Enrichment Analysis: Mutant Vs Wild-Type
Steve Pederson
05 March, 2020
Last updated: 2020-03-05
Checks: 7 0
Knit directory: 20170327_Psen2S4Ter_RNASeq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | bad81c1 | Steve Ped | 2020-03-05 | Updated MutVsWt UpSet plots for KEGG/HALLMARK |
| Rmd | 377410a | Steve Ped | 2020-03-05 | Finished revision of Enrichment Analysis |
| Rmd | c0b89ed | Steve Ped | 2020-03-05 | Reran after changing to cpmPotNorm instead of the incorrect fit$fitted.values |
| html | 7e59d3c | Steve Ped | 2020-02-20 | Recompiled |
| Rmd | 4ede752 | Steve Ped | 2020-02-20 | Added headings |
| html | 0168824 | Steve Ped | 2020-02-20 | Rebult after resizing |
| Rmd | de9c4b3 | Steve Ped | 2020-02-20 | Resized heatmaps again |
| html | 86ffa07 | Steve Ped | 2020-02-20 | Updated saample layout figure |
| Rmd | 4d2af23 | Steve Ped | 2020-02-20 | Modified heatmap sizes & initial network layout |
| Rmd | 8fe35b0 | Steve Ped | 2020-02-20 | Added multiple heatmaps & UpSet plots |
| html | 679b10d | Steve Ped | 2020-02-19 | Rendered OXPHOS Plot |
| Rmd | 66ba4e0 | Steve Ped | 2020-02-19 | Added OXPHOS plot |
| html | e0288c6 | Steve Ped | 2020-02-19 | Generated enrichment tables |
| 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)
library(UpSetR)
library(pheatmap)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$genotype))dgeList <- here::here("data/dgeList.rds") %>% read_rds()
cpmPostNorm <- here::here("data/cpmPostNorm.rds") %>% read_rds()
entrezGenes <- dgeList$genes %>%
dplyr::filter(!is.na(entrezid)) %>%
unnest(entrezid) %>%
dplyr::rename(entrez_gene = entrezid)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.
- Testing for enrichment within discrete sets of DE genes as defined in the previous steps
- 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:
fryas 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.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.fgseawhich 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 <- 3go <- 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 <- here::here("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 = here::here("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.
| 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."
)
)| 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.*"
)
)| 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$gene_id
) %>% sort()
np <- 1e5Genes were ranked by -sign(logFC)*log10(p) for approaches which required a ranked list. Multiple approaches were first calculated individually, before being combined for the final integrated set of results.
Hallmark Gene Sets
hmFry <- cpmPostNorm %>%
fry(
index = hmByID,
design = dgeList$design,
contrast = "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 <- cpmPostNorm %>%
camera(
index = hmByID,
design = dgeList$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"
)| Gene Set | Number DE | Set Size | Wilkinsonp | pFDR | pbonf |
|---|---|---|---|---|---|
| HALLMARK_UV_RESPONSE_UP | 7 | 141 | 5.90e-08 | 2.95e-06 | 2.95e-06 |
| HALLMARK_XENOBIOTIC_METABOLISM | 11 | 146 | 1.87e-05 | 0.0005 | 0.0009 |
| HALLMARK_MTORC1_SIGNALING | 11 | 194 | 9.13e-05 | 0.0012 | 0.0046 |
| HALLMARK_FATTY_ACID_METABOLISM | 6 | 135 | 9.46e-05 | 0.0012 | 0.0047 |
