Differential Expression for multiple contrasts
Lorena Pantano
2019-07-08
set up
Let’s define some parameters so it can be adapted to different analysis.
R> params=list(
+ se_file= "data/gse.rds",
+ design= "~ group",
+ contrast= "group.E_vs_D",
+ metadata= c("group"), # first one used in some plots
+ alpha= 0.05,
+ lfc= 0,
+ slot= "vst",
+ output_dir= "docs/results",
+ cache_dir= "cache",
+ cache_on= FALSE)Parent folder
Check params
R> library(SummarizedExperiment)
R> library(stringr)
R>
R> # add check function
R> if(!file.exists(params$se_file))
+ stop("se_file isn't found: ", params$se_file)
R>
R> se = readRDS(params$se_file)
R> stopifnot(class(se) == "SummarizedExperiment" | class(se) == "RangedSummarizedExperiment")
R>
R> stopifnot(sum(params$metadata %in% colnames(colData(se))) == length(params$metadata))
R>
R> comparisons = str_split(params$contrast, ",")[[1]]
R> contrasts = lapply(comparisons, function(c){
+ m = str_match(c, "(.+)\\.(.+)_vs_(.+)") %>%
+ as.vector()
+
+ if( ! m[2] %in% colnames(colData(se)))
+ stop(c, "in column ", m[2]," not in colnames: ",
+ paste(colnames(colData(se)), collapse = " ") )
+ values = unique(colData(se)[[m[2]]])
+ if(! m[3] %in% colData(se)[[m[2]]])
+ stop("value ", m[3], " not in column values: ",
+ paste(values))
+ if(! m[4] %in% colData(se)[[m[2]]])
+ stop("value ", m[4], " not in column values: ",
+ paste(values))
+ c(m[2], m[3], m[4])
+ })Load data
R> # Set seed for reproducibility
R> set.seed(1454944673)
R> library(knitr)
R> library(ggplot2)
R>
R> opts_chunk[["set"]](
+ autodep = TRUE,
+ bootstrap.show.code = FALSE,
+ cache = params$cache_on,
+ cache.lazy = params$cache_on,
+ cache.path = params$cache_dir,
+ dev = c("png", "pdf"),
+ error = FALSE,
+ fig.height = 7,
+ fig.retina = 2,
+ fig.width = 7,
+ highlight = TRUE,
+ message = FALSE,
+ prompt = TRUE,
+ # formatR required for tidy code
+ tidy = TRUE,
+ warning = FALSE)
R>
R> theme_set(
+ theme_light(base_size = 14))
R> theme_update(
+ legend.justification = "center",
+ legend.position = "bottom")R> library(DESeq2)
R> library(DEGreport)
R> library(pheatmap)
R> library(tidyverse)
R> library(readr)
R>
R> lfc = params$lfc
R> alpha = params$alpha
R> # Directory paths
R> output_dir <- params$output_dir
R> dataDir <- file.path(params$output_dir, "data")
R> dir.create(dataDir, showWarnings = FALSE, recursive = TRUE)
R> countsDir <- file.path(output_dir, "results", "counts")
R> dir.create(countsDir, showWarnings = FALSE, recursive = TRUE)
R> deDir <- file.path(output_dir, "results", "differential_expression")
R> dir.create(deDir, showWarnings = FALSE, recursive = TRUE)R> # help('design', 'DESeq2')
R> dds <- DESeqDataSetFromMatrix(countData = round(assay(se)), colData = colData(se),
+ design = formula(params$design)) %>% DESeq()
R> rld <- varianceStabilizingTransformation(dds)
R> save(dds, rld, file = file.path(dataDir, "dds.rda"))R> as.data.frame(assay(se)) %>% rownames_to_column("gene_id") %>% left_join(as.data.frame(rowData(se)),
+ by = "gene_id") %>% write_csv(file.path(countsDir, "raw.csv.gz"))
R>
R> as.data.frame(assay(rld)) %>% rownames_to_column("gene_id") %>% left_join(as.data.frame(rowData(se)),
+ by = "gene_id") %>% write_csv(file.path(countsDir, "log2_normalized.csv.gz"))Quality metrics
Full quality metrics can be found at [multiqc_report.html].
Number of genes
We calculate the number of genes with counts > 0 in each sample.
