Last updated: 2020-03-05
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Knit directory: 20170327_Psen2S4Ter_RNASeq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2c56cef | Steve Ped | 2020-03-05 | Tidied column names for enrichment output |
| html | bad81c1 | Steve Ped | 2020-03-05 | Updated MutVsWt UpSet plots for KEGG/HALLMARK |
| Rmd | c0b89ed | Steve Ped | 2020-03-05 | Reran after changing to cpmPotNorm instead of the incorrect fit$fitted.values |
| Rmd | afdf91e | Steve Ped | 2020-03-04 | Fixed typo |
| html | e0288c6 | Steve Ped | 2020-02-19 | Generated enrichment tables |
| Rmd | 541fbf0 | Steve Ped | 2020-02-19 | Revised Hom Vs Het Enrichment |
| html | 876e40f | Steve Ped | 2020-02-17 | Compiled after minor corrections |
| Rmd | 53ed0e3 | Steve Ped | 2020-02-17 | Corrected Ensembl Release |
| Rmd | f55be85 | Steve Ped | 2020-02-17 | Added commas |
| Rmd | 55143d4 | Steve Ped | 2020-02-17 | Corrected gsea analyses |
| html | 9104ecd | Steve Ped | 2020-01-28 | First draft of Hom Vs Het |
| Rmd | 2cc69b5 | Steve Ped | 2020-01-28 | Added data load correction |
| Rmd | 207cdc8 | Steve Ped | 2020-01-28 | Added code for Hom Vs Het Enrichment |
library(tidyverse)
library(magrittr)
library(edgeR)
library(scales)
library(pander)
library(goseq)
library(msigdbr)
library(AnnotationDbi)
library(RColorBrewer)
library(ngsReports)
library(fgsea)
library(metap)theme_set(theme_bw())
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
panderOptions("big.mark", ",")samples <- here::here("data/samples.csv") %>%
read_csv() %>%
distinct(sampleName, .keep_all = TRUE) %>%
dplyr::select(sample = sampleName, sampleID, genotype) %>%
mutate(
genotype = factor(genotype, levels = c("WT", "Het", "Hom")),
mutant = genotype %in% c("Het", "Hom"),
homozygous = genotype == "Hom"
)
genoCols <- samples$genotype %>%
levels() %>%
length() %>%
brewer.pal("Set1") %>%
setNames(levels(samples$genotype))dgeList <- here::here("data/dgeList.rds") %>% read_rds()
cpmPostNorm <- here::here("data/cpmPostNorm.rds") %>% read_rds()
entrezGenes <- dgeList$genes %>%
dplyr::filter(!is.na(entrezid)) %>%
unnest(entrezid) %>%
dplyr::rename(entrez_gene = entrezid)deTable <- here::here("output", "psen2HomVsHet.csv") %>%
read_csv() %>%
mutate(
entrezid = dgeList$genes$entrezid[gene_id]
)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
}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.
As the list of DE genes for this comparison was small (\(n_{\text{DE}} = 7\)), enrichment testing was only performed using ranked-list approaches. 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)) %>%
distinct(gs_name, gene_id, .keep_all = TRUE)
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 98). A total of 3,459 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)) %>%
distinct(gs_name, gene_id, .keep_all = TRUE)
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 3,614 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) %>%
distinct(gs_name, gene_id, .keep_all = TRUE)
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 11,245 Ensembl IDs were mapped to pathways from restricted database of 8,834 GO gene sets.
gsSizes <- bind_rows(hm, kg, go) %>%
dplyr::select(gs_name, gene_symbol, gene_id) %>%
chop(c(gene_symbol, gene_id)) %>%
mutate(gs_size = vapply(gene_symbol, length, integer(1)))rnk <- structure(
-sign(deTable$logFC)*log10(deTable$PValue),
names = deTable$gene_id
) %>% sort()
np <- 1e5Genes were ranked by -sign(logFC)*log10(p) for approaches which required a ranked list. Multiple approaches were first calculated individually, before being combined for the final integrated set of results.
