DifferentialExpression
Steve Pederson
21 January, 2020
Last updated: 2020-01-21
Checks: 6 1
Knit directory: 20170327_Psen2S4Ter_RNASeq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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(tidygraph)
library(ggraph)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.
create_ring(3) %>%
mutate(
name = factor(levels(samples$genotype), levels = levels(samples$genotype))
) %>%
activate(edges) %>%
mutate(comparison = c("Het Vs WT", "Hom Vs Het", "Hom Vs WT")) %>%
ggraph(layout = "kk") +
geom_edge_link2(
aes(label = comparison),
angle_calc = "along",
label_dodge = unit(0.02, "npc"),
start_cap = circle(0.04, "npc"),
end_cap = circle(0.04, "npc"),
label_size = 5,
arrow = arrow(
length = unit(0.06, "npc"),
ends = "both",
type = "closed"
)
) +
geom_node_label(
aes(label = name, colour = name),
size = 5,
fill = rgb(1,1,1,0.7)
) +
theme_void() +
theme(
legend.position = "none"
) 
| Version | Author | Date |
|---|---|---|
| 01512da | Steve Ped | 2020-01-21 |
d <- model.matrix(~ 0 + genotype, data = dgeList$samples) %>%
set_colnames(str_remove_all(colnames(.), "genotype"))
cont <- makeContrasts(
HetVsWT = Het - WT,
HomVsWT = Hom - WT,
HomVsHet = Hom - Het,
levels = d
)Normalisation
gcCqn <- cqn(
counts = dgeList$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)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.
Model Fitting
minLfc <- log2(1.5)
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 < 0.05 & 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.
For initial enrichment testing, genes were considered to be DE using an estimated logFC outside of the range \(\pm \log_2(1.5)\) and an FDR-adjusted p-value < 0.05. Using these criteria, the following initial DE genesets were defined:
pander(topTables %>% lapply(filter, DE) %>% vapply(nrow, integer(1)))| HetVsWT | HomVsWT | HomVsHet |
|---|---|---|
| 2,274 | 1,577 | 7 |
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) > 2.8)
) +
geom_text_repel(
aes(label = gene_name, colour = DE),
data = . %>% dplyr::filter(FDR < 0.05 & comparison == "HomVsHet")
) +
geom_smooth(se = FALSE) +
facet_wrap(~comparison, ncol = 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.
| Version | Author | Date |
|---|---|---|
| 01512da | Steve Ped | 2020-01-21 |
topTables %>%
bind_rows() %>%
mutate(stat = -sign(logFC)*log10(PValue)) %>%
ggplot(aes(gc_content, stat)) +
geom_point(aes(colour = DE)) +
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.
topTables %>%
bind_rows() %>%
mutate(stat = -sign(logFC)*log10(PValue)) %>%
ggplot(aes(length, stat)) +
geom_point(aes(colour = DE)) +
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.
Checks for both GC and length bias on differnetial expression showed that a small bias remained evident, despite using conditional-quantile normalisation.
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")
) +
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.
| Version | Author | Date |
|---|---|---|
| 01512da | Steve Ped | 2020-01-21 |
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
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tz Australia/Adelaide
date 2020-01-21
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