GSE72165
This report outlines the analysis of the GSE72165 data set, consisting of 3 samples from the carotid body and 3 samples from the adrenal medulla in mice.
Preparation
Reference directories and packages
basedir <- "/home/charlotte/gene_vs_tx_quantification"
refdir <- "/home/Shared/data/annotation"
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(tximport))
suppressPackageStartupMessages(library(iCOBRA))
suppressPackageStartupMessages(library(dplyr))Metadata definition
A metadata table was generated using the information provided in Gene Expression Omnibus.
meta <- read.delim(paste0(basedir, "/data/gse72165/gse72165_phenodata.txt"),
header = TRUE, as.is = TRUE)
rownames(meta) <- meta$srr.id
meta## gsm.id srr.id condition
## SRR2172551 GSM1856457 SRR2172551 carotid
## SRR2172552 GSM1856458 SRR2172552 carotid
## SRR2172553 GSM1856459 SRR2172553 carotid
## SRR2172554 GSM1856460 SRR2172554 medulla
## SRR2172555 GSM1856461 SRR2172555 medulla
## SRR2172556 GSM1856462 SRR2172556 medullaFASTQ file download
The code below downloads the FASTQ files for the six samples from the SRA.
fastq_files <- c(paste0("ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/00",
c(1:6), "/",
meta$srr.id, "/", meta$srr.id, ".fastq.gz"))
for (fq in fastq_files) {
if (!file.exists(paste0(basedir, "/data/gse72165/fastq/", basename(fq)))) {
cmd <- paste0("wget -P ", basedir, "/data/gse72165/fastq ", fq)
message(cmd)
system(cmd)
} else {
message(paste0(fq, " is already downloaded."))
}
}## ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/001/SRR2172551/SRR2172551.fastq.gz is already downloaded.
## ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/002/SRR2172552/SRR2172552.fastq.gz is already downloaded.
## ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/003/SRR2172553/SRR2172553.fastq.gz is already downloaded.
## ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/004/SRR2172554/SRR2172554.fastq.gz is already downloaded.
## ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/005/SRR2172555/SRR2172555.fastq.gz is already downloaded.
## ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/006/SRR2172556/SRR2172556.fastq.gz is already downloaded.Reference file preparation and index building
We use the GRCm38.82 Mus musculus Ensembl annotation for the analysis. Here, we download the fasta file with the cDNA sequences and build an index for quantification with Salmon.
cdna_fasta <- paste0(refdir, "/Mouse/Ensembl_GRCm38.82/cDNA/Mus_musculus.GRCm38.cdna.all.fa.gz")
salmon_index <- paste0(basedir, "/annotation/Mouse_Ensembl_GRCm38.82/salmon_index/Mus_musculus.GRCm38.82.gtf.sidx")
############################### cDNA FASTA ################################
if (!file.exists(cdna_fasta)) {
cmd <- paste0("wget -P ", refdir, "/Mouse/Ensembl_GRCm38.82/cDNA ",
"ftp://ftp.ensembl.org/pub/release-82/fasta/mus_musculus/",
"cdna/Mus_musculus.GRCm38.cdna.all.fa.gz")
message(cmd)
system(cmd)
cmd <- paste0("gunzip -c ", cdna_fasta, "> ", gsub("\\.gz$", "", cdna_fasta))
message(cmd)
system(cmd)
} else {
message(paste0("Reference cDNA fasta is already downloaded."))
}## Reference cDNA fasta is already downloaded.## Build Salmon index
cmd <- paste("salmon index -i", salmon_index, "-t", gsub("\\.gz$", "", cdna_fasta), "-p 5 --type quasi")
message(cmd)## salmon index -i /home/charlotte/gene_vs_tx_quantification/annotation/Mouse_Ensembl_GRCm38.82/salmon_index/Mus_musculus.GRCm38.82.gtf.sidx -t /home/Shared/data/annotation/Mouse/Ensembl_GRCm38.82/cDNA/Mus_musculus.GRCm38.cdna.all.fa -p 5 --type quasiif (!file.exists(paste0(salmon_index, "/hash.bin"))) {
system(cmd)
} else {
message("Salmon index already exists.")