| HALLMARK_GLYCOLYSIS | 8 | 172 | 0.0002 | 0.0015 | 0.0084 |
| HALLMARK_PEROXISOME | 2 | 95 | 0.0002 | 0.0015 | 0.0115 |
| HALLMARK_COAGULATION | 3 | 80 | 0.0003 | 0.0015 | 0.0127 |
| HALLMARK_MYC_TARGETS_V1 | 15 | 192 | 0.0003 | 0.0015 | 0.0138 |
| HALLMARK_CHOLESTEROL_HOMEOSTASIS | 2 | 69 | 0.0003 | 0.0015 | 0.0153 |
| HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY | 5 | 45 | 0.0003 | 0.0015 | 0.0158 |
| HALLMARK_BILE_ACID_METABOLISM | 1 | 86 | 0.0003 | 0.0015 | 0.0169 |
| HALLMARK_ESTROGEN_RESPONSE_LATE | 3 | 153 | 0.0004 | 0.0016 | 0.0188 |
| HALLMARK_ADIPOGENESIS | 13 | 186 | 0.0007 | 0.0027 | 0.0355 |
| HALLMARK_HYPOXIA | 9 | 169 | 0.0009 | 0.0032 | 0.0451 |
| HALLMARK_PROTEIN_SECRETION | 3 | 92 | 0.0009 | 0.0032 | 0.0475 |
| HALLMARK_UNFOLDED_PROTEIN_RESPONSE | 4 | 113 | 0.0017 | 0.0054 | 0.0864 |
| HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION | 7 | 156 | 0.0020 | 0.0060 | 0.1020 |
| HALLMARK_TNFA_SIGNALING_VIA_NFKB | 7 | 149 | 0.0037 | 0.0099 | 0.1841 |
| HALLMARK_IL2_STAT5_SIGNALING | 2 | 147 | 0.0038 | 0.0099 | 0.1889 |
| HALLMARK_OXIDATIVE_PHOSPHORYLATION | 15 | 201 | 0.0041 | 0.0101 | 0.2028 |
| HALLMARK_NOTCH_SIGNALING | 0 | 29 | 0.0135 | 0.0317 | 0.6744 |
| HALLMARK_ANGIOGENESIS | 1 | 26 | 0.0140 | 0.0317 | 0.6985 |
| HALLMARK_ANDROGEN_RESPONSE | 2 | 90 | 0.0222 | 0.0482 | 1.0000 |
Exploration of significant Hallmark Gene Sets
hmMeta %>%
dplyr::filter(nDE > 0, FDR < 0.05) %>%
dplyr::select(gs_name, DE) %>%
unnest(DE) %>%
mutate(
gs_name = str_remove(gs_name, "HALLMARK_"),
gs_name = fct_lump(gs_name, n = 12)
) %>%
split(f = .$gs_name) %>%
lapply(magrittr::extract2, "DE") %>%
fromList() %>%
upset(
nsets = length(.),
order.by = "freq",
mb.ratio = c(0.6, 0.4)
)
UpSet plot indicating distribution of DE genes within all significant HALLMARK gene sets. Most gene sets seem relatively independent of each other.
hmHeat <- hmMeta %>%
dplyr::filter(nDE > 10, FDR < 0.05) %>%
dplyr::select(gs_name, DE) %>%
unnest(DE) %>%
left_join(dgeList$genes, by = c("DE" = "gene_id")) %>%
dplyr::select(gs_name, DE, gene_name) %>%
mutate(belongs = TRUE) %>%
pivot_wider(
id_cols = c(DE, gene_name),
names_from = gs_name,
values_from = belongs,
values_fill = list(belongs = FALSE)
) %>%
left_join(
cpmPostNorm %>%
as.data.frame() %>%
rownames_to_column("DE")
)
hmHeat %>%
dplyr::select(gene_name, starts_with("Ps")) %>%
as.data.frame() %>%
column_to_rownames("gene_name") %>%
as.matrix() %>%
pheatmap(
color = viridis_pal(option = "magma")(100),
legend_breaks = c(seq(-2, 8, by = 2), max(.)),
legend_labels = c(seq(-2, 8, by = 2), "logCPM\n"),
labels_col = hmHeat %>%
dplyr::select(starts_with("Ps")) %>%
colnames() %>%
enframe(name = NULL) %>%
dplyr::rename(sample = value) %>%
left_join(dgeList$samples) %>%
dplyr::select(sample, sampleID) %>%
with(
structure(sampleID, names = sample)
),
cutree_rows = 6,
cutree_cols = 2,
annotation_names_row = FALSE,
annotation_row = hmHeat %>%
dplyr::select(gene_name, starts_with("HALLMARK_")) %>%
mutate_at(vars(starts_with("HALL")), as.character) %>%
as.data.frame() %>%
column_to_rownames("gene_name") %>%
set_colnames(
str_remove(colnames(.), "HALLMARK_")
)
)
Gene expression patterns for all DE genes in HALLMARK gene sets, containing more than 10 DE genes.
