Last updated: 2020-03-06

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Knit directory: 20170327_Psen2S4Ter_RNASeq/

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File Version Author Date Message
Rmd 154085b Steve Ped 2020-03-06 Reduced ranked lists to fry only
Rmd 433a729 Steve Ped 2020-03-06 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 2020-03-05 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

Setup

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
}

Introduction

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.

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.

Hallmark Gene Sets

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.

KEGG 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.

Gene Ontology 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 <- 3
go <- 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)),
    de_id = lapply(
      X = gene_id, 
      FUN = intersect, 
      y = dplyr::filter(deTable, DE)$gene_id
      ),
    de_size = vapply(de_id, length, integer(1))
  )

Enrichment Testing on Ranked Lists

Hallmark Gene Sets

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 65%. Similarly for a non-directional approach, no Hallmark Gene Sets were detected as different, with a minimum FDR.Mixed being 80%.

KEGG Gene Sets

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 21%.

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(
    caption = "The only KEGG Gene Set detected as different between mutants."
  )
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 0.0473

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.

GO Gene Sets

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 14%.

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(
    caption = "The only GO Gene Set detected as different between mutants."
  )
The only GO Gene Set detected as different between mutants.
GO Gene Set Expressed Genes PValue FDR
GO_REGULATION_OF_AUTOPHAGOSOME_MATURATION 11 5.17e-06 0.0457

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.

Data Export

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/Melbourne         
 date     2020-03-06                  

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