i + 1] %>%
qvalue(),
mc.cores = 4L)
eBayes.eFDR.2000.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- eBayes.eFDR.train[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(eBayes.eFDR.train) = names(eBayes.eFDR.2000.train) = var.pool.add
voom.test <- voom(counts = dat.expr.test,
design = design.test,
lib.size = colSums(dat.expr.test)*calcNormFactors(dat.expr.test))
eBayes.fit.test <- lmFit(voom.test[["E"]],
design = design.test,
weights = voom.test[["weights"]]) %>%
eBayes()
eBayes.eFDR.test <- mclapply(1:length(var.pool.add),
function(i) eBayes.fit.test[["p.value"]][, i + 1] %>%
qvalue(),
mc.cores = 4L)
eBayes.eFDR.2000.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- eBayes.eFDR.test[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(eBayes.eFDR.test) = names(eBayes.eFDR.2000.test) = var.pool.add
save(dat.expr.train, dat.expr.test, dat.pheno.train, dat.pheno.test, var.pool, var.pool.add, design.train, design.test, voom.train, eBayes.fit.train, eBayes.eFDR.train, eBayes.eFDR.2000.train, voom.test, eBayes.fit.test, eBayes.eFDR.test, eBayes.eFDR.2000.test, file = "../ApplicationData/derived/RandomSeed/eBayesMultiInt2390.RData")
##------edgeR------
#################################################################
## edgeR multivariate analysis ##
#################################################################
y.train <- DGEList(counts = dat.expr.train,
lib.size = colSums(dat.expr.train)) %>%
calcNormFactors() %>%
estimateGLMTrendedDisp(design.train)
edgeR.fit.train <- glmQLFit(y.train, design.train)
qlf.train <- mclapply(1:length(var.pool.add),
function(i) glmQLFTest(edgeR.fit.train, coef = i + 1),
mc.cores = 4L)
edgeR.eFDR.train <- mclapply(1:length(var.pool.add),
function(i) qlf.train[[i]][["table"]][["PValue"]] %>%
qvalue(),
mc.cores = 4L)
edgeR.eFDR.2000.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- edgeR.eFDR.train[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
edgeR.eFDR.2000.GeneName.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- edgeR.eFDR.train[[i]][["qvalues"]]
return(rownames(dat.expr.train)[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(qlf.train) = names(edgeR.eFDR.train) = names(edgeR.eFDR.2000.train) = names(edgeR.eFDR.2000.GeneName.train) = var.pool.add
y.test <- DGEList(counts = dat.expr.test,
lib.size = colSums(dat.expr.test)) %>%
calcNormFactors() %>%
estimateGLMTrendedDisp(design.test)
edgeR.fit.test <- glmQLFit(y.test, design.test)
qlf.test <- mclapply(1:length(var.pool.add),
function(i) glmQLFTest(edgeR.fit.test, coef = i + 1),
mc.cores = 4L)
edgeR.eFDR.test <- mclapply(1:length(var.pool.add),
function(i) qlf.test[[i]][["table"]][["PValue"]] %>%
qvalue(),
mc.cores = 4L)
edgeR.eFDR.2000.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- edgeR.eFDR.test[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
edgeR.eFDR.2000.GeneName.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- edgeR.eFDR.test[[i]][["qvalues"]]
return(rownames(dat.expr.test)[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(qlf.test) = names(edgeR.eFDR.test) = names(edgeR.eFDR.2000.test) = names(edgeR.eFDR.2000.GeneName.test) = var.pool.add
