Enrichment Analysis
Steve Pederson
27 January, 2020
Last updated: 2020-01-27
Checks: 6 1
Knit directory: 20170327_Psen2S4Ter_RNASeq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b086aff | Steve Ped | 2020-01-26 | Added first step of enrichment analysis |
| html | b086aff | Steve Ped | 2020-01-26 | Added first step of enrichment analysis |
| Rmd | ba189be | Steve Ped | 2020-01-26 | Corrected packages |
| Rmd | 68033fe | Steve Ped | 2020-01-26 | Corrected chunk labels after publishing DE analysis |
| Rmd | 25922d6 | Steve Ped | 2020-01-26 | Added first pass of enrichment |
Setup
library(tidyverse)
library(magrittr)
library(edgeR)
library(scales)
library(pander)
library(goseq)
library(msigdbr)
library(AnnotationDbi)
library(RColorBrewer)
library(ngsReports)theme_set(theme_bw())
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
panderOptions("big.mark", ",")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")),
mutant = genotype %in% c("Het", "Hom"),
homozygous = genotype == "Hom"
)
genoCols <- samples$genotype %>%
levels() %>%
length() %>%
brewer.pal("Set1") %>%
setNames(levels(samples$genotype))dgeList <- read_rds("data/dgeList.rds")
fit <- read_rds("data/fit.rds")
entrezGenes <- dgeList$genes %>%
dplyr::filter(!is.na(entrezid)) %>%
unnest(entrezid) %>%
dplyr::rename(entrez_gene = entrezid)formatP <- function(p, m = 0.0001){
out <- rep("", length(p))
out[p < m] <- sprintf("%.2e", p[p<m])
out[p >= m] <- sprintf("%.4f", p[p>=m])
out
}Introduction
Enrichment analysis for this datast present some challenges. Despite normalisation to account for gene length and GC bias, some appeared to still be present in the final results. In addition, the confounding of incomplete rRNA removal with genotype may lead to other distortions in both DE genes and ranking statistics.
Two methods for enrichment analysis will be undertaken.
- Testing for enrichment within discrete sets of DE genes as defined in the previous steps
- Testing for enrichment within ranked lists, regardless of DE status or statistical significance.
Testing for enrichment within discrete gene sets will be performed using goseq as this allows for the incorporation of a single covariate as a predictor of differential expression. GC content, gene length and correlation with rRNA removal can all be supplied as separate covariates.
Testing for enrichment with ranked lists will be performed using fry as this can take a dgeList directly. However, in order for testing to be robust in the context of bias, normalised counts after CQN will be used for generation of a new dgeList for testing.
Databases used for testing
Data was sourced using the msigdbr package. The initial database used for testing was the Hallmark Gene Sets, with mappings from gene-set to EntrezGene IDs performed by the package authors.
Discrete DE Gene Sets
topTables <- list.files(
path = "output", pattern = "Vs.+csv", full.names = TRUE
) %>%
sapply(read_csv, simplify = FALSE) %>%
set_names(basename(names(.))) %>%
set_names(str_remove(names(.), ".csv")) %>%
lapply(function(x){
x %>%
mutate(
entrezid = dgeList$genes$entrezid[gene_id]
)
})Effects of the *psen2S4Ter mutation
The first step of analysis using goseq, regardless of the geneset, is estimation of the probability weight function (PWF) which quantifies the probablity of a gene being considered as DE based on a single covariate. Two possible covariates (GC content & Gene Length) were checked
pwf <- c("gc_content", "length") %>%
sapply(
function(x){
topTables$psen2VsWT %>%
dplyr::select(gene_id, DE, covariate = one_of(x)) %>%
distinct(gene_id, .keep_all = TRUE) %>%
with(
nullp(
DEgenes = structure(
as.integer(DE), names = gene_id
),
genome = "danRer10",
id = "ensGene",
bias.data = covariate,
plot.fit = FALSE
)
)
},
simplify = FALSE
)par(mfrow = c(1, length(pwf)))
names(pwf) %>%
lapply(function(x){
plotPWF(pwf[[x]], main = x)
})
Bias data based on each of the possible covariates. Both appeared to impact the probability of a gene being DE to some extent, with gene length being selected as the initial covariate for usage in the goseq model.
