Last updated: 2020-02-19

Checks: 7 0

Knit directory: 20170327_Psen2S4Ter_RNASeq/

This reproducible R Markdown analysis was created with workflowr (version 1.6.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200119)

The command set.seed(20200119) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 541fbf0

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  output/Enrichment_Hom_V_Het.csv

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 541fbf0 Steve Ped 2020-02-19 Revised Hom Vs Het Enrichment
Rmd 77f167d Steve Ped 2020-02-19 Revised 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 8f29458 Steve Ped 2020-02-17 Minor tweaks to formatting
html 9104ecd Steve Ped 2020-01-28 First draft of Hom Vs Het
Rmd 207cdc8 Steve Ped 2020-01-28 Added code for Hom Vs Het Enrichment
Rmd 468e6e3 Steve Ped 2020-01-28 Ran enrichment of Mut Vs WT
html 468e6e3 Steve Ped 2020-01-28 Ran enrichment of Mut Vs WT
Rmd 3a9933c Steve Ped 2020-01-28 Finished Enrichment analysis on MutVsWt
html 3a9933c Steve Ped 2020-01-28 Finished Enrichment analysis on MutVsWt

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)) %>%
  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 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.

Version Author Date
468e6e3 Steve Ped 2020-01-28

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
  )

No gene-sets achieved significance in the DE genes 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 in the set of DE genes, 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 <- 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 <- 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, 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, including goseq 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)
  ) %>%
  left_join(
    dplyr::select(hmRiboGoseq, gs_name, goseq = PValue)
  ) %>% 
  nest(p = one_of(c("fry", "camera", "gsea", "goseq"))) %>%
  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 < 0.05) %>%
  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 Hallmark Gene Sets. All terms are significant to an FDR of 0.05.",
    justify = "lrrrrr"
  )
Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant to an FDR of 0.05.
Gene Set Number DE Set Size Wilkinsonp pFDR pbonf
HALLMARK_XENOBIOTIC_METABOLISM 11 146 0.0001 0.0057 0.0057
HALLMARK_MYC_TARGETS_V1 15 192 0.0003 0.0069 0.0138
HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY 5 45 0.0009 0.0143 0.0428
HALLMARK_ADIPOGENESIS 13 186 0.0019 0.0243 0.0971
HALLMARK_OXIDATIVE_PHOSPHORYLATION 15 201 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, 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, including goseq 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)
  ) %>%
  left_join(
    dplyr::select(kgRiboGoseq, gs_name, goseq = PValue)
  ) %>% 
  nest(p = one_of(c("fry", "camera", "gsea", "goseq"))) %>%
  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 < 0.05, nDE > 0) %>%
  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. Only Gene Sets with at least one DE gene are shown.",
    justify = "lrrrrr"
  )
Results from combining all above approaches for the KEGG Gene Sets. All terms are significant to an FDR of 0.05. Only Gene Sets with at least one DE gene are shown.
Gene Set Number DE Set Size Wilkinsonp pFDR pbonf
KEGG_RIBOSOME 26 80 5.11e-14 9.51e-12 9.51e-12
KEGG_PRIMARY_IMMUNODEFICIENCY 2 15 1.75e-05 0.0007 0.0033
KEGG_OXIDATIVE_PHOSPHORYLATION 14 119 3.59e-05 0.0010 0.0067
KEGG_CYSTEINE_AND_METHIONINE_METABOLISM 4 26 0.0001 0.0020 0.0189
KEGG_ASTHMA 1 3 0.0003 0.0038 0.0493
KEGG_PARKINSONS_DISEASE 13 112 0.0003 0.0038 0.0534
KEGG_RETINOL_METABOLISM 3 25 0.0005 0.0064 0.0960
KEGG_GLUTATHIONE_METABOLISM 4 36 0.0015 0.0150 0.2704
KEGG_ALLOGRAFT_REJECTION 1 4 0.0016 0.0156 0.2965
KEGG_HUNTINGTONS_DISEASE 15 159 0.0027 0.0227 0.5003
KEGG_HEMATOPOIETIC_CELL_LINEAGE 3 31 0.0047 0.0376 0.8812
KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 3 26 0.0048 0.0376 0.9014
KEGG_RENIN_ANGIOTENSIN_SYSTEM 1 8 0.0059 0.0430 1.0000
KEGG_DRUG_METABOLISM_CYTOCHROME_P450 3 26 0.0060 0.0430 1.0000
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS 5 86 0.0065 0.0450 1.0000

