Last updated: 2020-04-02
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Knit directory: 20170327_Psen2S4Ter_RNASeq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 33f5f93 | Steve Ped | 2020-03-07 | Updated html after minor formatting |
| Rmd | 3b120d9 | Steve Ped | 2020-03-07 | Corrected table justifications |
| html | a5cdf5d | Steve Ped | 2020-03-06 | Finished Revisions |
| Rmd | 154085b | Steve Ped | 2020-03-06 | Reduced ranked lists to fry only |
| Rmd | 433a729 | Steve Ped | 2020-03-05 | Prepared to remove EGSEA-type approach |
| Rmd | b617456 | Steve Ped | 2020-03-05 | Tweaked comparison of mutants |
| html | 1385ceb | Steve Ped | 2020-03-05 | Compiled after renaming columns |
| 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$fitted.values |
| Rmd | afdf91e | Steve Ped | 2020-03-04 | Fixed typo |
| html | e0288c6 | Steve Ped | 2020-02-19 | Generated enrichment tables |
| Rmd | 541fbf0 | Steve Ped | 2020-02-19 | Revised Hom Vs Het Enrichment |
| html | 876e40f | Steve Ped | 2020-02-17 | Compiled after minor corrections |
| Rmd | 53ed0e3 | Steve Ped | 2020-02-17 | Corrected Ensembl Release |
| Rmd | f55be85 | Steve Ped | 2020-02-17 | Added commas |
| Rmd | 55143d4 | Steve Ped | 2020-02-17 | Corrected gsea analyses |
| html | 9104ecd | Steve Ped | 2020-01-28 | First draft of Hom Vs Het |
| Rmd | 2cc69b5 | Steve Ped | 2020-01-28 | Added data load correction |
| Rmd | 207cdc8 | Steve Ped | 2020-01-28 | Added code for Hom Vs Het Enrichment |
library(tidyverse)
library(magrittr)
library(edgeR)
library(scales)
library(pander)
library(msigdbr)
library(AnnotationDbi)
library(RColorBrewer)
library(ngsReports)theme_set(theme_bw())
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
panderOptions("big.mark", ",")samples <- here::here("data/samples.csv") %>%
read_csv() %>%
distinct(sampleName, .keep_all = TRUE) %>%
dplyr::select(sample = sampleName, sampleID, genotype) %>%
mutate(
genotype = factor(genotype, levels = c("WT", "Het", "Hom")),
mutant = genotype %in% c("Het", "Hom"),
homozygous = genotype == "Hom"
)
genoCols <- samples$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. For enrichment within larger gene lists, fry can take into account inter-gene correlations. Values supplied will be logCPM for each gene/sample after being adjusted for GC and length biases.
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 3,375 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 3,530 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 10,975 Ensembl IDs were mapped to pathways from restricted database of 8,825 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)),
de_id = lapply(
X = gene_id,
FUN = intersect,
y = dplyr::filter(deTable, DE)$gene_id
),
de_size = vapply(de_id, length, integer(1))
)hmFry <- cpmPostNorm %>%
fry(
index = hmByID,
design = dgeList$design,
contrast = "homozygous",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()Using the directional results from fry, no Hallmark Gene Sets were detected as different between mutant genotypes, with a minimum FDR being found as 64%. Similarly for a non-directional approach, no Hallmark Gene Sets were detected as different, with a minimum FDR.Mixed being 81%.
kgFry <-cpmPostNorm%>%
fry(
index = kgByID,
design = dgeList$design,
contrast = "homozygous",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()Using the directional results from fry, no KEGG Gene Sets were detected as different between mutant genotypes, with a minimum FDR being found as 14%.
kgFry %>%
arrange(PValue.Mixed) %>%
dplyr::select(gs_name, NGenes, contains("Mixed")) %>%
set_names(str_remove(names(.), ".Mixed")) %>%
dplyr::filter(FDR < 0.05) %>%
dplyr::select(
`KEGG Gene Set` = gs_name,
`Expressed Genes` = NGenes,
PValue, FDR
) %>%
mutate_at(
vars(PValue, FDR), formatP
) %>%
pander(
justify = "lrrr",
caption = "The only KEGG Gene Set detected as different between mutants."
)| KEGG Gene Set | Expressed Genes | PValue | FDR |
|---|---|---|---|
| KEGG_GLYCINE_SERINE_AND_THREONINE_METABOLISM | 24 | 0.0003 | 0.0474 |
Using a non-directional (i.e. Mixed) approach, one KEGG gene set was detected as different. However, an FDR of 0.047 in this instance does not provide much confidence to this observation.
goFry <- cpmPostNorm %>%
fry(
index = goByID,
design = dgeList$design,
contrast = "homozygous",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()No GO Gene Sets were detected as different between mutant genotypes, with a minimum FDR being found as 15%.
goFry %>%
arrange(PValue.Mixed) %>%
dplyr::select(gs_name, NGenes, contains("Mixed")) %>%
set_names(str_remove(names(.), ".Mixed")) %>%
dplyr::filter(FDR < 0.05) %>%
dplyr::select(
`GO Gene Set` = gs_name,
`Expressed Genes` = NGenes,
PValue, FDR
) %>%
mutate_at(
vars(PValue, FDR), formatP
) %>%
pander(
justify = "lrrr",
caption = "The only GO Gene Set detected as different between mutants."
)| GO Gene Set | Expressed Genes | PValue | FDR |
|---|---|---|---|
| GO_REGULATION_OF_AUTOPHAGOSOME_MATURATION | 11 | 5.05e-06 | 0.0446 |
Again, using a non-directional (i.e. Mixed) approach, one GO gene set was detected as different. However, an FDR of 0.046 in this instance does not provide much confidence to this observation.
All enriched gene sets terms with an FDR adjusted p-value < 0.05 were exported as a single csv file.
bind_rows(
hmFry,
kgFry,
goFry
) %>%
dplyr::filter(FDR.Mixed < 0.05) %>%
left_join(gsSizes) %>%
dplyr::select(
gs_name, NGenes,
PValue = PValue.Mixed,
FDR = FDR.Mixed,
gene_symbol, de_size
) %>%
mutate(
DE = lapply(
X = gene_symbol,
FUN = intersect,
y = dplyr::filter(deTable, DE)$gene_name
),
DE = vapply(DE, paste, character(1), collapse = ";")
) %>%
dplyr::select(
`Gene Set` = gs_name,
`Nbr Detected Genes` = NGenes,
`Nbr DE Genes` = de_size,
PValue, FDR,
`DE Genes` = DE
) %>%
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 2020-04-02
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