| Version | Author | Date |
|---|---|---|
| b086aff | Steve Ped | 2020-01-26 |
[[1]]
NULL
[[2]]
NULLpar(mfrow = c(1, 1))Length Bias: Hallmark Gene Sets
hm <- msigdbr("Danio rerio", category = "H") %>%
left_join(entrezGenes) %>%
dplyr::filter(!is.na(gene_id)) hmByGene <- hm %>%
split(f = .$gene_id) %>%
lapply(extract2, "gs_name")Mappings are required from gene to pathway, and Ensembl identifiers were used to map from gene to pathway, based on the mappings in the previously used annotations (Ensembl Release 96). A total of 3459 Ensembl IDs were mapped to pathways from the hallmark gene sets. This contrasts with 3458 EntrezGene IDs.
hmGoseq <- goseq(pwf$length, gene2cat = hmByGene) %>%
as_tibble %>%
dplyr::filter(numDEInCat > 0) %>%
mutate(
adjP = p.adjust(over_represented_pvalue, method = "bonf"),
FDR = p.adjust(over_represented_pvalue, method = "fdr")
) %>%
dplyr::select(-contains("under"))No hallmark genesets were considered to be enriched after adjusting for the observed length bias in the data.
Length Bias: KEGG Gene Sets
The same mapping process was applied to KEGG gene sets.
kg <- msigdbr("Danio rerio", category = "C2", subcategory = "CP:KEGG") %>%
left_join(entrezGenes) %>%
dplyr::filter(!is.na(gene_id)) kgByGene <- kg %>%
split(f = .$gene_id) %>%
lapply(extract2, "gs_name")A total of 3614 Ensembl IDs were mapped to pathways from the KEGG gene sets. This contrasts with 3603 EntrezGene IDs.
kgGoseq <- goseq(pwf$length, gene2cat = kgByGene) %>%
as_tibble %>%
dplyr::filter(numDEInCat > 0) %>%
mutate(
adjP = p.adjust(over_represented_pvalue, method = "bonf"),
FDR = p.adjust(over_represented_pvalue, method = "fdr")
) %>%
dplyr::select(-contains("under"))kgGoseq %>%
dplyr::slice(1:5) %>%
dplyr::rename(p = over_represented_pvalue) %>%
mutate(
p = formatP(p),
adjP = formatP(adjP),
FDR = formatP(FDR)
) %>%
dplyr::select(
Category = category,
DE = numDEInCat,
`Set Size` = numInCat,
p,
`p~bonf~` = adjP,
`p~FDR~` = FDR
) %>%
pander(
justify = "lrrrrr",
caption = paste(
"The", nrow(.), "most highly-ranked KEGG pathways.",
"Bonferroni-adjusted p-values are the most stringent and give high",
"confidence when below 0.05."
)
)| Category | DE | Set Size | p | pbonf | pFDR |
|---|---|---|---|---|---|
| KEGG_RIBOSOME | 26 | 80 | 4.65e-12 | 5.20e-10 | 5.20e-10 |
| KEGG_OXIDATIVE_PHOSPHORYLATION | 14 | 119 | 0.0070 | 0.7892 | 0.3086 |
| KEGG_CYSTEINE_AND_METHIONINE_METABOLISM | 4 | 26 | 0.0101 | 1.0000 | 0.3086 |
| KEGG_PARKINSONS_DISEASE | 13 | 112 | 0.0118 | 1.0000 | 0.3086 |
| KEGG_HUNTINGTONS_DISEASE | 15 | 159 | 0.0138 | 1.0000 | 0.3086 |
Notably, the KEGG gene-set for Ribosomal genes was detected as enriched. This is likely to be due to the previously discussed contaminant.