| Version | Author | Date |
|---|---|---|
| 86ffa07 | Steve Ped | 2020-02-20 |
KEGG Gene Sets
kgFry <- cpmPostNorm %>%
fry(
index = kgByID,
design = dgeList$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 <- cpmPostNorm %>%
camera(
index = kgByID,
design = dgeList$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 > 2) %>%
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 three DE genes are shown.",
justify = "lrrrrr"
)| Gene Set | Number DE | Set Size | Wilkinsonp | pFDR | pbonf |
|---|---|---|---|---|---|
| KEGG_RIBOSOME | 26 | 80 | 8.32e-09 | 1.55e-06 | 1.55e-06 |
| KEGG_GLUTATHIONE_METABOLISM | 4 | 36 | 1.22e-06 | 7.56e-05 | 0.0002 |
| KEGG_CYSTEINE_AND_METHIONINE_METABOLISM | 4 | 26 | 0.0001 | 0.0013 | 0.0189 |
| KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 | 3 | 26 | 0.0002 | 0.0020 | 0.0393 |
| KEGG_DRUG_METABOLISM_CYTOCHROME_P450 | 3 | 26 | 0.0003 | 0.0021 | 0.0487 |
| KEGG_RETINOL_METABOLISM | 3 | 25 | 0.0005 | 0.0036 | 0.0960 |
| KEGG_PARKINSONS_DISEASE | 13 | 112 | 0.0006 | 0.0040 | 0.1122 |
| KEGG_PURINE_METABOLISM | 4 | 135 | 0.0007 | 0.0044 | 0.1310 |
| KEGG_HUNTINGTONS_DISEASE | 15 | 159 | 0.0027 | 0.0125 | 0.5003 |
| KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION | 3 | 44 | 0.0036 | 0.0148 | 0.6616 |
| KEGG_ALZHEIMERS_DISEASE | 12 | 141 | 0.0039 | 0.0159 | 0.7294 |
| KEGG_OXIDATIVE_PHOSPHORYLATION | 14 | 119 | 0.0046 | 0.0180 | 0.8633 |
| KEGG_HEMATOPOIETIC_CELL_LINEAGE | 3 | 31 | 0.0047 | 0.0180 | 0.8812 |
| KEGG_GLYCOLYSIS_GLUCONEOGENESIS | 3 | 39 | 0.0067 | 0.0229 | 1.0000 |
| KEGG_CARDIAC_MUSCLE_CONTRACTION | 3 | 63 | 0.0158 | 0.0451 | 1.0000 |
| KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS | 5 | 86 | 0.0182 | 0.0498 | 1.0000 |
Exploration of Significant KEGG Gene Sets
kgMeta %>%
dplyr::filter(nDE > 2, FDR < 0.05) %>%
dplyr::select(gs_name, DE) %>%
unnest(DE) %>%
mutate(
gs_name = str_remove(gs_name, "KEGG_"),
gs_name = fct_lump(gs_name, n = 8)
) %>%
split(f = .$gs_name) %>%
lapply(magrittr::extract2, "DE") %>%
fromList() %>%
upset(
nsets = length(.),
order.by = "freq",
mb.ratio = c(0.6, 0.4)
)
UpSet plot indicating distribution of DE genes within all significant terms from the KEGG gene sets. There is considerable overlap between Oxidative Phosphorylation and both Parkinson’s and Huntington’s Disease, indicating that these terms essentially capture the same signal.
riboOx <- kg %>%
dplyr::filter(
grepl("OXIDATIVE_PHOS", gs_name) | grepl("RIBOSOME", gs_name),
gene_id %in% dplyr::filter(deTable, DE)$gene_id
) %>%
mutate(gs_name = str_remove(gs_name, "(KEGG|HALLMARK)_")) %>%
distinct(gs_name, gene_id) %>%
left_join(
cpmPostNorm %>%
as.data.frame() %>%
rownames_to_column("gene_id") %>%
as_tibble()
) %>%
pivot_longer(cols = starts_with("Ps"), names_to = "sample", values_to = "CPM") %>%
left_join(dgeList$samples) %>%
left_join(dgeList$genes) %>%
dplyr::select(gs_name, gene_name, sampleID, genotype, CPM) %>%
pivot_wider(
id_cols = c(gs_name, gene_name),
values_from = CPM,
names_from = sampleID
)
riboOx %>%
dplyr::select(-gs_name) %>%
as.data.frame() %>%
column_to_rownames("gene_name") %>%
as.matrix() %>%
pheatmap(
annotation_row = data.frame(
GeneSet = riboOx$gs_name %>% str_replace("OXI.+", "OXPHOS"),
row.names = riboOx$gene_name
),
color = viridis_pal(option = "magma")(100),
legend_breaks = c(seq(-2, 8, by = 2), max(.)),
legend_labels = c(seq(-2, 8, by = 2), "logCPM\n"),
annotation_names_row = FALSE,
cutree_rows = 3,
cutree_cols = 2
)
Given that the largest gene sets within the KEGG results were Oxidative Phosphorylation and Ribosomal gene sets, all DE genes associated with these KEGG terms are displayed. All genes appear to show increased expression in mutant samples.
| Version | Author | Date |
|---|---|---|
| 86ffa07 | Steve Ped | 2020-02-20 |
GO Gene Sets
goFry <- cpmPostNorm %>%
fry(
index = goByID,
design = dgeList$design,
contrast = "mutant",
sort = "directional"
) %>%
rownames_to_column("gs_name") %>%
as_tibble()For analysis under camera inter-gene correlations were directly calculated for a more conservative result.
