Last updated: 2020-01-28

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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 <- 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
}

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.

Two steps for enrichment analysis will be undertaken.

  1. Testing for enrichment within discrete sets of DE genes as defined in the previous steps
  2. Testing for enrichment within ranked lists, regardless of DE status or statistical significance, before integration of all enrichment analyses using Fisher’s method for combining p-values.

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:

  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)) 
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.

KEGG 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.

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) 
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.

Enrichment in the DE Gene Set

deTable <- file.path("output", "psen2VsWT.csv") %>% 
  read_csv() %>%
  mutate(
    entrezid = dgeList$genes$entrezid[gene_id]
  )

The first step of analysis using goseq, regardless of the gene-set, is estimation of the probability weight function (PWF) which quantifies the probability of a gene being considered as DE based on a single covariate. As GC content and length should have been accounted for during conditional-quantile normalisation, these were not required for any bias. However, the gene-level correlations with rRNA contamination were instead used a predictor of bias in selection of a gene as being DE.

rawFqc <- list.files(
  path = "data/0_rawData/FastQC/",
  pattern = "zip",
  full.names = TRUE
) %>%
  FastqcDataList()
gc <- getModule(rawFqc, "Per_sequence_GC") %>%
  group_by(Filename) %>% 
  mutate(Freq = Count / sum(Count)) %>%
  ungroup()
gcDev <- gc %>%
  left_join(getGC(gcTheoretical, "Drerio", "Trans")) %>%
  mutate(
    sample = str_remove(Filename, "_R[12].fastq.gz"),
    resid = Freq - Drerio
  ) %>% 
  left_join(samples) %>%
  group_by(sample) %>%
  summarise(
    ss = sum(resid^2),
    n = n(),
    sd = sqrt(ss / (n - 1))
  )
riboVec <- structure(gcDev$sd, names = gcDev$sample)
riboCors <- cpm(dgeList, log = TRUE) %>%
  apply(1, function(x){
    cor(x, riboVec[names(x)])
  })

Values were calculated as per the previous steps, using the logCPM values for each gene, with the sample-level standard deviations from the theoretical GC distribution being used as a measure of rRNA contamination. For estimation of the probability weight function, squared correlations were used to place negative and positive correlations on the same scale. This accounts for genes which are both negatively & positively biased by the presence of excessive rRNA. Clearly, the confounding of genotype with rRNA means that some genes driven by the genuine biology may be down-weighted under this approach.

riboPwf <- deTable %>%
  mutate(riboCors = riboCors[gene_id]^2) %>%
  dplyr::select(gene_id, DE, riboCors) %>%
  distinct(gene_id, .keep_all = TRUE) %>% 
  with(
    nullp(
      DEgenes = structure(
        as.integer(DE), names = gene_id
      ), 
      genome = "danRer10", 
      id = "ensGene", 
      bias.data = riboCors,
      plot.fit = FALSE
    )
  )
plotPWF(riboPwf, main = "Bias from rRNA proportions")
*Using this approach, it was clear that correlation with rRNA proportions significantly biased the probability of a gene being considered as DE.*

Using this approach, it was clear that correlation with rRNA proportions significantly biased the probability of a gene being considered as DE.

All gene-sets were then tested using this PWF.

Hallmark Gene Sets

hmRiboGoseq <- goseq(riboPwf, gene2cat = hmByGene) %>%
  as_tibble %>%
  dplyr::filter(numDEInCat > 0) %>%
  mutate( 
    adjP = p.adjust(over_represented_pvalue, method = "bonf"),
    FDR = p.adjust(over_represented_pvalue, method = "fdr")
  ) %>%
  dplyr::select(-contains("under")) %>%
  dplyr::rename(
    gs_name = category,
    PValue = over_represented_pvalue
  )

