differential_expression.Rmd

---
title: "Differential expression across all donors"
author: "Davis J. McCarthy"
site: workflowr::wflow_site
---

Here, we will lok at differential expresion between clones across all lines (
i.e. donors) at the gene and gene set levels.

## Load libraries, data and DE results

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
library(tidyverse)
library(scater)
library(ggridges)
library(GenomicRanges)
library(RColorBrewer)
library(edgeR)
library(ggrepel)
library(ggcorrplot)
library(rlang)
library(limma)
library(org.Hs.eg.db)
library(ggforce)
library(superheat)
library(viridis)
library(IHW)
library(cowplot)
library(broom)
options(stringsAsFactors = FALSE)
dir.create("figures/differential_expression", showWarnings = FALSE, 
           recursive = TRUE)
```

Load the genewise differential expression results produced with the edgeR 
quasi-likelihood F test and gene set enrichment results produced with camera. 

```{r load-de}
params <- list()
params$callset <- "filt_lenient.cell_coverage_sites"
load(file.path("data/human_c6_v5p2.rdata"))
load(file.path("data/human_H_v5p2.rdata"))
load(file.path("data/human_c2_v5p2.rdata"))

de_res <- readRDS(paste0("data/de_analysis_FTv62/",
                         params$callset, 
                         ".de_results_unstimulated_cells.rds"))
```

Load SingleCellExpression objects with data used for differential expression 
analyses.

```{r load-sce}
fls <- list.files("data/sces")
fls <- fls[grepl(params$callset, fls)]
donors <- gsub(".*ce_([a-z]+)_.*", "\\1", fls)

sce_unst_list <- list()
for (don in donors) {
    sce_unst_list[[don]] <- readRDS(file.path("data/sces", 
        paste0("sce_", don, "_with_clone_assignments.", params$callset, ".rds")))
    cat(paste("reading", don, ":   ", ncol(sce_unst_list[[don]]), "cells.\n"))
}
```

The starting point for differential expression analysis was a set of 
`r length(donors)` donors, of which `r length(names(de_res$dge_list))` donors
had enough cells assigned to clones to conduct DE testing.

Summarise cell assignment information.

```{r assignments}
assignments_lst <- list()
for (don in donors) {
    assignments_lst[[don]] <- as_data_frame(
        colData(sce_unst_list[[don]])[, 
                                      c("donor_short_id", "highest_prob", 
                                        "assigned", "total_features",
                                        "total_counts_endogenous", "num_processed")])
}
assignments <- do.call("rbind", assignments_lst)
```

`r round(100 * mean(assignments$assigned != "unassigned"))`% of cells from these
donors are assigned with confidence to a clone.

Load donor info including evidence for selection dynamics in donors.

```{r load-donor-info}
df_donor_info <- read.table("data/donor_info_070818.txt")
```


## Genewise DE results

We first look at differential expression at the level of individual genes.

```{r genewise-de}
fdr_thresh <- 1
df_de_all_unst <- data_frame()
for (donor in names(de_res[["qlf_list"]])) {
    tmp <- de_res[["qlf_list"]][[donor]]$table
    tmp$gene <- rownames(de_res[["qlf_list"]][[donor]]$table)
    ihw_res <- ihw(PValue ~ logCPM, data = tmp, alpha = 0.05)
    tmp$FDR <- adj_pvalues(ihw_res)
    tmp <- tmp[tmp$FDR <= fdr_thresh,]
    if (nrow(tmp) > 0.5) {
        tmp[["donor"]] <- donor
        df_de_all_unst <- bind_rows(df_de_all_unst, tmp)
    }
}

df_ncells_de <- assignments %>% dplyr::filter(assigned != "unassigned", 
                              donor_short_id %in% names(de_res$qlf_list)) %>%
    group_by(donor_short_id) %>%
    dplyr::summarise(n_cells = n())
colnames(df_ncells_de)[1] <- "donor"

fdr_thresh <- 0.1
df_de_sig_unst <- data_frame()
for (donor in names(de_res[["qlf_list"]])) {
    tmp <- de_res[["qlf_list"]][[donor]]$table
    tmp$gene <- rownames(de_res[["qlf_list"]][[donor]]$table)
    ihw_res <- ihw(PValue ~ logCPM, data = tmp, alpha = 0.05)
    tmp$FDR <- adj_pvalues(ihw_res)
    tmp <- tmp[tmp$FDR < fdr_thresh,]
    if (nrow(tmp) > 0.5) {
        tmp[["donor"]] <- donor
        df_de_sig_unst <- bind_rows(df_de_sig_unst, tmp)
    }
}

df_de_sig_unst %>% 
    group_by(gene) %>% 
    dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% group_by(n_donors) %>%
    dplyr::summarise(count = n()) %>%
ggplot(aes(x = n_donors, y = count)) +
    geom_segment(aes(x = n_donors, xend = n_donors, y = count, yend = 0.1), 
                 colour = "gray50") +
        geom_point(size = 3) +
    scale_y_log10(breaks = c(10, 100, 1000)) +
    scale_x_continuous(breaks = 0:11) +
    coord_cartesian(ylim = c(1, 2000)) +
    theme_classic(20) +
    xlab("Number of lines in which significant (FDR < 10%)")  +
    ylab("Number of genes") +
    ggtitle("edgeR QL F test DE results")

ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes.png", 
       height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes.pdf", 
       height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes.svg", 
       height = 7, width = 10)
       
p1 <- df_de_sig_unst %>% 
    group_by(gene) %>% 
    dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% group_by(n_donors) %>%
    dplyr::summarise(count = n()) %>%
ggplot(aes(x = n_donors, y = count)) +
    geom_segment(aes(x = n_donors, xend = n_donors, y = count, yend = 0.1), 
                 colour = "gray50") +
        geom_point(size = 3) +
    scale_x_continuous(breaks = 0:11) +
    #coord_cartesian(ylim = c(1, 2200)) +
    theme_classic(16) +
    xlab("Number of lines significant (FDR < 10%)")  +
    ylab("Number of genes")
p1 

ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_linscale.png", 
       height = 7, width = 5.5)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_linscale.pdf", 
       height = 7, width = 5.5)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_linscale.svg", 
       height = 7, width = 5.5)
```


```{r, fig.height=14, fig.width=12}
p2 <- df_de_sig_unst %>% 
    group_by(gene) %>% 
    dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% dplyr::arrange(gene, n_donors) %>% ungroup() %>%
    dplyr::mutate(gene = gsub("ENSG.*_", "", gene)) %>%
    dplyr::filter(n_donors > 7.5) %>%
    ggplot(aes(y = n_donors, x = reorder(gene, n_donors, max))) +
    geom_point(alpha = 0.7, size = 4) +
    scale_y_continuous(breaks = 7:11) +
    ggthemes::scale_colour_tableau() +
    coord_flip() +
    theme_bw(16) +
    xlab("Gene") + ylab("Number of lines significant")
p2

ggsave("figures/differential_expression/alldonors_de_n_sig_donors_topgenes.png", 
       height = 7, width = 5.5)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_topgenes.pdf", 
       height = 7, width = 5.5)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_topgenes.svg", 
       height = 7, width = 5.5)


#cowplot::plot_grid(p1, p2, rel_heights = c(0.4, 0.6))


df_donor_n_de <- df_de_sig_unst %>% 
    group_by(gene) %>% 
    dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
    group_by(donor) %>%
    dplyr::summarise(count = n())
no_de_donor <- unique(df_de_all_unst[["donor"]])[!(unique(df_de_all_unst[["donor"]]) %in% df_donor_n_de[["donor"]])]
df_donor_n_de <- rbind(df_donor_n_de, data_frame(donor = no_de_donor, count = 0))

```

Permute gene labels to get a null distribution.