goCamera <- cpmPostNorm %>%
camera(
index = goByID,
design = dgeList$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 >= 10) %>%
mutate_at(vars(one_of(c("wilkinson_p", "FDR", "adjP"))), formatP) %>%
mutate(gs_name = str_trunc(gs_name, 70)) %>%
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 ten DE genes are shown.",
justify = "lrrrrr"
)| Gene Set | Number DE | Set Size | Wilkinsonp | pFDR | pbonf |
|---|---|---|---|---|---|
| GO_SYMPORTER_ACTIVITY | 10 | 94 | 7.34e-12 | 6.49e-08 | 6.49e-08 |
| GO_SECONDARY_ACTIVE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY | 13 | 154 | 2.41e-11 | 7.50e-08 | 2.13e-07 |
| GO_ANION_TRANSMEMBRANE_TRANSPORT | 15 | 203 | 2.55e-11 | 7.50e-08 | 2.25e-07 |
| GO_ORGANIC_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY | 11 | 144 | 6.48e-11 | 1.04e-07 | 5.72e-07 |
| GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_MEMBRANE | 30 | 287 | 7.06e-11 | 1.04e-07 | 6.23e-07 |
| GO_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY | 17 | 228 | 1.42e-10 | 1.79e-07 | 1.26e-06 |
| GO_ANION_TRANSPORT | 22 | 434 | 3.19e-10 | 3.38e-07 | 2.81e-06 |
| GO_PROTEIN_TARGETING_TO_MEMBRANE | 30 | 171 | 2.57e-09 | 2.27e-06 | 2.27e-05 |
| GO_PROTEIN_TARGETING | 34 | 379 | 7.09e-09 | 5.22e-06 | 6.26e-05 |
| GO_CYTOSOLIC_PART | 30 | 206 | 9.18e-09 | 5.52e-06 | 8.11e-05 |
| GO_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT | 11 | 41 | 9.40e-09 | 5.52e-06 | 8.30e-05 |
| GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ORGANELLE | 38 | 484 | 1.00e-08 | 5.52e-06 | 8.84e-05 |
| GO_TRANSLATIONAL_INITIATION | 29 | 177 | 1.29e-08 | 6.70e-06 | 0.0001 |
| GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT | 15 | 51 | 1.47e-08 | 7.20e-06 | 0.0001 |
| GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM | 27 | 105 | 1.58e-08 | 7.34e-06 | 0.0001 |
| GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE | 28 | 93 | 1.91e-08 | 8.46e-06 | 0.0002 |
| GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM | 27 | 128 | 2.36e-08 | 9.92e-06 | 0.0002 |
| GO_CYTOSOLIC_RIBOSOME | 27 | 97 | 3.13e-08 | 1.20e-05 | 0.0003 |
| GO_ORGANIC_ACID_TRANSPORT | 14 | 240 | 4.33e-08 | 1.59e-05 | 0.0004 |
| GO_ORGANIC_CYCLIC_COMPOUND_CATABOLIC_PROCESS | 40 | 474 | 5.44e-08 | 1.92e-05 | 0.0005 |
| GO_RNA_CATABOLIC_PROCESS | 34 | 337 | 7.51e-08 | 2.46e-05 | 0.0007 |
| GO_RESPONSE_TO_DRUG | 31 | 746 | 1.05e-07 | 3.30e-05 | 0.0009 |
| GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS | 30 | 183 | 1.61e-07 | 4.45e-05 | 0.0014 |
| GO_CYTOPLASMIC_TRANSLATION | 11 | 87 | 3.11e-07 | 7.09e-05 | 0.0028 |
| GO_ORGANONITROGEN_COMPOUND_BIOSYNTHETIC_PROCESS | 68 | 1,431 | 3.13e-07 | 7.09e-05 | 0.0028 |
| GO_AMIDE_BIOSYNTHETIC_PROCESS | 40 | 678 | 3.31e-07 | 7.31e-05 | 0.0029 |
| GO_CELLULAR_AMIDE_METABOLIC_PROCESS | 45 | 805 | 3.62e-07 | 7.79e-05 | 0.0032 |
| GO_INNER_MITOCHONDRIAL_MEMBRANE_PROTEIN_COMPLEX | 16 | 124 | 3.77e-07 | 7.94e-05 | 0.0033 |
| GO_SMALL_RIBOSOMAL_SUBUNIT | 11 | 69 | 3.96e-07 | 8.13e-05 | 0.0035 |