save(dat.expr.train, dat.expr.test, dat.pheno.train, dat.pheno.test, var.pool, var.pool.add, design.train, design.test, y.train, edgeR.fit.train, edgeR.eFDR.train, edgeR.eFDR.2000.train, edgeR.eFDR.2000.GeneName.train, y.test, edgeR.fit.test, edgeR.eFDR.test, edgeR.eFDR.2000.test, edgeR.eFDR.2000.GeneName.test, file = "../ApplicationData/derived/RandomSeed/edgeRMultiInt2390.RData")
##------DESeq2------
##################################################################
## DESeq2 multivariate analysis ##
##################################################################
cts.train <- as.matrix(dat.expr.train)
cts.test <- as.matrix(dat.expr.test)
coldata.train <- dat.pheno.train
coldata.test <- dat.pheno.test
name.formula <- c("BMI_high_vs_low",
"AGE_old_vs_young",
"SEX_male_vs_female",
"MHABNWBC_yes_vs_no",
"BMIhigh.SEXmale")
lib.size <- colSums(cts.train)
norm.factor <- calcNormFactors(cts.train, method = "TMM")
size.factor <- lib.size*norm.factor/exp(mean(log(lib.size*norm.factor)))
dds <- DESeqDataSetFromMatrix(countData = cts.train,
colData = coldata.train,
design = ~BMI + AGE + SEX + MHABNWBC + BMI*SEX)
sizeFactors(dds) <- size.factor
res.train <- mclapply(1:length(var.pool.add),
function(i) {
return(results(DESeq(dds), name = name.formula[i])) },
mc.cores = 4L)
DESeq2.eFDR.train <- mclapply(1:length(var.pool.add),
function(i) qvalue(res.train[[i]][["pvalue"]])$qvalues,
mc.cores = 4L)
DESeq2.eFDR.2000.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- DESeq2.eFDR.train[[i]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
DESeq2.eFDR.2000.GeneName.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- DESeq2.eFDR.train[[i]]
return(rownames(cts.train)[order(q.val)[1:2000]])},
mc.cores = 4L)
names(res.train) = names(DESeq2.eFDR.train) = names(DESeq2.eFDR.2000.train) = names(DESeq2.eFDR.2000.GeneName.train) = var.pool.add
lib.size <- colSums(cts.test)
norm.factor <- calcNormFactors(cts.test, method = "TMM")
size.factor <- lib.size*norm.factor/exp(mean(log(lib.size*norm.factor)))
dds <- DESeqDataSetFromMatrix(countData = cts.test,
colData = coldata.test,
design = ~BMI + AGE + SEX + MHABNWBC + BMI*SEX)
sizeFactors(dds) <- size.factor
res.test <- mclapply(1:length(var.pool.add),
function(i) {
return(results(DESeq(dds), name = name.formula[i])) },
mc.cores = 4L)
DESeq2.eFDR.test <- mclapply(1:length(var.pool.add),
function(i) qvalue(res.test[[i]][["pvalue"]])$qvalues,
mc.cores = 4L)
DESeq2.eFDR.2000.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- DESeq2.eFDR.test[[i]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
DESeq2.eFDR.2000.GeneName.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- DESeq2.eFDR.test[[i]]
return(rownames(cts.test)[order(q.val)[1:2000]])},
mc.cores = 4L)
names(res.test) = names(DESeq2.eFDR.test) = names(DESeq2.eFDR.2000.test) = names(DESeq2.eFDR.2000.GeneName.test) = var.pool.add
save(cts.train, cts.test, coldata.train, coldata.test, var.pool, var.pool.add, res.train, DESeq2.eFDR.train, DESeq2.eFDR.2000.train, DESeq2.eFDR.2000.GeneName.train, res.test, DESeq2.eFDR.test, DESeq2.eFDR.2000.test, DESeq2.eFDR.2000.GeneName.test, file = "../ApplicationData/derived/RandomSeed/DESeq2MultiInt2390.RData")
```
## Seed: 98907
```{r}
dat.expr.train <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.train98907.RDS")
dat.expr.test <- readRDS("../ApplicationData/derived/RandomSeed/dat.expr.test98907.RDS")