```{r permute-de}
df_de_nsig <- df_de_all_unst %>% dplyr::filter(FDR < 0.1) %>% 
    dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
    group_by(donor) %>% 
    dplyr::summarise(n_sig = n())
df_nsig_ncells_de <- full_join(df_ncells_de, df_de_nsig)
df_nsig_ncells_de$n_sig[is.na(df_nsig_ncells_de$n_sig)] <- 0

permute_gene_labels <- function(gene_names, n_de)  {
    sampled_genes <- c()
    for (i in seq_along(n_de))
        sampled_genes <- c(sampled_genes, sample(gene_names, size = n_de[i]))
    tab <- table(table(sampled_genes))
    df <- data_frame(n_donors = 1:11, n_genes = 0)
    df[names(tab), 2] <- tab
    df
}


n_perm <- 1000
df_perm <- list()
for (i in seq_len(n_perm))
    df_perm[[i]] <- permute_gene_labels(rownames(de_res$qlf_list$vass$table), 
                                        df_nsig_ncells_de[["n_sig"]])
df_perm <- do.call("rbind", df_perm)
df_perm <- dplyr::mutate(df_perm, data_type = "permuted")

df_perm %>% group_by(n_donors) %>% dplyr::summarise(min = min(n_genes), 
                                             median = median(n_genes),
                                             mean = mean(n_genes), 
                                             max = max(n_genes))

ppp <- df_de_sig_unst %>% 
    group_by(gene) %>% 
    dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% group_by(n_donors) %>%
    dplyr::summarise(n_genes = n()) %>% dplyr::mutate(data_type = "observed") %>%
ggplot(aes(x = n_donors, y = n_genes)) +
    # geom_segment(aes(x = n_donors, xend = n_donors, y = count, yend = 0.1), 
    #              colour = "gray50") +
#    geom_hline(yintercept = 0, linetype = 2) +
    geom_hline(yintercept = 0) +
    geom_boxplot(aes(group = n_donors, y = n_genes, colour = data_type), 
                 fill = "gray80", data = df_perm, show.legend = FALSE) +
    geom_point(aes(colour = data_type), shape = 17, size = 5) +
    scale_x_continuous(breaks = 0:11) +
    scale_y_sqrt(breaks = c(0, 10, 100, 500, 1000, 2000, 3000),
                 labels = c(0, 10, 100, 500, 1000, 2000, 3000),
                 limits = c(0, 4500)) +
    scale_colour_manual(name = '', 
                        values = c("observed" = "black", "permuted" = "gray50"), 
                        labels = c("observed", "permuted")) +
    coord_cartesian(ylim = c(0, 4500)) +
    theme_bw(18) +
    theme(legend.position = c(0.8, 0.88),
          panel.grid.major.x = element_blank(), 
          panel.grid.minor.x = element_blank()) +
    guides(colour = guide_legend(override.aes = list(shape = c(17, 19))),
           fill = FALSE, boxplot = FALSE) +
    xlab("Number of lines significant (FDR < 10%)")  +
    ylab("Number of genes")

ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_sqrtscale_perm.png", 
       height = 7, width = 10, plot = ppp)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_sqrtscale_perm.pdf", 
       height = 7, width = 10, plot = ppp)
ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_sqrtscale_perm.svg", 
       height = 7, width = 10, plot = ppp)

df_de_sig_unst %>% 
    group_by(gene) %>% 
    dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% group_by(n_donors) %>%
    dplyr::summarise(n_genes = n()) %>% dplyr::mutate(data_type = "observed") %>%
ggplot(aes(x = n_donors, y = n_genes)) +
    # geom_segment(aes(x = n_donors, xend = n_donors, y = count, yend = 0.1), 
    #              colour = "gray50") +
#    geom_hline(yintercept = 0, linetype = 2) +
    geom_hline(yintercept = 0) +
    geom_boxplot(aes(group = n_donors, y = n_genes, colour = data_type), 
                 fill = "gray80", data = df_perm, show.legend = FALSE) +
    geom_point(aes(colour = data_type), shape = 17, size = 5) +
    scale_x_continuous(breaks = 0:11) +
    scale_y_sqrt(breaks = c(0, 10, 100, 500, 1000, 2000, 3000),
                 labels = c(0, 10, 100, 500, 1000, 2000, 3000),
                 limits = c(0, 4500)) +
    scale_colour_manual(name = '', 
                        values = c("observed" = "black", "permuted" = "gray50"), 
                        labels = c("observed", "permuted")) +
    coord_cartesian(ylim = c(0, 4500)) +
    theme(legend.position = c(0.8, 0.88),
          panel.grid.major.x = element_blank(), 
          panel.grid.minor.x = element_blank()) +
    guides(colour = guide_legend(override.aes = list(shape = c(17, 19))),
           fill = FALSE, boxplot = FALSE) +
    xlab("Number of lines significant (FDR < 10%)")  +
    ylab("Number of genes")

ggsave("figures/differential_expression/alldonors_de_n_sig_donors_n_sig_genes_sqrtscale_perm_skinny.png", 
       height = 5.5, width = 6.5)

```

Look at recurrently DE genes.

```{r recurrent-de}
df_gene_n_de <- df_de_sig_unst %>% 
    group_by(gene) %>% 
    dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
    group_by(gene) %>%
    dplyr::summarise(count = n()) %>% 
    dplyr::arrange(desc(count)) %>%
    dplyr::mutate(ensembl_gene_id = gsub("_.*", "", gene),
                  hgnc_symbol = gsub(".*_", "", gene))
df_gene_n_de <- left_join(
    df_gene_n_de,
    dplyr::select(de_res$qlf_pairwise$joxm$clone2_clone1$table, 
                  ensembl_gene_id, hgnc_symbol, entrezid)
    )
df_gene_n_de <- dplyr::mutate(
    df_gene_n_de,
    cell_cycle_growth = (entrezid %in% 
                             c(Hs.H$HALLMARK_G2M_CHECKPOINT,
                               Hs.H$HALLMARK_MITOTIC_SPINDLE,
                               Hs.H$HALLMARK_E2F_TARGETS)),
    myc = (entrezid %in% c(Hs.H$HALLMARK_MYC_TARGETS_V1,
                           Hs.H$HALLMARK_MYC_TARGETS_V2)),
    emt = (entrezid %in% c(Hs.H$HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION))
)
df_gene_n_de %>% dplyr::filter(count >= 8) %>% 
  DT::datatable(.)
```

----------

## Camera results

First, aggregate gene set enrichment results across all donors.

