DTE analysis with STAR + RSEM input
Load required libraries
library(data.table)
library(DESeq2)
library(apeglm)
library(ggplot2)
library(ggrepel)
library(EnhancedVolcano)Import input data
Load clinical information and define file path
dir = "../../"
sampleData = paste0(dir, "clinical.txt")
sampleData = fread(sampleData)
rownames(sampleData) = sampleData$ENA_RUN
sampleData$individual = as.factor(sampleData$individual)
sampleData$paris_classification = as.factor(sampleData$paris_classification)
# Use relevel() to set adjacent normal samples as reference
sampleData$paris_classification = relevel(sampleData$paris_classification, ref = "normal")
files = list.files(paste0(dir, "rsem_star"), "*soforms.results$", full.names = T)> files
[1] "../../rsem_star/ERR2675454.isoforms.results"
[2] "../../rsem_star/ERR2675455.isoforms.results"
[3] "../../rsem_star/ERR2675458.isoforms.results"
[4] "../../rsem_star/ERR2675459.isoforms.results"
[5] "../../rsem_star/ERR2675460.isoforms.results"
[6] "../../rsem_star/ERR2675461.isoforms.results"
[7] "../../rsem_star/ERR2675464.isoforms.results"
[8] "../../rsem_star/ERR2675465.isoforms.results"
[9] "../../rsem_star/ERR2675468.isoforms.results"
[10] "../../rsem_star/ERR2675469.isoforms.results"
[11] "../../rsem_star/ERR2675472.isoforms.results"
[12] "../../rsem_star/ERR2675473.isoforms.results"
[13] "../../rsem_star/ERR2675476.isoforms.results"
[14] "../../rsem_star/ERR2675477.isoforms.results"
[15] "../../rsem_star/ERR2675478.isoforms.results"
[16] "../../rsem_star/ERR2675479.isoforms.results"
[17] "../../rsem_star/ERR2675480.isoforms.results"
[18] "../../rsem_star/ERR2675481.isoforms.results"
[19] "../../rsem_star/ERR2675484.isoforms.results"
[20] "../../rsem_star/ERR2675485.isoforms.results"Import RSEM result file and keep the 5th column containing the expected_count values. Build a countData data.frame to store counts
countData = data.frame(fread(files[1]))[c(1,5)]
# Loop and read the 5th column remaining files
for(i in 2:length(files)) {
countData = cbind(countData, data.frame(fread(files[i]))[5])
}
colnames(countData) = c("GeneID", gsub(paste0(dir,"rsem_star/"), "", files))
colnames(countData) = gsub(".isoforms.results", "", colnames(countData))
rownames(countData) = countData$GeneID
countData = countData[,c(2:ncol(countData))]
countData = round(countData) # convert to integer> countData[1:10,1:5]
ERR2675454 ERR2675455 ERR2675458 ERR2675459 ERR2675460
ENST00000373020.9 874 1921 627 1706 944
ENST00000494424.1 0 0 0 0 0
ENST00000496771.5 3 4 16 19 37
ENST00000612152.4 0 0 0 0 0
ENST00000614008.4 114 103 60 58 60
ENST00000373031.5 29 26 9 36 22
ENST00000485971.1 5 0 0 9 0
ENST00000371582.8 26 54 28 51 54
ENST00000371584.8 18 16 39 35 36
ENST00000371588.9 638 781 796 489 622Build a DESeqDataSet from countData with DESeqDataSetFromMatrix, providing also the sample information and a design formula
dds = DESeqDataSetFromMatrix(countData = countData,
colData = sampleData, design = ~ individual + paris_classification)> dds
class: DESeqDataSet
dim: 227912 20
metadata(1): version
assays(1): counts
rownames(227912): ENST00000373020.9 ENST00000494424.1 ... ENST00000673857.1 ENST00000673884.1
rowData names(0):
colnames(20): ERR2675454 ERR2675455 ... ERR2675484 ERR2675485
colData names(11): ENA_RUN individual ... braf_status kras_status* Import input data (tximport)
Alternatively, tximport can be used to import expected_count from RSEM, as follows
library(tximport)
txi = tximport(files, type = "none", txIn = TRUE, txOut = TRUE,
txIdCol = "transcript_id", abundanceCol = "TPM",
countsCol = "expected_count", lengthCol = "effective_length",
importer = function(x) readr::read_tsv(x))
