1_DE gene analysis
Yang & Steve
24/02/2020
Last updated: 2020-10-28
Checks: 6 1
Knit directory: 20190717_Lardelli_RNASeq_Larvae/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 075ff49 | yangdongau | 2020-03-12 | Add in description and gene_biotype |
| Rmd | 6c61705 | yangdongau | 2020-02-27 | Compiled DE Gene analysis |
| html | 6c61705 | yangdongau | 2020-02-27 | Compiled DE Gene analysis |
Setup
library(limma)
library(edgeR)
library(AnnotationHub)
library(tidyverse)
library(magrittr)
library(pander)
library(ggrepel)
library(scales)
library(RUVSeq)
theme_set(theme_bw())
panderOptions("big.mark", ",")
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
if (interactive()) setwd(here::here("analysis"))# Get GC content and lengths
gcTrans <- url("https://uofabioinformaticshub.github.io/Ensembl_GC/Release96/Danio_rerio.GRCz11.96.rds") %>%
readRDS()
gcGene <- readRDS(here::here("analysis", "gcGene.rds"))Annotation was set up as a EnsDb object based on Ensembl Release 96
ah <- AnnotationHub() %>%
subset(species == "Danio rerio") %>%
subset(dataprovider == "Ensembl") %>%
subset(rdataclass == "EnsDb")
ensDb <- ah[["AH69169"]]
genes <- genes(ensDb)
transGR <- transcripts(ensDb)
cols2keep <- c("gene_id", "gene_name", "gene_biotype", "entrezid","description","entrezid")
mcols(genes) <- mcols(genes)[cols2keep] %>%
as.data.frame() %>%
dplyr::select(-entrezid) %>%
left_join(as.data.frame(mcols(gcGene))) %>%
distinct(gene_id, .keep_all = TRUE) %>%
set_rownames(.$gene_id) %>%
DataFrame() %>%
.[names(genes),]
genesGR <- genes(ensDb)Prior to count-level analysis, the initial dataset was pre-processed using the following steps:
- Adapters were removed from any reads
- Bases were removed from the end of reads when the quality score dipped below 30
- Reads < 35bp after trimming were discarded
After trimming alignment was performed using STAR v2.5.3a to the Danio rerio genome included in Ensembl Release 96 (GRCz11). Aligned reads were counted for each gene if the following criteria were satisfied:
- Alignments were unique
- Alignments strictly overlapped exonic regions
minSamples <- 6
minCpm <- 1counts <- here::here("data", "2_alignedData", "featureCounts", "genes.out") %>%
read_tsv() %>%
set_colnames(basename(colnames(.))) %>%
set_colnames(str_remove(colnames(.), "Aligned.+")) %>%
gather(key = "Library", value = "Counts", -Geneid) %>%
dplyr::mutate(Sample = str_remove_all(Library, "_2")) %>%
group_by(Geneid, Sample) %>%
dplyr::summarise(Counts = sum(Counts)) %>%
tidyr::spread(key = "Sample", value = "Counts") %>%
as.data.frame() %>%
column_to_rownames("Geneid")
genes2keep <- cpm(counts) %>%
is_greater_than(minCpm) %>%
rowSums() %>%
is_weakly_greater_than(minSamples)Counts were then loaded and summed across replicate sequencing runs. Genes were only retained if receiving more than 1 cpm in at least 6 samples. This filtering step discarded 12,661 genes as undetectable and retained 19,396 genes for further analysis.
