3_Enrichment_MutantVsWT.Rmd

---
title: "Enrichment Analysis: Mutant Vs Wild-Type"
author: "Steve Pederson"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: workflowr::wflow_html
editor_options:
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
    autodep = TRUE,
    echo = TRUE,
    warning = FALSE,
    message = FALSE,
    fig.align = "center"
)
```


# Setup

```{r loadPackages}
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)
```

```{r setOpts}
theme_set(theme_bw())
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
panderOptions("big.mark", ",")
```



```{r samplesAndLabels}
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))
```


```{r loadFits}
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)
```

```{r formatP}
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

```{r hm}
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 `r comma(length(hmByGene))` Ensembl IDs were mapped to pathways from the hallmark gene sets.

## KEGG Gene Sets


```{r kg}
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 `r comma(length(kgByGene))` Ensembl IDs were mapped to pathways from the KEGG gene sets.

## Gene Ontology Gene Sets

```{r goSummaries}
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
```


```{r go}
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 `r minPath` or more steps back to the ontology root terms.
A total of `r comma(length(goByGene))` Ensembl IDs were mapped to pathways from restricted database of `r comma(length(goByID))` GO gene sets.

```{r gsSizes}
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

```{r topTables}
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.


```{r rawFqc}
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))
  )
```

```{r riboCors}
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.


```{r riboPwf, fig.height=4, fig.width=4, fig.cap="*Using this approach, it was clear that correlation with rRNA proportions significantly biased the probability of a gene being considered as DE.*"}
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")
```

All gene-sets were then tested using this PWF.

## Hallmark Gene Sets

```{r hmRiboGoSeq}
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 `r percent(min(hmRiboGoseq$FDR))`

## KEGG Gene Sets


```{r kgRiboGoseq}
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
  )
```

```{r kgRiboGoseqTable}
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."
    )
  )
```

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

```{r goRiboGoseq}
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
  )
```

```{r goRiboGoseqTable}
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.*"
    )
  )
```

# Enrichment Testing on Ranked Lists

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

Genes were ranked by -sign(logFC)*log~10~(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

```{r hmFry}
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.

```{r hmCamera}
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, `r comma(np)` permutations were conducted.

```{r hmGsea}
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.

```{r hmMeta}
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))
  )
```

```{r}
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"
  )
```

### Exploration of significant Hallmark Gene Sets

```{r hmMetaUpset, fig.width=10, fig.cap = "*UpSet plot indicating distribution of DE genes within all significant HALLMARK gene sets. Most gene sets seem relatively independent of each other.*"}
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)
    )
```

```{r hmHeat, fig.height=9, fig.width=10, fig.cap = "*Gene expression patterns for all DE genes in HALLMARK gene sets, containing more than 10 DE genes.*"}
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_")
      )
  )
```


## KEGG Gene Sets

```{r kgFry}
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.

```{r kgCamera}
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, `r comma(np)` permutations were conducted.

```{r kgGsea}
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.

```{r kgMeta}
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))
  )
```

```{r}
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"
  )
```

### Exploration of Significant KEGG Gene Sets

```{r kgMetaUpset, fig.width=10, fig.cap = "*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.*"}
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)
    )
```


```{r riboOxHeatmap, fig.width=8, fig.height=8, fig.cap = "*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.*"}
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
  )
```


## GO Gene Sets

```{r goFry}
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.

```{r goCamera}
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, `r comma(np)` permutations were conducted.

```{r goGsea}
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.

```{r goMeta}
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))
  )
```

```{r}
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"
  )
```

### Exploration of Significant GO Gene Sets

```{r goMetaUpset, fig.height=6, fig.width=10, fig.cap = "*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.*"}
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)
  )
```

### Genes Associated with RNA Metabolic Process

```{r heatDsDNA, fig.height=8, fig.width=8, fig.cap="*All DE genes associated with the GO term '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
  )
```

### Genes Associated with the Mitochondrial Envelope

```{r heatMitoEnvelope, fig.height=8, fig.width=8, fig.cap="*All DE genes associated with the GO term 'Mitochondrion'.*"}
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
  )
```

### Genes Associated with Ion Transport

```{r heatOrganoNitro, fig.height=6, fig.width=8, fig.cap="*All DE genes associated with the GO term '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
  )
```

### Genes Associated with Homeostasis

```{r heatHomeo, fig.height=8, fig.width=8, fig.cap="*All DE genes associated with the GO term 'Homeostatic Process'.*"}
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
  )
```

### Genes Associated with Gene Regulation

```{r heatReg, fig.height=8, fig.width=8, fig.cap="*All DE genes associated with the GO term 'Positive Regulation of Gene Expression'.*"}
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
  )
```


# Data Export

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

```{r}
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"))
```