dat.pheno.train <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.train98907.RDS")
dat.pheno.test <- readRDS("../ApplicationData/derived/RandomSeed/dat.pheno.test98907.RDS")
```
```{r}
var.pool <- c("BMI", "AGE", "SEX", "MHABNWBC")
var.pool.add <- c(var.pool, "BMIxSEX")
##------BMAseq------
##################################################################
## BMAseq multivariate analysis ##
##################################################################
output.multi.int1.train <- BMAseq.multi.postprob.norm(
dat.expr.counts = dat.expr.train,
dat.pheno = dat.pheno.train,
var.pool = var.pool,
max.nvar = 4,
interaction = "BMI&SEX",
cut.BF = 1)
output.multi.int2.train <- BMAseq.multi.DEG(
dat.pheno = output.multi.int1.train$dat.pheno.new,
model.space = output.multi.int1.train$model.space,
post.modelprob = output.multi.int1.train$post.modelprob,
var.pool = var.pool,
interact = T,
cut.FDR = 0.05)
output.multi.int1.test <- BMAseq.multi.postprob.norm(
dat.expr.counts = dat.expr.test,
dat.pheno = dat.pheno.test,
var.pool = var.pool,
max.nvar = 4,
interaction = "BMI&SEX",
cut.BF = 1)
output.multi.int2.test <- BMAseq.multi.DEG(
dat.pheno = output.multi.int1.test$dat.pheno.new,
model.space = output.multi.int1.test$model.space,
post.modelprob = output.multi.int1.test$post.modelprob,
var.pool = var.pool,
interact = T,
cut.FDR = 0.05)
BMAseq.eFDR.Main.2000.train <- mclapply(1:length(var.pool),
function(i) output.multi.int2.train$eFDR.Main[, i][order(output.multi.int2.train$eFDR.Main[, i])[1:2000]],
mc.cores = 4)
BMAseq.eFDR.Main.2000.test <- mclapply(1:length(var.pool),
function(i) output.multi.int2.test$eFDR.Main[, i][order(output.multi.int2.test$eFDR.Main[, i])[1:2000]],
mc.cores = 4)
names(BMAseq.eFDR.Main.2000.train) = names(BMAseq.eFDR.Main.2000.test) = var.pool
BMAseq.eFDR.Interaction.2000.train <- mclapply(1:length(var.pool),
function(i) output.multi.int2.train$eFDR.Interaction[, i][order(output.multi.int2.train$eFDR.Interaction[, i])[1:2000]],
mc.cores = 4)
BMAseq.eFDR.Interaction.2000.test <- mclapply(1:length(var.pool),
function(i) output.multi.int2.test$eFDR.Interaction[, i][order(output.multi.int2.test$eFDR.Interaction[, i])[1:2000]],
mc.cores = 4)
names(BMAseq.eFDR.Interaction.2000.train) = names(BMAseq.eFDR.Interaction.2000.test) = var.pool
save(dat.expr.train, dat.expr.test, dat.pheno.train, dat.pheno.test, var.pool, var.pool.add, output.multi.int1.train, output.multi.int2.train, output.multi.int1.test, output.multi.int2.test, BMAseq.eFDR.Main.2000.train, BMAseq.eFDR.Main.2000.test, BMAseq.eFDR.Interaction.2000.train, BMAseq.eFDR.Interaction.2000.test, file = "../ApplicationData/derived/RandomSeed/BMAseqMultiInt98907.RData")
##------voom + limma------
##################################################################
## voom + limma multivariate analysis ##
##################################################################
design.train <- model.matrix(~BMI + AGE + SEX + MHABNWBC + BMI*SEX, data = dat.pheno.train)
design.test <- model.matrix(~BMI + AGE + SEX + MHABNWBC + BMI*SEX, data = dat.pheno.test)
voom.train <- voom(counts = dat.expr.train,
design = design.train,
lib.size = colSums(dat.expr.train)*calcNormFactors(dat.expr.train))