| GO_ACTIVE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY | 15 | 254 | 4.19e-07 | 8.22e-05 | 0.0037 |
| GO_ORGANIC_ANION_TRANSPORT | 16 | 345 | 4.49e-07 | 8.63e-05 | 0.0040 |
| GO_PEPTIDE_BIOSYNTHETIC_PROCESS | 38 | 572 | 1.16e-06 | 0.0002 | 0.0102 |
| GO_RESPONSE_TO_CARBOHYDRATE | 11 | 176 | 1.31e-06 | 0.0002 | 0.0116 |
| GO_RIBOSOMAL_SUBUNIT | 28 | 175 | 1.34e-06 | 0.0002 | 0.0118 |
| GO_ORGANIC_ACID_BIOSYNTHETIC_PROCESS | 12 | 198 | 1.63e-06 | 0.0003 | 0.0144 |
| GO_RRNA_BINDING | 11 | 57 | 2.23e-06 | 0.0003 | 0.0197 |
| GO_RIBOSOME | 30 | 210 | 2.32e-06 | 0.0003 | 0.0205 |
| GO_REGULATION_OF_HORMONE_LEVELS | 19 | 362 | 2.48e-06 | 0.0003 | 0.0220 |
| GO_REGULATION_OF_HORMONE_SECRETION | 12 | 200 | 2.49e-06 | 0.0003 | 0.0220 |
| GO_RESPONSE_TO_TOXIC_SUBSTANCE | 20 | 380 | 3.18e-06 | 0.0004 | 0.0281 |
| GO_PROTEIN_LOCALIZATION_TO_MEMBRANE | 35 | 539 | 3.42e-06 | 0.0004 | 0.0302 |
| GO_VIRAL_GENE_EXPRESSION | 29 | 172 | 3.66e-06 | 0.0004 | 0.0323 |
| GO_LARGE_RIBOSOMAL_SUBUNIT | 17 | 108 | 3.84e-06 | 0.0005 | 0.0340 |
| GO_SMALL_MOLECULE_BIOSYNTHETIC_PROCESS | 23 | 478 | 6.98e-06 | 0.0007 | 0.0616 |
| GO_RNA_METABOLIC_PROCESS | 48 | 915 | 1.27e-05 | 0.0011 | 0.1119 |
| GO_OXIDATIVE_PHOSPHORYLATION | 15 | 122 | 1.35e-05 | 0.0011 | 0.1189 |
| GO_MONOCARBOXYLIC_ACID_METABOLIC_PROCESS | 10 | 315 | 2.46e-05 | 0.0016 | 0.2174 |
| GO_SEQUENCE_SPECIFIC_DNA_BINDING | 29 | 827 | 2.63e-05 | 0.0017 | 0.2327 |
| GO_HOMEOSTATIC_PROCESS | 55 | 1,368 | 3.07e-05 | 0.0018 | 0.2715 |
| GO_RESPONSE_TO_OXYGEN_CONTAINING_COMPOUND | 43 | 1,156 | 3.17e-05 | 0.0018 | 0.2803 |
| GO_LIPID_BIOSYNTHETIC_PROCESS | 13 | 508 | 3.25e-05 | 0.0018 | 0.2874 |
| GO_REGULATION_OF_SMALL_MOLECULE_METABOLIC_PROCESS | 11 | 262 | 3.61e-05 | 0.0020 | 0.3186 |
| GO_NEGATIVE_REGULATION_OF_CELL_DIFFERENTIATION | 23 | 549 | 4.09e-05 | 0.0021 | 0.3612 |
| GO_GENERATION_OF_PRECURSOR_METABOLITES_AND_ENERGY | 25 | 422 | 4.45e-05 | 0.0022 | 0.3931 |
| GO_REGULATORY_REGION_NUCLEIC_ACID_BINDING | 24 | 720 | 4.60e-05 | 0.0023 | 0.4059 |
| GO_ATP_SYNTHESIS_COUPLED_ELECTRON_TRANSPORT | 12 | 85 | 4.62e-05 | 0.0023 | 0.4078 |
| GO_RESPIRATORY_CHAIN_COMPLEX | 11 | 75 | 5.10e-05 | 0.0025 | 0.4508 |
| GO_LIPID_METABOLIC_PROCESS | 30 | 906 | 5.38e-05 | 0.0025 | 0.4750 |
| GO_ENZYME_INHIBITOR_ACTIVITY | 12 | 219 | 5.92e-05 | 0.0026 | 0.5226 |
| GO_CELLULAR_LIPID_METABOLIC_PROCESS | 16 | 649 | 6.23e-05 | 0.0027 | 0.5508 |
| GO_ION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY | 25 | 616 | 6.28e-05 | 0.0027 | 0.5544 |
| GO_ORGANIC_ACID_METABOLIC_PROCESS | 23 | 648 | 7.15e-05 | 0.0030 | 0.6318 |
| GO_ENERGY_DERIVATION_BY_OXIDATION_OF_ORGANIC_COMPOUNDS | 18 | 242 | 7.55e-05 | 0.0031 | 0.6666 |
| GO_ENDOPLASMIC_RETICULUM | 38 | 1,407 | 9.11e-05 | 0.0034 | 0.8048 |
| GO_POSITIVE_REGULATION_OF_TRANSCRIPTION_BY_RNA_POLYMERASE_II | 31 | 903 | 9.22e-05 | 0.0034 | 0.8141 |
| GO_NUCLEAR_BODY | 21 | 649 | 9.34e-05 | 0.0035 | 0.8250 |