}## Salmon index already exists.Definition of gene-to-transcript mapping
Next, we derive a mapping between transcript and gene identifiers from the cDNA file downloaded above.
feature_lengths_file <- paste0(basedir, "/annotation/Mouse_Ensembl_GRCm38.82/feature_lengths.Rdata")
tx_gene_file <- paste0(basedir, "/annotation/Mouse_Ensembl_GRCm38.82/tx_gene_map.Rdata")## Calculate gene and transcript lengths, get gene-transcript mapping
if (!file.exists(feature_lengths_file)) {
calc_lengths_mapping(gtf = NULL, cdna_fasta = cdna_fasta,
feature_lengths_file = feature_lengths_file,
tx_gene_file = tx_gene_file)
} else {
message("feature lengths and tx-to-gene map already calculated.")
}## feature lengths and tx-to-gene map already calculated.load(tx_gene_file)Salmon abundance quantification
Next, we use Salmon to calculate transcript abundance estimates for each of the six samples.
salmon_basedir <- paste0(basedir, "/quantifications/gse72165/salmon")
fqs <- list.files(paste0(basedir, "/data/gse72165/fastq"), full.names = TRUE)
names(fqs) <- gsub("\\.fastq.gz", "", basename(fqs))
for (i in 1:length(fqs)) {
if (!file.exists(paste0(salmon_basedir, "/", names(fqs)[i], "/quant.sf"))) {
cmd <- sprintf("bash -c 'salmon quant -i %s -l U -r %s -p 5 -o %s'",
salmon_index,
paste0("<(gunzip -c ", fqs[i], ")"),
paste0(salmon_basedir, "/", names(fqs)[i]))
cat(cmd, "\n")
system(cmd)
} else {
cat("Salmon results for", names(fqs)[i], "already exist.\n")
}
}## Salmon results for SRR2172551 already exist.
## Salmon results for SRR2172552 already exist.
## Salmon results for SRR2172553 already exist.
## Salmon results for SRR2172554 already exist.
## Salmon results for SRR2172555 already exist.
## Salmon results for SRR2172556 already exist.Summarization of Salmon results and offset estimation
We use the tximport package (https://github.com/mikelove/tximport) to generate count matrices and offset matrices (average transcript lengths) from the Salmon transcript-level estimates. We generate two different count matrices (simplesum and scaledTPM), and additionally create offsets to be used with the simplesum matrix.
salmon_files <- list.files(salmon_basedir, pattern = "SRR", full.names = TRUE)
salmon_files <- salmon_files[file.info(salmon_files)$isdir]
salmon_files <- paste0(salmon_files, "/quant.sf")
salmon_files <- salmon_files[file.exists(salmon_files)]
names(salmon_files) <- basename(gsub("/quant.sf", "", salmon_files))
txi_salmonsimplesum <- tximport(files = salmon_files, type = "salmon", txIn = TRUE,
txOut = FALSE, countsFromAbundance = "no",
gene2tx = gene2tx)## reading in files
## 1 2 3 4 5 6
## summarizing abundance
## summarizing counts
## summarizing lengthtxi_salmonscaledtpm <- tximport(files = salmon_files, type = "salmon", txIn = TRUE,
txOut = FALSE, countsFromAbundance = "scaledTPM",
gene2tx = gene2tx)## reading in files
## 1 2 3 4 5 6
## summarizing abundance
## summarizing counts
## summarizing lengthsalmon_quant <- list(geneCOUNT_sal_simplesum = txi_salmonsimplesum$counts,
geneCOUNT_sal_scaledTPM = txi_salmonscaledtpm$counts,
avetxlength = txi_salmonsimplesum$length,
geneTPM_sal = txi_salmonsimplesum$abundance,
txi_salmonsimplesum = txi_salmonsimplesum,
txi_salmonscaledtpm = txi_salmonscaledtpm)Differential expression analysis
Given the gene count matrices defined above we apply edgeR and DESeq2 to perform differential gene expression. For the simplesum matrix, we also apply edgeR and DESeq2 using the offsets derived from the average transcript lengths (simplesum_avetxl).