Length Bias: GO Gene Sets
The same mapping process was applied to GO gene sets.
goSummaries <- url("https://uofabioinformaticshub.github.io/summaries2GO/data/goSummaries.RDS") %>%
readRDS() %>%
mutate(
Term = Term(id),
gs_name = Term %>% str_to_upper() %>% str_replace_all("[ -]", "_"),
gs_name = paste0("GO_", gs_name)
)go <- msigdbr("Danio rerio", category = "C5") %>%
left_join(entrezGenes) %>%
dplyr::filter(!is.na(gene_id)) minPath <- 3
goByGene <- go %>%
left_join(goSummaries) %>%
dplyr::filter(shortest_path >= minPath) %>%
split(f = .$gene_id) %>%
lapply(extract2, "gs_name")A total of 11245 Ensembl IDs were mapped to pathways from the GO gene sets. This contrasts with 11359 EntrezGene IDs. In addition GO genesets were restricted to those with 3 or more steps back to each ontology root.
goGoseq <- goseq(pwf$length, gene2cat = goByGene) %>%
as_tibble %>%
dplyr::filter(numDEInCat > 0) %>%
mutate(
adjP = p.adjust(over_represented_pvalue, method = "bonf"),
FDR = p.adjust(over_represented_pvalue, method = "fdr")
) %>%
dplyr::select(-contains("under"))goGoseq %>%
dplyr::filter(adjP < 0.05) %>%
dplyr::rename(p = over_represented_pvalue) %>%
mutate(
p = formatP(p),
adjP = formatP(adjP),
FDR = formatP(FDR)
) %>%
dplyr::select(
Category = category,
DE = numDEInCat,
`Set Size` = numInCat,
p,
`p~bonf~` = adjP,
`p~FDR~` = FDR
) %>%
pander(
justify = "lrrrrr",
caption = paste(
"*The", nrow(.), "most highly-ranked GO terms.",
"Bonferroni-adjusted p-values are the most stringent and give high",
"confidence when below 0.05, and all terms reached this threshold.",
"However, most terms once again indicate the presence of rRNA.*"
)
)| Category | DE | Set Size | p | pbonf | pFDR |
|---|---|---|---|---|---|
| GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE | 28 | 93 | 2.42e-13 | 9.33e-10 | 9.33e-10 |
| GO_CYTOSOLIC_RIBOSOME | 27 | 97 | 3.74e-12 | 1.44e-08 | 7.19e-09 |
| GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM | 27 | 105 | 1.17e-11 | 4.50e-08 | 1.50e-08 |
| GO_PROTEIN_TARGETING_TO_MEMBRANE | 30 | 171 | 1.67e-10 | 6.43e-07 | 1.55e-07 |
| GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM | 27 | 128 | 2.01e-10 | 7.74e-07 | 1.55e-07 |
| GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS | 30 | 183 | 1.03e-09 | 3.97e-06 | 6.62e-07 |
| GO_VIRAL_GENE_EXPRESSION | 29 | 172 | 1.59e-09 | 6.12e-06 | 8.74e-07 |
| GO_TRANSLATIONAL_INITIATION | 29 | 177 | 4.88e-09 | 1.88e-05 | 2.35e-06 |
| GO_CYTOSOLIC_PART | 30 | 206 | 8.07e-09 | 3.10e-05 | 3.45e-06 |
| GO_RNA_BINDING | 88 | 1,408 | 4.79e-08 | 0.0002 | 1.84e-05 |
| GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT | 15 | 51 | 1.26e-07 | 0.0005 | 4.40e-05 |
| GO_RIBOSOMAL_SUBUNIT | 28 | 175 | 3.31e-07 | 0.0013 | 0.0001 |
| GO_RIBOSOME | 30 | 210 | 3.83e-07 | 0.0015 | 0.0001 |
| GO_RNA_CATABOLIC_PROCESS | 34 | 337 | 7.18e-07 | 0.0028 | 0.0002 |
| GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_MEMBRANE | 30 | 287 | 1.69e-06 | 0.0065 | 0.0004 |
| GO_ORGANIC_CYCLIC_COMPOUND_CATABOLIC_PROCESS | 40 | 474 | 1.82e-06 | 0.0070 | 0.0004 |
| GO_PROTEIN_TARGETING | 34 | 379 | 7.10e-06 | 0.0273 | 0.0016 |
| GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ORGANELLE | 38 | 484 | 9.76e-06 | 0.0376 | 0.0021 |
rRNA Correlation Bias: Hallmark Gene Sets
Given the results above all pointing to DE genes being enriched for ribosomal related processes, an alternative approch was sought. Under this approach, each gene’s correlation with the estimates of rRNA proportions was used for bias correction. Values were calculated as per the previous steps, using the logCPM values for each gene, with the sample-level standard deviations from the theoretical GC distribution being used as measures of rRNA bias.