R> data.frame(samples = colnames(se), num_genes = colSums(assay(se) > 0)) %>% ggplot(aes(samples,
+ num_genes)) + geom_bar(stat = "identity") + coord_flip()
PCA
Principal component analysis is a technique to reduce the dimensionality of the data to allow visualization in two dimensions PCA. It takes all the gene abundances for the samples and creates a series of principal components (PCs) to explain the variability in the data. We normally plot the first two PCs for simplicity.
R> assays(se)[["vst"]] = assay(rld)
R>
R> degPCA(assays(se)[[params$slot]], colData(se), condition = params$metadata[1])
Results
We calculate the different contrasts (comparisons) in a single step with the degComps function, and all results are stored in a single variable.
R> # ?degComps: Read how to get the different contrasts for coefficients from
R> # one column: degComps(dds, combs = column_name) for all possible paris:
R> # degComps(dds, combs = column_name, pairs = TRUE) for specific contrasts:
R> # degComps(dds, contrasts = list(c(column_name, group1, group2),
R> # c(column_name, group1, group3)))
R>
R> comparisons = degComps(dds, contrast = contrasts, type = "ashr")
R>
R> save(comparisons, file = file.path(dataDir, "comparisons.rda"))R> lapply(file.path(dataDir, c("dds.rda", "comparisons.rda")), load, environment()) %>%
+ invisible()We performed the analysis using a BH adjusted P value cutoff of 0.05 and a log fold-change (LFC) ratio cutoff of 0.
Alpha level (FDR) cutoffs
Let’s take a look at the number of genes we get with different false discovery rate (FDR) cutoffs. These tests subset P values that have been multiple test corrected using the Benjamini Hochberg (BH) method [@Benjamini:1995ws].
R> lapply(names(comparisons), function(x) {
+ cat("### ", x, "\n")
+ degSummary(comparisons[[x]], kable = TRUE) %>% show
+ cat("\n\n")
+ }) %>% invisible()group_E_vs_D
| 0.1 | 0.05 | 0.01 | |
|---|---|---|---|
| LFC > 0 (up) | LFC > 0 (up) : 533, 3% | LFC > 0 (up) : 402, 2.3% | LFC > 0 (up) : 206, 1.2% |
| LFC < 0 (down) | LFC < 0 (down) : 1016, 5.7% | LFC < 0 (down) : 883, 5% | LFC < 0 (down) : 709, 4% |
| outliers | outliers [1] : 0, 0% | outliers [1] : 0, 0% | outliers [1] : 0, 0% |
| low counts | low counts [2] : 9521, 54% | low counts [2] : 9521, 54% | low counts [2] : 9521, 54% |
| cutoff | (mean count < 7) | (mean count < 7) | (mean count < 7) |
| NULL |
Plots
Mean average (MA)
An MA plot compares transformed counts on M (log ratio) and A (mean average) scales [@Yang:2002ty]. Blue arrows represent a correction of the log2 Fold Change values due to variability. Normally this happens when the gene shows high variation and the log2FC is not accurate, here the model tries to estimate them. See this paper for more information:
Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15:550. https://doi.org/10.1186/s13059-014-0550-8
R> lapply(names(comparisons), function(x) {
+ cat("### ", x, "\n")
+ print(degMA(comparisons[[x]], limit = 5))
+ cat("\n\n")
+ }) %>% invisible()group_E_vs_D
Volcano
A volcano plot compares significance (BH-adjusted P value) against fold change (log2) [@Cui:2003kh; @Li:2014fv]. Genes in the green box with text labels have an adjusted P value are likely to be the top candidate genes of interest.
R> lapply(names(comparisons), function(x) {
+ cat("### ", x, "\n")
+ print(degVolcano(comparisons[[x]]))
+ cat("\n\n")
+ }) %>% invisible()group_E_vs_D
Heatmap
This plot shows only differentially expressed genes on a per-sample basis. We have scaled the data by row and used the ward.D2 method for clustering [@WardJr:1963eu].
R> # lapply(names(comparisons), function(x){
R> for (x in names(comparisons)) {
+ cat("### ", x, "\n")
+ s = significants(comparisons[[x]])
+ if (length(s) < 2) {
+ cat("Less than two genes, no possible to plot")
+ } else {
+ p = pheatmap(assays(se)[[params$slot]][s, ], scale = "row", annotation_col = as.data.frame(colData(se)[,
+ params$metadata, drop = F]), show_rownames = FALSE, show_colnames = FALSE,
+ clustering_method = "ward.D2", clustering_distance_cols = "correlation")
+ print(p)
+ }
+ cat("\n\n")
+ }group_E_vs_D
PCA
Principal component analysis is a technique to reduce the dimensionality of the data to allow visualization in two dimensions PCA. It takes all the gene abundances for the samples and creates a series of principal components (PCs) to explain the variability in the data. We normally plot the first two PCs for simplicity.