| GO_REGULATION_OF_NUCLEOBASE_CONTAINING_COMPOUND_METABOLIC_PROCESS | 24 | 421 | 9.38e-05 | 0.0035 | 0.8286 |
| GO_ENDOPLASMIC_RETICULUM_PART | 26 | 1,018 | 9.68e-05 | 0.0035 | 0.8551 |
| GO_REGULATION_OF_APOPTOTIC_SIGNALING_PATHWAY | 16 | 301 | 0.0001 | 0.0039 | 0.9840 |
| GO_CELLULAR_MACROMOLECULE_CATABOLIC_PROCESS | 48 | 939 | 0.0001 | 0.0039 | 1.0000 |
| GO_PROTEIN_LOCALIZATION_TO_ORGANELLE | 43 | 788 | 0.0001 | 0.0039 | 1.0000 |
| GO_DOUBLE_STRANDED_DNA_BINDING | 30 | 740 | 0.0001 | 0.0040 | 1.0000 |
| GO_SEQUENCE_SPECIFIC_DOUBLE_STRANDED_DNA_BINDING | 23 | 668 | 0.0001 | 0.0043 | 1.0000 |
| GO_TRANSMEMBRANE_TRANSPORT | 38 | 1,149 | 0.0002 | 0.0047 | 1.0000 |
| GO_RESPIRATORY_ELECTRON_TRANSPORT_CHAIN | 12 | 103 | 0.0002 | 0.0047 | 1.0000 |
| GO_PROXIMAL_PROMOTER_SEQUENCE_SPECIFIC_DNA_BINDING | 15 | 413 | 0.0002 | 0.0049 | 1.0000 |
| GO_ELECTRON_TRANSPORT_CHAIN | 14 | 153 | 0.0002 | 0.0051 | 1.0000 |
| GO_ION_TRANSPORT | 41 | 1,184 | 0.0002 | 0.0051 | 1.0000 |
| GO_INFLAMMATORY_RESPONSE | 18 | 376 | 0.0002 | 0.0052 | 1.0000 |
| GO_COFACTOR_METABOLIC_PROCESS | 10 | 317 | 0.0002 | 0.0052 | 1.0000 |
| GO_APOPTOTIC_SIGNALING_PATHWAY | 24 | 458 | 0.0002 | 0.0052 | 1.0000 |
| GO_HORMONE_TRANSPORT | 12 | 242 | 0.0002 | 0.0053 | 1.0000 |
| GO_TRANSCRIPTION_FACTOR_BINDING | 24 | 563 | 0.0002 | 0.0060 | 1.0000 |
| GO_ORGANIC_HYDROXY_COMPOUND_METABOLIC_PROCESS | 15 | 342 | 0.0002 | 0.0061 | 1.0000 |
| GO_RESPONSE_TO_CYTOKINE | 32 | 752 | 0.0003 | 0.0062 | 1.0000 |
| GO_RESPONSE_TO_OXIDATIVE_STRESS | 14 | 347 | 0.0003 | 0.0064 | 1.0000 |
| GO_SECRETORY_VESICLE | 24 | 713 | 0.0003 | 0.0072 | 1.0000 |
| GO_NUCLEOSIDE_PHOSPHATE_BIOSYNTHETIC_PROCESS | 10 | 227 | 0.0003 | 0.0074 | 1.0000 |
| GO_CELLULAR_RESPONSE_TO_OXYGEN_CONTAINING_COMPOUND | 31 | 816 | 0.0004 | 0.0079 | 1.0000 |
| GO_NEGATIVE_REGULATION_OF_APOPTOTIC_SIGNALING_PATHWAY | 10 | 171 | 0.0004 | 0.0079 | 1.0000 |
| GO_MACROMOLECULE_CATABOLIC_PROCESS | 52 | 1,116 | 0.0004 | 0.0079 | 1.0000 |
| GO_REGULATION_OF_DNA_METABOLIC_PROCESS | 15 | 236 | 0.0004 | 0.0080 | 1.0000 |
| GO_CATION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY | 18 | 458 | 0.0004 | 0.0081 | 1.0000 |
| GO_CYTOPLASMIC_VESICLE_PART | 22 | 1,141 | 0.0005 | 0.0087 | 1.0000 |
| GO_ION_TRANSMEMBRANE_TRANSPORT | 29 | 826 | 0.0005 | 0.0088 | 1.0000 |
| GO_PROTEIN_HOMOOLIGOMERIZATION | 10 | 246 | 0.0005 | 0.0090 | 1.0000 |
| GO_POSITIVE_REGULATION_OF_RNA_BIOSYNTHETIC_PROCESS | 43 | 1,232 | 0.0005 | 0.0093 | 1.0000 |
| GO_VESICLE_MEMBRANE | 11 | 609 | 0.0005 | 0.0093 | 1.0000 |
| GO_MONOVALENT_INORGANIC_CATION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY | 13 | 268 | 0.0005 | 0.0097 | 1.0000 |
| GO_INTRACELLULAR_TRANSPORT | 65 | 1,571 | 0.0005 | 0.0098 | 1.0000 |
| GO_TRANSMEMBRANE_RECEPTOR_PROTEIN_TYROSINE_KINASE_SIGNALING_PATHWAY | 12 | 569 | 0.0006 | 0.0098 | 1.0000 |
| GO_CARBOHYDRATE_DERIVATIVE_BIOSYNTHETIC_PROCESS | 18 | 540 | 0.0006 | 0.0099 | 1.0000 |
Exploration of Significant GO Gene Sets
goMeta %>%
