2_DifferentialExpression.Rmd

---
title: "DifferentialExpression"
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(ngsReports)
library(tidyverse)
library(magrittr)
library(edgeR)
library(AnnotationHub)
library(ensembldb)
library(scales)
library(pander)
library(cowplot)
library(cqn)
library(ggrepel)
library(UpSetR)
```

```{r setOptions}
if (interactive()) setwd(here::here())
theme_set(theme_bw())
panderOptions("big.mark", ",")
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
twoCols <- c(rgb(0.8, 0.1, 0.1), rgb(0.2, 0.2, 0.8))
```

## Annotations

```{r annotationSetup}
ah <- AnnotationHub() %>%
    subset(species == "Danio rerio") %>%
    subset(rdataclass == "EnsDb")
ensDb <- ah[["AH74989"]]
```

```{r transAnnotation}
grTrans <- transcripts(ensDb)
trLengths <- exonsBy(ensDb, "tx") %>%
    width() %>%
    vapply(sum, integer(1))
mcols(grTrans)$length <- trLengths[names(grTrans)]
```


```{r geneAnnotation}
gcGene <- grTrans %>%
    mcols() %>%
    as.data.frame() %>%
    dplyr::select(gene_id, tx_id, gc_content, length) %>%
    as_tibble() %>%
    group_by(gene_id) %>%
    summarise(
        gc_content = sum(gc_content*length) / sum(length),
        length = ceiling(median(length))
    )
grGenes <- genes(ensDb)
mcols(grGenes) %<>%
    as.data.frame() %>%
    left_join(gcGene) %>%
    as.data.frame() %>%
    DataFrame()
```

Similarly to the Quality Assessment steps, `GRanges` objects were formed at the gene and transcript levels, to enable estimation of GC content and length for each transcript and gene.
GC content and transcript length are available for each transcript, and for gene-level estimates, GC content was taken as the sum of all GC bases divided by the sum of all transcript lengths, effectively averaging across all transcripts.
Gene length was defined as the median transcript length.


```{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")))
```

Sample metadata was also loaded, with only the sampleID and genotype being retained. 
All other fields were considered irrelevant.

## Count Data

```{r dgeList}
minCPM <- 1.5
minSamples <- 4
dgeList <- file.path("data", "2_alignedData", "featureCounts", "genes.out") %>%
    read_delim(delim = "\t") %>%
    set_names(basename(names(.))) %>%
    as.data.frame() %>%
    column_to_rownames("Geneid") %>%
    as.matrix() %>% 
    set_colnames(str_remove(colnames(.), "Aligned.sortedByCoord.out.bam")) %>%
    .[rowSums(cpm(.) >= minCPM) >= minCPM,] %>%
    DGEList(
        samples = tibble(sample = colnames(.)) %>%
            left_join(samples),
        genes = grGenes[rownames(.)] %>%
            as.data.frame() %>%
            dplyr::select(
                chromosome = seqnames, start, end, 
                gene_id, gene_name, gene_biotype, description, 
                entrezid, gc_content, length
            )
    ) %>%
    .[!grepl("rRNA", .$genes$gene_biotype),] %>%
    calcNormFactors()
```

Gene-level count data as output by `featureCounts`, was loaded and formed into a `DGEList` object.
During this process, genes were removed if:

- They were not considered as detectable (CPM < `r minCPM` in > `r ncol(dgeList) - minSamples` samples). This translates to > `r ceiling(min(minCPM * dgeList$samples$lib.size/1e6))` reads assigned a gene in all samples from one or more of the genotype groups
- The `gene_biotype` was any type of `rRNA`.

These filtering steps returned gene-level counts for `r comma(nrow(dgeList))` genes, with total library sizes between `r pander(comma(range(dgeList$samples$lib.size)))` reads assigned to genes.
It was noted that these library sizes were about 1.5-fold larger than the transcript-level counts used for the QA steps.