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 inter-gene correlations were directly 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, 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, including goseq 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)
  ) %>%
  left_join(
    dplyr::select(goRiboGoseq, gs_name, goseq = PValue)
  )  %>% 
  nest(p = one_of(c("fry", "camera", "gsea", "goseq"))) %>%
  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(FDR < 0.01, nDE > 0) %>%
  mutate_at(vars(one_of(c("wilkinson_p", "FDR", "adjP"))), formatP) %>%
  mutate(gs_name = str_trunc(gs_name, 80)) %>%
  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 using an FDR < 0.01. Only Gene Sets with at least one DE gene are shown.",
    justify = "lrrrrr"
  )
Results from combining all above approaches for the GO Gene Sets. All terms are significant using an FDR < 0.01. Only Gene Sets with at least one DE gene are shown.
Gene Set Number DE Set Size Wilkinsonp pFDR pbonf
GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT 15 51 4.68e-14 5.23e-11 4.14e-10
GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE 28 93 5.44e-14 5.23e-11 4.81e-10
GO_CYTOSOLIC_RIBOSOME 27 97 5.51e-14 5.23e-11 4.86e-10
GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM 27 105 5.60e-14 5.23e-11 4.95e-10
GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM 27 128 5.96e-14 5.23e-11 5.27e-10
GO_PROTEIN_TARGETING_TO_MEMBRANE 30 171 6.54e-14 5.23e-11 5.78e-10
GO_VIRAL_GENE_EXPRESSION 29 172 6.57e-14 5.23e-11 5.81e-10
GO_RIBOSOMAL_SUBUNIT 28 175 6.58e-14 5.23e-11 5.81e-10
GO_TRANSLATIONAL_INITIATION 29 177 6.61e-14 5.23e-11 5.84e-10
GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS 30 183 6.67e-14 5.23e-11 5.90e-10
GO_CYTOSOLIC_PART 30 206 7.06e-14 5.23e-11 6.24e-10
GO_RIBOSOME 30 210 7.10e-14 5.23e-11 6.27e-10
GO_LARGE_RIBOSOMAL_SUBUNIT 17 108 8.71e-12 5.92e-09 7.70e-08
GO_RNA_CATABOLIC_PROCESS 34 337 3.70e-11 2.33e-08 3.27e-07
GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_MEMBRANE 30 287 4.55e-11 2.68e-08 4.02e-07
GO_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT 11 41 6.56e-11 3.62e-08 5.80e-07
GO_PROTEIN_TARGETING 34 379 2.20e-10 1.14e-07 1.94e-06
GO_RRNA_BINDING 11 57 3.48e-10 1.71e-07 3.07e-06
GO_SYMPORTER_ACTIVITY 10 94 5.13e-09 2.27e-06 4.53e-05
GO_DRUG_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 8 76 1.08e-08 4.53e-06 9.52e-05
GO_ORGANIC_ACID_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 9 105 1.81e-08 6.96e-06 0.0002
GO_ORGANIC_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 11 144 2.33e-08 8.58e-06 0.0002