dplyr::filter(nDE >= 25, FDR < 0.05) %>%
dplyr::select(gs_name, DE) %>%
unnest(DE) %>%
mutate(
gs_name = str_remove(gs_name, "GO_"),
gs_name = fct_lump(gs_name, n = 20)
) %>%
split(f = .$gs_name) %>%
lapply(magrittr::extract2, "DE") %>%
.[setdiff(names(.), "Other")] %>%
fromList() %>%
upset(
nsets = length(.),
order.by = "freq",
mb.ratio = c(0.55, 0.45)
)
UpSet plot indicating distribution of DE genes within significant terms from the GO gene sets. For this visualisation, GO terms were restricted to those with 25 or more DE genes and an FDR < 0.05. A group of about 300 genes is widely spread across multiple terms and not shown, with the next largest group being 19 genes uniquely associated with RNA Metabolic Process. A group of 15 genes is shared by a large group of terms, indicating that these genes largely drive the signal for these terms. These genes tend to indicate Ribosomal activity (i.e. protein synthesis). However, a set of 14 genes seems to more uniquely indicate Mitochondrial activity, followed by 12 uniquely associated with Ion Transport, and a set of 9 genes associated with homeostasis followed by 25 (9 + + 8) involved with regulation of gene expression.
Genes Associated with RNA Metabolic Process
go %>%
dplyr::filter(
gs_name == "GO_RNA_METABOLIC_PROCESS",
gene_id %in% dplyr::filter(deTable, DE)$gene_id
) %>%
dplyr::select(
gene_id, gene_name
) %>%
left_join(
cpmPostNorm %>%
as.data.frame() %>%
rownames_to_column("gene_id")
) %>%
dplyr::select(gene_name, starts_with("Ps")) %>%
as.data.frame() %>%
column_to_rownames("gene_name") %>%
as.matrix() %>%
pheatmap(
color = viridis_pal(option = "magma")(100),
legend_breaks = c(seq(-2, 8, by = 2), max(.)),
legend_labels = c(seq(-2, 8, by = 2), "logCPM\n"),
labels_col = hmHeat %>%
dplyr::select(starts_with("Ps")) %>%
colnames() %>%
enframe(name = NULL) %>%
dplyr::rename(sample = value) %>%
left_join(dgeList$samples) %>%
dplyr::select(sample, sampleID) %>%
with(
structure(sampleID, names = sample)
),
cutree_rows = 4,
cutree_cols = 2
)
All DE genes associated with the GO term ‘RNA Metabolic Process’.
Genes Associated with the Mitochondrial Envelope
go %>%
dplyr::filter(
gs_name == "GO_MITOCHONDRION",
gene_id %in% dplyr::filter(deTable, DE)$gene_id
) %>%
dplyr::select(
gene_id, gene_name
) %>%
left_join(
cpmPostNorm %>%
as.data.frame() %>%
rownames_to_column("gene_id")
) %>%
dplyr::select(gene_name, starts_with("Ps")) %>%
as.data.frame() %>%
column_to_rownames("gene_name") %>%
as.matrix() %>%
pheatmap(
color = viridis_pal(option = "magma")(100),
legend_breaks = c(seq(-2, 8, by = 2), max(.)),
legend_labels = c(seq(-2, 8, by = 2), "logCPM\n"),
labels_col = hmHeat %>%
dplyr::select(starts_with("Ps")) %>%
colnames() %>%
enframe(name = NULL) %>%
dplyr::rename(sample = value) %>%
left_join(dgeList$samples) %>%
dplyr::select(sample, sampleID) %>%
with(
structure(sampleID, names = sample)
),
cutree_rows = 5,
cutree_cols = 2
)
All DE genes associated with the GO term ‘Mitochondrion’.
