Last updated: 2020-03-05

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File Version Author Date Message
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(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
}

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. Testing for enrichment with ranked lists will be performed using:

  1. 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.
  2. 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.
  3. 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.

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)))

Enrichment Testing on Ranked Lists

rnk <- structure(
  -sign(deTable$logFC)*log10(deTable$PValue), 
  names = deTable$gene_id
) %>% sort()
np <- 1e5

Genes 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.

Hallmark Gene Sets

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"
  )
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
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

KEGG Gene Sets

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"
  )
Results from combining all above approaches for the KEGG Gene Sets. All terms are significant to an FDR of 0.05.
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

GO Gene Sets

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"
  )
Results from combining all above approaches for the GO Gene Sets. All terms are significant to an FDR of 0.05.
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

Data Export

All enriched gene sets terms with an FDR adjusted p-value < 0.05 were exported as a single csv file.

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(
    gs_name, gs_size, nDE, wilkinson_p, FDR, fry, camera, gsea, 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     2020-03-05                  

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 here                   0.1        2017-05-28 [2] CRAN (R 3.6.0)
 hms                    0.5.3      2020-01-08 [2] CRAN (R 3.6.2)
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[1] /home/steveped/R/x86_64-pc-linux-gnu-library/3.6
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library