Once again, no gene-sets achieved significance with the lowest FDR being 41%

KEGG Gene Sets

kgRiboGoseq <- goseq(riboPwf, gene2cat = kgByGene) %>%
  as_tibble %>%
  dplyr::filter(numDEInCat > 0) %>%
  mutate( 
    adjP = p.adjust(over_represented_pvalue, method = "bonf"),
    FDR = p.adjust(over_represented_pvalue, method = "fdr")
  ) %>%
  dplyr::select(-contains("under"))  %>%
  dplyr::rename(
    gs_name = category,
    PValue = over_represented_pvalue
  )
kgRiboGoseq %>%
  dplyr::slice(1:5) %>%
  mutate(
    p = formatP(PValue),
    adjP = formatP(adjP),
    FDR = formatP(FDR)
  ) %>%
  dplyr::select(
    `Gene Set` = gs_name,
    DE = numDEInCat,
    `Set Size` = numInCat,
    PValue,
    `p~bonf~` = adjP,
    `p~FDR~` = FDR
  ) %>%
  pander(
    justify = "lrrrrr",
    caption = paste(
      "The", nrow(.), "most highly-ranked KEGG pathways.",
      "Bonferroni-adjusted p-values are the most stringent and give high",
      "confidence when below 0.05."
    )
  )
The 5 most highly-ranked KEGG pathways. Bonferroni-adjusted p-values are the most stringent and give high confidence when below 0.05.
Gene Set DE Set Size PValue pbonf pFDR
KEGG_RIBOSOME 26 80 4.754e-09 5.32e-07 5.32e-07
KEGG_PRIMARY_IMMUNODEFICIENCY 2 15 0.01642 1.0000 0.7173
KEGG_CYSTEINE_AND_METHIONINE_METABOLISM 4 26 0.02959 1.0000 0.7173
KEGG_ASTHMA 1 3 0.04088 1.0000 0.7173
KEGG_RETINOL_METABOLISM 3 25 0.0512 1.0000 0.7173

Notably, the KEGG gene-set for Ribosomal genes was detected as enriched with no other KEGG gene-sets being considered significant.

GO Gene Sets

goRiboGoseq <- goseq(riboPwf, gene2cat = goByGene) %>%
  as_tibble %>%
  dplyr::filter(numDEInCat > 0) %>%
  mutate( 
    adjP = p.adjust(over_represented_pvalue, method = "bonf"),
    FDR = p.adjust(over_represented_pvalue, method = "fdr")
  ) %>%
  dplyr::select(-contains("under")) %>%
  dplyr::rename(
    gs_name = category,
    PValue = over_represented_pvalue
  )
goRiboGoseq %>%
  dplyr::filter(adjP < 0.05) %>%
  mutate(
    p = formatP(PValue),
    adjP = formatP(adjP),
    FDR = formatP(FDR)
  ) %>%
  dplyr::select(
    `Gene Set` = gs_name,
    DE = numDEInCat,
    `Set Size` = numInCat,
    PValue,
    `p~bonf~` = adjP,
    `p~FDR~` = FDR
  ) %>%
  pander(
    justify = "lrrrrr",
    caption = paste(
      "*The", nrow(.), "most highly-ranked GO terms.",
      "Bonferroni-adjusted p-values are the most stringent and give high",
      "confidence when below 0.05, with all terms here reaching this threshold.",
      "However, most terms once again indicate the presence of rRNA.*"
    )
  )
The 14 most highly-ranked GO terms. Bonferroni-adjusted p-values are the most stringent and give high confidence when below 0.05, with all terms here reaching this threshold. However, most terms once again indicate the presence of rRNA.
Gene Set DE Set Size PValue pbonf pFDR
GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE 28 93 7.36e-10 2.83e-06 2.03e-06
GO_CYTOSOLIC_RIBOSOME 27 97 1.056e-09 4.06e-06 2.03e-06
GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM 27 105 1.6e-08 6.16e-05 2.05e-05
GO_TRANSLATIONAL_INITIATION 29 177 2.178e-07 0.0008 0.0002
GO_PROTEIN_TARGETING_TO_MEMBRANE 30 171 2.533e-07 0.0010 0.0002
GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM 27 128 3.849e-07 0.0015 0.0002
GO_CYTOSOLIC_PART 30 206 4.424e-07 0.0017 0.0002
GO_VIRAL_GENE_EXPRESSION 29 172 6.939e-07 0.0027 0.0003
GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS 30 183 7.911e-07 0.0030 0.0003
GO_RIBOSOMAL_SUBUNIT 28 175 1.99e-06 0.0077 0.0008
GO_RIBOSOME 30 210 2.659e-06 0.0102 0.0009
GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT 15 51 2.826e-06 0.0109 0.0009
GO_SYMPORTER_ACTIVITY 10 94 7.571e-06 0.0291 0.0022
GO_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 17 228 1.23e-05 0.0473 0.0034