hmFry <- cpmPostNorm %>%
fry(
index = hmByID,
design = dgeList$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 <- cpmPostNorm %>%
camera(
index = hmByID,
design = dgeList$design,
contrast = "homozygous",
inter.gene.cor = NULL
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For generation of the GSEA ranked list, 100,000 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 were then combined using Wilkinson’s method to combine p-values. For a conservative approach, under \(m\) tests, the \(m - 1^{\text{th}}\) 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)
) %>%
nest(p = one_of(c("fry", "camera", "gsea"))) %>%
mutate(
n_p = vapply(p, function(x){sum(!is.na(unlist(x)))}, integer(1)),
wilkinson_p = vapply(p, function(x){
x <- unlist(x)
x <- x[!is.na(x)]
wilkinsonp(x, length(x) - 1)$p
}, numeric(1)),
FDR = p.adjust(wilkinson_p, "fdr"),
adjP = p.adjust(wilkinson_p, "bonferroni")
) %>%
arrange(wilkinson_p) %>%
unnest(p) %>%
left_join(gsSizes) %>%
mutate(
DE = lapply(gene_id, intersect, dplyr::filter(deTable, DE)$gene_id),
DE = lapply(DE, unique),
nDE = vapply(DE, length, integer(1))
)hmMeta %>%
dplyr::filter(FDR < 0.1) %>%
mutate_at(vars(one_of(c("wilkinson_p", "FDR", "adjP"))), formatP) %>%
dplyr::select(`Gene Set` = gs_name, `Number DE` = nDE, `Set Size` = gs_size, `Wilkinson~p~` = wilkinson_p, `p~FDR~` = FDR, `p~bonf~` = adjP) %>%
pander(
caption = "Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant to an FDR of 0.1, with none passing the initial filter of FDR < 0.05",
justify = "lrrrrr"
)| Gene Set | Number DE | Set Size | Wilkinsonp | pFDR | pbonf |
|---|---|---|---|---|---|
| HALLMARK_INTERFERON_ALPHA_RESPONSE | 0 | 60 | 0.0034 | 0.0622 | 0.1675 |
| HALLMARK_MYOGENESIS | 0 | 148 | 0.0036 | 0.0622 | 0.1812 |
| HALLMARK_INTERFERON_GAMMA_RESPONSE | 0 | 126 | 0.0037 | 0.0622 | 0.1865 |
| HALLMARK_UNFOLDED_PROTEIN_RESPONSE | 0 | 113 | 0.0086 | 0.0810 | 0.4300 |
| HALLMARK_DNA_REPAIR | 1 | 143 | 0.0101 | 0.0810 | 0.5062 |
| HALLMARK_KRAS_SIGNALING_UP | 0 | 142 | 0.0102 | 0.0810 | 0.5076 |
| HALLMARK_ESTROGEN_RESPONSE_EARLY | 0 | 166 | 0.0113 | 0.0810 | 0.5672 |
kgFry <-cpmPostNorm%>%
fry(
index = kgByID,
design = dgeList$design,
contrast = "homozygous",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For analysis under camera when inter-gene correlations were calculated for a more conservative result.