```{r agg-camera-res}
fdr_thresh <- 1
df_camera_all_unst <- data_frame()
for (geneset in names(de_res[["camera"]])) {
    for (donor in names(de_res[["camera"]][[geneset]])) {
        for (coeff in names(de_res[["camera"]][[geneset]][[donor]])) {
            for (stat in names(de_res[["camera"]][[geneset]][[donor]][[coeff]])) {
                tmp <- de_res[["camera"]][[geneset]][[donor]][[coeff]][[stat]]
                tmp <- tmp[tmp$FDR <= fdr_thresh,]
                if (nrow(tmp) > 0.5) {
                    tmp[["collection"]] <- geneset
                    tmp[["geneset"]] <- rownames(tmp)
                    tmp[["coeff"]] <- coeff
                    tmp[["donor"]] <- donor
                    tmp[["stat"]] <- stat
                    df_camera_all_unst <- bind_rows(df_camera_all_unst, tmp)
                }
            }
        }
    }
}

fdr_thresh <- 0.05
df_camera_sig_unst <- data_frame()
for (geneset in names(de_res[["camera"]])) {
    for (donor in names(de_res[["camera"]][[geneset]])) {
        for (coeff in names(de_res[["camera"]][[geneset]][[donor]])) {
            for (stat in names(de_res[["camera"]][[geneset]][[donor]][[coeff]])) {
                tmp <- de_res[["camera"]][[geneset]][[donor]][[coeff]][[stat]]
                tmp <- tmp[tmp$FDR <= fdr_thresh,]
                if (nrow(tmp) > 0.5) {
                    tmp[["collection"]] <- geneset
                    tmp[["geneset"]] <- rownames(tmp)
                    tmp[["coeff"]] <- coeff
                    tmp[["donor"]] <- donor
                    tmp[["stat"]] <- stat
                    df_camera_sig_unst <- bind_rows(df_camera_sig_unst, tmp)
                }
            }
        }
    }
}

df_camera_sig_unst <- dplyr::mutate(
    df_camera_sig_unst,
    contrast = gsub("_", " - ", coeff),
    msigdb_collection = plyr::mapvalues(collection, from = c("c2", "c6", "H"), to = c("MSigDB curated (c2)", "MSigDB oncogenic (c6)", "MSigDB Hallmark")))

df_camera_all_unst <- dplyr::mutate(
    df_camera_all_unst,
    contrast = gsub("_", " - ", coeff),
    msigdb_collection = plyr::mapvalues(collection, from = c("c2", "c6", "H"), to = c("MSigDB curated (c2)", "MSigDB oncogenic (c6)", "MSigDB Hallmark")))
```

We now have a dataframe for significant (FDR <5%) results from the camera 
gene set enrichment results.

```{r}
head(df_camera_sig_unst)
```

And a dataframe with all results.

```{r}
head(df_camera_all_unst)
```

For now, focus on gene set enrichment results computed using log-fold change 
statistics for pairwise comparisons of clones estimated from the edgeR QL-F 
models.

We can look at all significant results summarised by donor, geneset and pairwise
contrast of clones.

```{r camera-allcontr-allsets-sig-bydonor-pvals, fig.height=17, fig.width=11}
df_camera_sig_unst %>% 
    dplyr::filter(stat == "logFC") %>%
    dplyr::mutate(donor = factor(donor, levels = rev(levels(factor(donor))))) %>%
ggplot(aes(y = -log10(PValue), x = donor, colour = contrast)) +
    geom_sina(alpha = 0.7) +
    facet_grid(contrast ~ msigdb_collection) + 
    scale_colour_brewer(palette = "Accent") +
    coord_flip() + theme_bw()
```

Similarly, we can look at all results summarised by donor, geneset and pairwise
contrast of clones.

```{r camera-allcontr-allsets-all-bydonor-fdr, fig.height=17, fig.width=11}
df_camera_all_unst %>%
    dplyr::filter(stat == "logFC") %>%
    dplyr::mutate(donor = factor(donor, levels = rev(levels(factor(donor))))) %>%
ggplot(aes(y = -log10(FDR), x = donor, colour = contrast)) +
    geom_sina(alpha = 0.7) +
    geom_hline(yintercept = -log10(0.05), linetype = 2, colour = "firebrick") +
    facet_grid(contrast ~ msigdb_collection, scales = "free_x") + 
    scale_colour_brewer(palette = "Accent") +
    coord_flip() + theme_bw()

ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_all_results.png", 
       height = 16, width = 14)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_all_results.pdf", 
       height = 16, width = 14)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_all_results.svg", 
       height = 16, width = 14)
```

We can check the number of significant gene sets for each donor, for each MSigDB
gene set collection.

```{r camera-nsig-donor}
df_camera_sig_unst %>% 
    dplyr::filter(stat == "logFC") %>%
    dplyr::filter(FDR < 0.05) %>%
    group_by(donor, msigdb_collection) %>%
    dplyr::summarise(n_sig = n()) %>% print(n = Inf)
```

We can look at the number of significant gene sets for each donor.

```{r alldonors_camera_enrichment_by_donor_simple, fig.height=12, fig.width=8}
## simpler version
df_camera_all_unst %>% 
    dplyr::filter(stat == "logFC") %>%
    group_by(donor, msigdb_collection) %>%
    dplyr::summarise(n_sig = sum(FDR < 0.05)) %>% ungroup() %>% 
    dplyr::mutate(donor = factor(donor, levels = rev(levels(factor(donor))))) %>%
    ggplot(aes(y = n_sig, x = donor)) +
    geom_point(alpha = 1, size = 4) +
    facet_wrap(~ msigdb_collection, scales = "free_x") + 
    scale_fill_brewer(palette = "Accent") +
    coord_flip() +
    theme_bw(16) +
    xlab("Donor") + ylab("Number of significant genesets (FDR < 5%)") +
    ggtitle("Camera MSigDB gene set enrichment by line")

ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple.png", 
       height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple.pdf", 
       height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple.svg", 
       height = 7, width = 10)
```

We can look at the effect of the the number of cells for each donor on the 
DE results obtained.