Enrichment Testing on Ranked Lists

rnk <- structure(
  -sign(deTable$logFC)*log10(deTable$PValue), 
  names = deTable$gene_id
) %>% sort()
np <- 5e5

Genes were ranked by p-value alone 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 <- fit$fitted.values %>%
  cpm(log = TRUE) %>%
  fry(
    index = hmByID,
    design = fit$design,
    contrast = "mutant",
    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 = "mutant",
    inter.gene.cor = NULL
    ) %>%
  rownames_to_column("gs_name") %>%
  as_tibble()

For generation of the GSEA ranked list, 510^{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)
  ) %>%
  left_join(
    dplyr::select(hmRiboGoseq, gs_name, goseq = PValue)
  ) %>%
  pivot_longer(
    cols = one_of(c("fry", "camera", "gsea", "goseq")),
    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 < 0.05) %>%
  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"
  )
Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant to an FDR of 0.05.
gs_name n p FDR adjP
HALLMARK_XENOBIOTIC_METABOLISM 4 0.0001 0.0057 0.0057
HALLMARK_MYC_TARGETS_V1 4 0.0003 0.0069 0.0138
HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY 4 0.0009 0.0143 0.0428
HALLMARK_ADIPOGENESIS 4 0.0019 0.0243 0.0971
HALLMARK_OXIDATIVE_PHOSPHORYLATION 4 0.0024 0.0243 0.1217

KEGG Gene Sets

kgFry <- fit$fitted.values %>%
  cpm(log = TRUE) %>%
  fry(
    index = kgByID,
    design = fit$design,
    contrast = "mutant",
    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 = "mutant",
    inter.gene.cor = NULL
    ) %>%
  rownames_to_column("gs_name") %>%
  as_tibble()

For generation of the GSEA ranked list, 510^{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)
  ) %>%
  left_join(
    dplyr::select(kgRiboGoseq, gs_name, goseq = PValue)
  ) %>%
  pivot_longer(
    cols = one_of(c("fry", "camera", "gsea", "goseq")),
    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 < 0.01) %>%
  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 to an FDR of 0.01.",
    justify = "lrrrr"
  )
Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant to an FDR of 0.01.
gs_name n p FDR adjP
KEGG_RIBOSOME 4 4.22e-16 7.85e-14 7.85e-14
KEGG_BUTANOATE_METABOLISM 3 9.17e-09 8.53e-07 1.71e-06
KEGG_PORPHYRIN_AND_CHLOROPHYLL_METABOLISM 3 1.80e-07 1.11e-05 3.34e-05
KEGG_VALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION 3 2.87e-07 1.33e-05 5.33e-05
KEGG_LIMONENE_AND_PINENE_DEGRADATION 3 1.77e-06 5.52e-05 0.0003
KEGG_ASCORBATE_AND_ALDARATE_METABOLISM 3 1.78e-06 5.52e-05 0.0003
KEGG_BIOSYNTHESIS_OF_UNSATURATED_FATTY_ACIDS 3 4.26e-06 0.0001 0.0008
KEGG_PRIMARY_IMMUNODEFICIENCY 4 1.75e-05 0.0004 0.0033
KEGG_PROXIMAL_TUBULE_BICARBONATE_RECLAMATION 3 1.78e-05 0.0004 0.0033
KEGG_OXIDATIVE_PHOSPHORYLATION 4 3.59e-05 0.0007 0.0067
KEGG_CYSTEINE_AND_METHIONINE_METABOLISM 4 0.0001 0.0017 0.0189
KEGG_TERPENOID_BACKBONE_BIOSYNTHESIS 3 0.0001 0.0023 0.0274
KEGG_PEROXISOME 3 0.0002 0.0033 0.0427
KEGG_ASTHMA 4 0.0003 0.0035 0.0493
KEGG_PARKINSONS_DISEASE 4 0.0003 0.0035 0.0534
KEGG_VALINE_LEUCINE_AND_ISOLEUCINE_BIOSYNTHESIS 3 0.0003 0.0035 0.0566
KEGG_RETINOL_METABOLISM 4 0.0005 0.0056 0.0960