kgCamera <- cpmPostNorm %>%
camera(
index = kgByID,
design = dgeList$design,
contrast = "homozygous",
inter.gene.cor = NULL
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For generation of the GSEA ranked list, 100,000 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 were then combined using Wilkinson’s method to combine p-values. For a conservative approach, under \(m\) tests, the \(m - 1^{\text{th}}\) 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)
) %>%
nest(p = one_of(c("fry", "camera", "gsea"))) %>%
mutate(
n_p = vapply(p, function(x){sum(!is.na(unlist(x)))}, integer(1)),
wilkinson_p = vapply(p, function(x){
x <- unlist(x)
x <- x[!is.na(x)]
wilkinsonp(x, length(x) - 1)$p
}, numeric(1)),
FDR = p.adjust(wilkinson_p, "fdr"),
adjP = p.adjust(wilkinson_p, "bonferroni")
) %>%
arrange(wilkinson_p) %>%
unnest(p) %>%
left_join(gsSizes) %>%
mutate(
DE = lapply(gene_id, intersect, dplyr::filter(deTable, DE)$gene_id),
DE = lapply(DE, unique),
nDE = vapply(DE, length, integer(1))
)kgMeta %>%
dplyr::filter(FDR < 0.01) %>%
mutate_at(vars(one_of(c("wilkinson_p", "FDR", "adjP"))), formatP) %>%
dplyr::select(`Gene Set` = gs_name, `Number DE` = nDE, `Set Size` = gs_size, `Wilkinson~p~` = wilkinson_p, `p~FDR~` = FDR, `p~bonf~` = adjP) %>%
pander(
caption = "Results from combining all above approaches for the KEGG Gene Sets. All terms are significant to an FDR of 0.05.",
justify = "lrrrrr"
)| Gene Set | Number DE | Set Size | Wilkinsonp | pFDR | pbonf |
|---|---|---|---|---|---|
| KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY | 0 | 64 | 8.79e-06 | 0.0013 | 0.0016 |
| KEGG_FC_EPSILON_RI_SIGNALING_PATHWAY | 0 | 55 | 1.74e-05 | 0.0013 | 0.0032 |
| KEGG_CHEMOKINE_SIGNALING_PATHWAY | 0 | 116 | 2.14e-05 | 0.0013 | 0.0040 |
| KEGG_HOMOLOGOUS_RECOMBINATION | 1 | 19 | 7.71e-05 | 0.0036 | 0.0143 |
| KEGG_ACUTE_MYELOID_LEUKEMIA | 0 | 55 | 0.0001 | 0.0037 | 0.0222 |
| KEGG_BASAL_TRANSCRIPTION_FACTORS | 0 | 27 | 0.0001 | 0.0037 | 0.0250 |
| KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY | 0 | 35 | 0.0001 | 0.0037 | 0.0262 |
| KEGG_FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS | 0 | 85 | 0.0002 | 0.0053 | 0.0458 |
| KEGG_NEUROTROPHIN_SIGNALING_PATHWAY | 0 | 119 | 0.0003 | 0.0053 | 0.0476 |
| KEGG_CHRONIC_MYELOID_LEUKEMIA | 0 | 67 | 0.0005 | 0.0089 | 0.0892 |
goFry <- cpmPostNorm %>%
fry(
index = goByID,
design = dgeList$design,
contrast = "homozygous",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For analysis under camera when inter-gene correlations were calculated for a more conservative result.
goCamera <- cpmPostNorm %>%
camera(
index = goByID,
design = dgeList$design,
contrast = "homozygous",
inter.gene.cor = NULL
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For generation of the GSEA ranked list, 100,000 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 were then combined using Wilkinson’s method to combine p-values. For a conservative approach, under \(m\) tests, the \(m - 1^{\text{th}}\) 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)
) %>%
nest(p = one_of(c("fry", "camera", "gsea"))) %>%
mutate(
n_p = vapply(p, function(x){sum(!is.na(unlist(x)))}, integer(1)),
wilkinson_p = vapply(p, function(x){
x <- unlist(x)
x <- x[!is.na(x)]
wilkinsonp(x, length(x) - 1)$p
}, numeric(1)),
FDR = p.adjust(wilkinson_p, "fdr"),
adjP = p.adjust(wilkinson_p, "bonferroni")
) %>%
arrange(wilkinson_p) %>%
unnest(p) %>%
left_join(gsSizes) %>%
mutate(
DE = lapply(gene_id, intersect, dplyr::filter(deTable, DE)$gene_id),
DE = lapply(DE, unique),
nDE = vapply(DE, length, integer(1))
)goMeta %>%
dplyr::filter(adjP < 0.05) %>%
mutate_at(vars(one_of(c("wilkinson_p", "FDR", "adjP"))), formatP) %>%
dplyr::select(`Gene Set` = gs_name, `Number DE` = nDE, `Set Size` = gs_size, `Wilkinson~p~` = wilkinson_p, `p~FDR~` = FDR, `p~bonf~` = adjP) %>%
pander(
caption = "Results from combining all above approaches for the GO Gene Sets. All terms are significant to an FDR of 0.05.",
justify = "lrrrrr"
)| Gene Set | Number DE | Set Size | Wilkinsonp | pFDR | pbonf |
|---|---|---|---|---|---|
| GO_PHOSPHOLIPASE_A2_INHIBITOR_ACTIVITY | 0 | 3 | 2.29e-06 | 0.0129 | 0.0202 |
| GO_CELL_SUBSTRATE_JUNCTION_ASSEMBLY | 0 | 90 | 2.93e-06 | 0.0129 | 0.0259 |
All enriched gene sets terms with an FDR adjusted p-value < 0.05 were exported as a single csv file.