```{r plotDensities, fig.width=5, fig.height=4, fig.cap="*Expression density plots for all samples after filtering, showing logCPM values.*"}
cpm(dgeList, log = TRUE) %>%
    as.data.frame() %>%
    pivot_longer(
        cols = everything(),
        names_to = "sample",
        values_to = "logCPM"
    ) %>%
    split(f = .$sample) %>%
    lapply(function(x){
        d <- density(x$logCPM)
        tibble(
            sample = unique(x$sample),
            x = d$x,
            y = d$y
        )
    }) %>%
    bind_rows() %>%
    left_join(samples) %>%
    ggplot(aes(x, y, colour = genotype, group = sample)) +
    geom_line() +
    labs(
        x = "logCPM",
        y = "Density",
        colour = "Genotype"
    )
```

### Additional Functions

```{r labellers}
contLabeller <- as_labeller(
    c(
        HetVsWT = "S4Ter/+ Vs +/+",
        HomVsWT = "S4Ter/S4Ter Vs +/+",
        HomVsHet = "S4Ter/S4Ter Vs S4Ter/+",
        Hom = "S4Ter/S4Ter",
        Het = "S4Ter/+",
        WT = "+/+"
    )
)
geneLabeller <- structure(grGenes$gene_name, names = grGenes$gene_id) %>%
    as_labeller()
```

Labeller functions for genotypes, contrasts and gene names were additionally defined for simpler plotting using `ggplot2`.

# Analysis

## PCA

```{r pca}
pca <- dgeList %>%
    cpm(log = TRUE) %>%
    t() %>%
    prcomp() 
pcaVars <- percent_format(0.1)(summary(pca)$importance["Proportion of Variance",])
```


```{r plotPCA, fig.width=5, fig.height=4, fig.cap="*PCA of gene-level counts.*"}
pca$x %>%
    as.data.frame() %>%
    rownames_to_column("sample") %>%
    left_join(samples) %>%
    as_tibble() %>%
    ggplot(aes(PC1, PC2, colour = genotype, fill = genotype)) +
    geom_point() +
    geom_text_repel(aes(label = sampleID), show.legend = FALSE) +
    stat_ellipse(geom = "polygon", alpha = 0.05, show.legend = FALSE) +
    guides(fill = FALSE) +
    labs(
        x = paste0("PC1 (", pcaVars[["PC1"]], ")"),
        y = paste0("PC2 (", pcaVars[["PC2"]], ")"),
        colour = "Genotype"
    )
```

A Principal Component Analysis (PCA) was also performed using logCPM values from each sample.
Both mutant genotypes appear to cluster together, however it has previously been noted that GC content appears to track closely with PC1, as a result of varable rRNA removal.

## Model Description

Model and contrast matrices were defined setting each genotype separately, and comparing between each pair of genotypes.

```{r plotLayout, echo=FALSE, fig.width=4, fig.height=4}
knitr::include_graphics("figure/index.Rmd/plotLayout-1.png")
```


```{r setModelMatrix}
d <- model.matrix(~ 0 + genotype, data = dgeList$samples) %>%
    set_colnames(str_remove_all(colnames(.), "genotype"))
cont <- makeContrasts(
    HetVsWT = Het - WT,
    HomVsWT = Hom - WT,
    HomVsHet = Hom - Het,
    levels = d
)
```


### Normalisation

As GC content and length was noted as being of concern for this dataset, *conditional-quantile normalisation* was performed using the `cqn` package.
This adds a gene and sample-level offset for each count which takes into account any systemic bias, such as that identified previously as an artefact of variable rRNA removal.
The resultant `glm.offset` values were added to the original `DGEList` object.

