Last updated: 2020-02-17

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Knit directory: 20170327_Psen2S4Ter_RNASeq/

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File Version Author Date Message
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 <- 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$genes %>%
  dplyr::filter(!is.na(entrezid)) %>%
  unnest(entrezid) %>%
  dplyr::rename(entrez_gene = entrezid)
deTable <- file.path("output", "psen2HomVsHet.csv") %>% 
  read_csv() %>%
  mutate(
    entrezid = dgeList$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 (\(n_{\text{DE}} = 7\)), t enrichment esting 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)) 
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)) 
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) 
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.

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 = "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 <- fit$fitted.values %>%
  cpm(log = TRUE) %>%
  camera(
    index = hmByID,
    design = fit$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)
  ) %>%
  pivot_longer(
    cols = one_of(c("fry", "camera", "gsea")),
    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 < 0.1) %>%
  mutate_at(vars(one_of(c("p", "FDR", "adjP"))), formatP) %>%
  dplyr::select(`Gene Set` = gs_name, 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.",
    justify = "lrrr"
  )
Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant to an FDR of 0.1.
Gene Set p pFDR pbonf
HALLMARK_OXIDATIVE_PHOSPHORYLATION 0.0011 0.0315 0.0537
HALLMARK_KRAS_SIGNALING_DN 0.0013 0.0315 0.0631
HALLMARK_INTERFERON_ALPHA_RESPONSE 0.0022 0.0375 0.1125
HALLMARK_ESTROGEN_RESPONSE_EARLY 0.0044 0.0552 0.2206
HALLMARK_ALLOGRAFT_REJECTION 0.0075 0.0753 0.3763
HALLMARK_MYOGENESIS 0.0097 0.0811 0.4864

KEGG Gene Sets

kgFry <- fit$fitted.values %>%
  cpm(log = TRUE) %>%
  fry(
    index = kgByID,
    design = fit$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 <- fit$fitted.values %>%
  cpm(log = TRUE) %>%
  camera(
    index = kgByID,
    design = fit$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)
  ) %>%
  pivot_longer(
    cols = one_of(c("fry", "camera", "gsea")),
    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 < 0.05) %>%
  mutate_at(vars(one_of(c("p", "FDR", "adjP"))), formatP) %>%
  dplyr::select(`Gene Set` = gs_name, 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 = "lrrr"
  )
Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant to an FDR of 0.05.
Gene Set p pFDR pbonf
KEGG_RIBOSOME 7.88e-10 1.47e-07 1.47e-07
KEGG_OXIDATIVE_PHOSPHORYLATION 2.88e-08 2.68e-06 5.35e-06
KEGG_PARKINSONS_DISEASE 9.90e-08 6.14e-06 1.84e-05
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS 2.24e-05 0.0010 0.0042
KEGG_HUNTINGTONS_DISEASE 0.0001 0.0053 0.0264
KEGG_PROTEASOME 0.0003 0.0092 0.0550
KEGG_ALZHEIMERS_DISEASE 0.0004 0.0101 0.0742
KEGG_PRIMARY_IMMUNODEFICIENCY 0.0004 0.0101 0.0811
KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY 0.0011 0.0225 0.2028
KEGG_LEISHMANIA_INFECTION 0.0017 0.0320 0.3205