GO Gene Sets

goFry <- fit$fitted.values %>%
  cpm(log = TRUE) %>%
  fry(
    index = goByID,
    design = fit$design,
    contrast = "mutant",
    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 = "mutant",
    inter.gene.cor = NULL
    ) %>%
  rownames_to_column("gs_name") %>%
  as_tibble()

For generation of the GSEA ranked list, 510^{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)
  ) %>%
  left_join(
    dplyr::select(goRiboGoseq, gs_name, goseq = PValue)
  ) %>%
  pivot_longer(
    cols = one_of(c("fry", "camera", "gsea", "goseq")),
    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"
  )
Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant using a Bonferroni-adjusted p-value < 0.01.
gs_name n p FDR adjP
GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT 4 3.87e-16 4.30e-13 3.42e-12
GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE 4 4.34e-16 4.30e-13 3.83e-12
GO_CYTOSOLIC_RIBOSOME 4 4.49e-16 4.30e-13 3.96e-12
GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM 4 4.50e-16 4.30e-13 3.97e-12
GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM 4 4.77e-16 4.30e-13 4.22e-12
GO_VIRAL_GENE_EXPRESSION 4 5.31e-16 4.30e-13 4.69e-12
GO_PROTEIN_TARGETING_TO_MEMBRANE 4 5.31e-16 4.30e-13 4.69e-12
GO_TRANSLATIONAL_INITIATION 4 5.38e-16 4.30e-13 4.76e-12
GO_RIBOSOMAL_SUBUNIT 4 5.43e-16 4.30e-13 4.80e-12
GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS 4 5.43e-16 4.30e-13 4.80e-12
GO_CYTOSOLIC_PART 4 5.84e-16 4.30e-13 5.16e-12
GO_RIBOSOME 4 5.84e-16 4.30e-13 5.16e-12
GO_LARGE_RIBOSOMAL_SUBUNIT 4 8.71e-12 5.92e-09 7.70e-08
GO_RNA_CATABOLIC_PROCESS 4 3.70e-11 2.33e-08 3.27e-07
GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_MEMBRANE 4 4.55e-11 2.68e-08 4.02e-07
GO_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT 4 6.56e-11 3.62e-08 5.80e-07
GO_PROTEIN_TARGETING 4 2.20e-10 1.14e-07 1.94e-06
GO_RRNA_BINDING 4 3.48e-10 1.71e-07 3.07e-06