voom.fit.train <- lmFit(voom.train[["E"]],
design = design.train,
weights = voom.train[["weights"]])
voom.eFDR.train <- mclapply(1:length(var.pool.add),
function(i) {
t <- voom.fit.train[["coefficients"]][, i + 1]/voom.fit.train[["stdev.unscaled"]][, i+1]/voom.fit.train[["sigma"]]
p <- 2*pt(-abs(t), df = voom.fit.train[["df.residual"]])
return(qvalue(p)) },
mc.cores = 4L)
voom.eFDR.2000.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- voom.eFDR.train[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(voom.eFDR.train) = names(voom.eFDR.2000.train) = var.pool.add
voom.test <- voom(counts = dat.expr.test,
design = design.test,
lib.size = colSums(dat.expr.test)*calcNormFactors(dat.expr.test))
voom.fit.test <- lmFit(voom.test[["E"]],
design = design.test,
weights = voom.test[["weights"]])
voom.eFDR.test <- mclapply(1:length(var.pool.add),
function(i) {
t <- voom.fit.test[["coefficients"]][, i + 1]/voom.fit.test[["stdev.unscaled"]][, i+1]/voom.fit.test[["sigma"]]
p <- 2*pt(-abs(t), df = voom.fit.test[["df.residual"]])
return(qvalue(p)) },
mc.cores = 4L)
voom.eFDR.2000.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- voom.eFDR.test[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(voom.eFDR.test) = names(voom.eFDR.2000.test) = var.pool.add
save(dat.expr.train, dat.expr.test, dat.pheno.train, dat.pheno.test, var.pool, var.pool.add, design.train, design.test, voom.train, voom.fit.train, voom.eFDR.train, voom.eFDR.2000.train, voom.test, voom.fit.test, voom.eFDR.test, voom.eFDR.2000.test, file = "../ApplicationData/derived/RandomSeed/VoomLimmaMultiInt98907.RData")
##------voom + limma + eBayes------
#################################################################
## voom + limma + eBayes multivariate analysis ##
#################################################################
voom.train <- voom(counts = dat.expr.train,
design = design.train,
lib.size = colSums(dat.expr.train)*calcNormFactors(dat.expr.train))
eBayes.fit.train <- lmFit(voom.train[["E"]],
design = design.train,
weights = voom.train[["weights"]]) %>%
eBayes()
eBayes.eFDR.train <- mclapply(1:length(var.pool.add),
function(i) eBayes.fit.train[["p.value"]][, i + 1] %>%
qvalue(),
mc.cores = 4L)
eBayes.eFDR.2000.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- eBayes.eFDR.train[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(eBayes.eFDR.train) = names(eBayes.eFDR.2000.train) = var.pool.add
voom.test <- voom(counts = dat.expr.test,
design = design.test,
lib.size = colSums(dat.expr.test)*calcNormFactors(dat.expr.test))
eBayes.fit.test <- lmFit(voom.test[["E"]],
design = design.test,
weights = voom.test[["weights"]]) %>%
eBayes()
eBayes.eFDR.test <- mclapply(1:length(var.pool.add),
function(i) eBayes.fit.test[["p.value"]][, i + 1] %>%
qvalue(),
mc.cores = 4L)
eBayes.eFDR.2000.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- eBayes.eFDR.test[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(eBayes.eFDR.test) = names(eBayes.eFDR.2000.test) = var.pool.add
save(dat.expr.train, dat.expr.test, dat.pheno.train, dat.pheno.test, var.pool, var.pool.add, design.train, design.test, voom.train, eBayes.fit.train, eBayes.eFDR.train, eBayes.eFDR.2000.train, voom.test, eBayes.fit.test, eBayes.eFDR.test, eBayes.eFDR.2000.test, file = "../ApplicationData/derived/RandomSeed/eBayesMultiInt98907.RData")