edgeR
res_sal_simplesum_edgeR <- diff_expression_edgeR(counts = salmon_quant$geneCOUNT_sal_simplesum,
meta = meta, cond_name = "condition",
sample_name = "srr.id",
gene_length_matrix = NULL)
res_sal_simplesum_avetxl_edgeR <- diff_expression_edgeR(counts = salmon_quant$geneCOUNT_sal_simplesum,
meta = meta, cond_name = "condition",
sample_name = "srr.id",
gene_length_matrix = salmon_quant$avetxlength)
res_sal_scaledTPM_edgeR <- diff_expression_edgeR(counts = salmon_quant$geneCOUNT_sal_scaledTPM,
meta = meta, cond_name = "condition",
sample_name = "srr.id",
gene_length_matrix = NULL)Diagnostics
dfh <- data.frame(pvalue = c(res_sal_scaledTPM_edgeR$tt$PValue,
res_sal_simplesum_avetxl_edgeR$tt$PValue,
res_sal_simplesum_edgeR$tt$PValue),
mth = c(rep("scaledTPM_salmon, edgeR", nrow(res_sal_scaledTPM_edgeR$tt)),
rep("simplesum_salmon_avetxl, edgeR", nrow(res_sal_simplesum_avetxl_edgeR$tt)),
rep("simplesum_salmon, edgeR", nrow(res_sal_simplesum_edgeR$tt))))
ggplot(dfh, aes(x = pvalue)) + geom_histogram() + facet_wrap(~mth) +
plot_theme() +
xlab("p-value") + ylab("count")
par(mfrow = c(1, 3))
plotBCV(res_sal_scaledTPM_edgeR$dge, main = "scaledTPM_salmon, edgeR")
plotBCV(res_sal_simplesum_avetxl_edgeR$dge, main = "simplesum_salmon_avetxl, edgeR")
plotBCV(res_sal_simplesum_edgeR$dge, main = "simplesum_salmon, edgeR")
par(mfrow = c(1, 3))
plotSmear(res_sal_scaledTPM_edgeR$dge, main = "scaledTPM_salmon, edgeR", ylim = c(-13, 13))
plotSmear(res_sal_simplesum_avetxl_edgeR$dge, main = "simplesum_salmon_avetxl, edgeR", ylim = c(-13, 13))
plotSmear(res_sal_simplesum_edgeR$dge, main = "simplesum_salmon, edgeR", ylim = c(-13, 13))
par(mfrow = c(1, 1))Comparison of significant genes found with different matrices
cobra_edgeR <- COBRAData(padj = data.frame(simplesum_salmon = res_sal_simplesum_edgeR$tt$FDR,
row.names = rownames(res_sal_simplesum_edgeR$tt)))
cobra_edgeR <- COBRAData(padj = data.frame(scaledTPM_salmon = res_sal_scaledTPM_edgeR$tt$FDR,
row.names = rownames(res_sal_scaledTPM_edgeR$tt)),
object_to_extend = cobra_edgeR)
cobra_edgeR <- COBRAData(padj = data.frame(simplesum_salmon_avetxl = res_sal_simplesum_avetxl_edgeR$tt$FDR,
row.names = rownames(res_sal_simplesum_avetxl_edgeR$tt)),
object_to_extend = cobra_edgeR)
cobraperf_edgeR <- calculate_performance(cobra_edgeR, aspects = "overlap", thr_venn = 0.05)
cobraplot1_edgeR <- prepare_data_for_plot(cobraperf_edgeR, incltruth = FALSE,
colorscheme = c("blue", "red", "black"))
plot_overlap(cobraplot1_edgeR, cex = c(1, 0.7, 0.7))
title("GSE72165, edgeR", line = 0)
Comparison of logFC estimates - all methods
df1 <- Reduce(function(...) merge(..., by = "gene", all = TRUE),
list(data.frame(gene = rownames(res_sal_scaledTPM_edgeR$tt),
scaledTPM_salmon = res_sal_scaledTPM_edgeR$tt$logFC,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_simplesum_edgeR$tt),
simplesum_salmon = res_sal_simplesum_edgeR$tt$logFC,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_simplesum_avetxl_edgeR$tt),
simplesum_salmon_avetxl =
res_sal_simplesum_avetxl_edgeR$tt$logFC,
stringsAsFactors = FALSE)))
rownames(df1) <- df1$gene
df1$gene <- NULL
pairs(df1, upper.panel = panel_smooth, lower.panel = panel_cor)
Comparison of logFC estimates - simplesum vs scaledTPM
df2 <- Reduce(function(...) merge(..., by = "gene", all = TRUE),
list(data.frame(gene = rownames(salmon_quant$geneTPM_sal),
salmon_quant$geneTPM_sal,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_scaledTPM_edgeR$tt),
