DifferentialExpression
Steve Pederson
22 January, 2020
Last updated: 2020-01-22
Checks: 6 1
Knit directory: 20170327_Psen2S4Ter_RNASeq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 10a285b | Steve Ped | 2020-01-22 | Expanded description in index and tried reusing an image |
| html | 10a285b | Steve Ped | 2020-01-22 | Expanded description in index and tried reusing an image |
| html | aabb570 | Steve Ped | 2020-01-21 | Rebuilt to get rid of warnings |
| Rmd | 71b8832 | Steve Ped | 2020-01-21 | Added DE QC for GC bias |
| html | 71b8832 | Steve Ped | 2020-01-21 | Added DE QC for GC bias |
| Rmd | e825637 | Steve Ped | 2020-01-21 | Minor updates to DE plots |
| html | 01512da | Steve Ped | 2020-01-21 | Added initial DE analysis to index |
| Rmd | fbb6242 | Steve Ped | 2020-01-21 | Paused DE analysis |
| Rmd | c560637 | Steve Ped | 2020-01-20 | Started DE analysis |
Setup
library(ngsReports)
library(tidyverse)
library(magrittr)
library(edgeR)
library(AnnotationHub)
library(ensembldb)
library(scales)
library(pander)
library(cowplot)
library(cqn)
library(ggrepel)
library(UpSetR)if (interactive()) setwd(here::here())
theme_set(theme_bw())
panderOptions("big.mark", ",")
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
twoCols <- c(rgb(0.8, 0.1, 0.1), rgb(0.2, 0.2, 0.8))Annotations
ah <- AnnotationHub() %>%
subset(species == "Danio rerio") %>%
subset(rdataclass == "EnsDb")
ensDb <- ah[["AH74989"]]grTrans <- transcripts(ensDb)
trLengths <- exonsBy(ensDb, "tx") %>%
width() %>%
vapply(sum, integer(1))
mcols(grTrans)$length <- trLengths[names(grTrans)]gcGene <- grTrans %>%
mcols() %>%
as.data.frame() %>%
dplyr::select(gene_id, tx_id, gc_content, length) %>%
as_tibble() %>%
group_by(gene_id) %>%
summarise(
gc_content = sum(gc_content*length) / sum(length),
length = ceiling(median(length))
)
grGenes <- genes(ensDb)
mcols(grGenes) %<>%
as.data.frame() %>%
left_join(gcGene) %>%
as.data.frame() %>%
DataFrame()Similarly to the Quality Assessment steps, GRanges objects were formed at the gene and transcript levels, to enable estimation of GC content and length for each transcript and gene. GC content and transcript length are available for each transcript, and for gene-level estimates, GC content was taken as the sum of all GC bases divided by the sum of all transcript lengths, effectively averaging across all transcripts. Gene length was defined as the median transcript length.
samples <- read_csv("data/samples.csv") %>%
distinct(sampleName, .keep_all = TRUE) %>%
dplyr::select(sample = sampleName, sampleID, genotype) %>%
mutate(genotype = factor(genotype, levels = c("WT", "Het", "Hom")))Sample metadata was also loaded, with only the sampleID and genotype being retained. All other fields were considered irrelevant.
Count Data
minCPM <- 1.5
minSamples <- 4
dgeList <- file.path("data", "2_alignedData", "featureCounts", "genes.out") %>%
read_delim(delim = "\t") %>%
set_names(basename(names(.))) %>%
as.data.frame() %>%
column_to_rownames("Geneid") %>%
as.matrix() %>%
set_colnames(str_remove(colnames(.), "Aligned.sortedByCoord.out.bam")) %>%
.[rowSums(cpm(.) >= minCPM) >= minCPM,] %>%
DGEList(
samples = tibble(sample = colnames(.)) %>%
left_join(samples),
genes = grGenes[rownames(.)] %>%
as.data.frame() %>%
dplyr::select(
chromosome = seqnames, start, end,
gene_id, gene_name, gene_biotype, description,
entrezid, gc_content, length
)
) %>%
.[!grepl("rRNA", .$genes$gene_biotype),] %>%
calcNormFactors()Gene-level count data as output by featureCounts, was loaded and formed into a DGEList object. During this process, genes were removed if:
- They were not considered as detectable (CPM < 1.5 in > 8 samples). This translates to > 18 reads assigned a gene in all samples from one or more of the genotype groups
- The
gene_biotypewas any type ofrRNA.