GO_PROTON_EXPORTING_ATPASE_ACTIVITY 3 4.98e-09 2.27e-06 4.40e-05
GO_SYMPORTER_ACTIVITY 4 5.13e-09 2.27e-06 4.53e-05
GO_DRUG_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 4 1.08e-08 4.53e-06 9.52e-05
GO_PHAGOSOME_ACIDIFICATION 3 1.52e-08 6.10e-06 0.0001
GO_ORGANIC_ACID_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 4 1.81e-08 6.96e-06 0.0002
GO_ORGANIC_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 4 2.33e-08 8.58e-06 0.0002
GO_TRABECULA_FORMATION 4 2.79e-08 9.86e-06 0.0002
GO_ORGANIC_CYCLIC_COMPOUND_CATABOLIC_PROCESS 4 3.73e-08 1.27e-05 0.0003
GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ORGANELLE 4 7.96e-08 2.60e-05 0.0007
GO_ORGANIC_ACID_TRANSMEMBRANE_TRANSPORT 4 8.97e-08 2.83e-05 0.0008
GO_NEGATIVE_REGULATION_OF_INSULIN_SECRETION_INVOLVED_IN_CELLULAR_RESPONSE_TO_GLUCOSE_STIMULUS 4 1.06e-07 3.22e-05 0.0009
GO_PROTON_TRANSPORTING_V_TYPE_ATPASE_COMPLEX 3 1.36e-07 3.99e-05 0.0012
GO_SECONDARY_ACTIVE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 4 1.87e-07 5.32e-05 0.0016
GO_RIBOSOME_ASSEMBLY 4 1.97e-07 5.40e-05 0.0017
GO_DRUG_TRANSMEMBRANE_TRANSPORT 4 2.02e-07 5.40e-05 0.0018
GO_POLYSOMAL_RIBOSOME 4 2.58e-07 6.60e-05 0.0023
GO_L_AMINO_ACID_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 4 2.62e-07 6.60e-05 0.0023
GO_AEROBIC_ELECTRON_TRANSPORT_CHAIN 4 2.99e-07 7.34e-05 0.0026
GO_CYTOPLASMIC_TRANSLATION 4 3.11e-07 7.44e-05 0.0028
GO_INNER_MITOCHONDRIAL_MEMBRANE_PROTEIN_COMPLEX 4 3.77e-07 8.77e-05 0.0033
GO_SMALL_RIBOSOMAL_SUBUNIT 4 3.93e-07 8.90e-05 0.0035
GO_ACTIVE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 4 4.19e-07 9.25e-05 0.0037
GO_PYRUVATE_DEHYDROGENASE_COMPLEX 3 4.59e-07 9.89e-05 0.0041
GO_UBIQUITIN_LIGASE_INHIBITOR_ACTIVITY 4 6.91e-07 0.0001 0.0061
GO_AMINO_ACID_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 4 7.80e-07 0.0002 0.0069
GO_GONADAL_MESODERM_DEVELOPMENT 3 7.82e-07 0.0002 0.0069
GO_PEPTIDE_BIOSYNTHETIC_PROCESS 4 9.11e-07 0.0002 0.0081 