```{r gcCqn}
gcCqn <- cqn(
    counts = dgeList$counts,
    x = dgeList$genes$gc_content,
    lengths = dgeList$genes$length,
    sizeFactors = dgeList$samples$lib.size
)
```


```{r plotCQN, fig.cap ="*Model fits for GC content and gene length under the CQN model. Genotype-specific effects are clearly visible.*"}
par(mfrow = c(1, 2))
cols <- as.integer(dgeList$samples$genotype)
cqnplot(gcCqn, n = 1, xlab = "GC Content", col = cols)
cqnplot(gcCqn, n = 2, xlab = "Length", col = cols)
legend("bottomright", legend = levels(samples$genotype), col = seq_along(levels(samples$genotype)), lty = 1)
par(mfrow = c(1, 1))
```


```{r addOffset}
dgeList$offset <- gcCqn$glm.offset 
dgeList %<>% estimateDisp(design = d)
```


## Model Fitting

```{r fitModel}
minLfc <- log2(1.5)
alpha <- 0.01
fit <- glmFit(dgeList)
topTables <- colnames(cont) %>%
    sapply(function(x){
        glmLRT(fit, contrast = cont[,x]) %>%
            topTags(n = Inf) %>%
            .[["table"]] %>%
            as_tibble() %>%
            dplyr::select(
                gene_id, gene_name, logFC, logCPM, PValue, FDR, everything()  
            ) %>%
            mutate(
                comparison = x,
                DE = FDR < alpha & abs(logFC) > minLfc
            )
    },
    simplify = FALSE) 
```

Models were fit using the negative-binomial approaches of `glmFit()`.
Top Tables were then obtained using pairwise likelihood-ratio tests in `glmLRT()`.
These test the standard $H_0$ that there is no difference in gene expression estimates between genotypes, the gene expression estimates are obtained under the negative binomial model.

```{r expandDeLists}
alpha2 <- 0.05
topTables %<>% 
  bind_rows() %>% 
  split(f = .$gene_id) %>% 
  lapply(function(x){mutate(x, DE = any(DE) & FDR < alpha2)}) %>%
  bind_rows() %>%
  split(f = .$comparison)
```

For enrichment testing, genes were initially considered to be DE using an estimated logFC outside of the range $\pm \log_2(`r 2^minLfc`)$ and an FDR-adjusted p-value < `r alpha`.
For genes in any of these initial lists, the logFC filter was subsequently removed from subsequent comparisons in order to minimise issues introduced by the use of a hard cutoff.
Similarly the FDR threshold was raised to `r alpha2` in secondary comparisons for genes which passed the initial round of selection.

Using these criteria, the following initial DE genesets were defined:

```{r printInitialDE, results='asis'}
topTables %>%
  lapply(dplyr::filter, DE) %>% 
  vapply(nrow, integer(1)) %>%
  pander()
```


## Model Checking

```{r maPlots, fig.height=5, fig.width=10, fig.cap="*MA plots checking for any logFC bias across the range of expression values. The small curve in the average at the low end of expression values was considered to be an artefact of the sparse points at this end. Initial DE genes are shown in red, with select points labelled.*"}
topTables %>%
  bind_rows() %>%
  ggplot(aes(logCPM, logFC)) +
  geom_point(aes(colour = DE), alpha = 0.4) +
  geom_text_repel(
    aes(label = gene_name, colour = DE),
    data = . %>% dplyr::filter(DE & abs(logFC) > 3)
  ) +
  geom_text_repel(
    aes(label = gene_name, colour = DE),
    data = . %>% dplyr::filter(FDR < 0.05 & comparison == "HomVsHet")
  ) +
  geom_smooth(se = FALSE) +
  geom_hline(
    yintercept = c(-1, 1)*minLfc,
    linetype = 2,
    colour = "red"
  ) +
  facet_wrap(~comparison, nrow = 1, labeller = contLabeller) +
  scale_y_continuous(breaks = seq(-8, 8, by = 2)) +
  scale_colour_manual(values = c("grey50", "red")) +
  theme(legend.position = "none")
```