| Version | Author | Date |
|---|---|---|
| 86ffa07 | Steve Ped | 2020-02-20 |
Genes Associated with Ion Transport
go %>%
dplyr::filter(
gene_id %in% dplyr::filter(deTable, DE)$gene_id,
gs_name %in% c("GO_ION_TRANSPORT")
) %>%
group_by(gene_id, gene_name) %>%
tally() %>%
ungroup() %>%
dplyr::filter(n == 1) %>%
dplyr::select(
gene_id, gene_name
) %>%
left_join(
cpmPostNorm %>%
as.data.frame() %>%
rownames_to_column("gene_id")
) %>%
dplyr::select(gene_name, starts_with("Ps")) %>%
as.data.frame() %>%
column_to_rownames("gene_name") %>%
as.matrix() %>%
pheatmap(
color = viridis_pal(option = "magma")(100),
legend_breaks = c(seq(-2, 8, by = 2), max(.)),
legend_labels = c(seq(-2, 8, by = 2), "logCPM\n"),
labels_col = hmHeat %>%
dplyr::select(starts_with("Ps")) %>%
colnames() %>%
enframe(name = NULL) %>%
dplyr::rename(sample = value) %>%
left_join(dgeList$samples) %>%
dplyr::select(sample, sampleID) %>%
with(
structure(sampleID, names = sample)
),
cutree_rows = 4,
cutree_cols = 2
)
All DE genes associated with the GO term ‘Ion Transport’.
Genes Associated with Homeostasis
go %>%
dplyr::filter(
gene_id %in% dplyr::filter(deTable, DE)$gene_id,
gs_name %in% c("GO_HOMEOSTATIC_PROCESS")
) %>%
group_by(gene_id, gene_name) %>%
tally() %>%
ungroup() %>%
dplyr::filter(n == 1) %>%
dplyr::select(
gene_id, gene_name
) %>%
left_join(
cpmPostNorm %>%
as.data.frame() %>%
rownames_to_column("gene_id")
) %>%
dplyr::select(gene_name, starts_with("Ps")) %>%
as.data.frame() %>%
column_to_rownames("gene_name") %>%
as.matrix() %>%
pheatmap(
color = viridis_pal(option = "magma")(100),
legend_breaks = c(seq(-2, 8, by = 2), max(.)),
legend_labels = c(seq(-2, 8, by = 2), "logCPM\n"),
labels_col = hmHeat %>%
dplyr::select(starts_with("Ps")) %>%
colnames() %>%
enframe(name = NULL) %>%
dplyr::rename(sample = value) %>%
left_join(dgeList$samples) %>%
dplyr::select(sample, sampleID) %>%
with(
structure(sampleID, names = sample)
),
cutree_rows = 4,
cutree_cols = 2
)
All DE genes associated with the GO term ‘Homeostatic Process’.
Genes Associated with Gene Regulation
go %>%
dplyr::filter(
gene_id %in% dplyr::filter(deTable, DE)$gene_id,
gs_name %in% c("GO_POSITIVE_REGULATION_OF_GENE_EXPRESSION")
) %>%
group_by(gene_id, gene_name) %>%
tally() %>%
ungroup() %>%
dplyr::filter(n == 1) %>%
dplyr::select(
gene_id, gene_name
) %>%
left_join(
cpmPostNorm %>%
as.data.frame() %>%
rownames_to_column("gene_id")
) %>%
dplyr::select(gene_name, starts_with("Ps")) %>%
as.data.frame() %>%
column_to_rownames("gene_name") %>%
as.matrix() %>%
pheatmap(
color = viridis_pal(option = "magma")(100),
legend_breaks = c(seq(-2, 8, by = 2), max(.)),
legend_labels = c(seq(-2, 8, by = 2), "logCPM\n"),
labels_col = hmHeat %>%
dplyr::select(starts_with("Ps")) %>%
colnames() %>%
enframe(name = NULL) %>%
dplyr::rename(sample = value) %>%
left_join(dgeList$samples) %>%
dplyr::select(sample, sampleID) %>%
with(
structure(sampleID, names = sample)
),
cutree_rows = 4,
cutree_cols = 2
)
All DE genes associated with the GO term ‘Positive Regulation of Gene Expression’.
Data Export
All enriched gene sets with an FDR adjusted p-value < 0.05 were exported as a single csv file.
bind_rows(
hmMeta,
kgMeta,
goMeta
) %>%
dplyr::filter(FDR < 0.05) %>%
mutate(
DE = lapply(DE, function(x){dplyr::filter(deTable, gene_id %in% x)$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 3.6.3 (2020-02-29)
os Ubuntu 18.04.4 LTS
system x86_64, linux-gnu
ui X11
language en_AU:en
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2020-03-05
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
AnnotationDbi * 1.48.0 2019-10-29 [2] Bioconductor
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backports 1.1.5 2019-10-02 [2] CRAN (R 3.6.1)
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biomaRt 2.42.0 2019-10-29 [2] Bioconductor
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callr 3.4.2 2020-02-12 [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)
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FactoMineR 2.2 2020-02-05 [2] CRAN (R 3.6.2)
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farver 2.0.3 2020-01-16 [2] CRAN (R 3.6.2)
fastmatch 1.1-0 2017-01-28 [2] CRAN (R 3.6.0)
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knitr 1.28 2020-02-06 [2] CRAN (R 3.6.2)
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[1] /home/steveped/R/x86_64-pc-linux-gnu-library/3.6
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library