Last updated: 2020-01-28
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Knit directory: 20170327_Psen2S4Ter_RNASeq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2cc69b5 | Steve Ped | 2020-01-28 | Added data load correction |
| Rmd | 207cdc8 | Steve Ped | 2020-01-28 | Added code for Hom Vs Het Enrichment |
library(tidyverse)
library(magrittr)
library(edgeR)
library(scales)
library(pander)
library(goseq)
library(msigdbr)
library(AnnotationDbi)
library(RColorBrewer)
library(ngsReports)
library(fgsea)
library(metap)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
}ALL INTRODUCTORY TEXT NEEDS CHANGING. RESULTS ARE OK
Enrichment analysis for this dataset 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 steps for enrichment analysis will be undertaken.
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 into account inter-gene correlations. Values supplied will be fitted values for each gene/sample as these have been corrected for GC and length biases.camera, which also accommodates inter-gene correlations. Values supplied will be fitted values for each gene/sample as these have been corrected for GC and length biases.fgsea which is an R implementation of GSEA. This approach simply takes a ranked list and doesn’t directly account for correlations. However, the ranked list will be derived from analysis using CQN-normalisation so will be robust to these technical artefacts.In the case of camera, inter-gene correlations will be calculated for each gene-set prior to analysis to ensure more conservative p-values are obtained.
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.
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")
hmByID <- hm %>%
split(f = .$gs_name) %>%
lapply(extract2, "gene_id")Mappings are required from gene to pathway, and Ensembl identifiers were used to map from gene to pathway, based on the mappings in the previously used annotations (Ensembl Release 96). A total of 3459 Ensembl IDs were mapped to pathways from the hallmark gene sets.
kg <- msigdbr("Danio rerio", category = "C2", subcategory = "CP:KEGG") %>%
left_join(entrezGenes) %>%
dplyr::filter(!is.na(gene_id))
kgByGene <- kg %>%
split(f = .$gene_id) %>%
lapply(extract2, "gs_name")
kgByID <- kg %>%
split(f = .$gs_name) %>%
lapply(extract2, "gene_id")The same mapping process was applied to KEGG gene sets. A total of 3614 Ensembl IDs were mapped to pathways from the KEGG gene sets.
goSummaries <- url("https://uofabioinformaticshub.github.io/summaries2GO/data/goSummaries.RDS") %>%
readRDS() %>%
mutate(
Term = Term(id),
gs_name = Term %>% str_to_upper() %>% str_replace_all("[ -]", "_"),
gs_name = paste0("GO_", gs_name)
)
minPath <- 3go <- msigdbr("Danio rerio", category = "C5") %>%
left_join(entrezGenes) %>%
dplyr::filter(!is.na(gene_id)) %>%
left_join(goSummaries) %>%
dplyr::filter(shortest_path >= minPath)
goByGene <- go %>%
split(f = .$gene_id) %>%
lapply(extract2, "gs_name")
goByID <- go %>%
split(f = .$gs_name) %>%
lapply(extract2, "gene_id")For analysis of gene-sets from the GO database, gene-sets were restricted to those with 3 or more steps back to the ontology root terms. A total of 11245 Ensembl IDs were mapped to pathways from restricted database of 8834 GO gene sets.
deTable <- file.path("output", "psen2VsWT.csv") %>%
read_csv() %>%
mutate(
entrezid = dgeList$genes$entrezid[gene_id]
)rnk <- structure(
-sign(deTable$logFC)*log10(deTable$PValue),
names = deTable$gene_id
) %>% sort()
np <- 1e5Genes were ranked by -sign(logFC)*log10(p) for approaches which required a ranked list. Multiple approaches were first calculated individually, before being combined for the final integrated set of results.
hmFry <- fit$fitted.values %>%
cpm(log = TRUE) %>%
fry(
index = hmByID,
design = fit$design,
contrast = "homozygous",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For analysis under camera when inter-gene correlations were calculated for a more conservative result.
hmCamera <- fit$fitted.values %>%
cpm(log = TRUE) %>%
camera(
index = hmByID,
design = fit$design,
contrast = "homozygous",
inter.gene.cor = NULL
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For generation of the GSEA ranked list, 10^{5} permutations were conducted.
hmGsea <- fgsea(
pathways = hmByID,
stats = rnk,
nperm = np
) %>%
as_tibble() %>%
dplyr::rename(gs_name = pathway, PValue = pval) %>%
arrange(PValue)Results for all analyses, including goseq were then combined using Wilkinson’s method to combine p-values. For a conservative approach, under \(m\) tests, the \(m - 1\)th smallest p-value was chosen.