# fix "Error: all(lengths > 0) is not TRUE" error
txi$length[txi$length == 0] = 0.01
dds = DESeqDataSetFromTximport(txi,
colData = sampleData, ~ individual + paris_classification)> dds
class: DESeqDataSet
dim: 227912 20
metadata(1): version
assays(2): counts avgTxLength
rownames(227912): ENST00000373020.9 ENST00000494424.1 ... ENST00000673857.1 ENST00000673884.1
rowData names(0):
colnames: NULL
colData names(11): ENA_RUN individual ... braf_status kras_statusDifferential expression analysis
Run DESeq2 analysis using DESeq, which performs (1) estimation of size factors, (2) estimation of dispersion, then (3) Negative Binomial GLM fitting and Wald statistics. The results tables (log2 fold changes and p-values) can be generated using the results function
dds = DESeq(dds)> cbind(resultsNames(dds))
[,1]
[1,] "Intercept"
[2,] "individual_S11_vs_S1"
[3,] "individual_S12_vs_S1"
[4,] "individual_S15_vs_S1"
[5,] "individual_S17_vs_S1"
[6,] "individual_S3_vs_S1"
[7,] "individual_S5_vs_S1"
[8,] "individual_S6_vs_S1"
[9,] "individual_S7_vs_S1"
[10,] "individual_S9_vs_S1"
[11,] "paris_classification_0.IIa_vs_normal"We set the adjusted p-value cutoff (FDR) to be 0.05, hence we change the default significance cutoff used for optimizing the independent filtering alpha from 0.1 to 0.05
res <- results(dds, name = "paris_classification_0.IIa_vs_normal", alpha = 0.05)At a FDR of 0.05, we have 2945 genes up-regulated and 2871 genes down-regulated in 0-IIa pre-lesion versus Normal
> summary(res)
out of 159634 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 4210, 2.6%
LFC < 0 (down) : 3136, 2%
outliers [1] : 0, 0%
low counts [2] : 87733, 55%
(mean count < 3)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultsThe results res object contains the follow columns
> mcols(res)$description
[1] "mean of normalized counts for all samples"
[2] "log2 fold change (MLE): paris classification 0.IIa vs normal"
[3] "standard error: paris classification 0.IIa vs normal"
[4] "Wald statistic: paris classification 0.IIa vs normal"
[5] "Wald test p-value: paris classification 0.IIa vs normal"
[6] "BH adjusted p-values"
> head(res)
log2 fold change (MLE): paris classification 0.IIa vs normal
Wald test p-value: paris classification 0.IIa vs normal
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
ENST00000373020.9 1187.48517486989 -0.63220378627308 0.194294561388125 -3.25384190764961 0.00113855622353168 0.0158582088422363
ENST00000494424.1 0 NA NA NA NA NA
ENST00000496771.5 10.8614532030735 0.332252420760318 0.385893227572389 0.860995728923465 0.389240395417396 0.748731313595519
ENST00000612152.4 0.77778022898225 1.06375257452071 3.00091767488387 0.354475760339502 0.722982366693673 NA
ENST00000614008.4 83.2226874117913 -0.176481709375863 0.356493402266472 -0.495049019852396 0.620565518581719 0.892120965062597
ENST00000373031.5 25.5200220663835 0.0278313866560059 0.389927929551535 0.0713757198362666 0.943098733077851 0.990814121769475Read about authors' note on p-values and adjusted p-values set to NA here
Exploring results
MA-plot
We use MA-plot to show the log2 fold changes attributable to a given variable (i.e. pre-lesion) over the mean of normalized counts for all the samples. Points will be colored red if the adjusted p value is less than 0.05. Points which fall out of the window are plotted as open triangles pointing either up or down
resLFC = lfcShrink(dds, coef = "paris_classification_0.IIa_vs_normal",
type="apeglm")
png("DTE_MA-plot.RSEM.png", width=7, height=5, units = "in", res = 300)
plotMA(resLFC, alpha = 0.05, ylim=c(-6,6),
main = "MA-plot for the shrunken log2 fold changes")
dev.off()
Principal component plot of the samples