##------edgeR------
#################################################################
## edgeR multivariate analysis ##
#################################################################
y.train <- DGEList(counts = dat.expr.train,
lib.size = colSums(dat.expr.train)) %>%
calcNormFactors() %>%
estimateGLMTrendedDisp(design.train)
edgeR.fit.train <- glmQLFit(y.train, design.train)
qlf.train <- mclapply(1:length(var.pool.add),
function(i) glmQLFTest(edgeR.fit.train, coef = i + 1),
mc.cores = 4L)
edgeR.eFDR.train <- mclapply(1:length(var.pool.add),
function(i) qlf.train[[i]][["table"]][["PValue"]] %>%
qvalue(),
mc.cores = 4L)
edgeR.eFDR.2000.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- edgeR.eFDR.train[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
edgeR.eFDR.2000.GeneName.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- edgeR.eFDR.train[[i]][["qvalues"]]
return(rownames(dat.expr.train)[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(qlf.train) = names(edgeR.eFDR.train) = names(edgeR.eFDR.2000.train) = names(edgeR.eFDR.2000.GeneName.train) = var.pool.add
y.test <- DGEList(counts = dat.expr.test,
lib.size = colSums(dat.expr.test)) %>%
calcNormFactors() %>%
estimateGLMTrendedDisp(design.test)
edgeR.fit.test <- glmQLFit(y.test, design.test)
qlf.test <- mclapply(1:length(var.pool.add),
function(i) glmQLFTest(edgeR.fit.test, coef = i + 1),
mc.cores = 4L)
edgeR.eFDR.test <- mclapply(1:length(var.pool.add),
function(i) qlf.test[[i]][["table"]][["PValue"]] %>%
qvalue(),
mc.cores = 4L)
edgeR.eFDR.2000.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- edgeR.eFDR.test[[i]][["qvalues"]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
edgeR.eFDR.2000.GeneName.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- edgeR.eFDR.test[[i]][["qvalues"]]
return(rownames(dat.expr.test)[order(q.val)[1:2000]])
},
mc.cores = 4L)
names(qlf.test) = names(edgeR.eFDR.test) = names(edgeR.eFDR.2000.test) = names(edgeR.eFDR.2000.GeneName.test) = var.pool.add
save(dat.expr.train, dat.expr.test, dat.pheno.train, dat.pheno.test, var.pool, var.pool.add, design.train, design.test, y.train, edgeR.fit.train, edgeR.eFDR.train, edgeR.eFDR.2000.train, edgeR.eFDR.2000.GeneName.train, y.test, edgeR.fit.test, edgeR.eFDR.test, edgeR.eFDR.2000.test, edgeR.eFDR.2000.GeneName.test, file = "../ApplicationData/derived/RandomSeed/edgeRMultiInt98907.RData")
##------DESeq2------
##################################################################
## DESeq2 multivariate analysis ##
##################################################################
cts.train <- as.matrix(dat.expr.train)
cts.test <- as.matrix(dat.expr.test)
coldata.train <- dat.pheno.train
coldata.test <- dat.pheno.test
name.formula <- c("BMI_high_vs_low",
"AGE_old_vs_young",
"SEX_male_vs_female",
"MHABNWBC_yes_vs_no",
"BMIhigh.SEXmale")
lib.size <- colSums(cts.train)
norm.factor <- calcNormFactors(cts.train, method = "TMM")
size.factor <- lib.size*norm.factor/exp(mean(log(lib.size*norm.factor)))
dds <- DESeqDataSetFromMatrix(countData = cts.train,
colData = coldata.train,
design = ~BMI + AGE + SEX + MHABNWBC + BMI*SEX)
sizeFactors(dds) <- size.factor
res.train <- mclapply(1:length(var.pool.add),