scaledTPM_salmon_logFC = res_sal_scaledTPM_edgeR$tt$logFC,
scaledTPM_salmon_logCPM = res_sal_scaledTPM_edgeR$tt$logCPM,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_simplesum_edgeR$tt),
simplesum_salmon_logFC = res_sal_simplesum_edgeR$tt$logFC,
simplesum_salmon_logCPM = res_sal_simplesum_edgeR$tt$logCPM,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_simplesum_avetxl_edgeR$tt),
simplesum_salmon_avetxl_logFC =
res_sal_simplesum_avetxl_edgeR$tt$logFC,
simplesum_salmon_avetxl_logCPM =
res_sal_simplesum_avetxl_edgeR$tt$logCPM,
stringsAsFactors = FALSE)))
rownames(df2) <- df2$gene
df2$gene <- NULL
df2$scaledTPM_salmon_logCPMbinary <- Hmisc::cut2(df2$scaledTPM_salmon_logCPM, g = 2)
df2$simplesum_salmon_logCPMbinary <- Hmisc::cut2(df2$simplesum_salmon_logCPM, g = 2)
df2$simplesum_salmon_avetxl_logCPMbinary <- Hmisc::cut2(df2$simplesum_salmon_avetxl_logCPM, g = 2)
df2$sumA <- rowSums(df2[, meta$srr.id[meta$condition == "carotid"]])
df2$sumB <- rowSums(df2[, meta$srr.id[meta$condition == "medulla"]])
df2$allzero_onecond <- "expressed in both groups"
df2$allzero_onecond[union(which(df2$sumA == 0), which(df2$sumB == 0))] <- "expressed in one group"
df2$onecol <- rep("", nrow(df2))
ggplot(df2, aes(x = simplesum_salmon_logFC, y = scaledTPM_salmon_logFC, col = onecol)) +
geom_abline(intercept = 0, slope = 1) +
geom_point(size = 2, alpha = 0.5) +
plot_theme() + ggtitle("GSE72165") + theme(legend.position = "bottom") +
scale_color_manual(values = c("blue"), name = "") +
theme(legend.background = element_rect(fill = "white"), legend.key = element_blank()) +
xlab("simplesum_salmon, logFC") + ylab("scaledTPM_salmon, logFC") +
guides(colour = guide_legend(override.aes = list(size = 0)))
ggplot(df2, aes(x = simplesum_salmon_logFC, y = scaledTPM_salmon_logFC, col = allzero_onecond)) +
geom_abline(intercept = 0, slope = 1) +
geom_point(size = 2, alpha = 0.5) +
plot_theme() + ggtitle("GSE72165") + theme(legend.position = "bottom") +
scale_color_manual(values = c("blue", "red"), name = "") +
xlab("simplesum_salmon, logFC") + ylab("scaledTPM_salmon, logFC") +
guides(colour = guide_legend(override.aes = list(size = 7)))
ggplot(subset(df2, !is.na(scaledTPM_salmon_logCPMbinary)),
aes(x = simplesum_salmon_logFC, y = scaledTPM_salmon_logFC,
col = scaledTPM_salmon_logCPMbinary)) +
geom_abline(intercept = 0, slope = 1) +
geom_point(size = 2, alpha = 0.5) +
facet_wrap(~scaledTPM_salmon_logCPMbinary) +
plot_theme() + ggtitle("GSE72165") + theme(legend.position = "bottom") +
xlab("simplesum_salmon, logFC") + ylab("scaledTPM_salmon, logFC") +
scale_color_manual(values = c("red", "blue"), name = "scaledTPM_salmon, logCPM") +
guides(colour = guide_legend(override.aes = list(size = 7)))
DESeq2
res_sal_simplesum_deseq2 <- diff_expression_DESeq2(txi = NULL,
counts = salmon_quant$geneCOUNT_sal_simplesum,
meta = meta, cond_name = "condition",
level1 = "carotid",
level2 = "medulla",
sample_name = "srr.id")
res_sal_simplesum_avetxl_deseq2 <- diff_expression_DESeq2(txi = salmon_quant$txi_salmonsimplesum,
counts = NULL,
meta = meta, cond_name = "condition",
level1 = "carotid",
level2 = "medulla",
sample_name = "srr.id")
res_sal_scaledTPM_deseq2 <- diff_expression_DESeq2(txi = NULL,
counts = salmon_quant$geneCOUNT_sal_scaledTPM,
meta = meta, cond_name = "condition",
level1 = "carotid",
level2 = "medulla",
sample_name = "srr.id")Diagnostics