```{r alldonors_camera_enrichment_by_donor_simple_size_by_ncells, fig.height=12, fig.width=8}
ncells_by_donor <- rep(NA, length(sce_unst_list))
names(ncells_by_donor) <- names(sce_unst_list)
for (don in names(sce_unst_list))
    ncells_by_donor[don] <- ncol(sce_unst_list[[don]])

df_camera_all_unst %>% 
    dplyr::filter(stat == "logFC") %>%
    group_by(donor, msigdb_collection) %>%
    dplyr::summarise(n_sig = sum(FDR < 0.05)) %>% ungroup() -> df_to_plot
df_to_plot <- inner_join(df_to_plot, 
                        data_frame(donor = names(ncells_by_donor),
                                   ncells = ncells_by_donor))
df_to_plot %>%
    dplyr::mutate(donor = factor(donor, levels = rev(levels(factor(donor))))) %>%
    ggplot(aes(y = n_sig, x = donor, size = ncells)) +
    geom_point(alpha = 1) +
    facet_wrap(~ msigdb_collection, scales = "free_x") + 
    scale_fill_brewer(palette = "Accent") +
    coord_flip() +
    theme_bw(16) +
    xlab("Donor") + ylab("Number of significant genesets (FDR < 5%)") +
    ggtitle("Camera MSigDB gene set enrichment by line")

ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple_size_by_ncells.png", 
       height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple_size_by_ncells.pdf", 
       height = 7, width = 10)
ggsave("figures/differential_expression/alldonors_camera_enrichment_by_donor_simple_size_by_ncells.svg", 
       height = 7, width = 10)

```

### Hallmark gene set

Focus now on looking at DE results for the MSigDB Hallmark gene set (50 of the 
best-characterised gene sets as determined by MSigDB).

Look at the gene sets that are found to be enriched in multiple donors.

```{r alldonors_camera_enrichment_H_by_geneset, fig.height=7, fig.width=10}

## Hallmark geneset
df_camera_sig_unst %>% dplyr::filter(collection == "H") %>% 
    dplyr::filter(stat == "logFC") %>%
    group_by(geneset) %>% 
    dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
    dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
ggplot(aes(y = n_donors, x = reorder(geneset, n_donors, max))) +
    geom_point(alpha = 0.7, size = 4) +
    ggthemes::scale_colour_tableau() +
    coord_flip() +
    theme_bw(14) +
    xlab("Gene set") + ylab("Number of lines significant")

ggsave("figures/differential_expression/alldonors_camera_enrichment_H_by_geneset.png", 
       height = 7, width = 9.5)
ggsave("figures/differential_expression/alldonors_camera_enrichment_H_by_geneset.pdf", 
       height = 7, width = 9.5)
ggsave("figures/differential_expression/alldonors_camera_enrichment_H_by_geneset.svg", 
       height = 7, width = 9.5)
## number of donors with at least one significant geneset
tmp <- df_camera_sig_unst %>% dplyr::filter(collection == "H") %>% 
    dplyr::filter(stat == "logFC") %>%
    group_by(geneset)
unique(tmp[["donor"]])
```

`r length(unique(tmp[["donor"]]))` donors have at least one significantly 
enriched Hallmark gene set.

For gene sets related directly to cell cycle and growth, we see contrasts being
both up- and down- regulated, but for EMT, coagulation and angiogenesis pathways,
we only see these down-regulated.

```{r alldonors_camera_enrichment_H_by_geneset_by_dir, fig.height=10, fig.width=16}
df_camera_sig_unst %>% dplyr::filter(collection == "H") %>% 
  dplyr::filter(stat == "logFC") %>%
  group_by(geneset, Direction) %>% 
  dplyr::mutate(id = paste0(donor, geneset)) %>% 
  dplyr::summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
  ggplot(aes(y = n_donors, x = reorder(geneset, n_donors, max),
             colour = Direction)) +
  geom_point(alpha = 0.7, size = 4, position = position_dodge(width = 0.5)) +
  ggthemes::scale_colour_tableau() +
  coord_flip() +
  theme_bw(16) +
  xlab("Gene set") + ylab("Number of lines significant")  +
  ggtitle("Camera MSigDB Hallmark gene set enrichment")
```



## Heatmap of results for camera Hallmark geneset testing

We can get an overview of all the Hallmark gene set results by producing a 
heatmap, first showing just the significant (FDR < 5%) results across all 
donors and pairwise contrasts of clones.

```{r top_genesets_H_direction_heatmap, fig.height=7, fig.width=12}
repeated_sig_H_genesets <- df_camera_sig_unst %>% 
    dplyr::filter(collection == "H", stat == "logFC") %>% 
    group_by(geneset) %>% 
    dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% dplyr::arrange(n_donors) %>% 
    dplyr::filter(n_donors > 0.5) 
repeated_sig_H_genesets_vec <- unique(repeated_sig_H_genesets[["geneset"]])
repeated_sig_H_genesets_vec <- gsub("_", " ", gsub("HALLMARK_", "", 
                                                   repeated_sig_H_genesets_vec))
     
df_4_heatmap <- df_camera_sig_unst %>% 
  dplyr::filter(collection == "H", stat == "logFC") %>%
  dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
  dplyr::mutate(geneset = 
                  factor(geneset, levels = repeated_sig_H_genesets_vec)) %>%
  dplyr::filter(geneset %in% repeated_sig_H_genesets_vec) %>%
  dplyr::mutate(id = paste0(donor, ": ", contrast))
  

div_lines <- gsub(": c.*", "",
     sort(unique(paste0(df_4_heatmap[["donor"]], ": ", 
                        df_4_heatmap[["contrast"]])))) %>% table %>% cumsum + 0.5
    
df_4_heatmap %>%
    ggplot(aes(x = id, y = geneset, fill = Direction)) +
    geom_tile() +
    geom_vline(xintercept = div_lines, colour = "gray70") +
    scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1))

ggsave("figures/differential_expression/top_genesets_H_direction_heatmap.png", height = 6, width = 12)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap.pdf", height = 6, width = 12)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap.svg", height = 6, width = 12)
```

We can do the same for all results for the gene sets that are significantly 
enriched in at least two donors.

```{r top_genesets_H_direction_heatmap_all_contrasts, fig.height=9, fig.width=18}
df_camera_all_unst %>% 
    dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
    dplyr::mutate(geneset = 
                      factor(geneset, levels = repeated_sig_H_genesets_vec)) %>%
    dplyr::filter(geneset %in% repeated_sig_H_genesets_vec) %>%
    dplyr::mutate(id = paste0(donor, ": ", contrast)) ->
    df_4_heatmap_all

df_4_heatmap_all <- dplyr::mutate(
    df_4_heatmap_all,
    minlog10P = cut(-log10(PValue), breaks = c(0, 1, 2, 3, 4, 5, 30)))

div_lines_all <- gsub(": c.*", "",
     sort(unique(paste0(df_4_heatmap_all[["donor"]], ": ", 
                        df_4_heatmap_all[["contrast"]])))) %>% table %>% cumsum + 0.5

pp <- df_4_heatmap_all %>%
    ggplot(aes(x = id, y = geneset, fill = Direction, alpha = minlog10P)) +
    geom_tile() +
    geom_point(alpha = 1, data = df_4_heatmap, pch = 19, size = 0.5, show.legend = FALSE) +
    geom_vline(xintercept = div_lines_all, colour = "gray70") +
    scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
    scale_alpha_discrete(name = "-log10(P)") +
    ylab("Gene set") +
    xlab("Line and clone comparison") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1),
          legend.position = "right")
pp    

ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts.png", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts.pdf", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts.svg", height = 9, width = 20)
```

We can also add a panel to this figure showing the number of donors in which
each of these gene sets is significantly enriched.