GO_TRABECULA_FORMATION 4 17 2.79e-08 9.86e-06 0.0002
GO_ORGANIC_CYCLIC_COMPOUND_CATABOLIC_PROCESS 40 474 3.73e-08 1.27e-05 0.0003
GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ORGANELLE 38 484 7.96e-08 2.60e-05 0.0007
GO_ORGANIC_ACID_TRANSMEMBRANE_TRANSPORT 9 106 8.97e-08 2.83e-05 0.0008
GO_NEGATIVE_REGULATION_OF_INSULIN_SECRETION_INVOLVED_IN_CELLULAR_RESPONSE_TO_… 3 9 1.55e-07 4.72e-05 0.0014
GO_SECONDARY_ACTIVE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 13 154 1.87e-07 5.50e-05 0.0016
GO_RIBOSOME_ASSEMBLY 9 57 1.97e-07 5.57e-05 0.0017
GO_DRUG_TRANSMEMBRANE_TRANSPORT 6 65 2.02e-07 5.57e-05 0.0018
GO_POLYSOMAL_RIBOSOME 7 28 2.58e-07 6.80e-05 0.0023
GO_L_AMINO_ACID_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 4 44 2.62e-07 6.80e-05 0.0023
GO_AEROBIC_ELECTRON_TRANSPORT_CHAIN 3 19 2.99e-07 7.55e-05 0.0026
GO_CYTOPLASMIC_TRANSLATION 11 87 3.11e-07 7.64e-05 0.0028
GO_INNER_MITOCHONDRIAL_MEMBRANE_PROTEIN_COMPLEX 16 124 3.77e-07 9.01e-05 0.0033
GO_SMALL_RIBOSOMAL_SUBUNIT 11 69 3.93e-07 9.13e-05 0.0035
GO_ACTIVE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 15 254 4.19e-07 9.49e-05 0.0037
GO_UBIQUITIN_LIGASE_INHIBITOR_ACTIVITY 3 4 6.91e-07 0.0002 0.0061
GO_AMINO_ACID_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 5 60 7.80e-07 0.0002 0.0069
GO_PEPTIDE_BIOSYNTHETIC_PROCESS 38 572 9.11e-07 0.0002 0.0081
GO_ORGANIC_ACID_BIOSYNTHETIC_PROCESS 12 198 1.63e-06 0.0003 0.0144
GO_UBIQUITIN_PROTEIN_TRANSFERASE_INHIBITOR_ACTIVITY 3 5 2.02e-06 0.0004 0.0178
GO_RESPIRATORY_CHAIN_COMPLEX_IV 2 14 2.15e-06 0.0004 0.0190
GO_CELLULAR_AMIDE_METABOLIC_PROCESS 45 805 3.20e-06 0.0006 0.0283
GO_AMIDE_BIOSYNTHETIC_PROCESS 40 678 3.83e-06 0.0007 0.0338
GO_PLATELET_DENSE_GRANULE_LUMEN 2 7 4.18e-06 0.0007 0.0369
GO_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 17 228 4.28e-06 0.0007 0.0378
GO_HEART_TRABECULA_FORMATION 3 11 4.73e-06 0.0008 0.0418
GO_PROSTANOID_BIOSYNTHETIC_PROCESS 3 17 5.26e-06 0.0009 0.0464
GO_ORGANIC_ACID_TRANSPORT 14 240 5.61e-06 0.0009 0.0495
GO_NEGATIVE_REGULATION_OF_UBIQUITIN_PROTEIN_LIGASE_ACTIVITY 3 7 5.85e-06 0.0009 0.0517
GO_POSITIVE_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR 4 17 6.25e-06 0.0010 0.0552
GO_REGULATION_OF_UBIQUITIN_PROTEIN_TRANSFERASE_ACTIVITY 6 44 6.25e-06 0.0010 0.0553
GO_DNA_METHYLATION_OR_DEMETHYLATION 7 56 6.73e-06 0.0010 0.0595
GO_PROTON_TRANSPORTING_ATP_SYNTHASE_COMPLEX 4 20 8.60e-06 0.0012 0.0760
GO_DRUG_CATABOLIC_PROCESS 5 55 1.00e-05 0.0014 0.0883
GO_DRUG_TRANSPORT 8 152 1.05e-05 0.0015 0.0926