```{r checkGCBias, fig.height=5, fig.width=10, fig.cap="*Checks for GC bias in differential expression. GC content is shown against the ranking statistic, using -log10(p) multiplied by the sign of log fold-change. A small amount of bias was noted particularly in the comparison between homozygous mutants and wild-type.*"}
topTables %>%
    bind_rows() %>%
    mutate(stat = -sign(logFC)*log10(PValue)) %>%
    ggplot(aes(gc_content, stat)) +
    geom_point(aes(colour = DE), alpha = 0.4) +
    geom_smooth(se = FALSE) +
    facet_wrap(~comparison, labeller = contLabeller)  +
    labs(
        x = "GC content (%)",
        y = "Ranking Statistic"
    ) +
    coord_cartesian(ylim = c(-10, 10)) +
    scale_colour_manual(values = c("grey50", "red")) +
    theme(legend.position = "none")
```

```{r checkLengthBias, fig.height=5, fig.width=10, fig.cap="*Checks for length bias in differential expression. Gene length is shown against the ranking statistic, using -log10(p) multiplied by the sign of log fold-change. Again, a small amount of bias was noted particularly in the comparison between homozygous mutants and wild-type.*"}
topTables %>%
    bind_rows() %>%
    mutate(stat = -sign(logFC)*log10(PValue)) %>%
    ggplot(aes(length, stat)) +
    geom_point(aes(colour = DE), alpha = 0.4) +
    geom_smooth(se = FALSE) +
    facet_wrap(~comparison, labeller = contLabeller)  +
    labs(
        x = "Gene Length (bp)",
        y = "Ranking Statistic"
    ) +
    coord_cartesian(ylim = c(-10, 10)) +
    scale_x_log10(labels = comma) +
    scale_colour_manual(values = c("grey50", "red")) +
    theme(legend.position = "none")
```

Checks for both GC and length bias on differential expression showed that a small bias remained evident, despite using conditional-quantile normalisation.
However, manual checking against the same dataset **excluding the CQN steps** revealed vastly different and exaggerated bias (*not shown*).
As such, the impact of CQN normalisation was considered to be appropriate.

# Results

```{r volcanoPlots, fig.height=5, fig.width=10, fig.cap="*Volcano Plots showing DE genes against logFC.*"}
topTables %>%
    bind_rows() %>%
    ggplot(aes(logFC, -log10(PValue), colour = DE)) +
    geom_point(alpha = 0.4) +
    geom_text_repel(
        aes(label = gene_name),
        data = . %>% dplyr::filter(PValue < 0.2e-12 | logFC > 4)
    ) +
    geom_text_repel(
        aes(label = gene_name),
        data = . %>% dplyr::filter(FDR < 0.05 & comparison == "HomVsHet")
    ) +
  geom_vline(
    xintercept = c(-1, 1)*minLfc,
    linetype = 2,
    colour = "red"
  ) +
  facet_wrap(~comparison, nrow = 1, labeller = contLabeller) +
  scale_colour_manual(values = c("grey50", "red")) +
  scale_x_continuous(breaks = seq(-8, 8, by = 2)) +
  theme(legend.position = "none") 
```


```{r deGenes}
deGenes <- topTables %>%
  lapply(dplyr::filter, DE) %>%
  lapply(magrittr::extract2, "gene_id") 
```

```{r upSetPlot, fig.cap="UpSet plot showing the common genes from each comparison. A strong overlap was seen between both Mutant Vs WT comparisons."}
upSet <- deGenes %>%
  fromList() %>%
  upset(order.by = "freq")
upSet
```

```{r}
grps <- deGenes %>%
  with(
    list(
      common = intersect(HetVsWT, HomVsWT),
      onlyHet = intersect(HetVsWT, HomVsHet),
      onlyHom = intersect(HomVsWT, HomVsHet)
    )
  )
```


## Common Genes

A set of `r comma(length(grps$common))` genes was identified as commonly DE for both Mutant Vs WT comparisons.
Given that no difference was detected between mutants for this group, this was considered to be the common signature.