hmMeta <- hmFry %>%
dplyr::select(gs_name, fry = PValue) %>%
left_join(
dplyr::select(hmCamera, gs_name, camera = PValue)
) %>%
left_join(
dplyr::select(hmGsea, gs_name, gsea = PValue)
) %>%
pivot_longer(
cols = one_of(c("fry", "camera", "gsea")),
names_to = "method",
values_to = "p"
) %>%
dplyr::filter(!is.na(p)) %>%
group_by(gs_name) %>%
summarise(
n = n(),
p = wilkinsonp(p, r = n - 1)$p
) %>%
arrange(p) %>%
mutate(
FDR = p.adjust(p, "fdr"),
adjP = p.adjust(p, "bonf")
) hmMeta %>%
dplyr::filter(FDR < 0.1) %>%
mutate_at(vars(one_of(c("p", "FDR", "adjP"))), formatP) %>%
pander(
caption = "Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant to an FDR of 0.1.",
justify = "lrrrr"
)| gs_name | n | p | FDR | adjP |
|---|---|---|---|---|
| HALLMARK_OXIDATIVE_PHOSPHORYLATION | 3 | 0.0011 | 0.0537 | 0.0537 |
kgFry <- fit$fitted.values %>%
cpm(log = TRUE) %>%
fry(
index = kgByID,
design = fit$design,
contrast = "homozygous",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For analysis under camera when inter-gene correlations were calculated for a more conservative result.
kgCamera <- fit$fitted.values %>%
cpm(log = TRUE) %>%
camera(
index = kgByID,
design = fit$design,
contrast = "homozygous",
inter.gene.cor = NULL
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For generation of the GSEA ranked list, 10^{5} permutations were conducted.
kgGsea <- fgsea(
pathways = kgByID,
stats = rnk,
nperm = np
) %>%
as_tibble() %>%
dplyr::rename(gs_name = pathway, PValue = pval) %>%
arrange(PValue)Results for all analyses, including goseq were then combined using Wilkinson’s method to combine p-values. For a conservative approach, under \(m\) tests, the \(m - 1\)th smallest p-value was chosen.
kgMeta <- kgFry %>%
dplyr::select(gs_name, fry = PValue) %>%
left_join(
dplyr::select(kgCamera, gs_name, camera = PValue)
) %>%
left_join(
dplyr::select(kgGsea, gs_name, gsea = PValue)
) %>%
pivot_longer(
cols = one_of(c("fry", "camera", "gsea")),
names_to = "method",
values_to = "p"
) %>%
dplyr::filter(!is.na(p)) %>%
group_by(gs_name) %>%
summarise(
n = n(),
p = wilkinsonp(p, r = n - 1)$p
) %>%
arrange(p) %>%
mutate(
FDR = p.adjust(p, "fdr"),
adjP = p.adjust(p, "bonf")
) kgMeta %>%
dplyr::filter(FDR < 0.05) %>%
mutate_at(vars(one_of(c("p", "FDR", "adjP"))), formatP) %>%
pander(
caption = "Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant to an FDR of 0.05.",
justify = "lrrrr"
)| gs_name | n | p | FDR | adjP |
|---|---|---|---|---|
| KEGG_RIBOSOME | 3 | 1.65e-09 | 3.07e-07 | 3.07e-07 |
| KEGG_OXIDATIVE_PHOSPHORYLATION | 3 | 2.88e-08 | 2.68e-06 | 5.35e-06 |
| KEGG_PARKINSONS_DISEASE | 3 | 9.90e-08 | 6.14e-06 | 1.84e-05 |
| KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS | 3 | 2.24e-05 | 0.0010 | 0.0042 |
| KEGG_HUNTINGTONS_DISEASE | 3 | 0.0001 | 0.0053 | 0.0264 |
| KEGG_PROTEASOME | 3 | 0.0003 | 0.0092 | 0.0550 |
| KEGG_ALZHEIMERS_DISEASE | 3 | 0.0004 | 0.0101 | 0.0742 |
| KEGG_PRIMARY_IMMUNODEFICIENCY | 3 | 0.0004 | 0.0101 | 0.0811 |
| KEGG_ASTHMA | 3 | 0.0005 | 0.0104 | 0.0933 |
| KEGG_AUTOIMMUNE_THYROID_DISEASE | 3 | 0.0018 | 0.0330 | 0.3300 |
| KEGG_ECM_RECEPTOR_INTERACTION | 3 | 0.0021 | 0.0356 | 0.3919 |
| KEGG_OLFACTORY_TRANSDUCTION | 3 | 0.0029 | 0.0450 | 0.5403 |
goFry <- fit$fitted.values %>%
cpm(log = TRUE) %>%
fry(
index = goByID,
design = fit$design,
contrast = "homozygous",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For analysis under camera when inter-gene correlations were calculated for a more conservative result.
goCamera <- fit$fitted.values %>%
cpm(log = TRUE) %>%
camera(
index = goByID,
design = fit$design,
contrast = "homozygous",
inter.gene.cor = NULL
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For generation of the GSEA ranked list, 10^{5} permutations were conducted.