add_prefix <- function(x, pre = "p_"){
paste0(pre, x)
}
bind_rows(
hmMeta,
kgMeta,
goMeta
) %>%
dplyr::filter(FDR < 0.05) %>%
mutate(
DE = lapply(DE, function(x){dplyr::filter(deTable, gene_id %in% x)$gene_name}),
DE = lapply(DE, unique),
DE = vapply(DE, paste, character(1), collapse = ";")
) %>%
arrange(wilkinson_p) %>%
dplyr::select(
`Gene Set` = gs_name,
`Nbr Detected Genes` = gs_size,
`Nbr DE Genes` = nDE,
combined = wilkinson_p, FDR,
fry, camera, gsea,
`DE Genes` = DE
) %>%
rename_at(
vars(combined, fry, camera, gsea),
add_prefix
) %>%
write_csv(
here::here("output", "Enrichment_Hom_V_Het.csv")
)
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 3.6.3 (2020-02-29)
os Ubuntu 18.04.4 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 2020-03-05
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
AnnotationDbi * 1.48.0 2019-10-29 [2] Bioconductor
askpass 1.1 2019-01-13 [2] CRAN (R 3.6.0)
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backports 1.1.5 2019-10-02 [2] CRAN (R 3.6.1)
BiasedUrn * 1.07 2015-12-28 [2] CRAN (R 3.6.1)
bibtex 0.4.2.2 2020-01-02 [2] CRAN (R 3.6.2)
Biobase * 2.46.0 2019-10-29 [2] Bioconductor
BiocFileCache 1.10.2 2019-11-08 [2] Bioconductor
BiocGenerics * 0.32.0 2019-10-29 [2] Bioconductor
BiocParallel 1.20.1 2019-12-21 [2] Bioconductor
biomaRt 2.42.0 2019-10-29 [2] Bioconductor
Biostrings 2.54.0 2019-10-29 [2] Bioconductor
bit 1.1-15.2 2020-02-10 [2] CRAN (R 3.6.2)
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bitops 1.0-6 2013-08-17 [2] CRAN (R 3.6.0)
blob 1.2.1 2020-01-20 [2] CRAN (R 3.6.2)
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callr 3.4.2 2020-02-12 [2] CRAN (R 3.6.2)
cellranger 1.1.0 2016-07-27 [2] CRAN (R 3.6.0)
cli 2.0.1 2020-01-08 [2] CRAN (R 3.6.2)
cluster 2.1.0 2019-06-19 [2] CRAN (R 3.6.1)
codetools 0.2-16 2018-12-24 [4] CRAN (R 3.6.0)
colorspace 1.4-1 2019-03-18 [2] CRAN (R 3.6.0)
crayon 1.3.4 2017-09-16 [2] CRAN (R 3.6.0)
curl 4.3 2019-12-02 [2] CRAN (R 3.6.2)
data.table 1.12.8 2019-12-09 [2] CRAN (R 3.6.2)
DBI 1.1.0 2019-12-15 [2] CRAN (R 3.6.2)
dbplyr 1.4.2 2019-06-17 [2] CRAN (R 3.6.0)
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desc 1.2.0 2018-05-01 [2] CRAN (R 3.6.0)
devtools 2.2.2 2020-02-17 [2] CRAN (R 3.6.2)
digest 0.6.25 2020-02-23 [2] CRAN (R 3.6.2)
dplyr * 0.8.4 2020-01-31 [2] CRAN (R 3.6.2)
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fansi 0.4.1 2020-01-08 [2] CRAN (R 3.6.2)
fastmatch 1.1-0 2017-01-28 [2] CRAN (R 3.6.0)
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