These filtering steps returned gene-level counts for 16,640 genes, with total library sizes between 11,852,141 and 16,997,219 reads assigned to genes. It was noted that these library sizes were about 1.5-fold larger than the transcript-level counts used for the QA steps.
cpm(dgeList, log = TRUE) %>%
as.data.frame() %>%
pivot_longer(
cols = everything(),
names_to = "sample",
values_to = "logCPM"
) %>%
split(f = .$sample) %>%
lapply(function(x){
d <- density(x$logCPM)
tibble(
sample = unique(x$sample),
x = d$x,
y = d$y
)
}) %>%
bind_rows() %>%
left_join(samples) %>%
ggplot(aes(x, y, colour = genotype, group = sample)) +
geom_line() +
labs(
x = "logCPM",
y = "Density",
colour = "Genotype"
)
Expression density plots for all samples after filtering, showing logCPM values.
| Version | Author | Date |
|---|---|---|
| 01512da | Steve Ped | 2020-01-21 |
Additional Functions
contLabeller <- as_labeller(
c(
HetVsWT = "S4Ter/+ Vs +/+",
HomVsWT = "S4Ter/S4Ter Vs +/+",
HomVsHet = "S4Ter/S4Ter Vs S4Ter/+",
Hom = "S4Ter/S4Ter",
Het = "S4Ter/+",
WT = "+/+"
)
)
geneLabeller <- structure(grGenes$gene_name, names = grGenes$gene_id) %>%
as_labeller()Labeller functions for genotypes, contrasts and gene names were additionally defined for simpler plotting using ggplot2.
Analysis
PCA
pca <- dgeList %>%
cpm(log = TRUE) %>%
t() %>%
prcomp()
pcaVars <- percent_format(0.1)(summary(pca)$importance["Proportion of Variance",])pca$x %>%
as.data.frame() %>%
rownames_to_column("sample") %>%
left_join(samples) %>%
as_tibble() %>%
ggplot(aes(PC1, PC2, colour = genotype, fill = genotype)) +
geom_point() +
geom_text_repel(aes(label = sampleID), show.legend = FALSE) +
stat_ellipse(geom = "polygon", alpha = 0.05, show.legend = FALSE) +
guides(fill = FALSE) +
labs(
x = paste0("PC1 (", pcaVars[["PC1"]], ")"),
y = paste0("PC2 (", pcaVars[["PC2"]], ")"),
colour = "Genotype"
)
PCA of gene-level counts.
| Version | Author | Date |
|---|---|---|
| 01512da | Steve Ped | 2020-01-21 |
A Principal Component Analysis (PCA) was also performed using logCPM values from each sample. Both mutant genotypes appear to cluster together, however it has previously been noted that GC content appears to track closely with PC1, as a result of varable rRNA removal.
Model Description
Model and contrast matrices were defined setting each genotype separately, and comparing between each pair of genotypes.
| Version | Author | Date |
|---|---|---|
| 10a285b | Steve Ped | 2020-01-22 |
d <- model.matrix(~ 0 + genotype, data = dgeList$samples) %>%
set_colnames(str_remove_all(colnames(.), "genotype"))
cont <- makeContrasts(
HetVsWT = Het - WT,
HomVsWT = Hom - WT,
HomVsHet = Hom - Het,
levels = d
)Normalisation
As GC content and length was noted as being of concern for this dataset, conditional-quantile normalisation was performed using the cqn package. This adds a gene and sample-level offset for each count which takes into account any systemic bias, such as that identified previously as an artefact of variable rRNA removal. The resultant glm.offset values were added to the original DGEList object.
gcCqn <- cqn(
counts = dgeList$counts,
x = dgeList$genes$gc_content,
lengths = dgeList$genes$length,
sizeFactors = dgeList$samples$lib.size
)par(mfrow = c(1, 2))
cols <- as.integer(dgeList$samples$genotype)
cqnplot(gcCqn, n = 1, xlab = "GC Content", col = cols)
cqnplot(gcCqn, n = 2, xlab = "Length", col = cols)
legend("bottomright", legend = levels(samples$genotype), col = seq_along(levels(samples$genotype)), lty = 1)
Model fits for GC content and gene length under the CQN model. Genotype-specific effects are clearly visible.
| Version | Author | Date |
|---|---|---|
| 01512da | Steve Ped | 2020-01-21 |
par(mfrow = c(1, 1))dgeList$offset <- gcCqn$glm.offset
dgeList %<>% estimateDisp(design = d)Model Fitting
minLfc <- log2(1.5)
alpha <- 0.01
fit <- glmFit(dgeList)
topTables <- colnames(cont) %>%
sapply(function(x){
glmLRT(fit, contrast = cont[,x]) %>%
topTags(n = Inf) %>%
.[["table"]] %>%
as_tibble() %>%
dplyr::select(
gene_id, gene_name, logFC, logCPM, PValue, FDR, everything()
) %>%
mutate(
comparison = x,
DE = FDR < alpha & abs(logFC) > minLfc
)
},
simplify = FALSE) Models were fit using the negative-binomial approaches of glmFit(). Top Tables were then obtained using pairwise likelihood-ratio tests in glmLRT(). These test the standard \(H_0\) that there is no difference in gene expression estimates between genotypes, the gene expression estimates are obtained under the negative binomial model.