R> lapply(names(comparisons), function(x) {
+ cat("### ", x, "\n")
+ s = significants(comparisons[[x]])
+ if (length(s) < 2) {
+ cat("Less than two genes, no possible to plot")
+ } else {
+ degPCA(assays(se)[[params$slot]][s, ], colData(se), condition = params$metadata[1]) %>%
+ print
+
+ }
+ cat("\n\n")
+ }) %>% invisible()group_E_vs_D
Gene Expression Patterns
In general, it is useful to cluster the significant genes together in similar patterns across samples. degPatterns uses standard expression correlation technique to generate a similarity matrix that can be clustered hierarchically and then split into groups of genes that follow similar expression patterns degPatterns.
We defined significance as genes with abs(log2FC) > and FDR < .
R> # choose the significants genes. In this case the first is used. `sig`
R> # should be a character vector with genes.
R> sig = significants(comparisons, fc = params$lfc, padj = params$alpha)
R> if (length(sig) > 20) {
+ colData(se)[[params$metadata[1]]] = as.factor(colData(se)[[params$metadata[1]]])
+ pattern = degPatterns(assays(se)[[params$slot]][sig, ], colData(se), time = params$metadata[1],
+ minc = length(sig) * 0.01)
+ } else {
+ cat("Not enough genes to make a pattern analysis.")
+ }
Top genes
R> resTbl = lapply(names(comparisons), function(x) {
+ cat("### ", x, "\n")
+ sign = significants(comparisons[[x]])
+ if (length(sign) == 0) {
+ cat("\n\n**Not significant genes**\n\n")
+ cat("\n\n")
+ return(NULL)
+ }
+ n = min(15, length(sign))
+ p = degPlot(se, genes = sign[1:n], xs = params$metadata[1], slot = params$slot,
+ log2 = FALSE, ann = c("gene_id", "gene_name")) + geom_boxplot(alpha = 0.3) +
+ theme(axis.text.x = element_text(angle = 45, hjust = 0.5, vjust = 0.5))
+ print(p)
+ cat("\n\n")
+ res = deg(comparisons[[x]], tidy = "tibble") %>% left_join(rowData(se) %>%
+ as.data.frame, by = c(gene = "gene_id"))
+ if (exists("pattern"))
+ res = left_join(res, pattern[["df"]], by = c(gene = "genes"))
+ dir.create(file.path(deDir, x), showWarnings = FALSE, recursive = TRUE)
+ write_csv(res, file.path(deDir, x, paste0(x, ".csv.gz")))
+ res
+ })
Top tables
Top 10 genes are shown order by False Discovery Rate. Genes below 0.05 are considered significant.
R> lapply(names(resTbl), function(x) {
+ cat("### ", x, "\n")
+ head(resTbl[[x]], 10) %>% kable %>% show
+ cat("\n\n")
+ }) %>% invisible()group_E_vs_D
| gene | baseMean | log2FoldChange | lfcSE | pvalue | padj | gene_name | cluster |
|---|---|---|---|---|---|---|---|
| ENSMUSG00000022868 | 15751.9 | -6.301 | 0.2635 | 0 | 0 | Ahsg | 2 |
| ENSMUSG00000031722 | 1666.4 | -6.989 | 0.3141 | 0 | 0 | Hp | 2 |
| ENSMUSG00000040564 | 4369.6 | -5.198 | 0.2564 | 0 | 0 | Apoc1 | 2 |
| ENSMUSG00000039323 | 1968.6 | -7.259 | 0.3719 | 0 | 0 | Igfbp2 | 2 |
| ENSMUSG00000022875 | 1578.0 | -5.269 | 0.2737 | 0 | 0 | Kng1 | 2 |
| ENSMUSG00000000049 | 1523.7 | -5.433 | 0.2891 | 0 | 0 | Apoh | 2 |
| ENSMUSG00000054422 | 2443.4 | -5.540 | 0.2970 | 0 | 0 | Fabp1 | 2 |
| ENSMUSG00000002985 | 15557.3 | -4.470 | 0.2400 | 0 | 0 | Apoe | 2 |
| ENSMUSG00000030359 | 843.2 | -5.960 | 0.3407 | 0 | 0 | Pzp | 2 |
| ENSMUSG00000030895 | 1734.3 | -5.185 | 0.2986 | 0 | 0 | Hpx | 2 |
File downloads
The results are saved as gzip-compressed comma separated values (CSV). Gzip compression is natively supported on macOS and Linux-based operating systems. If you’re running Windows, we recommend installing 7-Zip. CSV files can be opened in Excel or RStudio.