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 ───────────────────────────────────────────────────────────────────
 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)
 assertthat             0.2.1      2019-03-21 [2] CRAN (R 3.6.0)
 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                    1.1-15.1   2020-01-14 [2] CRAN (R 3.6.2)
 bit64                  0.9-7      2017-05-08 [2] CRAN (R 3.6.0)
 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)
 broom                  0.5.3      2019-12-14 [2] CRAN (R 3.6.2)
 callr                  3.4.1      2020-01-24 [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)
 DelayedArray           0.12.2     2020-01-06 [2] Bioconductor  
 desc                   1.2.0      2018-05-01 [2] CRAN (R 3.6.0)
 devtools               2.2.1      2019-09-24 [2] CRAN (R 3.6.1)
 digest                 0.6.23     2019-11-23 [2] CRAN (R 3.6.1)
 dplyr                * 0.8.3      2019-07-04 [2] CRAN (R 3.6.1)
 edgeR                * 3.28.0     2019-10-29 [2] Bioconductor  
 ellipsis               0.3.0      2019-09-20 [2] CRAN (R 3.6.1)
 evaluate               0.14       2019-05-28 [2] CRAN (R 3.6.0)
 FactoMineR             2.1        2020-01-17 [2] CRAN (R 3.6.2)
 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)
 fgsea                * 1.12.0     2019-10-29 [2] Bioconductor  
 flashClust             1.01-2     2012-08-21 [2] CRAN (R 3.6.1)
 forcats              * 0.4.0      2019-02-17 [2] CRAN (R 3.6.0)
 fs                     1.3.1      2019-05-06 [2] CRAN (R 3.6.0)
 gbRd                   0.4-11     2012-10-01 [2] CRAN (R 3.6.0)
 geneLenDataBase      * 1.22.0     2019-11-05 [2] Bioconductor  
 generics               0.0.2      2018-11-29 [2] CRAN (R 3.6.0)
 GenomeInfoDb           1.22.0     2019-10-29 [2] Bioconductor  
 GenomeInfoDbData       1.2.2      2019-11-21 [2] Bioconductor  
 GenomicAlignments      1.22.1     2019-11-12 [2] Bioconductor  
 GenomicFeatures        1.38.1     2020-01-22 [2] Bioconductor  
 GenomicRanges          1.38.0     2019-10-29 [2] Bioconductor  
 ggdendro               0.1-20     2016-04-27 [2] CRAN (R 3.6.0)
 ggplot2              * 3.2.1      2019-08-10 [2] CRAN (R 3.6.1)
 ggrepel                0.8.1      2019-05-07 [2] CRAN (R 3.6.0)
 git2r                  0.26.1     2019-06-29 [2] CRAN (R 3.6.1)
 glue                   1.3.1      2019-03-12 [2] CRAN (R 3.6.0)
 GO.db                  3.10.0     2019-11-21 [2] Bioconductor  
 goseq                * 1.38.0     2019-10-29 [2] Bioconductor  
 gridExtra              2.3        2017-09-09 [2] CRAN (R 3.6.0)
 gtable                 0.3.0      2019-03-25 [2] CRAN (R 3.6.0)
 haven                  2.2.0      2019-11-08 [2] CRAN (R 3.6.1)
 highr                  0.8        2019-03-20 [2] CRAN (R 3.6.0)
 hms                    0.5.3      2020-01-08 [2] CRAN (R 3.6.2)
 htmltools              0.4.0      2019-10-04 [2] CRAN (R 3.6.1)
 htmlwidgets            1.5.1      2019-10-08 [2] CRAN (R 3.6.1)
 httpuv                 1.5.2      2019-09-11 [2] CRAN (R 3.6.1)
 httr                   1.4.1      2019-08-05 [2] CRAN (R 3.6.1)
 hwriter                1.3.2      2014-09-10 [2] CRAN (R 3.6.0)
 IRanges              * 2.20.2     2020-01-13 [2] Bioconductor  