goGsea <- fgsea(
pathways = goByID,
stats = rnk,
nperm = np
) %>%
as_tibble() %>%
dplyr::rename(gs_name = pathway, PValue = pval) %>%
arrange(PValue)Results for all analyses, including goseq were then combined using Wilkinson’s method to combine p-values. For a conservative approach, under \(m\) tests, the \(m - 1\)th smallest p-value was chosen.
goMeta <- goFry %>%
dplyr::select(gs_name, fry = PValue) %>%
left_join(
dplyr::select(goCamera, gs_name, camera = PValue)
) %>%
left_join(
dplyr::select(goGsea, gs_name, gsea = PValue)
) %>%
pivot_longer(
cols = one_of(c("fry", "camera", "gsea")),
names_to = "method",
values_to = "p"
) %>%
dplyr::filter(!is.na(p)) %>%
group_by(gs_name) %>%
summarise(
n = n(),
p = wilkinsonp(p, r = n - 1)$p
) %>%
arrange(p) %>%
mutate(
FDR = p.adjust(p, "fdr"),
adjP = p.adjust(p, "bonf")
) goMeta %>%
dplyr::filter(adjP < 0.01) %>%
mutate_at(vars(one_of(c("p", "FDR", "adjP"))), formatP) %>%
pander(
caption = "Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant using a Bonferroni-adjusted p-value < 0.01.",
justify = "lrrrr"
)| gs_name | n | p | FDR | adjP |
|---|---|---|---|---|
| GO_AEROBIC_ELECTRON_TRANSPORT_CHAIN | 3 | 1.41e-09 | 1.78e-06 | 1.24e-05 |
| GO_PROTON_TRANSPORTING_ATP_SYNTHASE_COMPLEX | 3 | 1.41e-09 | 1.78e-06 | 1.25e-05 |
| GO_POLYSOMAL_RIBOSOME | 3 | 1.45e-09 | 1.78e-06 | 1.28e-05 |
| GO_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT | 3 | 1.54e-09 | 1.78e-06 | 1.36e-05 |
| GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT | 3 | 1.59e-09 | 1.78e-06 | 1.40e-05 |
| GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE | 3 | 1.71e-09 | 1.78e-06 | 1.51e-05 |
| GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM | 3 | 1.75e-09 | 1.78e-06 | 1.55e-05 |
| GO_CYTOSOLIC_RIBOSOME | 3 | 1.75e-09 | 1.78e-06 | 1.55e-05 |
| GO_INNATE_IMMUNE_RESPONSE_IN_MUCOSA | 3 | 1.81e-09 | 1.78e-06 | 1.60e-05 |
| GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM | 3 | 3.56e-09 | 3.15e-06 | 3.15e-05 |
| GO_ORGAN_OR_TISSUE_SPECIFIC_IMMUNE_RESPONSE | 3 | 5.54e-09 | 4.45e-06 | 4.89e-05 |
| GO_SMALL_RIBOSOMAL_SUBUNIT | 3 | 8.64e-09 | 6.36e-06 | 7.63e-05 |
| GO_RIBOSOMAL_SMALL_SUBUNIT_ASSEMBLY | 3 | 1.26e-08 | 8.53e-06 | 0.0001 |
| GO_MITOCHONDRIAL_RESPIRATORY_CHAIN_COMPLEX_I | 3 | 7.01e-08 | 4.18e-05 | 0.0006 |
| GO_NADH_DEHYDROGENASE_ACTIVITY | 3 | 7.09e-08 | 4.18e-05 | 0.0006 |
| GO_RESPIRATORY_CHAIN_COMPLEX_IV | 3 | 8.18e-08 | 4.52e-05 | 0.0007 |
| GO_OXIDATIVE_PHOSPHORYLATION | 3 | 1.13e-07 | 5.87e-05 | 0.0010 |
| GO_RIBOSOMAL_SUBUNIT | 3 | 1.45e-07 | 6.86e-05 | 0.0013 |
| GO_ATP_SYNTHESIS_COUPLED_ELECTRON_TRANSPORT | 3 | 1.48e-07 | 6.86e-05 | 0.0013 |
| GO_RESPIRATORY_CHAIN_COMPLEX | 3 | 1.84e-07 | 8.13e-05 | 0.0016 |
| GO_CRISTAE_FORMATION | 3 | 3.97e-07 | 0.0002 | 0.0035 |
| GO_VIRAL_GENE_EXPRESSION | 3 | 6.14e-07 | 0.0002 | 0.0054 |
| GO_ANTIBACTERIAL_HUMORAL_RESPONSE | 3 | 1.03e-06 | 0.0004 | 0.0091 |
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 2020-01-28
─ Packages ───────────────────────────────────────────────────────────────────
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