```{r plotLfcComparisons, fig.height=6, fig.width=5, fig.cap="*Comparison of logFC estimates for both Mutant Vs WT comparisons.*"}
topTables %>% 
  bind_rows() %>% 
  dplyr::filter(grepl("WT", comparison)) %>%
  dplyr::select(starts_with("gene"), logFC, logCPM, comparison) %>%
  pivot_wider(names_from = comparison, values_from = logFC) %>%
  mutate(group = case_when(
    gene_id %in% grps$common ~ "Common",
    gene_id %in% grps$onlyHet ~ "Het Only",
    gene_id %in% grps$onlyHom ~ "Hom Only",
    !gene_id %in% unlist(grps) ~ "No Group"
  )) %>%
  dplyr::filter(logCPM > 0) %>%
  arrange(desc(group)) %>%
  ggplot(aes(HetVsWT, HomVsWT)) + 
  geom_point(aes(colour = group, alpha = logCPM)) + 
  geom_text_repel(
    aes(label = gene_name, colour = group, alpha = logCPM),
    data = . %>% 
      dplyr::filter(
        group == "Common",
        HomVsWT < -2.2 | HomVsWT > 2 | HetVsWT > 1.9
      ),
    show.legend = FALSE
  ) +
  geom_text_repel(
    aes(label = gene_name, colour = group, alpha = logCPM),
    data = . %>% 
      dplyr::filter(
        grepl("Only", group)
      ),
    show.legend = FALSE
  ) +
  geom_smooth(se = FALSE, method = "lm") +
  geom_hline(
    yintercept = c(-1, 1)*minLfc,
    linetype = 2,
    colour = "grey50"
  ) +
  geom_vline(
    xintercept = c(-1, 1)*minLfc,
    linetype = 2,
    colour = "grey50"
  ) +
  scale_colour_manual(
    values = c(
      rgb(0.2, 0.8, 0.2), "blue", "red", rgb(0.5, 0.5, 0.5, 0.5)
    )
  ) +
  scale_x_continuous(breaks = seq(-8, 8, by = 2)) +
  scale_alpha_continuous(trans = "sqrt")
```



```{r commonSummaryHeatmap, fig.height=8, fig.width=8}
dgeList %>%
  cpm(log = TRUE) %>%
  .[grps$common,] %>%
  pheatmap::pheatmap(
    scale = "none", 
    color = viridis_pal()(100),
    labels_col = colnames(.) %>%
      str_replace_all(".+(Het|Hom|WT).+F3(_[0-9]{2}).+", "\\1\\2"),
    show_rownames = FALSE,
    annotation_col = samples %>%
      dplyr::select(sample, Genotype = genotype) %>%
      as.data.frame() %>%
      column_to_rownames("sample")
  )
```

```{r plotTopCommon}
dgeList %>%
  cpm(log = TRUE) %>% 
  extract(
    topTables %>%
      .[c("HetVsWT", "HomVsWT")] %>%
      bind_rows() %>%
      dplyr::filter(gene_id %in% grps$common) %>%
      group_by(gene_id) %>%
      mutate(minLfc = min(abs(logFC)), minFDR = min(FDR)) %>%
      ungroup() %>%
      dplyr::arrange(minFDR, desc(minLfc)) %>%
      distinct(gene_id) %>%
      dplyr::slice(1:20) %>%
      unlist(),
  ) %>%
  as.data.frame() %>%
  rownames_to_column("gene_id") %>%
  pivot_longer(
    cols = starts_with("PS2Ex"),
    names_to = "sample",
    values_to = "CPM"
  ) %>%
  left_join(samples) %>%
  ggplot(aes(genotype, CPM, fill = genotype)) +
  geom_boxplot() +
  facet_wrap(~gene_id, scales = "free_y", labeller = geneLabeller)
```