plotDensities(cpm(counts, log = TRUE), legend = FALSE, main = "a) All genes")
plotDensities(cpm(counts[genes2keep,], log = TRUE), legend = FALSE, main = "b) Retained genes")
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
Distribution of logCPM values for a) all and b) retained genes
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
dgeList <- counts %>%
.[genes2keep,] %>%
DGEList(
samples = tibble(
sample = colnames(.),
group = str_replace_all(sample, "[0-9]*[A-Z]([12])Lardelli", "\\1"),
pair = str_replace_all(sample, "[0-9]*([A-Z])[0-9].+", "\\1")
) %>%
as.data.frame(),
genes = genes[rownames(.)] %>%
as.data.frame() %>%
dplyr::select(
ensembl_gene_id = gene_id,
chromosome_name = seqnames,
description,
gene_biotype,
external_gene_name = gene_name,
entrez_gene = entrezid.1,
aveLength,
aveGc,
maxLength,
longestGc
)
) %>%
calcNormFactors(method = "TMM") %>%
estimateDisp()Design matrix not provided. Switch to the classic mode.dgeList$samples$Genotype <- c("Q96K97del/+", "WT")[dgeList$samples$group] %>%
factor(levels = c("WT", "Q96K97del/+"))Counts were then formed into a DGEList object. Library sizes after alignment, counting and gene filtering ranged between 44,498,373 and 52,779,608 reads.
Data Inspection
pca <- dgeList %>%
cpm(log = TRUE) %>%
t() %>%
prcomp()summary(pca)$importance %>% pander(split.tables = Inf) | PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | PC11 | PC12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Standard deviation | 21.59 | 17.49 | 13.54 | 12.32 | 11.16 | 10.77 | 10.38 | 10.13 | 9.387 | 7.599 | 7.202 | 4.868e-14 |
| Proportion of Variance | 0.2655 | 0.1742 | 0.1045 | 0.08647 | 0.07089 | 0.06608 | 0.06135 | 0.0585 | 0.05019 | 0.03289 | 0.02955 | 0 |
| Cumulative Proportion | 0.2655 | 0.4396 | 0.5441 | 0.6306 | 0.7015 | 0.7675 | 0.8289 | 0.8874 | 0.9376 | 0.9705 | 1 | 1 |
PCA of all samples.
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
DGE Analysis
Design Marix
Next we setup a design matrix using each pool as its own intercept, with the genotype as the common difference, and recalculated the dispersions
expDesign <- model.matrix(~0 + pair + Genotype, dgeList$samples) %>%
set_colnames(str_replace(colnames(.), pattern = "Genotype.+", "Mutant"))
dgeList %<>%
estimateGLMCommonDisp(expDesign) %>%
estimateGLMTagwiseDisp(expDesign)deGenes <- dgeList %>%
glmFit(expDesign) %>%
glmLRT(coef = "Mutant") %>%
topTags(n = Inf) %>%
as.data.frame() %>%
set_colnames(gsub("ID.", "", colnames(.))) %>%
as_tibble() %>%
mutate(
Sig = FDR < 0.05,
rankstat = -sign(logFC)*log10(PValue)
) %>%
dplyr::select(
ensembl_gene_id, external_gene_name, logFC, logCPM, LR,
PValue, FDR, Sig,aveGc, aveLength, rankstat
) %>%
arrange(PValue)
deGenesSig <- deGenes %>%
dplyr::filter(Sig)
Volcano plot highlighting DE genes. Genes indicated in red were considered as DE, which receive a FDR < 0.05.
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
logFC plotted against expression level with significant DE genes shown in red.