rawFqc <- list.files(
path = "data/0_rawData/FastQC/",
pattern = "zip",
full.names = TRUE
) %>%
FastqcDataList()
gc <- getModule(rawFqc, "Per_sequence_GC") %>%
group_by(Filename) %>%
mutate(Freq = Count / sum(Count)) %>%
ungroup()
rawGC <- gc %>%
dplyr::filter(GC_Content > 70) %>%
group_by(Filename) %>%
summarise(Freq = sum(Freq)) %>%
arrange(desc(Freq)) %>%
mutate(sample = str_remove(Filename, "_R[12].fastq.gz")) %>%
group_by(sample) %>%
summarise(Freq = mean(Freq)) %>%
left_join(samples) gcDev <- gc %>%
left_join(getGC(gcTheoretical, "Drerio", "Trans")) %>%
mutate(
sample = str_remove(Filename, "_R[12].fastq.gz"),
resid = Freq - Drerio
) %>%
left_join(samples) %>%
group_by(sample) %>%
summarise(
ss = sum(resid^2),
n = n(),
sd = sqrt(ss / (n - 1))
)riboVec <- structure(gcDev$sd, names = gcDev$sample)
riboCors <- cpm(dgeList, log = TRUE) %>%
apply(1, function(x){
cor(x, riboVec[names(x)])
})For estimation of the probability weight function, squared correlations were used to place negative and positive correlations on the same scale. This accounts for genes which are both negatively & positively biased by the presence of excessive rRNA. Clearly, the confounding of genotype with rRNA means that some genes driven by the genuine biology may be down-weighted under this approach.
riboPwf <- topTables$psen2VsWT %>%
mutate(riboCors = riboCors[gene_id]^2) %>%
dplyr::select(gene_id, DE, riboCors) %>%
distinct(gene_id, .keep_all = TRUE) %>%
with(
nullp(
DEgenes = structure(
as.integer(DE), names = gene_id
),
genome = "danRer10",
id = "ensGene",
bias.data = riboCors,
plot.fit = FALSE
)
)
plotPWF(riboPwf, main = "Bias from rRNA proportions")
Using this approach, it was clear that correlation with rRNA proportions significantly biased the probability of a gene being considered as DE.
The hallmark gene-sets were then tested using this alterntive PWF.
hmRiboGoseq <- goseq(riboPwf, gene2cat = hmByGene) %>%
as_tibble %>%
dplyr::filter(numDEInCat > 0) %>%
mutate(
adjP = p.adjust(over_represented_pvalue, method = "bonf"),
FDR = p.adjust(over_represented_pvalue, method = "fdr")
) %>%
dplyr::select(-contains("under"))Once again, no genesets acheived significance with the lower FDR being 41%
rRNA Correlation Bias: KEGG Gene Sets
kgRiboGoseq <- goseq(riboPwf, gene2cat = kgByGene) %>%
as_tibble %>%
dplyr::filter(numDEInCat > 0) %>%
mutate(
adjP = p.adjust(over_represented_pvalue, method = "bonf"),
FDR = p.adjust(over_represented_pvalue, method = "fdr")
) %>%
dplyr::select(-contains("under"))kgRiboGoseq %>%
dplyr::slice(1:5) %>%
dplyr::rename(p = over_represented_pvalue) %>%
mutate(
p = formatP(p),
adjP = formatP(adjP),
FDR = formatP(FDR)
) %>%
dplyr::select(
Category = category,
DE = numDEInCat,
`Set Size` = numInCat,
p,
`p~bonf~` = adjP,
`p~FDR~` = FDR
) %>%
pander(
justify = "lrrrrr",
caption = paste(
"The", nrow(.), "most highly-ranked KEGG pathways.",
"Bonferroni-adjusted p-values are the most stringent and give high",
"confidence when below 0.05."
