Identifying active genes for subsequent analysis
We calculate zFPKM for existing FPKM from gse94802.
library(dplyr)
library(GEOquery)
library(stringr)
library(SummarizedExperiment)
library(tidyr)
getSpecificGEOSupp <- function(url) {
temp <- tempfile()
download.file(url, temp)
out <- read.csv(gzfile(temp), row.names=1, check.names=FALSE)
out <- select(out, -MGI_Symbol)
return(out)
}
gse94802_fpkm <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94802/suppl/GSE94802_Minkina_etal_normalized_FPKM.csv.gz"
gse94802_counts <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94802/suppl/GSE94802_Minkina_etal_raw_counts.csv.gz"
if (file.exists("gse94802.rds")) {
esetlist <- readRDS("gse94802.rds")
} else {
esetlist <- getGEO("gse94802")
}
doe <- pData(esetlist[[1]])
colData <- DataFrame(
condition=ifelse(str_detect(doe$title, regex("control", ignore_case=TRUE)), "control", "mutant"),
sample_id=str_match(doe$title, "rep\\d_(.+)")[, 2],
row.names=str_match(doe$title, "rep\\d_(.+)")[, 2])
se <- SummarizedExperiment(assays=SimpleList(fpkm=getSpecificGEOSupp(gse94802_fpkm),
counts=getSpecificGEOSupp(gse94802_counts)),
colData=colData)
# clear namespace
rm(esetlist, gse94802_fpkm, gse94802_counts, doe, colData, getSpecificGEOSupp)We compute zFPKM.
library(zFPKM)
assay(se, "zfpkm") <- zFPKM(se)We can also plot the Guassian fit to the FPKM data for which the z-scores are based.
zFPKMPlot(se)
To determine which genes are active, we compute the median expression within each group.
activeGenes <- assay(se, "zfpkm") %>%
mutate(gene=rownames(assay(se, "zfpkm"))) %>%
gather(sample_id, zfpkm, -gene) %>%
left_join(select(as.data.frame(colData(se)), sample_id, condition), by="sample_id") %>%
group_by(gene, condition) %>%
summarize(median_zfpkm=median(zfpkm)) %>%
ungroup() %>%
mutate(active=(median_zfpkm > -3)) %>%
filter(active) %>%
select(gene) %>%
distinct()
seActive <- SummarizedExperiment(
assays=SimpleList(counts=as.matrix(assay(se, "counts")[activeGenes$gene, ])),
colData=colData(se))In the following DE analysis, we only use genes that were active in either group.
library(limma)
library(edgeR)
# Generate normalized log2CPM from counts AFTER we filter for protein-coding
# genes that are detectably expressed.
dge <- DGEList(counts=assay(seActive, "counts"))
dge <- calcNormFactors(dge)
design <- model.matrix(~ 0 + condition, data=colData(seActive))
vq <- voomWithQualityWeights(dge, design, plot=TRUE)
fit <- lmFit(vq, design)
contrastMatrix <- makeContrasts(conditioncontrol - conditionmutant, levels=design)
fit <- contrasts.fit(fit, contrastMatrix)
fit <- eBayes(fit, robust=TRUE)
deGenes <- topTable(fit, number=Inf)References
Hart T, Komori HK, LaMere S, Podshivalova K, Salomon DR. Finding the active genes in deep RNA-seq gene expression studies. BMC Genomics. 2013 Nov 11;14:778. doi: 10.1186/1471-2164-14-778.
sessionInfo()## R version 3.4.2 (2017-09-28)
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## attached base packages:
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## other attached packages:
## [1] edgeR_3.20.0 limma_3.34.0
## [3] bindrcpp_0.2 zFPKM_1.0.0
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## loaded via a namespace (and not attached):
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