 jpeg                   0.1-8.1    2019-10-24 [2] CRAN (R 3.6.1)
 jsonlite               1.6        2018-12-07 [2] CRAN (R 3.6.0)
 kableExtra             1.1.0      2019-03-16 [2] CRAN (R 3.6.1)
 knitr                  1.27       2020-01-16 [2] CRAN (R 3.6.2)
 later                  1.0.0      2019-10-04 [2] CRAN (R 3.6.1)
 lattice                0.20-38    2018-11-04 [4] CRAN (R 3.6.0)
 latticeExtra           0.6-29     2019-12-19 [2] CRAN (R 3.6.2)
 lazyeval               0.2.2      2019-03-15 [2] CRAN (R 3.6.0)
 leaps                  3.1        2020-01-16 [2] CRAN (R 3.6.2)
 lifecycle              0.1.0      2019-08-01 [2] CRAN (R 3.6.1)
 limma                * 3.42.0     2019-10-29 [2] Bioconductor  
 locfit                 1.5-9.1    2013-04-20 [2] CRAN (R 3.6.0)
 lubridate              1.7.4      2018-04-11 [2] CRAN (R 3.6.0)
 magrittr             * 1.5        2014-11-22 [2] CRAN (R 3.6.0)
 MASS                   7.3-51.5   2019-12-20 [4] CRAN (R 3.6.2)
 Matrix                 1.2-18     2019-11-27 [2] CRAN (R 3.6.1)
 matrixStats            0.55.0     2019-09-07 [2] CRAN (R 3.6.1)
 memoise                1.1.0      2017-04-21 [2] CRAN (R 3.6.0)
 metap                * 1.3        2020-01-23 [2] CRAN (R 3.6.2)
 mgcv                   1.8-31     2019-11-09 [4] CRAN (R 3.6.1)
 mnormt                 1.5-5      2016-10-15 [2] CRAN (R 3.6.1)
 modelr                 0.1.5      2019-08-08 [2] CRAN (R 3.6.1)
 msigdbr              * 7.0.1      2019-09-04 [2] CRAN (R 3.6.2)
 multcomp               1.4-12     2020-01-10 [2] CRAN (R 3.6.2)
 multtest               2.42.0     2019-10-29 [2] Bioconductor  
 munsell                0.5.0      2018-06-12 [2] CRAN (R 3.6.0)
 mutoss                 0.1-12     2017-12-04 [2] CRAN (R 3.6.2)
 mvtnorm                1.0-12     2020-01-09 [2] CRAN (R 3.6.2)
 ngsReports           * 1.1.2      2019-10-16 [1] Bioconductor  
 nlme                   3.1-143    2019-12-10 [4] CRAN (R 3.6.2)
 numDeriv               2016.8-1.1 2019-06-06 [2] CRAN (R 3.6.2)
 openssl                1.4.1      2019-07-18 [2] CRAN (R 3.6.1)
 pander               * 0.6.3      2018-11-06 [2] CRAN (R 3.6.0)
 pillar                 1.4.3      2019-12-20 [2] CRAN (R 3.6.2)
 pkgbuild               1.0.6      2019-10-09 [2] CRAN (R 3.6.1)
 pkgconfig              2.0.3      2019-09-22 [2] CRAN (R 3.6.1)
 pkgload                1.0.2      2018-10-29 [2] CRAN (R 3.6.0)
 plotly                 4.9.1      2019-11-07 [2] CRAN (R 3.6.1)
 plotrix                3.7-7      2019-12-05 [2] CRAN (R 3.6.2)
 plyr                   1.8.5      2019-12-10 [2] CRAN (R 3.6.2)
 png                    0.1-7      2013-12-03 [2] CRAN (R 3.6.0)
 prettyunits            1.1.1      2020-01-24 [2] CRAN (R 3.6.2)
 processx               3.4.1      2019-07-18 [2] CRAN (R 3.6.1)
 progress               1.2.2      2019-05-16 [2] CRAN (R 3.6.0)
 promises               1.1.0      2019-10-04 [2] CRAN (R 3.6.1)
 ps                     1.3.0      2018-12-21 [2] CRAN (R 3.6.0)
 purrr                * 0.3.3      2019-10-18 [2] CRAN (R 3.6.1)
 R6                     2.4.1      2019-11-12 [2] CRAN (R 3.6.1)
 rappdirs               0.3.1      2016-03-28 [2] CRAN (R 3.6.0)
 RColorBrewer         * 1.1-2      2014-12-07 [2] CRAN (R 3.6.0)
 Rcpp                 * 1.0.3      2019-11-08 [2] CRAN (R 3.6.1)