)
)| Category | DE | Set Size | p | pbonf | pFDR |
|---|---|---|---|---|---|
| KEGG_RIBOSOME | 26 | 80 | 4.75e-09 | 5.32e-07 | 5.32e-07 |
| KEGG_PRIMARY_IMMUNODEFICIENCY | 2 | 15 | 0.0164 | 1.0000 | 0.7173 |
| KEGG_CYSTEINE_AND_METHIONINE_METABOLISM | 4 | 26 | 0.0296 | 1.0000 | 0.7173 |
| KEGG_ASTHMA | 1 | 3 | 0.0409 | 1.0000 | 0.7173 |
| KEGG_RETINOL_METABOLISM | 3 | 25 | 0.0512 | 1.0000 | 0.7173 |
Notably, the KEGG gene-set for Ribosomal genes was once again detected as enriched with no other KEGG gene-sets being considered sigificant.
rRNA Correlation Bias: GO Gene Sets
goRiboGoseq <- goseq(riboPwf, gene2cat = goByGene) %>%
as_tibble %>%
dplyr::filter(numDEInCat > 0) %>%
mutate(
adjP = p.adjust(over_represented_pvalue, method = "bonf"),
FDR = p.adjust(over_represented_pvalue, method = "fdr")
) %>%
dplyr::select(-contains("under"))goRiboGoseq %>%
dplyr::filter(adjP < 0.05) %>%
dplyr::rename(p = over_represented_pvalue) %>%
mutate(
p = formatP(p),
adjP = formatP(adjP),
FDR = formatP(FDR)
) %>%
dplyr::select(
Category = category,
DE = numDEInCat,
`Set Size` = numInCat,
p,
`p~bonf~` = adjP,
`p~FDR~` = FDR
) %>%
pander(
justify = "lrrrrr",
caption = paste(
"*The", nrow(.), "most highly-ranked GO terms.",
"Bonferroni-adjusted p-values are the most stringent and give high",
"confidence when below 0.05, and all terms reached this threshold.",
"However, most terms once again indicate the presence of rRNA.*"
)
)| Category | DE | Set Size | p | pbonf | pFDR |
|---|---|---|---|---|---|
| GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE | 28 | 93 | 7.36e-10 | 2.83e-06 | 2.03e-06 |
| GO_CYTOSOLIC_RIBOSOME | 27 | 97 | 1.06e-09 | 4.06e-06 | 2.03e-06 |
| GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM | 27 | 105 | 1.60e-08 | 6.16e-05 | 2.05e-05 |
| GO_TRANSLATIONAL_INITIATION | 29 | 177 | 2.18e-07 | 0.0008 | 0.0002 |
| GO_PROTEIN_TARGETING_TO_MEMBRANE | 30 | 171 | 2.53e-07 | 0.0010 | 0.0002 |
| GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM | 27 | 128 | 3.85e-07 | 0.0015 | 0.0002 |
| GO_CYTOSOLIC_PART | 30 | 206 | 4.42e-07 | 0.0017 | 0.0002 |
| GO_VIRAL_GENE_EXPRESSION | 29 | 172 | 6.94e-07 | 0.0027 | 0.0003 |
| GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS | 30 | 183 | 7.91e-07 | 0.0030 | 0.0003 |
| GO_RIBOSOMAL_SUBUNIT | 28 | 175 | 1.99e-06 | 0.0077 | 0.0008 |
| GO_RIBOSOME | 30 | 210 | 2.66e-06 | 0.0102 | 0.0009 |
| GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT | 15 | 51 | 2.83e-06 | 0.0109 | 0.0009 |
| GO_SYMPORTER_ACTIVITY | 10 | 94 | 7.57e-06 | 0.0291 | 0.0022 |
| GO_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY | 17 | 228 | 1.23e-05 | 0.0473 | 0.0034 |
Effects of Homozygous Vs Heterozygous mutants
As only a small number of genes were detected as being classified as DE for this comparison, no testing was performed on this as a discrete set of DE genes.
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 2020-01-27
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
AnnotationDbi * 1.48.0 2019-10-29 [2] Bioconductor
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Biostrings 2.54.0 2019-10-29 [2] Bioconductor
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DBI 1.1.0 2019-12-15 [2] CRAN (R 3.6.2)
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devtools 2.2.1 2019-09-24 [2] CRAN (R 3.6.1)
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