GO_NEGATIVE_REGULATION_OF_SMOOTH_MUSCLE_CELL_DIFFERENTIATION 3 14 1.28e-05 0.0017 0.1127
GO_UBIQUITIN_PROTEIN_TRANSFERASE_REGULATOR_ACTIVITY 5 13 1.28e-05 0.0017 0.1127
GO_ANION_TRANSMEMBRANE_TRANSPORT 15 203 1.31e-05 0.0017 0.1160
GO_OXIDATIVE_PHOSPHORYLATION 15 122 1.35e-05 0.0017 0.1189
GO_NEGATIVE_REGULATION_OF_MACROPHAGE_CHEMOTAXIS 2 2 1.78e-05 0.0022 0.1575
GO_MONOCARBOXYLIC_ACID_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 3 28 1.85e-05 0.0023 0.1632
GO_POSITIVE_REGULATION_OF_CYTOKINE_BIOSYNTHETIC_PROCESS 3 28 2.10e-05 0.0025 0.1855
GO_HIGH_DENSITY_LIPOPROTEIN_PARTICLE 2 9 2.52e-05 0.0029 0.2224
GO_SMALL_MOLECULE_BIOSYNTHETIC_PROCESS 23 478 2.97e-05 0.0033 0.2622
GO_FAT_SOLUBLE_VITAMIN_METABOLIC_PROCESS 3 27 3.05e-05 0.0034 0.2698
GO_POLYSOME 8 65 3.25e-05 0.0035 0.2871
GO_REGULATION_OF_INTRINSIC_APOPTOTIC_SIGNALING_PATHWAY_BY_P53_CLASS_MEDIATOR 4 21 3.65e-05 0.0038 0.3220
GO_MACROLIDE_BINDING 3 8 3.65e-05 0.0038 0.3225
GO_REGULATION_OF_HISTONE_H3_K9_TRIMETHYLATION 3 9 3.78e-05 0.0038 0.3343
GO_ACYLGLYCEROL_TRANSPORT 2 4 4.02e-05 0.0039 0.3553
GO_ORGANIC_ANION_TRANSPORT 16 345 4.19e-05 0.0041 0.3701
GO_GENERATION_OF_PRECURSOR_METABOLITES_AND_ENERGY 25 422 4.45e-05 0.0042 0.3931
GO_ATP_SYNTHESIS_COUPLED_ELECTRON_TRANSPORT 12 85 4.62e-05 0.0043 0.4078
GO_RESPIRATORY_CHAIN_COMPLEX 11 75 5.10e-05 0.0046 0.4508
GO_REGULATION_OF_SYSTEMIC_ARTERIAL_BLOOD_PRESSURE_BY_CIRCULATORY_RENIN_ANGIOT… 2 8 5.24e-05 0.0046 0.4632
GO_HETEROCHROMATIN 7 66 5.32e-05 0.0046 0.4698
GO_HUMORAL_IMMUNE_RESPONSE 7 91 5.40e-05 0.0046 0.4769
GO_ORGANONITROGEN_COMPOUND_BIOSYNTHETIC_PROCESS 68 1,431 5.40e-05 0.0046 0.4771
GO_RIBOSOMAL_LARGE_SUBUNIT_ASSEMBLY 5 28 5.50e-05 0.0046 0.4856
GO_PLASMINOGEN_ACTIVATION 3 21 6.30e-05 0.0052 0.5564
GO_NEGATIVE_REGULATION_OF_UBIQUITIN_PROTEIN_TRANSFERASE_ACTIVITY 3 13 6.56e-05 0.0053 0.5796
GO_ANTIBIOTIC_CATABOLIC_PROCESS 4 25 7.09e-05 0.0056 0.6261
GO_INHIBITORY_EXTRACELLULAR_LIGAND_GATED_ION_CHANNEL_ACTIVITY 2 15 7.33e-05 0.0057 0.6480
GO_NEGATIVE_REGULATION_OF_LEUKOCYTE_MIGRATION 3 21 7.54e-05 0.0058 0.6657
GO_ENERGY_DERIVATION_BY_OXIDATION_OF_ORGANIC_COMPOUNDS 18 242 7.55e-05 0.0058 0.6666
GO_ICOSANOID_BIOSYNTHETIC_PROCESS 4 24 8.61e-05 0.0062 0.7606
GO_POSITIVE_REGULATION_OF_INTRINSIC_APOPTOTIC_SIGNALING_PATHWAY_BY_P53_CLASS_… 2 4 9.01e-05 0.0065 0.7957
GO_REGULATION_OF_FATTY_ACID_BIOSYNTHETIC_PROCESS 3 30 9.20e-05 0.0065 0.8130
GO_REGULATION_OF_NUCLEOBASE_CONTAINING_COMPOUND_METABOLIC_PROCESS 24 421 9.38e-05 0.0066 0.8286
GO_POSITIVE_REGULATION_OF_INTERLEUKIN_2_BIOSYNTHETIC_PROCESS 2 6 9.51e-05 0.0066 0.8398