```{r top_genesets_H_direction_heatmap_all_contrasts_with_nsig_donors, fig.height=9, fig.width=20}
## Hallmark geneset
pp_nsig <- df_camera_sig_unst %>% dplyr::filter(collection == "H") %>% 
    dplyr::filter(stat == "logFC") %>%
    group_by(geneset) %>% 
    dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
    dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
    ggplot(aes(y = n_donors, x = reorder(geneset, n_donors, max))) +
    geom_hline(yintercept = 0, colour = "gray50") +
    geom_segment(aes(xend = reorder(geneset, n_donors, max), yend = 0),
                 colour = "gray50") +
    geom_point(size = 4, colour = "gray30", alpha = 1) +
    ggthemes::scale_colour_tableau() +
    coord_flip() +
    xlab("Gene set") + ylab("Number of lines significant") +
    theme(axis.title.y = element_blank(),
          axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.line.y = element_blank())

prow <- plot_grid(pp + theme(legend.position = "none"), 
                  pp_nsig, align = 'h', rel_widths = c(7, 1))
lgnd <- get_legend(pp)
plot_grid(prow, lgnd, rel_widths = c(3, .3))

ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts_with_nsig_donors.png", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts_with_nsig_donors.pdf", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_all_contrasts_with_nsig_donors.svg", height = 9, width = 20)
```

However, the plot above is very complicated, so we may want to focus just on the 
lines for which there are multiple clones that show differing behaviour amongst
each other. To simplify, let us just look at 12 donors that have significant 
geneset enrichment for at least 2 contrasts and just look at the 9 gene sets 
that are significant in at least three lines.

```{r top_genesets_H_direction_heatmap_all_contrasts-simple, fig.height=9, fig.width=18}
repeated_sig_H_genesets3 <- df_camera_sig_unst %>% 
    dplyr::filter(collection == "H", stat == "logFC") %>% 
    group_by(geneset) %>% 
    dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% dplyr::arrange(n_donors) %>% 
    dplyr::filter(n_donors > 2.5) 
repeated_sig_H_genesets_vec3 <- unique(repeated_sig_H_genesets3[["geneset"]])
repeated_sig_H_genesets_vec3 <- gsub("_", " ", gsub("HALLMARK_", "", 
                                                   repeated_sig_H_genesets_vec3))

selected_donors <- c("fawm", "fikt", "hipn", "ieki", "laey", "lexy", "qayj", 
                     "qonc", "rozh", "ualf", "wahn", "zoxy")

df_4_heatmap_filt <- df_camera_all_unst %>% 
  dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
  dplyr::mutate(geneset = 
                  factor(geneset, levels = repeated_sig_H_genesets_vec3)) %>%
  dplyr::filter(geneset %in% repeated_sig_H_genesets_vec3,
                donor %in% selected_donors) %>%
  dplyr::mutate(id = paste0(donor, ": ", contrast))

df_4_heatmap_filt_sig <- df_camera_sig_unst %>% 
  dplyr::filter(collection == "H", stat == "logFC") %>%
  dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
  dplyr::mutate(geneset = 
                  factor(geneset, levels = repeated_sig_H_genesets_vec3)) %>%
  dplyr::filter(geneset %in% repeated_sig_H_genesets_vec3,
                donor %in% selected_donors) %>%
  dplyr::mutate(id = paste0(donor, ": ", contrast))

df_4_heatmap_filt <- dplyr::mutate(
    df_4_heatmap_filt,
    minlog10P = cut(-log10(PValue), breaks = c(0, 1, 2, 3, 4, 5, 30)))

div_lines_filt <- gsub(": c.*", "",
                       sort(unique(paste0(df_4_heatmap_filt[["donor"]], ": ", 
                                          df_4_heatmap_filt[["contrast"]])))) %>% 
  table %>% cumsum + 0.5

pp_filt <- df_4_heatmap_filt %>%
    ggplot(aes(x = id, y = geneset, fill = Direction, alpha = minlog10P)) +
    geom_tile() +
    geom_point(alpha = 1, data = df_4_heatmap_filt_sig, pch = 19, size = 0.5, 
               show.legend = FALSE) +
    geom_vline(xintercept = div_lines_filt, colour = "gray70") +
    scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
    scale_alpha_discrete(name = "-log10(P)") +
    ylab("Gene set") +
    xlab("Line and clone comparison") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1),
          legend.position = "right")
pp_filt    

ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_filt_contrasts.png", plot = pp_filt, height = 5, width = 12)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_filt_contrasts.pdf", plot = pp_filt, height = 5, width = 12)
ggsave("figures/differential_expression/top_genesets_H_direction_heatmap_filt_contrasts.svg", plot = pp_filt, height = 5, width = 12)
```


## Correlation of gene set results and genes contained

Let's look at the correlation between gene set results (Spearman correlation of
signed -log10(P-values) from _camera_ tests) and compare to the proportion of 
genes overlapping between pairs of gene sets.

```{r corr-maps, fig.height=9, fig.width=13}
repeated_sig_H_genesets_vec2 <- paste0("HALLMARK_", 
                                       gsub(" ", "_", repeated_sig_H_genesets_vec))
## all results
df_H_pvals <- df_camera_all_unst %>% dplyr::filter(collection == "H") %>% 
  dplyr::filter(stat == "logFC", geneset %in% repeated_sig_H_genesets_vec2) %>%
  dplyr::mutate(donor_coeff = paste(donor, coeff, sep = "."),
                sign = ifelse(Direction == "Down", -1, 1),
                signed_P = sign * -log10(PValue)) %>%
  dplyr::select(geneset, donor_coeff, signed_P) %>%
  tidyr::spread(key = donor_coeff, value = signed_P)

mat_H_pvals <- as.matrix(df_H_pvals[, -1])
rownames(mat_H_pvals) <- gsub("_", " ", gsub("HALLMARK_", "", df_H_pvals[[1]]))
cor_H_pvals <- cor(t(mat_H_pvals), method = "spearman")
p.mat <- cor_pmat(t(mat_H_pvals))
ggcorrplot(cor_H_pvals, hc.order = TRUE, p.mat = p.mat, insig = "blank") +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

hclust_cor <- hclust(as.dist(1 - cor_H_pvals))
corrplot1 <- ggcorrplot(cor_H_pvals[hclust_cor$order, hclust_cor$order], 
           p.mat = p.mat[hclust_cor$order, hclust_cor$order], insig = "blank") +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

mat_H_gene_overlap <- matrix(nrow = nrow(cor_H_pvals), ncol = ncol(cor_H_pvals),
                             dimnames = dimnames(cor_H_pvals))
for (i in seq_along(repeated_sig_H_genesets_vec2)) {
  for (j in seq_along(repeated_sig_H_genesets_vec2)) {
    gs1 <- paste0("HALLMARK_", gsub(" ", "_", rownames(mat_H_gene_overlap)[i]))
    gs2 <- paste0("HALLMARK_", gsub(" ", "_", rownames(mat_H_gene_overlap)[j]))
    mat_H_gene_overlap[i, j] <- mean(Hs.H[[gs1]] %in% Hs.H[[gs2]])
  }
}

corrplot2 <- ggcorrplot(mat_H_gene_overlap[hclust_cor$order, hclust_cor$order]) +
  scale_fill_gradient(name = "Gene set\noverlap", low = "white", high = "black") +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())
corrplot2
corrplot3 <- corrplot2 + theme(axis.text.y = element_blank())
```

```{r corr-plot-combined, fig.height=9, fig.width=20}
plot_grid(corrplot1 + theme(plot.margin = unit(c(0,0,0,0), "cm")), 
          corrplot3 + theme(plot.margin = unit(c(0,0,0,0), "cm")), 
          align = "h", axis = "b", rel_widths = c(0.58, 0.42))

ggsave("figures/differential_expression/top_genesets_H_corrplots.png", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_corrplots.pdf", height = 9, width = 20)
ggsave("figures/differential_expression/top_genesets_H_corrplots.svg", height = 9, width = 20)
```

Plot gene set correlation with the number of donors in which each gene set is 
significant.