Count matrices
Tables are under docs/results/results/counts folder:
normalizedCounts.csv.gz: Use to evaluate individual genes and/or generate plots. These counts are normalized for the variation in sequencing depth across samples.tpm.csv.gz: Transcripts per million, scaled by length and also suitable for plotting.rawCounts.csv.gz: Only use to perform a new differential expression analysis. These counts will vary across samples due to differences in sequencing depth, and have not been normalized. Do not use this file for plotting genes.
Differential expression tables
Tables are under docs/results/results/differential_expression folder:
DEG tables are sorted by BH-adjusted P value, and contain the following columns:
gene: Ensembl gene identifier.baseMean: Mean of the normalized counts per gene for all samples.log2FoldChange: log2 fold change.lfcSE: log2 standard error.stat: Wald statistic.pvalue: Walt test P value.padj: BH adjusted Wald test P value (corrected for multiple comparisons; aka FDR).gene_name: Ensembl name (a.k.a. symbol).cluster: Group defined in the Gene Expression Patterns section.
Methods
RNA-seq counts were generated by nf-core and nextflow using the rnaseq pipeline that integrates salmon for quantification. Counts were imported into R using tximport and DESeq2. Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 . Heatmaps were generated by pheatmap. DE analysis plots were done by DEGreport.
R session information
## ─ Session info ──────────────────────────────────────────────────────────
## setting value
## version R version 3.6.0 (2019-04-26)
## os macOS Mojave 10.14.3
## system x86_64, darwin15.6.0
## ui RStudio
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2019-07-08
##
## ─ Packages ──────────────────────────────────────────────────────────────
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## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
## shape 1.4.4 2018-02-07 [1] CRAN (R 3.6.0)
## SQUAREM 2017.10-1 2017-10-07 [1] CRAN (R 3.6.0)
## stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
## stringr * 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
## SummarizedExperiment * 1.14.0 2019-05-02 [1] Bioconductor
## survival 2.44-1.1 2019-04-01 [1] CRAN (R 3.6.0)
## testthat 2.1.1 2019-04-23 [1] CRAN (R 3.6.0)
## tibble * 2.1.2 2019-05-29 [1] CRAN (R 3.6.0)
## tidyr * 0.8.3 2019-03-01 [1] CRAN (R 3.6.0)
## tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.6.0)
## tidyverse * 1.2.1 2017-11-14 [1] CRAN (R 3.6.0)
## truncnorm 1.0-8 2018-02-27 [1] CRAN (R 3.6.0)
## tximport * 1.12.0 2019-05-02 [1] Bioconductor
## usethis 1.5.0 2019-04-07 [1] CRAN (R 3.6.0)
## withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
## xfun 0.7 2019-05-14 [1] CRAN (R 3.6.0)
## XML 3.98-1.19 2019-03-06 [1] CRAN (R 3.6.0)
## xml2 1.2.0 2018-01-24 [1] CRAN (R 3.6.0)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 3.6.0)
## XVector 0.24.0 2019-05-02 [1] Bioconductor
## yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.0)
## zlibbioc 1.30.0 2019-05-02 [1] Bioconductor
##
## [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library## $se_file
## [1] "data/gse.rds"
##
## $design
## [1] "~ group"
##
## $contrast
## [1] "group.E_vs_D"
##
## $metadata
## [1] "group"
##
## $alpha
## [1] 0.05
##
## $lfc
## [1] 0
##
## $slot
## [1] "vst"
##
## $output_dir
## [1] "docs/results"
##
## $cache_dir
## [1] "cache"
##
## $cache_on
## [1] FALSEThese materials have been developed by members of the teaching team at the PILM - MIT Bioinformatics Core (PILMBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.