dfh <- data.frame(pvalue = c(res_sal_scaledTPM_deseq2$res$pvalue,
res_sal_simplesum_avetxl_deseq2$res$pvalue,
res_sal_simplesum_deseq2$res$pvalue),
mth = c(rep("scaledTPM_salmon, DESeq2", nrow(res_sal_scaledTPM_deseq2$res)),
rep("simplesum_salmon_avetxl, DESeq2", nrow(res_sal_simplesum_avetxl_deseq2$res)),
rep("simplesum_salmon, DESeq2", nrow(res_sal_simplesum_deseq2$res))))
ggplot(dfh, aes(x = pvalue)) + geom_histogram() + facet_wrap(~mth) +
plot_theme() +
xlab("p-value") + ylab("count")
par(mfrow = c(1, 3))
plotDispEsts(res_sal_scaledTPM_deseq2$dsd, main = "scaledTPM_salmon, DESeq2")
plotDispEsts(res_sal_simplesum_avetxl_deseq2$dsd, main = "simplesum_salmon_avetxl, DESeq2")
plotDispEsts(res_sal_simplesum_deseq2$dsd, main = "simplesum_salmon, DESeq2")
par(mfrow = c(1, 3))
DESeq2::plotMA(res_sal_scaledTPM_deseq2$dsd, main = "scaledTPM_salmon, DESeq2")
DESeq2::plotMA(res_sal_simplesum_avetxl_deseq2$dsd, main = "simplesum_salmon_avetxl, DESeq2")
DESeq2::plotMA(res_sal_simplesum_deseq2$dsd, main = "simplesum_salmon, DESeq2")
par(mfrow = c(1, 1))Comparison of significant genes found with different matrices
cobra_deseq2 <- COBRAData(padj = data.frame(simplesum_salmon = res_sal_simplesum_deseq2$res$padj,
row.names = rownames(res_sal_simplesum_deseq2$res)))
cobra_deseq2 <- COBRAData(padj = data.frame(scaledTPM_salmon = res_sal_scaledTPM_deseq2$res$padj,
row.names = rownames(res_sal_scaledTPM_deseq2$res)),
object_to_extend = cobra_deseq2)
cobra_deseq2 <- COBRAData(padj = data.frame(simplesum_salmon_avetxl = res_sal_simplesum_avetxl_deseq2$res$padj,
row.names = rownames(res_sal_simplesum_avetxl_deseq2$res)),
object_to_extend = cobra_deseq2)
cobraperf_deseq2 <- calculate_performance(cobra_deseq2, aspects = "overlap", thr_venn = 0.05)
cobraplot1_deseq2 <- prepare_data_for_plot(cobraperf_deseq2, incltruth = FALSE,
colorscheme = c("blue", "red", "black"))
plot_overlap(cobraplot1_deseq2, cex = c(1, 0.7, 0.7))
title("GSE72165, DESeq2", line = 0)
Comparison of logFC estimates - all methods
df1 <- Reduce(function(...) merge(..., by = "gene", all = TRUE),
list(data.frame(gene = rownames(res_sal_scaledTPM_deseq2$res),
scaledTPM_salmon = res_sal_scaledTPM_deseq2$res$log2FoldChange,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_simplesum_deseq2$res),
simplesum_salmon = res_sal_simplesum_deseq2$res$log2FoldChange,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_simplesum_avetxl_deseq2$res),
simplesum_salmon_avetxl =
res_sal_simplesum_avetxl_deseq2$res$log2FoldChange,
stringsAsFactors = FALSE)))
rownames(df1) <- df1$gene
df1$gene <- NULL
pairs(df1, upper.panel = panel_smooth, lower.panel = panel_cor)
Comparison of logFC estimates - simplesum vs scaledTPM
df2 <- Reduce(function(...) merge(..., by = "gene", all = TRUE),
list(data.frame(gene = rownames(salmon_quant$geneTPM_sal),
salmon_quant$geneTPM_sal,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_scaledTPM_deseq2$res),
scaledTPM_salmon_logFC = res_sal_scaledTPM_deseq2$res$log2FoldChange,
scaledTPM_salmon_basemean = res_sal_scaledTPM_deseq2$res$baseMean,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_simplesum_deseq2$res),
simplesum_salmon_logFC = res_sal_simplesum_deseq2$res$log2FoldChange,
simplesum_salmon_basemean = res_sal_simplesum_deseq2$res$baseMean,
stringsAsFactors = FALSE),
data.frame(gene = rownames(res_sal_simplesum_avetxl_deseq2$res),
simplesum_salmon_avetxl_logFC =
res_sal_simplesum_avetxl_deseq2$res$log2FoldChange,