```{r, fig.height=7, fig.width=12}
pp_nsig <- df_camera_sig_unst %>% dplyr::filter(collection == "H") %>% 
    dplyr::filter(stat == "logFC") %>%
    group_by(geneset) %>% 
    dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
    dplyr::summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
    dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
    dplyr::mutate(geneset = factor(
      geneset, levels = rownames(mat_H_gene_overlap)[hclust_cor$order])) %>%
    ggplot(aes(y = n_donors, x = geneset)) +
    geom_hline(yintercept = 0, colour = "gray50") +
    geom_segment(aes(xend = geneset, yend = 0),
                 colour = "gray50") +
    geom_point(size = 4, colour = "gray30", alpha = 1) +
    ggthemes::scale_colour_tableau() +
    coord_flip() +
    xlab("Gene set") + ylab("Number of lines\nsignificant") +
    theme(axis.title.y = element_blank(),
          axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.line.y = element_blank())

ggdraw() +
  draw_plot(corrplot1 + theme(legend.position = "top"), 
            x = 0,  y = 0, width = 0.8, scale = 1) +
  draw_plot(pp_nsig, 
            x = 0.685,  y = 0.25, width = 0.25, height = 0.6445)

ggsave("figures/differential_expression/top_genesets_H_corrplot_with_nsig_donor.png", 
       height = 7, width = 12)
ggsave("figures/differential_expression/top_genesets_H_corrplot_with_nsig_donor.pdf", 
       height = 7, width = 12)

```



## Linking DE to selection

```{r}
df_donor_info <- read.table("data/donor_info_070818.txt")
df_donor_info <- as_data_frame(df_donor_info)
df_donor_info$donor <- df_donor_info$donor_short

df_ncells_de <- assignments %>% dplyr::filter(assigned != "unassigned", 
                              donor_short_id %in% names(de_res$qlf_list)) %>%
    group_by(donor_short_id) %>%
    dplyr::summarise(n_cells = n())
colnames(df_ncells_de)[1] <- "donor"

df_prop_assigned <- assignments %>% 
  dplyr::filter(donor_short_id %in% names(de_res$qlf_list)) %>%
    group_by(donor_short_id) %>%
    dplyr::summarise(prop_assigned = mean(assigned != "unassigned"))
colnames(df_prop_assigned)[1] <- "donor"

df_nvars_by_cat <- readr::read_tsv("output/nvars_by_category_by_donor.tsv")
df_nvars_by_cat_wd <- tidyr::spread(
  df_nvars_by_cat[, 1:3], consequence, n_vars_all_genes)
df_nvars_by_cat_wd <- left_join(
  dplyr::summarise(group_by(df_nvars_by_cat, donor), nvars_all = sum(n_vars_all_genes)),
  df_nvars_by_cat_wd
)
df_nvars_by_cat_wd <- df_nvars_by_cat %>% 
  dplyr::filter(consequence %in% c("missense", "splicing", "nonsense")) %>%
  group_by(donor) %>%
  dplyr::summarise(nvars_misnonspli = sum(n_vars_all_genes)) %>%
  left_join(., df_nvars_by_cat_wd)

df_donor_info <- left_join(df_ncells_de, df_donor_info)
df_donor_info <- left_join(df_prop_assigned, df_donor_info)
df_donor_info <- left_join(df_donor_n_de, df_donor_info)
df_donor_info$n_de_genes <- df_donor_info$count
df_donor_info <- left_join(df_donor_info, df_nvars_by_cat_wd)

nbglm_nde <- MASS::glm.nb(n_de_genes ~ n_cells, data = df_donor_info)
df_nbglm_nde <- broom::augment(nbglm_nde) %>%
  left_join(df_donor_info)

## n_de vs n_cells
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = n_cells, y = n_de_genes, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes") +
  xlab("Number of cells") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))

ggsave("figures/differential_expression/n_de_genes_vs_n_cells.png", 
       height = 5.5, width = 5.5)

## selection, n_de resid boxplot
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = selection, y = .resid)) +
  geom_violin(aes(fill = selection), alpha = 0.7) +
  geom_boxplot(outlier.alpha = 0, width = 0.2) +
  ggbeeswarm::geom_quasirandom(aes(fill = selection), size = 3, shape = 21) +
  ylab("Number of DE genes (residual from NB GLM)") +
  xlab("Inferred selection status") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
  coord_flip()

ggsave("figures/differential_expression/n_de_resid_selection_boxplot.png", 
       height = 4.5, width = 6.5)

summary(lm(.resid ~ selection, data = df_nbglm_nde))

## selection, n_de (sqrt scale) boxplot
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = selection, y = n_de_genes)) +
  geom_violin(aes(fill = selection), alpha = 0.7) +
  geom_boxplot(outlier.alpha = 0, width = 0.2) +
  ggbeeswarm::geom_quasirandom(aes(fill = selection), size = 3, shape = 21) +
  ylab("Number of DE genes") +
  xlab("Inferred selection status") +
  scale_y_sqrt(breaks = c(0, 100, 500, 1000, 1500, 2000, 2500)) +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
  coord_flip()

ggsave("figures/differential_expression/n_de_sqrt_selection_boxplot.png", 
       height = 5.5, width = 6.5)

## n_de (resids) vs goodness of fit cumul. mutation model
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = rsq_ntrtestr, y = .resid, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes (residual from NB GLM)") +
  xlab("Goodness of fit: cumulative mutations") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))

ggsave("figures/differential_expression/n_de_resid_selection_vs_gof_cumul_mut_model.png", 
       height = 6.5, width = 5.5)


## n_de (sqrt scale) vs goodness of fit cumul. mutation model
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = rsq_ntrtestr, y = n_de_genes, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes") +
  xlab("Goodness of fit: cumulative mutations") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))

ggsave("figures/differential_expression/n_de_sqrt_selection_vs_gof_cumul_mut_model.png", 
       height = 5.5, width = 6.5)

## n_de (resids) vs goodness of fit NB model
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = rsq_negbinfit, y = .resid, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes (residual from NB GLM)") +
  xlab("Goodness of fit: negative binomial distribution") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))

ggsave("figures/differential_expression/n_de_resid_selection_vs_gof_negbin_model.png", 
       height = 5.5, width = 6.5)