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
RUV treatment (Remove Unwanted Variation)
Find some ‘unchanged’ genes. Grab the lowest ranked 5000
genes4Control <- deGenes %>%
arrange(desc(PValue)) %>%
dplyr::slice(1:5000) %>%
.[["ensembl_gene_id"]]
length(genes4Control)[1] 5000k <- 1
ruv <- RUVg(
dgeList$counts,
cIdx = genes4Control,
k = k,
isLog = FALSE,
round = TRUE
)
dgeList$samples <- cbind(dgeList$samples, ruv$W)pcaRUV <- ruv$normalizedCounts %>%
cpm(log = TRUE) %>%
t() %>%
prcomp()
summary(pcaRUV)$importance %>% pander(split.tables = Inf) | PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | PC11 | PC12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Standard deviation | 17.5 | 13.6 | 12.57 | 11.23 | 10.84 | 10.43 | 10.21 | 9.495 | 7.642 | 7.232 | 0.3216 | 4.41e-14 |
| Proportion of Variance | 0.2344 | 0.1415 | 0.1208 | 0.09644 | 0.08994 | 0.08332 | 0.07973 | 0.069 | 0.0447 | 0.04003 | 8e-05 | 0 |
| Cumulative Proportion | 0.2344 | 0.3759 | 0.4968 | 0.5932 | 0.6831 | 0.7665 | 0.8462 | 0.9152 | 0.9599 | 0.9999 | 1 | 1 |
pcaRUV$x %>%
cbind(dgeList$samples[rownames(.),]) %>%
ggplot(aes(PC1, PC2, colour = Genotype)) +
geom_point() +
geom_text_repel(aes(label = pair), show.legend = FALSE) +
# stat_ellipse(aes(fill = Genotype), geom = "polygon", alpha = 0.05) +
xlab(
paste0(
"PC1 (", percent(summary(pcaRUV)$importance[2, "PC1"]), ")"
)
) +
ylab(
paste0(
"PC2 (", percent(summary(pcaRUV)$importance[2, "PC2"]), ")"
)
)
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
expDesignRUV <- model.matrix(~0 + pair + Genotype + W_1, dgeList$samples) %>%
set_colnames(str_replace(colnames(.), pattern = "Genotype.+", "Mutant"))Recalculate the dispersions
dgeList %<>%
estimateGLMCommonDisp(expDesignRUV) %>%
estimateGLMTagwiseDisp(expDesignRUV)Now run DE Analysis after RUV
topTable <- dgeList %>%
glmFit(expDesignRUV) %>%
glmLRT(coef = "Mutant") %>%
topTags(n = Inf) %>%
as.data.frame() %>%
set_colnames(gsub("ID.", "", colnames(.))) %>%
as_tibble() %>%
mutate(
DE = FDR < 0.01,
rankstat = -sign(logFC)*log10(PValue)
) %>%
dplyr::select(
ensembl_gene_id, external_gene_name, description, gene_biotype, logFC, logCPM, LR,
PValue, FDR, DE,aveGc, aveLength, rankstat
) %>%
arrange(PValue)
topTableDE <- topTable %>%
dplyr::filter(DE == TRUE)Check the MA plot
tested <- c("si:ch211-213a13.2", "si:ch211-11p18.6", "mdh1ab", "CABZ01034698.2")
# topTable %>%
topTable %>%
ggplot(aes(logCPM, logFC)) +
geom_point(aes(colour = DE), alpha = 0.5) +
geom_smooth(se = FALSE) +
geom_hline(yintercept = c(-1, 1)*0.5, linetype = 2) +
geom_text_repel(
aes(label = external_gene_name, colour = DE),
data = . %>%
dplyr::filter(abs(logFC) > 1.2 & DE | external_gene_name %in% tested),
show.legend = FALSE
) +
scale_colour_manual(values = c("grey50", "red"))
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
Check the volcano plot
topTable %>%
mutate(DE = FDR < 0.01) %>%
ggplot(aes(logFC, -log10(PValue))) +
geom_point(aes(colour = DE), alpha = 0.5) +
geom_vline(xintercept = c(-1, 1)*0.5, linetype = 2) +
geom_text_repel(
aes(label = external_gene_name, colour = DE),
data = . %>%
dplyr::filter(abs(logFC) > 1.2 & DE | external_gene_name %in% tested),
show.legend = FALSE
) +
scale_colour_manual(values = c("grey50", "red"))
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
Compare the two lists
topTable %>%
dplyr::select(ensembl_gene_id, RUV = logFC, DE) %>%
left_join(deGenes) %>%
dplyr::rename(Raw = logFC) %>%
mutate(
Status = case_when(
Sig & DE ~ "Both",
Sig & !DE ~ "Raw Only",
!Sig & DE ~ "RUV Only",
!Sig & !DE ~ "Not DE"
)
) %>%
ggplot(aes(Raw, RUV,)) +
geom_point(aes(colour = Status), alpha = 0.5) +
geom_abline(slope= 1, linetype = 2) +
geom_vline(xintercept = c(-1, 1)*0.5) +
geom_hline(yintercept = c(-1, 1)*0.5) +
scale_colour_manual(values = c("green", "grey50", "red", "blue"))
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
The 228 genes considered as DE using an FDR of 0.01 and logFC beyond the range \(\pm 0.5\) were then inspected to confirm that the counts support their inclusion as DE.