First, we perform count data transformation with regularized logarithm rlog or variance stabilizing transformations vst. You can read here about which transformation to choose
rld = rlog(dds)
vsd = vst(dds)Perform sample PCA for transformed data using plotPCA, then plot with ggplot
# rlog
pcaData = plotPCA(rld, intgroup=c("individual","paris_classification"),
returnData=TRUE)
percentVar = round(100 * attr(pcaData, "percentVar"))
png("DTE_PCA-rlog.RSEM.png", width=7, height=7, units = "in", res = 300)
ggplot(pcaData, aes(PC1, PC2, colour = paris_classification)) +
geom_point(size = 2) + theme_bw() +
scale_color_manual(values = c("blue", "red")) +
geom_text_repel(aes(label = individual), nudge_x = -1, nudge_y = 0.2, size = 3) +
ggtitle("Principal Component Analysis (PCA)", subtitle = "rlog transformation") +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance"))
dev.off()
# vst
pcaData = plotPCA(vsd, intgroup=c("individual","paris_classification"),
returnData=TRUE)
percentVar = round(100 * attr(pcaData, "percentVar"))
png("DTE_PCA-vst.RSEM.png", width=7, height=7, units = "in", res = 300)
ggplot(pcaData, aes(PC1, PC2, colour = paris_classification)) +
geom_point(size = 2) + theme_bw() +
scale_color_manual(values = c("blue", "red")) +
geom_text_repel(aes(label = individual), nudge_x = -1, nudge_y = 0.2, size = 3) +
ggtitle("Principal Component Analysis (PCA)", subtitle = "vst transformation") +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance"))
dev.off()
Volcano plots
We use EnhancedVolcano to generate volcano plot to visualise the results of differential expression analyses
pCutoff = 0.05
FCcutoff = 1.0
p = EnhancedVolcano(data.frame(res), lab = NA, x = 'log2FoldChange', y = 'padj',
xlab = bquote(~Log[2]~ 'fold change'), ylab = bquote(~-Log[10]~adjusted~italic(P)),
pCutoff = pCutoff, FCcutoff = FCcutoff, pointSize = 1.0, labSize = 2.0,
title = "Volcano plot", subtitle = "SSA/P vs. Normal",
caption = paste0('log2 FC cutoff: ', FCcutoff, '; p-value cutoff: ', pCutoff, '\nTotal = ', nrow(res), ' variables'),
legend=c('NS','Log2 FC','Adjusted p-value', 'Adjusted p-value & Log2 FC'),
legendPosition = 'bottom', legendLabSize = 14, legendIconSize = 5.0)
png("DTE_VolcanoPlots.RSEM.png", width=7, height=7, units = "in", res = 300)
print(p)
dev.off()
Exporting results
We construct a normData data.frame to store per-group normalised mean and normalised counts of all samples, and a deData data.frame to store the DESeq results. We merge both data.frame with a annoData data.frame that contain per-transcript annotation derived from GENCODE GTF file before exporting the results in tabular format
annoData = "/home/USER/db/refanno/gencode.v33.annotation_transcripts.txt"
annoData = data.frame(fread(annoData))
normCounts = as.data.frame(counts(dds, normalized = TRUE))
baseMeans = as.data.frame(sapply( levels(dds$paris_classification),
function(lvl) rowMeans( counts(dds, normalized = TRUE)[, dds$paris_classification == lvl, drop = FALSE] ) ))
normData = merge(annoData, merge(baseMeans, normCounts, by.x = 'row.names', by.y = 'row.names'), by.x = 'TranscriptID', by.y = 'Row.names')
normData = normData[order(normData$Chromosome, normData$Start, normData$End),]
deData = data.frame(res[,c(1,2,5,6)])
colnames(deData) = c("baseMean","log2fc","pvalue","padj")
deData = merge(annoData, deData, by.x = 'TranscriptID', by.y = 'row.names')
deData = deData[order(deData$Chromosome, deData$Start, deData$End),]
write.table(normData, file="DTE_DESeq2_Means_and_NormalisedCount.RSEM.txt",
sep = "\t", quote = F, row.names = F, col.names = T)
write.table(deData, file="DTE_DESeq2_DE_results.RSEM.txt", sep = "\t",
quote = F, row.names = F, col.names = T)> normData[normData$GeneSymbol == "ZIC2", 1:14]