## n_de (resids) vs mutational load (all)
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = nvars_all, y = .resid, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes (residual from NB GLM)") +
  xlab("Number of somatic variants") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
  scale_x_log10(breaks = c(5, 10, 20, 50, 100, 500))

ggsave("figures/differential_expression/n_de_resid_selection_vs_n_somatic_vars_all.png", 
       height = 6.5, width = 5.5)

## n_de (sqrt scale) vs mutational load (all)
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = nvars_all, y = n_de_genes, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes") +
  xlab("Number of somatic variants") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
  scale_x_log10(breaks = c(5, 10, 20, 50, 100, 500)) +
  scale_y_sqrt(breaks = c(10, 100, 500, 1000, 1500, 2000, 2500))

ggsave("figures/differential_expression/n_de_sqrt_selection_vs_n_somatic_vars_all.png", 
       height = 5.5, width = 6.5)

## n_de (resids) vs mutational load (missense)
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = missense, y = .resid, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes (residual from NB GLM)") +
  xlab("Number of somatic missense variants") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
  scale_x_log10(breaks = c(5, 10, 20, 50, 100, 500))

ggsave("figures/differential_expression/n_de_resid_selection_vs_n_somatic_vars_missense.png", 
       height = 5.5, width = 6.5)

## n_de (resids) vs mutational load (missense, nonsense, splicing)
df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = nvars_misnonspli, y = .resid, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes (residual from NB GLM)") +
  xlab("Number of missense, nonsense & splicing variants") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
  scale_x_log10(breaks = c(5, 10, 20, 50, 100, 500))

ggsave("figures/differential_expression/n_de_resid_selection_vs_n_somatic_vars_misnonspli.png", 
       height = 6.5, width = 5.5)

df_nbglm_nde %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = nvars_all, y = rsq_ntrtestr, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Goodness of fit: cumulative mutations ") +
  xlab("Number of somatic variants") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4")) +
  scale_x_log10(breaks = c(5, 10, 20, 50, 100, 500))

ggsave("figures/differential_expression/gof_cumul_mut_model_vs_n_somatic_vars.png", 
       height = 6.5, width = 5.5)

# 
# fdr_thresh <- 0.1
# df_de_sig_unst <- data_frame()
# for (donor in names(de_res[["qlf_list"]])) {
#     tmp <- de_res[["qlf_list"]][[donor]]$table
#     tmp$gene <- rownames(de_res[["qlf_list"]][[donor]]$table)
#     ihw_res <- ihw(PValue ~ logCPM, data = tmp, alpha = 0.05)
#     tmp$FDR <- adj_pvalues(ihw_res)
#     tmp <- tmp[tmp$FDR < fdr_thresh,]
#     if (nrow(tmp) > 0.5) {
#         tmp[["donor"]] <- donor
#         df_de_sig_unst <- bind_rows(df_de_sig_unst, tmp)
#     }
# }
# 
# 
# df_donor_n_de <- df_de_sig_unst %>% 
#     group_by(gene) %>% 
#     dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
#     group_by(donor) %>%
#     dplyr::summarise(count = n())

```


## Linking DE to assignment accuracy

Simulation results provide us with an estimate of the assignment accuracy for
each line. 

We load the simulation results for individual lines.

```{r}
all_files <- paste0(lines, ".simulate.rds")
assign_0 <- matrix(0, nrow = 500, ncol = length(lines))
assign_1 <- matrix(0, nrow = 500, ncol = length(lines))
prob_all <- matrix(0, nrow = 500, ncol = length(lines))

for (i in seq_len(length(all_files))) {
  afile <- all_files[i]
  sim_dat <- readRDS(file.path("data", "simulations", afile))
  assign_0[, i] <- get_prob_label(sim_dat$I_sim)
  assign_1[, i] <- get_prob_label(sim_dat$prob_Gibbs)
  prob_all[, i] <- get_prob_value(sim_dat$prob_Gibbs, mode = "best")
}


precision_simu <- rep(0, length(lines))
for (i in seq_len(length(lines))) {
  idx <- prob_all[, i] > 0.5
  precision_simu[i] <- mean(assign_0[idx, i] == assign_1[idx, i])
}

df <- data.frame(line = lines, n_sites = n_sites, n_clone = n_clone, 
                 recall_real = recall_all, recall_simu = colMeans(prob_all > 0.5),
                 precision_simu = precision_simu)
```

We can now look at the relationship between the number of DE genes detected 
per line and the assignment accuracy and assignment rate per line.

```{r plot-de-assignment-acc}
df <- inner_join(df, dplyr::mutate(df_nbglm_nde, line = donor))
## n_de (resids) vs assignment accuracy
p1 <- df %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = precision_simu, y = .resid, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes (residual from NB GLM)") +
  xlab("Assignment accuracy (precision) from simulations") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))

ggsave("figures/differential_expression/n_de_resid_selection_vs_sim_assign_acc.png", 
       plot = p1, height = 5.5, width = 6.5)

## n_de (resids) vs assignment rate
p2 <- df %>%
  dplyr::mutate(selection = factor(
    selection, levels = c("neutral", "undetermined", "selected"))) %>%
ggplot(aes(x = recall_simu, y = .resid, fill = selection)) +
  geom_smooth(aes(group = 1), colour = "firebrick", method = "lm", level = 0.9) +
  geom_point(size = 3, shape = 21) +
  ylab("Number of DE genes (residual from NB GLM)") +
  xlab("Assignment rate (recall) from simulations") +
  scale_fill_manual(values = c("dodgerblue", "#CCCCCC", "dodgerblue4"))

ggsave("figures/differential_expression/n_de_resid_selection_vs_sim_assign_rate.png", 
       plot = p2, height = 5.5, width = 6.5)

p3 <- plot_grid(p1, p2, labels = "auto", nrow = 2)

ggsave("figures/differential_expression/n_de_resid_selection_vs_sim_assign_acc_rate_combined.png", 
       plot = p3, height = 10, width = 6.5)