simplesum_salmon_avetxl_basemean =
res_sal_simplesum_avetxl_deseq2$res$baseMean,
stringsAsFactors = FALSE)))
rownames(df2) <- df2$gene
df2$gene <- NULL
df2$scaledTPM_salmon_basemeanbinary <- Hmisc::cut2(df2$scaledTPM_salmon_basemean, g = 2)
df2$simplesum_salmon_basemeanbinary <- Hmisc::cut2(df2$simplesum_salmon_basemean, g = 2)
df2$simplesum_salmon_avetxl_basemeanbinary <- Hmisc::cut2(df2$simplesum_salmon_avetxl_basemean, g = 2)
df2$sumA <- rowSums(df2[, meta$srr.id[meta$condition == "carotid"]])
df2$sumB <- rowSums(df2[, meta$srr.id[meta$condition == "medulla"]])
df2$allzero_onecond <- "expressed in both groups"
df2$allzero_onecond[union(which(df2$sumA == 0), which(df2$sumB == 0))] <- "expressed in one group"
df2$onecol <- rep("", nrow(df2))
ggplot(df2, aes(x = simplesum_salmon_logFC, y = scaledTPM_salmon_logFC, col = onecol)) +
geom_abline(intercept = 0, slope = 1) +
geom_point(size = 2, alpha = 0.5) +
plot_theme() + ggtitle("GSE72165") + theme(legend.position = "bottom") +
scale_color_manual(values = c("blue"), name = "") +
theme(legend.background = element_rect(fill = "white"), legend.key = element_blank()) +
xlab("simplesum_salmon, logFC") + ylab("scaledTPM_salmon, logFC") +
guides(colour = guide_legend(override.aes = list(size = 0)))
ggplot(df2, aes(x = simplesum_salmon_logFC, y = scaledTPM_salmon_logFC, col = allzero_onecond)) +
geom_abline(intercept = 0, slope = 1) +
geom_point(size = 2, alpha = 0.5) +
plot_theme() + ggtitle("GSE72165") + theme(legend.position = "bottom") +
scale_color_manual(values = c("blue", "red"), name = "") +
xlab("simplesum_salmon, logFC") + ylab("scaledTPM_salmon, logFC") +
guides(colour = guide_legend(override.aes = list(size = 7)))
ggplot(subset(df2, !is.na(scaledTPM_salmon_basemeanbinary)),
aes(x = simplesum_salmon_logFC, y = scaledTPM_salmon_logFC,
col = scaledTPM_salmon_basemeanbinary)) +
geom_abline(intercept = 0, slope = 1) +
geom_point(size = 2, alpha = 0.5) +
facet_wrap(~scaledTPM_salmon_basemeanbinary) +
plot_theme() + ggtitle("GSE72165") + theme(legend.position = "bottom") +
xlab("simplesum_salmon, logFC") + ylab("scaledTPM_salmon, logFC") +
scale_color_manual(values = c("red", "blue"), name = "scaledTPM_salmon, base mean") +
guides(colour = guide_legend(override.aes = list(size = 7)))
Help functions
panel_cor <- function(x, y, digits = 3, cex.cor) {
## Panel function to print Pearson and Spearman correlations
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r1 <- abs(cor(x, y, method = "pearson", use = "complete"))
txt1 <- format(c(r1, 0.123456789), digits = digits)[1]
r2 <- abs(cor(x, y, method = "spearman", use = "complete"))
txt2 <- format(c(r2, 0.123456789), digits = digits)[1]
text(0.5, 0.35, paste("pearson =", txt1), cex = 1.1)
text(0.5, 0.65, paste("spearman =", txt2), cex = 1.1)
}panel_smooth<-function (x, y, col = "blue", bg = NA, pch = ".",
cex = 0.8, ...) {
## Panel function to plot points
points(x, y, pch = pch, col = col, bg = bg, cex = cex)
}plot_theme <- function() {
## ggplot2 plotting theme
theme_grey() +
theme(legend.position = "right",
panel.background = element_rect(fill = "white", colour = "black"),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
strip.text = element_text(size = 10),
strip.background = element_rect(fill = NA, colour = "black"),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 15),
axis.title.y = element_text(size = 15),
plot.title = element_text(colour = "black", size = 20))