 RCurl                  1.98-1.1   2020-01-19 [2] CRAN (R 3.6.2)
 Rdpack                 0.11-1     2019-12-14 [2] CRAN (R 3.6.2)
 readr                * 1.3.1      2018-12-21 [2] CRAN (R 3.6.0)
 readxl                 1.3.1      2019-03-13 [2] CRAN (R 3.6.0)
 remotes                2.1.0      2019-06-24 [2] CRAN (R 3.6.0)
 reprex                 0.3.0      2019-05-16 [2] CRAN (R 3.6.0)
 reshape2               1.4.3      2017-12-11 [2] CRAN (R 3.6.0)
 rlang                  0.4.3      2020-01-24 [2] CRAN (R 3.6.2)
 rmarkdown              2.1        2020-01-20 [2] CRAN (R 3.6.2)
 rprojroot              1.3-2      2018-01-03 [2] CRAN (R 3.6.0)
 Rsamtools              2.2.1      2019-11-06 [2] Bioconductor  
 RSQLite                2.2.0      2020-01-07 [2] CRAN (R 3.6.2)
 rstudioapi             0.10       2019-03-19 [2] CRAN (R 3.6.0)
 rtracklayer            1.46.0     2019-10-29 [2] Bioconductor  
 rvest                  0.3.5      2019-11-08 [2] CRAN (R 3.6.1)
 S4Vectors            * 0.24.3     2020-01-18 [2] Bioconductor  
 sandwich               2.5-1      2019-04-06 [2] CRAN (R 3.6.1)
 scales               * 1.1.0      2019-11-18 [2] CRAN (R 3.6.1)
 scatterplot3d          0.3-41     2018-03-14 [2] CRAN (R 3.6.1)
 sessioninfo            1.1.1      2018-11-05 [2] CRAN (R 3.6.0)
 ShortRead              1.44.1     2019-12-19 [2] Bioconductor  
 sn                     1.5-4      2019-05-14 [2] CRAN (R 3.6.2)
 statmod                1.4.33     2020-01-10 [2] CRAN (R 3.6.2)
 stringi                1.4.5      2020-01-11 [2] CRAN (R 3.6.2)
 stringr              * 1.4.0      2019-02-10 [2] CRAN (R 3.6.0)
 SummarizedExperiment   1.16.1     2019-12-19 [2] Bioconductor  
 survival               3.1-8      2019-12-03 [2] CRAN (R 3.6.2)
 testthat               2.3.1      2019-12-01 [2] CRAN (R 3.6.1)
 TFisher                0.2.0      2018-03-21 [2] CRAN (R 3.6.2)
 TH.data                1.0-10     2019-01-21 [2] CRAN (R 3.6.1)
 tibble               * 2.1.3      2019-06-06 [2] CRAN (R 3.6.0)
 tidyr                * 1.0.2      2020-01-24 [2] CRAN (R 3.6.2)
 tidyselect             0.2.5      2018-10-11 [2] CRAN (R 3.6.0)
 tidyverse            * 1.3.0      2019-11-21 [2] CRAN (R 3.6.1)
 truncnorm              1.0-8      2018-02-27 [2] CRAN (R 3.6.0)
 usethis                1.5.1      2019-07-04 [2] CRAN (R 3.6.1)
 vctrs                  0.2.2      2020-01-24 [2] CRAN (R 3.6.2)
 viridisLite            0.3.0      2018-02-01 [2] CRAN (R 3.6.0)
 webshot                0.5.2      2019-11-22 [2] CRAN (R 3.6.1)
 whisker                0.4        2019-08-28 [2] CRAN (R 3.6.1)
 withr                  2.1.2      2018-03-15 [2] CRAN (R 3.6.0)
 workflowr            * 1.6.0      2019-12-19 [2] CRAN (R 3.6.2)
 xfun                   0.12       2020-01-13 [2] CRAN (R 3.6.2)
 XML                    3.99-0.3   2020-01-20 [2] CRAN (R 3.6.2)
 xml2                   1.2.2      2019-08-09 [2] CRAN (R 3.6.1)
 XVector                0.26.0     2019-10-29 [2] Bioconductor  
 yaml                   2.2.0      2018-07-25 [2] CRAN (R 3.6.0)
 zlibbioc               1.32.0     2019-10-29 [2] Bioconductor  
 zoo                    1.8-7      2020-01-10 [2] CRAN (R 3.6.2)

[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