alpha2 <- 0.05
topTables %<>%
bind_rows() %>%
split(f = .$gene_id) %>%
lapply(function(x){mutate(x, DE = any(DE) & FDR < alpha2)}) %>%
bind_rows() %>%
split(f = .$comparison)For enrichment testing, genes were initially considered to be DE using an estimated logFC outside of the range \(\pm \log_2(1.5)\) and an FDR-adjusted p-value < 0.01. For genes in any of these initial lists, the logFC filter was subsequently removed from subsequent comparisons in order to minimise issues introduced by the use of a hard cutoff. Similarly the FDR threshold was raised to 0.05 in secondary comparisons for genes which passed the initial round of selection.
Using these criteria, the following initial DE genesets were defined:
topTables %>%
lapply(dplyr::filter, DE) %>%
vapply(nrow, integer(1)) %>%
pander()Model Checking
topTables %>%
bind_rows() %>%
ggplot(aes(logCPM, logFC)) +
geom_point(aes(colour = DE), alpha = 0.4) +
geom_text_repel(
aes(label = gene_name, colour = DE),
data = . %>% dplyr::filter(DE & abs(logFC) > 3)
) +
geom_text_repel(
aes(label = gene_name, colour = DE),
data = . %>% dplyr::filter(FDR < 0.05 & comparison == "HomVsHet")
) +
geom_smooth(se = FALSE) +
geom_hline(
yintercept = c(-1, 1)*minLfc,
linetype = 2,
colour = "red"
) +
facet_wrap(~comparison, nrow = 1, labeller = contLabeller) +
scale_y_continuous(breaks = seq(-8, 8, by = 2)) +
scale_colour_manual(values = c("grey50", "red")) +
theme(legend.position = "none")
MA plots checking for any logFC bias across the range of expression values. The small curve in the average at the low end of expression values was considered to be an artefact of the sparse points at this end. Initial DE genes are shown in red, with select points labelled.
topTables %>%
bind_rows() %>%
mutate(stat = -sign(logFC)*log10(PValue)) %>%
ggplot(aes(gc_content, stat)) +
geom_point(aes(colour = DE), alpha = 0.4) +
geom_smooth(se = FALSE) +
facet_wrap(~comparison, labeller = contLabeller) +
labs(
x = "GC content (%)",
y = "Ranking Statistic"
) +
coord_cartesian(ylim = c(-10, 10)) +
scale_colour_manual(values = c("grey50", "red")) +
theme(legend.position = "none")
Checks for GC bias in differential expression. GC content is shown against the ranking statistic, using -log10(p) multiplied by the sign of log fold-change. A small amount of bias was noted particularly in the comparison between homozygous mutants and wild-type.
| Version | Author | Date |
|---|---|---|
| 71b8832 | Steve Ped | 2020-01-21 |
topTables %>%
bind_rows() %>%
mutate(stat = -sign(logFC)*log10(PValue)) %>%
ggplot(aes(length, stat)) +
geom_point(aes(colour = DE), alpha = 0.4) +
geom_smooth(se = FALSE) +
facet_wrap(~comparison, labeller = contLabeller) +
labs(
x = "Gene Length (bp)",
y = "Ranking Statistic"
) +
coord_cartesian(ylim = c(-10, 10)) +
scale_x_log10(labels = comma) +
scale_colour_manual(values = c("grey50", "red")) +
theme(legend.position = "none")
Checks for length bias in differential expression. Gene length is shown against the ranking statistic, using -log10(p) multiplied by the sign of log fold-change. Again, a small amount of bias was noted particularly in the comparison between homozygous mutants and wild-type.
| Version | Author | Date |
|---|---|---|
| 71b8832 | Steve Ped | 2020-01-21 |
Checks for both GC and length bias on differential expression showed that a small bias remained evident, despite using conditional-quantile normalisation. However, manual checking against the same dataset excluding the CQN steps revealed vastly different and exaggerated bias (not shown). As such, the impact of CQN normalisation was considered to be appropriate.