}calc_lengths_mapping <- function(gtf, cdna_fasta, feature_lengths_file,
tx_gene_file) {
## Function to calculate gene and transcript lengths from transcript cDNA
## fasta and gtf file. Also generate mapping between transcript and gene IDs.
suppressPackageStartupMessages(library(GenomicFeatures))
suppressPackageStartupMessages(library(Biostrings))
## Gene/transcript lengths ===============================================
## Transcripts/genes present in gtf file
if (!is.null(gtf)) {
txdb <- makeTxDbFromGFF(gtf, format = "gtf")
ebg <- exonsBy(txdb, "gene")
ebt <- exonsBy(txdb, "tx", use.names = TRUE)
ebg_red <- reduce(ebg)
gene_length <- sum(width(ebg_red))
ebt_red <- reduce(ebt)
tx_length <- sum(width(ebt_red))
} else {
tx_length <- c()
gene_length <- c()
}
## Extend with transcripts from cDNA fasta
cdna <- readDNAStringSet(gsub("\\.gz", "", cdna_fasta))
tx_length2 <- width(cdna)
names(tx_length2) <- sapply(names(cdna), function(i) strsplit(i, " ")[[1]][1])
tx_length2 <- tx_length2[setdiff(names(tx_length2), names(tx_length))]
tx_length <- c(tx_length, tx_length2)
## Gene/transcript mapping ===============================================
## Transcripts/genes present in gtf file
if (!is.null(gtf)) {
tbg <- transcriptsBy(txdb, "gene")
tx2gene <- stack(lapply(tbg, function(w) w$tx_name))
colnames(tx2gene) <- c("transcript", "gene")
} else {
tx2gene <- data.frame(transcript = c(), gene = c())
}
## Extend with mappings from cDNA fasta file
tx <- sapply(names(cdna), function(i) strsplit(i, " ")[[1]][1])
gn <- sapply(names(cdna), function(i) gsub("gene:", "", strsplit(i, " ")[[1]][4]))
tx2gene2 <- data.frame(transcript = tx, gene = gn,
stringsAsFactors = FALSE)
rownames(tx2gene2) <- NULL
tx2gene2 <- tx2gene2[match(setdiff(tx2gene2$transcript,
tx2gene$transcript), tx2gene2$transcript), ]
tx2gene <- rbind(tx2gene, tx2gene2)
gene2tx <- tx2gene[, c("gene", "transcript")]
save(gene_length, tx_length, file = feature_lengths_file)
save(gene2tx, tx2gene, file = tx_gene_file)
}diff_expression_edgeR <- function(counts, meta, cond_name, sample_name,
gene_length_matrix = NULL) {
## Differential expression analysis with edgeR
suppressPackageStartupMessages(library(edgeR))
## Prepare DGEList
counts <- round(counts)
cts <- counts[rowSums(is.na(counts)) == 0, ]
cts <- cts[rowSums(cts) != 0, ]
dge <-
DGEList(counts = cts, group = meta[, cond_name][match(colnames(cts),
meta[, sample_name])])
## If average transcript lengths provided, add as offset
if (!is.null(gene_length_matrix)) {
egf <- gene_length_matrix[match(rownames(cts), rownames(gene_length_matrix)),
match(colnames(cts), colnames(gene_length_matrix))]
egf <- egf / exp(rowMeans(log(egf)))
o <- log(calcNormFactors(cts/egf)) + log(colSums(cts/egf))
dge$offset <- t(t(log(egf)) + o)
} else {
dge <- calcNormFactors(dge)
}
## Estimate dispersions and fit model
design <- model.matrix(~dge$samples$group)
dge <- estimateGLMCommonDisp(dge, design = design)
dge <- estimateGLMTrendedDisp(dge, design = design)
dge <- estimateGLMTagwiseDisp(dge, design = design)
fit <- glmFit(dge, design = design)
lrt <- glmLRT(fit)
tt <- topTags(lrt, n = Inf)$table
return(list(dge = dge, tt = tt))
}diff_expression_DESeq2 <- function(txi = NULL, counts, meta, cond_name,
level1, level2, sample_name) {
## Differential expression analysis with DESeq2
suppressPackageStartupMessages(library(DESeq2,
lib.loc = "/home/charlotte/R/x86_64-pc-linux-gnu-library/3.2"))