function(i) {
return(results(DESeq(dds), name = name.formula[i])) },
mc.cores = 4L)
DESeq2.eFDR.train <- mclapply(1:length(var.pool.add),
function(i) qvalue(res.train[[i]][["pvalue"]])$qvalues,
mc.cores = 4L)
DESeq2.eFDR.2000.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- DESeq2.eFDR.train[[i]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
DESeq2.eFDR.2000.GeneName.train <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- DESeq2.eFDR.train[[i]]
return(rownames(cts.train)[order(q.val)[1:2000]])},
mc.cores = 4L)
names(res.train) = names(DESeq2.eFDR.train) = names(DESeq2.eFDR.2000.train) = names(DESeq2.eFDR.2000.GeneName.train) = var.pool.add
lib.size <- colSums(cts.test)
norm.factor <- calcNormFactors(cts.test, method = "TMM")
size.factor <- lib.size*norm.factor/exp(mean(log(lib.size*norm.factor)))
dds <- DESeqDataSetFromMatrix(countData = cts.test,
colData = coldata.test,
design = ~BMI + AGE + SEX + MHABNWBC + BMI*SEX)
sizeFactors(dds) <- size.factor
res.test <- mclapply(1:length(var.pool.add),
function(i) {
return(results(DESeq(dds), name = name.formula[i])) },
mc.cores = 4L)
DESeq2.eFDR.test <- mclapply(1:length(var.pool.add),
function(i) qvalue(res.test[[i]][["pvalue"]])$qvalues,
mc.cores = 4L)
DESeq2.eFDR.2000.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- DESeq2.eFDR.test[[i]]
return(q.val[order(q.val)[1:2000]])
},
mc.cores = 4L)
DESeq2.eFDR.2000.GeneName.test <- mclapply(1:length(var.pool.add),
function(i) {
q.val <- DESeq2.eFDR.test[[i]]
return(rownames(cts.test)[order(q.val)[1:2000]])},
mc.cores = 4L)
names(res.test) = names(DESeq2.eFDR.test) = names(DESeq2.eFDR.2000.test) = names(DESeq2.eFDR.2000.GeneName.test) = var.pool.add
save(cts.train, cts.test, coldata.train, coldata.test, var.pool, var.pool.add, res.train, DESeq2.eFDR.train, DESeq2.eFDR.2000.train, DESeq2.eFDR.2000.GeneName.train, res.test, DESeq2.eFDR.test, DESeq2.eFDR.2000.test, DESeq2.eFDR.2000.GeneName.test, file = "../ApplicationData/derived/RandomSeed/DESeq2MultiInt98907.RData")
```
# Violin plot - distributions of eFDR/q-value of the top ranked 2000 genes
## Seed: 2390
```{r}
load("../ApplicationData/derived/RandomSeed/BMAseqMultiInt2390.RData")
load("../ApplicationData/derived/RandomSeed/VoomLimmaMultiInt2390.RData")
load("../ApplicationData/derived/RandomSeed/eBayesMultiInt2390.RData")
load("../ApplicationData/derived/RandomSeed/edgeRMultiInt2390.RData")
load("../ApplicationData/derived/RandomSeed/DESeq2MultiInt2390.RData")
```
### BMI
```{r}
BMI.eFDR.q.dat.2390 <- data.frame(Estimate = c(
BMAseq.eFDR.Main.2000.train$BMI |> log10(),
BMAseq.eFDR.Main.2000.test$BMI |> log10(),
DESeq2.eFDR.2000.train$BMI |> log10(),
DESeq2.eFDR.2000.test$BMI |> log10(),
edgeR.eFDR.2000.train$BMI |> log10(),
edgeR.eFDR.2000.test$BMI |> log10(),
eBayes.eFDR.2000.train$BMI |> log10(),
eBayes.eFDR.2000.test$BMI |> log10(),
voom.eFDR.2000.train$BMI |> log10(),
voom.eFDR.2000.test$BMI |> log10()),
Data.type = rep(c("Train", "Test"), each = 2000, times = 5),
DEG.method = rep(c("BMAseq", "DESeq2", "edgeR", "voom+limma+eBayes", "voom+limma"), each = 4000)
)
BMI.eFDR.q.dat.2390$DEG.method <- factor(BMI.eFDR.q.dat.2390$DEG.method, levels = c("BMAseq", "DESeq2", "edgeR", "voom+limma+eBayes", "voom+limma"))
##------Log10------