TranscriptID GeneID GeneSymbol Chromosome Start End TranscriptName Class Strand Length normal 0-IIa ERR2675454 ERR2675455
25492 ENST00000376335.8 ENSG00000043355.12 ZIC2 chr13 99981784 99986765 ZIC2-201 protein_coding + 4981 1.658049 168.7328173 260.0938 0
93460 ENST00000490085.5 ENSG00000043355.12 ZIC2 chr13 99984289 99985492 ZIC2-205 processed_transcript + 1203 0.000000 0.0000000 0.0000 0
75945 ENST00000468291.1 ENSG00000043355.12 ZIC2 chr13 99984689 99985492 ZIC2-202 processed_transcript + 803 0.000000 0.7729482 0.0000 0
83115 ENST00000477213.1 ENSG00000043355.12 ZIC2 chr13 99984789 99985474 ZIC2-203 processed_transcript + 685 0.000000 0.8654115 0.0000 0
86616 ENST00000481565.1 ENSG00000043355.12 ZIC2 chr13 99985494 99985904 ZIC2-204 processed_transcript + 410 0.000000 0.0000000 0.0000 0
> deData[deData$GeneSymbol == "ZIC2",]
TranscriptID GeneID GeneSymbol Chromosome Start End TranscriptName Class Strand Length baseMean log2fc pvalue padj
25492 ENST00000376335.8 ENSG00000043355.12 ZIC2 chr13 99981784 99986765 ZIC2-201 protein_coding + 4981 85.1954333 6.7850301 2.059453e-42 5.288456e-39
93460 ENST00000490085.5 ENSG00000043355.12 ZIC2 chr13 99984289 99985492 ZIC2-205 processed_transcript + 1203 0.0000000 NA NA NA
75945 ENST00000468291.1 ENSG00000043355.12 ZIC2 chr13 99984689 99985492 ZIC2-202 processed_transcript + 803 0.3864741 0.6503018 8.289198e-01 NA
83115 ENST00000477213.1 ENSG00000043355.12 ZIC2 chr13 99984789 99985474 ZIC2-203 processed_transcript + 685 0.4327057 1.2410927 6.797049e-01 NA
86616 ENST00000481565.1 ENSG00000043355.12 ZIC2 chr13 99985494 99985904 ZIC2-204 processed_transcript + 410 0.0000000 NA NA NASession info
> sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS
Matrix products: default
BLAS/LAPACK: /home/USER/miniconda3/lib/libopenblasp-r0.3.9.so
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] EnhancedVolcano_1.4.0 ggrepel_0.8.2
[3] ggplot2_3.3.0 apeglm_1.8.0
[5] DESeq2_1.26.0 SummarizedExperiment_1.16.1
[7] DelayedArray_0.12.3 BiocParallel_1.20.1
[9] matrixStats_0.56.0 Biobase_2.46.0
[11] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[13] IRanges_2.20.2 S4Vectors_0.24.4
[15] BiocGenerics_0.32.0 data.table_1.12.8
loaded via a namespace (and not attached):
[1] bit64_0.9-7 splines_3.6.2 Formula_1.2-3
[4] assertthat_0.2.1 latticeExtra_0.6-29 blob_1.2.1
[7] GenomeInfoDbData_1.2.2 numDeriv_2016.8-1.1 pillar_1.4.4
[10] RSQLite_2.2.0 backports_1.1.6 lattice_0.20-41
[13] glue_1.4.0 bbmle_1.0.23.1 digest_0.6.25
[16] RColorBrewer_1.1-2 XVector_0.26.0 checkmate_2.0.0
[19] colorspace_1.4-1 plyr_1.8.6 htmltools_0.4.0
[22] Matrix_1.2-18 XML_3.99-0.3 pkgconfig_2.0.3
[25] emdbook_1.3.12 genefilter_1.68.0 zlibbioc_1.32.0
[28] mvtnorm_1.1-0 purrr_0.3.4 xtable_1.8-4
[31] scales_1.1.0 jpeg_0.1-8.1 htmlTable_1.13.3
[34] tibble_3.0.1 annotate_1.64.0 farver_2.0.3
[37] ellipsis_0.3.0 withr_2.2.0 nnet_7.3-14
[40] survival_3.1-12 magrittr_1.5 crayon_1.3.4
[43] memoise_1.1.0 MASS_7.3-51.6 foreign_0.8-76
[46] tools_3.6.2 lifecycle_0.2.0 stringr_1.4.0
[49] locfit_1.5-9.4 munsell_0.5.0 cluster_2.1.0
[52] AnnotationDbi_1.48.0 compiler_3.6.2 rlang_0.4.6
[55] grid_3.6.2 RCurl_1.98-1.2 rstudioapi_0.11
[58] htmlwidgets_1.5.1 labeling_0.3 bitops_1.0-6
[61] base64enc_0.1-3 gtable_0.3.0 DBI_1.1.0
[64] R6_2.4.1 gridExtra_2.3 bdsmatrix_1.3-4
[67] knitr_1.28 dplyr_0.8.5 bit_1.1-15.2
[70] Hmisc_4.4-0 stringi_1.4.6 Rcpp_1.0.4.6
[73] geneplotter_1.64.0 vctrs_0.2.4 rpart_4.1-15
[76] acepack_1.4.1 png_0.1-7 coda_0.19-3
[79] tidyselect_1.0.0 xfun_0.13 PreviousDTE analysis with Salmon/Kallisto inputNextDGE and DTE analysis of Salmon/Kallisto inputs using Sleuth
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