## If tximport object provided, generate DESeqDataSet from it. Otherwise,
## use the provided count matrix.
if (!is.null(txi)) {
txi$counts <- round(txi$counts)
keep_feat <- rownames(txi$counts[rowSums(is.na(txi$counts)) == 0 & rowSums(txi$counts) != 0, ])
txi <- lapply(txi, function(w) {
if (!is.null(dim(w))) w[match(keep_feat, rownames(w)), ]
else w
})
dsd <- DESeqDataSetFromTximport(txi,
colData = meta[match(colnames(txi$counts),
meta[, sample_name]), ],
design = as.formula(paste0("~", cond_name)))
} else {
counts <- round(counts)
cts = counts[rowSums(is.na(counts)) == 0, ]
cts <- cts[rowSums(cts) != 0, ]
dsd <- DESeqDataSetFromMatrix(countData = round(cts),
colData = meta[match(colnames(cts),
meta[, sample_name]), ],
design = as.formula(paste0("~", cond_name)))
}
## Estimate dispersions and fit model
dsd <- DESeq(dsd, test = "Wald", fitType = "local", betaPrior = TRUE)
res <- as.data.frame(results(dsd, contrast = c(cond_name, level2, level1),
cooksCutoff = FALSE, independentFiltering = FALSE))
return(list(dsd = dsd, res = res))
}Session info
sessionInfo()## R version 3.2.2 (2015-08-14)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.3 LTS
##
## locale:
## [1] LC_CTYPE=C LC_NUMERIC=C
## [3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
## [5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
## [7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] DESeq2_1.11.6 RcppArmadillo_0.6.200.2.0
## [3] Rcpp_0.12.2 SummarizedExperiment_1.0.1
## [5] Biobase_2.30.0 GenomicRanges_1.22.1
## [7] GenomeInfoDb_1.6.1 IRanges_2.4.4
## [9] S4Vectors_0.8.3 BiocGenerics_0.16.1
## [11] edgeR_3.12.0 limma_3.26.3
## [13] dplyr_0.4.3 iCOBRA_0.99.3
## [15] tximport_0.0.7 ggplot2_1.0.1
##
## loaded via a namespace (and not attached):
## [1] splines_3.2.2 gtools_3.5.0 Formula_1.2-1
## [4] shiny_0.12.2 assertthat_0.1 latticeExtra_0.6-26
## [7] yaml_2.1.13 RSQLite_1.0.0 lattice_0.20-33
## [10] digest_0.6.8 RColorBrewer_1.1-2 XVector_0.10.0
## [13] colorspace_1.2-6 htmltools_0.2.6 httpuv_1.3.3
## [16] plyr_1.8.3 XML_3.98-1.3 genefilter_1.52.0
## [19] zlibbioc_1.16.0 xtable_1.8-0 scales_0.3.0
## [22] gdata_2.17.0 BiocParallel_1.4.0 annotate_1.48.0
## [25] DT_0.1 ROCR_1.0-7 nnet_7.3-10
## [28] proto_0.3-10 survival_2.38-3 magrittr_1.5
## [31] mime_0.4 evaluate_0.8 MASS_7.3-43
## [34] gplots_2.17.0 foreign_0.8-65 shinydashboard_0.5.1
## [37] tools_3.2.2 formatR_1.2.1 stringr_1.0.0
## [40] locfit_1.5-9.1 munsell_0.4.2 cluster_2.0.3
## [43] AnnotationDbi_1.32.0 lambda.r_1.1.7 caTools_1.17.1
## [46] futile.logger_1.4.1 grid_3.2.2 htmlwidgets_0.5
## [49] bitops_1.0-6 shinyBS_0.61 labeling_0.3
## [52] rmarkdown_0.8.1 gtable_0.1.2 DBI_0.3.1
## [55] reshape2_1.4.1 R6_2.1.1 gridExtra_2.0.0
## [58] knitr_1.11 Hmisc_3.17-0 futile.options_1.0.0
## [61] KernSmooth_2.23-15 stringi_1.0-1 geneplotter_1.48.0
## [64] rpart_4.1-10 acepack_1.3-3.3