Results
topTables %>%
bind_rows() %>%
ggplot(aes(logFC, -log10(PValue), colour = DE)) +
geom_point(alpha = 0.4) +
geom_text_repel(
aes(label = gene_name),
data = . %>% dplyr::filter(PValue < 0.2e-12 | logFC > 4)
) +
geom_text_repel(
aes(label = gene_name),
data = . %>% dplyr::filter(FDR < 0.05 & comparison == "HomVsHet")
) +
geom_vline(
xintercept = c(-1, 1)*minLfc,
linetype = 2,
colour = "red"
) +
facet_wrap(~comparison, nrow = 1, labeller = contLabeller) +
scale_colour_manual(values = c("grey50", "red")) +
scale_x_continuous(breaks = seq(-8, 8, by = 2)) +
theme(legend.position = "none") 
Volcano Plots showing DE genes against logFC.
deGenes <- topTables %>%
lapply(dplyr::filter, DE) %>%
lapply(magrittr::extract2, "gene_id") upSet <- deGenes %>%
fromList() %>%
upset(order.by = "freq")
upSet
UpSet plot showing the common genes from each comparison. A strong overlap was seen between both Mutant Vs WT comparisons.
grps <- deGenes %>%
with(
list(
common = intersect(HetVsWT, HomVsWT),
onlyHet = intersect(HetVsWT, HomVsHet),
onlyHom = intersect(HomVsWT, HomVsHet)
)
)Common Genes
A set of 1,484 genes was identified as commonly DE for both Mutant Vs WT comparisons. Given that no difference was detected between mutants for this group, this was considered to be the common signature.
topTables %>%
bind_rows() %>%
dplyr::filter(grepl("WT", comparison)) %>%
dplyr::select(starts_with("gene"), logFC, logCPM, comparison) %>%
pivot_wider(names_from = comparison, values_from = logFC) %>%
mutate(group = case_when(
gene_id %in% grps$common ~ "Common",
gene_id %in% grps$onlyHet ~ "Het Only",
gene_id %in% grps$onlyHom ~ "Hom Only",
!gene_id %in% unlist(grps) ~ "No Group"
)) %>%
dplyr::filter(logCPM > 0) %>%
arrange(desc(group)) %>%
ggplot(aes(HetVsWT, HomVsWT)) +
geom_point(aes(colour = group, alpha = logCPM)) +
geom_text_repel(
aes(label = gene_name, colour = group, alpha = logCPM),
data = . %>%
dplyr::filter(
group == "Common",
HomVsWT < -2.2 | HomVsWT > 2 | HetVsWT > 1.9
),
show.legend = FALSE
) +
geom_text_repel(
aes(label = gene_name, colour = group, alpha = logCPM),
data = . %>%
dplyr::filter(
grepl("Only", group)
),
show.legend = FALSE
) +
geom_smooth(se = FALSE, method = "lm") +
geom_hline(
yintercept = c(-1, 1)*minLfc,
linetype = 2,
colour = "grey50"
) +
geom_vline(
xintercept = c(-1, 1)*minLfc,
linetype = 2,
colour = "grey50"
) +
scale_colour_manual(
values = c(
rgb(0.2, 0.8, 0.2), "blue", "red", rgb(0.5, 0.5, 0.5, 0.5)
)
) +
scale_x_continuous(breaks = seq(-8, 8, by = 2)) +
scale_alpha_continuous(trans = "sqrt")
Comparison of logFC estimates for both Mutant Vs WT comparisons.
dgeList %>%
cpm(log = TRUE) %>%
.[grps$common,] %>%
pheatmap::pheatmap(
scale = "none",
color = viridis_pal()(100),
labels_col = colnames(.) %>%
str_replace_all(".+(Het|Hom|WT).+F3(_[0-9]{2}).+", "\\1\\2"),
show_rownames = FALSE,
annotation_col = samples %>%
dplyr::select(sample, Genotype = genotype) %>%
as.data.frame() %>%
column_to_rownames("sample")
)
dgeList %>%
cpm(log = TRUE) %>%
extract(
topTables %>%
.[c("HetVsWT", "HomVsWT")] %>%
bind_rows() %>%
dplyr::filter(gene_id %in% grps$common) %>%
group_by(gene_id) %>%
mutate(minLfc = min(abs(logFC)), minFDR = min(FDR)) %>%
ungroup() %>%
dplyr::arrange(minFDR, desc(minLfc)) %>%
distinct(gene_id) %>%
dplyr::slice(1:20) %>%
unlist(),
) %>%
as.data.frame() %>%
rownames_to_column("gene_id") %>%
pivot_longer(
cols = starts_with("PS2Ex"),
names_to = "sample",
values_to = "CPM"
) %>%
left_join(samples) %>%
ggplot(aes(genotype, CPM, fill = genotype)) +
geom_boxplot() +
facet_wrap(~gene_id, scales = "free_y", labeller = geneLabeller)
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 3.6.2 (2019-12-12)
os Ubuntu 18.04.3 LTS
system x86_64, linux-gnu
ui X11
language en_AU:en
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Adelaide
date 2020-01-22
─ Packages ───────────────────────────────────────────────────────────────────
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