g <- BMI.eFDR.q.dat.2390 %>%
ggplot(aes(x = Data.type,
y = Estimate,
fill = Data.type)) +
geom_violin() +
geom_boxplot(width = 0.1) +
facet_wrap(~ DEG.method, nrow = 1) +
scale_x_discrete(limits = c("Train", "Test")) +
# scale_y_log10() +
scale_fill_brewer(palette = "Set1",
direction = -1) +
labs(x = "Data Type",
y = "Log10 (estimate)",
title = "The distributions of top 2000 lowest eFDRs and q-values \nassociated with the main effect of BMI in training and test sets per approach") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "none")
date.analysis <- format(Sys.Date(), "%Y%b%d")
seed.num <- 2390
ggsave(filename = paste0("../ApplicationResult/Multi_Interaction/RandomSeed/TMM_Top2000/violinplot/2390/", date.analysis, "_", "BMIMain_eFDR_qvalue", "_", seed.num, ".png"),
plot = g,
device = "png",
width = 8,
height = 4,
units = "in")
# date.analysis <- format(Sys.Date(), "%Y%b%d")
# model.type <- "Multi_Interaction"
# plot.type <- "violinplot"
# seed.num <- 2390
# file.path <- sprintf("../ApplicationResult/%s/RandomSeed/TMM_Top2000/%s/%s/", model.type, plot.type, seed.num)
# file.path
# paste0(file.path, date.analysis, "_", "BMIMain_eFDR_qvalue", "_", seed.num, ".png")
```
### AGE
```{r}
AGE.eFDR.q.dat.2390 <- data.frame(Estimate = c(
BMAseq.eFDR.Main.2000.train$AGE |> log10(),
BMAseq.eFDR.Main.2000.test$AGE |> log10(),
DESeq2.eFDR.2000.train$AGE |> log10(),
DESeq2.eFDR.2000.test$AGE |> log10(),
edgeR.eFDR.2000.train$AGE |> log10(),
edgeR.eFDR.2000.test$AGE |> log10(),
eBayes.eFDR.2000.train$AGE |> log10(),
eBayes.eFDR.2000.test$AGE |> log10(),
voom.eFDR.2000.train$AGE |> log10(),
voom.eFDR.2000.test$AGE |> log10()),
Data.type = rep(c("Train", "Test"), each = 2000, times = 5),
DEG.method = rep(c("BMAseq", "DESeq2", "edgeR", "voom+limma+eBayes", "voom+limma"), each = 4000)
)
AGE.eFDR.q.dat.2390$DEG.method <- factor(AGE.eFDR.q.dat.2390$DEG.method, levels = c("BMAseq", "DESeq2", "edgeR", "voom+limma+eBayes", "voom+limma"))
g <- AGE.eFDR.q.dat.2390 %>%
ggplot(aes(x = Data.type,
y = Estimate,
fill = Data.type)) +
geom_violin() +
geom_boxplot(width = 0.1) +
facet_wrap(~ DEG.method, nrow = 1) +
scale_x_discrete(limits = c("Train", "Test")) +
scale_fill_brewer(palette = "Set1",
direction = -1) +
labs(x = "Data Type",
y = "Log10 (estimate)",
title = "The distributions of top 2000 lowest eFDRs and q-values \nassociated with the main effect of AGE in training and test sets per approach") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5),
legend.position = "none")
date.analysis <- format(Sys.Date(), "%Y%b%d")
seed.num <- 2390
ggsave(filename = paste0("../ApplicationResult/Multi_Interaction/RandomSeed/TMM_Top2000/violinplot/2390/", date.analysis, "_", "AGEMain_eFDR_qvalue", "_", seed.num, ".png"),
plot = g,
device = "png",
width = 8,
height = 4,
units = "in")
```
### SEX
```{r}
SEX.eFDR.q.dat.2390 <- data.frame(Estimate = c(
BMAseq.eFDR.Main.2000.train$SEX |> log10(),
BMAseq.eFDR.Main.2000.test$SEX |> log10(),
DESeq2.eFDR.2000.train$SEX |> log10(),
DESeq2.eFDR.2000.test$SEX |> log10(),
edgeR.eFDR.2000.train$SEX |> log10(),
edgeR.eFDR.2000.test$SEX |> log10(),
eBayes.eFDR.2000.train$SEX |> log10(),
eBayes.eFDR.2000.test$SEX |> log10(),
voom.eFDR.2000.train$SEX |> log10(),
voom.eFDR.2000.test$SEX |> log10()),