Here plot CPM for DE genes having a FDR below 0.01 and logFC beyond the range \(\pm 1.2\)
ruv$normalizedCounts %>%
cpm(log = TRUE) %>%
.[dplyr::filter(topTable, FDR < 0.01 & abs(logFC) > 1.2)$ensembl_gene_id,] %>%
as.data.frame() %>%
rownames_to_column("ensembl_gene_id") %>%
as_tibble() %>%
gather(key = "sample", value = "CPM", -ensembl_gene_id) %>%
left_join(dgeList$samples) %>%
left_join(dgeList$genes) %>%
ggplot(aes(Genotype, CPM)) +
geom_point(aes(colour = Genotype)) +
geom_line(
aes(group = pair),
colour = "grey70"
) +
facet_wrap(~external_gene_name, scales = "free_y") +
theme(legend.position = "none") +
labs(y = "logCPM")
| Version | Author | Date |
|---|---|---|
| 6c61705 | yangdongau | 2020-02-27 |
Data Export
Final gene lists were exported as separate csv files, along with dgeList objects.
write.csv(topTableDE, here::here("output", "DEgenes.csv"))
write.csv(topTable,here::here("output", "topTable.csv"))
write_rds(dgeList, here::here("data", "dgeList.rds"), compress = "gz")
devtools::session_info()─ Session info ──────────────────────────────────────────────────────────
setting value
version R version 3.6.0 (2019-04-26)
os macOS Mojave 10.14.6
system x86_64, darwin15.6.0
ui X11
language (EN)
collate en_AU.UTF-8
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tz Australia/Adelaide
date 2020-10-28
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rappdirs 0.3.1 2016-03-28 [1] CRAN (R 3.6.0)
RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 3.6.0)
Rcpp 1.0.2 2019-07-25 [1] CRAN (R 3.6.0)
RCurl 1.95-4.12 2019-03-04 [1] CRAN (R 3.6.0)
readr * 1.3.1 2018-12-21 [1] CRAN (R 3.6.0)
readxl 1.3.1 2019-03-13 [1] CRAN (R 3.6.0)
remotes 2.1.1 2020-02-15 [1] CRAN (R 3.6.0)
rlang 0.4.4 2020-01-28 [1] CRAN (R 3.6.0)
rmarkdown 1.15 2019-08-21 [1] CRAN (R 3.6.0)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.0)
Rsamtools * 2.0.0 2019-05-02 [1] Bioconductor
RSQLite 2.1.2 2019-07-24 [1] CRAN (R 3.6.0)
rstudioapi 0.10 2019-03-19 [1] CRAN (R 3.6.0)
rtracklayer 1.44.3 2019-08-24 [1] Bioconductor
RUVSeq * 1.18.0 2019-05-02 [1] Bioconductor
rvest 0.3.4 2019-05-15 [1] CRAN (R 3.6.0)
S4Vectors * 0.22.0 2019-05-02 [1] Bioconductor
scales * 1.0.0 2018-08-09 [1] CRAN (R 3.6.0)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
shiny 1.3.2 2019-04-22 [1] CRAN (R 3.6.0)
ShortRead * 1.42.0 2019-05-02 [1] Bioconductor
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 * 1.14.1 2019-07-31 [1] Bioconductor
survival 2.44-1.1 2019-04-01 [1] CRAN (R 3.6.0)
testthat 2.3.1 2019-12-01 [1] CRAN (R 3.6.0)
tibble * 2.1.3 2019-06-06 [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)
usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.0)
vctrs 0.2.0 2019-07-05 [1] CRAN (R 3.6.0)
whisker 0.4 2019-08-28 [1] CRAN (R 3.6.0)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
workflowr 1.6.0 2019-12-19 [1] CRAN (R 3.6.0)
xfun 0.9 2019-08-21 [1] CRAN (R 3.6.0)
XML 3.98-1.20 2019-06-06 [1] CRAN (R 3.6.0)
xml2 1.2.2 2019-08-09 [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)
zeallot 0.1.0 2018-01-28 [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