GO Gene Sets

goFry <- fit$fitted.values %>%
  cpm(log = TRUE) %>%
  fry(
    index = goByID,
    design = fit$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 <- fit$fitted.values %>%
  cpm(log = TRUE) %>%
  camera(
    index = goByID,
    design = fit$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)
  ) %>%
  pivot_longer(
    cols = one_of(c("fry", "camera", "gsea")),
    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) %>%
  dplyr::select(`Gene Set` = gs_name, p, `p~FDR~` = FDR, `p~bonf~` = adjP) %>%  
  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 = "lrrr"
  )
Results from combining all above approaches for the Hallmark Gene Sets. All terms are significant using a Bonferroni-adjusted p-value < 0.01.
Gene Set p pFDR pbonf
GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM 7.46e-10 9.37e-07 6.59e-06
GO_CYTOSOLIC_RIBOSOME 7.47e-10 9.37e-07 6.60e-06
GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE 7.61e-10 9.37e-07 6.72e-06
GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT 8.26e-10 9.37e-07 7.30e-06
GO_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT 8.55e-10 9.37e-07 7.55e-06
GO_POLYSOMAL_RIBOSOME 9.12e-10 9.37e-07 8.06e-06
GO_PROTON_TRANSPORTING_ATP_SYNTHASE_COMPLEX 9.37e-10 9.37e-07 8.28e-06
GO_INNATE_IMMUNE_RESPONSE_IN_MUCOSA 1.26e-09 9.37e-07 1.12e-05
GO_MHC_CLASS_II_RECEPTOR_ACTIVITY 1.30e-09 9.37e-07 1.15e-05
GO_INTERMEDIATE_FILAMENT_BUNDLE_ASSEMBLY 1.44e-09 9.37e-07 1.27e-05
GO_KERATIN_FILAMENT_BINDING 1.45e-09 9.37e-07 1.28e-05
GO_HEMIDESMOSOME_ASSEMBLY 1.49e-09 9.37e-07 1.32e-05
GO_CORNIFIED_ENVELOPE 1.57e-09 9.37e-07 1.39e-05
GO_INTERMEDIATE_FILAMENT_ORGANIZATION 1.58e-09 9.37e-07 1.39e-05
GO_PEPTIDE_CROSS_LINKING 1.59e-09 9.37e-07 1.41e-05
GO_KERATIN_FILAMENT 1.73e-09 9.56e-07 1.53e-05
GO_CORNIFICATION 2.20e-09 1.14e-06 1.94e-05
GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM 3.56e-09 1.75e-06 3.15e-05
GO_ORGAN_OR_TISSUE_SPECIFIC_IMMUNE_RESPONSE 5.54e-09 2.57e-06 4.89e-05
GO_INTERMEDIATE_FILAMENT_BINDING 6.09e-09 2.69e-06 5.38e-05
GO_SMALL_RIBOSOMAL_SUBUNIT 8.64e-09 3.63e-06 7.63e-05
GO_RIBOSOMAL_SMALL_SUBUNIT_ASSEMBLY 1.06e-08 4.27e-06 9.40e-05
GO_AEROBIC_ELECTRON_TRANSPORT_CHAIN 1.51e-08 5.80e-06 0.0001
GO_PHOSPHOLIPASE_A2_INHIBITOR_ACTIVITY 4.69e-08 1.72e-05 0.0004
GO_NUCLEAR_NUCLEOSOME 6.31e-08 2.23e-05 0.0006
GO_MITOCHONDRIAL_RESPIRATORY_CHAIN_COMPLEX_I 7.01e-08 2.32e-05 0.0006
GO_NADH_DEHYDROGENASE_ACTIVITY 7.09e-08 2.32e-05 0.0006
GO_OXIDATIVE_PHOSPHORYLATION 1.13e-07 3.56e-05 0.0010
GO_RESPIRATORY_CHAIN_COMPLEX_IV 1.17e-07 3.57e-05 0.0010
GO_RIBOSOMAL_SUBUNIT 1.45e-07 4.21e-05 0.0013
GO_ATP_SYNTHESIS_COUPLED_ELECTRON_TRANSPORT 1.48e-07 4.21e-05 0.0013
GO_RESPIRATORY_CHAIN_COMPLEX 1.84e-07 5.07e-05 0.0016
GO_PHOTORECEPTOR_DISC_MEMBRANE 1.89e-07 5.07e-05 0.0017
GO_STRAND_INVASION 2.60e-07 6.75e-05 0.0023
GO_CRISTAE_FORMATION 3.97e-07 0.0001 0.0035
GO_INTERLEUKIN_8_SECRETION 4.84e-07 0.0001 0.0043
GO_INTERMEDIATE_FILAMENT_CYTOSKELETON 5.94e-07 0.0001 0.0052
GO_CELL_SUBSTRATE_JUNCTION_ASSEMBLY 6.00e-07 0.0001 0.0053
GO_VIRAL_GENE_EXPRESSION 6.14e-07 0.0001 0.0054
GO_O_PALMITOYLTRANSFERASE_ACTIVITY 8.00e-07 0.0002 0.0071
GO_ANTIBACTERIAL_HUMORAL_RESPONSE 1.03e-06 0.0002 0.0091

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     2020-02-17                  

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date       lib source        
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 biomaRt                2.42.0     2019-10-29 [2] Bioconductor  
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 digest                 0.6.23     2019-11-23 [2] CRAN (R 3.6.1)
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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