GO_NEGATIVE_REGULATION_OF_CHROMATIN_ORGANIZATION 5 52 0.0001 0.0073 0.9565
GO_INTERLEUKIN_2_BIOSYNTHETIC_PROCESS 2 8 0.0001 0.0073 0.9576
GO_AMINO_ACID_TRANSMEMBRANE_TRANSPORT 5 69 0.0001 0.0073 0.9649
GO_ANION_TRANSPORT 22 434 0.0001 0.0074 1.0000
GO_TETRAPYRROLE_BINDING 5 61 0.0001 0.0074 1.0000
GO_ACTIVIN_BINDING 3 15 0.0001 0.0074 1.0000
GO_E_BOX_BINDING 4 41 0.0001 0.0074 1.0000
GO_FIBRONECTIN_BINDING 2 23 0.0001 0.0074 1.0000
GO_NEGATIVE_REGULATION_OF_MITOCHONDRION_ORGANIZATION 4 46 0.0001 0.0074 1.0000
GO_DOUBLE_STRANDED_DNA_BINDING 30 740 0.0001 0.0076 1.0000
GO_REGULATION_OF_SYSTEMIC_ARTERIAL_BLOOD_PRESSURE_BY_RENIN_ANGIOTENSIN 2 11 0.0001 0.0082 1.0000
GO_PROTEIN_LOCALIZATION_TO_MEMBRANE 35 539 0.0001 0.0082 1.0000
GO_REGULATION_OF_INFLAMMATORY_RESPONSE_TO_ANTIGENIC_STIMULUS 2 17 0.0001 0.0084 1.0000
GO_DNA_METHYLATION 5 44 0.0001 0.0085 1.0000
GO_DNA_MODIFICATION 7 75 0.0001 0.0085 1.0000
GO_NEGATIVE_REGULATION_OF_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY 3 26 0.0001 0.0088 1.0000
GO_NITRIC_OXIDE_MEDIATED_SIGNAL_TRANSDUCTION 3 22 0.0002 0.0089 1.0000
GO_RESPIRATORY_ELECTRON_TRANSPORT_CHAIN 12 103 0.0002 0.0091 1.0000
GO_ELECTRON_TRANSPORT_CHAIN 14 153 0.0002 0.0098 1.0000
GO_RIBOSOMAL_SMALL_SUBUNIT_ASSEMBLY 3 16 0.0002 0.0098 1.0000

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, goseq, DE
  ) %>%
  write_csv(here::here("output", "Enrichment_Mutant_V_WT.csv"))

devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 3.6.2 (2019-12-12)
 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     2020-02-19                  

─ 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                  0.5.4      2020-01-27 [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                * 0.8.4      2020-01-31 [2] CRAN (R 3.6.2)
 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)
 here                   0.1        2017-05-28 [2] CRAN (R 3.6.0)
 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                * 3.42.1     2020-01-29 [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                   3.1-144    2020-02-06 [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                  0.4.4      2020-01-28 [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                     1.5-5      2020-01-30 [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             1.0.0      2020-01-27 [2] CRAN (R 3.6.2)
 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                   2.2.1      2020-02-01 [2] CRAN (R 3.6.2)
 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