p3
```



## Linking pathway results to selection

Not yet implemented

```{r, eval=FALSE, include=FALSE} 
# df_donor_info <- read.table("data/donor_info_070818.txt")
# df_camera_all_unst$classification <- NA
# df_camera_all_unst$classification[
#     df_camera_all_unst$donor %in% df_donor_info$sampleID[df_donor_info$cons_classification == "selection"]
# ] <- "selection"
# df_camera_all_unst$classification[
#     df_camera_all_unst$donor %in% df_donor_info$sampleID[df_donor_info$cons_classification == "undetermined"]
# ] <- "undetermined"
# df_camera_all_unst$classification[
#     df_camera_all_unst$donor %in% df_donor_info$sampleID[df_donor_info$cons_classification == "neutral"]
# ] <- "neutral"
# df_camera_all_unst$classification <- factor(df_camera_all_unst$classification, 
#                                             levels = c("neutral", "undetermined", "selection"))
# 
# df_camera_nsig <- df_camera_all_unst %>% dplyr::filter(collection == "H", FDR < 0.05) %>% 
#     dplyr::filter(geneset %in% c("HALLMARK_E2F_TARGETS", "HALLMARK_G2M_CHECKPOINT", "HALLMARK_MYC_TARGETS_V1")) %>%
#     group_by(geneset, classification, Direction) %>% 
#     dplyr::mutate(id = paste0(donor, geneset, Direction)) %>% distinct(id, .keep_all = TRUE) %>%
#     dplyr::summarise(n_sig = n())
# df_camera_nsig
# 
# 
# ## test for different proportions of Up/Down sig genesets
# e2f_select <- df_camera_nsig %>% dplyr::filter(geneset == "HALLMARK_E2F_TARGETS") %>% ungroup() %>% dplyr::select(-geneset) %>%
#     spread(classification, n_sig)
# e2f_mat <- as.matrix(e2f_select[, -1])
# rownames(e2f_mat) <- e2f_select[["e2f_select"]]
# fisher.test(e2f_mat)
# g2m_select <- df_camera_nsig %>% dplyr::filter(geneset == "HALLMARK_G2M_CHECKPOINT") %>% ungroup() %>% dplyr::select(-geneset) %>%
#     spread(classification, n_sig)
# g2m_mat <- as.matrix(g2m_select[, -1])
# rownames(g2m_mat) <- g2m_select[["e2f_select"]]
# fisher.test(g2m_mat)
# myc_select <- df_camera_nsig %>% dplyr::filter(geneset == "HALLMARK_MYC_TARGETS_V1") %>% ungroup() %>% dplyr::select(-geneset) %>%
#     spread(classification, n_sig)
# myc_mat <- as.matrix(myc_select[, -1])
# rownames(myc_mat) <- myc_select[["e2f_select"]]
# fisher.test(myc_mat)
# 
# df_camera_all_unst %>% dplyr::filter(collection == "H") %>% 
#     dplyr::filter(geneset %in% c("HALLMARK_E2F_TARGETS", "HALLMARK_G2M_CHECKPOINT", "HALLMARK_MYC_TARGETS_V1")) %>%
#     group_by(geneset, classification, Direction) %>% 
#     dplyr::mutate(id = paste0(donor, geneset, Direction)) %>% distinct(id, .keep_all = TRUE) %>%
#     ungroup() %>%
#     dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
#     ggplot(aes(y = -log10(FDR), x = classification, colour = Direction)) +
#     ggbeeswarm::geom_quasirandom(alpha = 0.7, size = 4) +
#     geom_hline(yintercept = -log10(0.05), linetype = 2) +
#     facet_wrap(~geneset, nrow = 3) +
#     scale_colour_manual(values = c("gray40", "firebrick")) +
#     coord_flip() +
#     theme_bw(14) +
#     xlab("Selection classification") + ylab("-log10(False discovery rate)") +
#     theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank())
# 
# ggsave("figures/differential_expression/alldonors_camera_enrichment_H_top_genesets_by_selection.png", 
#        height = 7, width = 10)
# ggsave("figures/differential_expression/alldonors_camera_enrichment_H_top_genesets_by_selection.pdf", 
#        height = 7, width = 10)
# ggsave("figures/differential_expression/alldonors_camera_enrichment_H_top_genesets_by_selection.svg", 
#        height = 7, width = 10)
# 
# df_camera_all_unst %>% dplyr::filter(collection == "H", FDR < 0.05) %>% 
#     dplyr::filter(geneset %in% c("HALLMARK_E2F_TARGETS", "HALLMARK_G2M_CHECKPOINT")) %>%
#     group_by(geneset, classification) %>% 
#     dplyr::mutate(id = paste0(donor, geneset)) %>% distinct(id, .keep_all = TRUE) %>%
#     dplyr::summarise(n_donors = n()) %>% dplyr::arrange(geneset, n_donors) %>% ungroup() %>%
#     dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
#     ggplot(aes(y = n_donors, x = geneset)) +
#     geom_point(alpha = 0.7, size = 4) +
#     ggthemes::scale_colour_tableau() +
#     coord_flip() +
#     theme_bw(14) +
#     xlab("Gene set") + ylab("Number of significant donors")



df_e2f <- df_camera_all_unst %>% 
    dplyr::filter(grepl("HALLMARK_E2F_TARGETS", geneset)) %>%
    dplyr::mutate(id = paste(donor, contrast, sep = ": "), sig = FDR < 0.05)
ggplot(df_e2f, aes(x = Direction, y = -log10(PValue), colour = sig, 
                      label = id)) +
    geom_violin(aes(group = Direction), fill = "gray90", colour = "gray90") +
    geom_point(size = 4) +
    geom_label_repel(show.legend = FALSE, point.padding = 0.5,
                    data = subset(df_e2f, FDR < 0.05)) +
    scale_colour_manual(values = c("gray50", "firebrick"), 
                        label = c("N.S.", "FDR < 5%"), name = "", drop = FALSE) +
    guides(alpha = FALSE) + 
    ggtitle("E2F TARGETS") +
    theme_classic(20)

ggsave("figures/differential_expression/alldonors_camera_H_e2f_targets.png", height = 8.5, width = 10)
ggsave("figures/differential_expression/alldonors_camera_H_e2f_targets.pdf", height = 8.5, width = 10)
ggsave("figures/differential_expression/alldonors_camera_H_e2f_targets.svg", height = 8.5, width = 10)

df_g2m <- df_camera_all_unst %>% 
    dplyr::filter(grepl("HALLMARK_G2M_CHECKPOINT", geneset)) %>%
    dplyr::mutate(id = paste(donor, contrast, sep = ": "), sig = FDR < 0.05)
ggplot(df_g2m, aes(x = Direction, y = -log10(PValue), colour = sig, 
                      label = id)) +
    geom_violin(aes(group = Direction), fill = "gray90", colour = "gray90") +
    geom_point(size = 4) +
    geom_label_repel(show.legend = FALSE, point.padding = 0.5,
                    data = subset(df_g2m, FDR < 0.05)) +
    scale_colour_manual(values = c("gray50", "firebrick"), 
                        label = c("N.S.", "FDR < 5%"), name = "", drop = FALSE) +
    guides(alpha = FALSE) + 
    ggtitle("G2M CHECKPOINT") +
    theme_classic(20)

ggsave("figures/differential_expression/alldonors_camera_H_g2m_checkpoint.png", height = 8.5, width = 10)
ggsave("figures/differential_expression/alldonors_camera_H_g2m_checkpoint.pdf", height = 8.5, width = 10)
ggsave("figures/differential_expression/alldonors_camera_H_g2m_checkpoint.svg", height = 8.5, width = 10)
```


## Compare pathway results to clone prevalence

Hallmark gene sets. Not yet implemented.

```{r, eval=FALSE, include=FALSE}
head(df_camera_nsig)
head(df_camera_all_unst)

head(de_res$qlf_list$euts$coefficients)
head(de_res$qlf_list$zoxy$coefficients)

df_clone_prevalence <- list()
for (don in names(de_res$qlf_list)) {
    df_clone_prevalence[[don]] <- data_frame(
        donor = don,
        clone = rownames(cell_assign_list[[don]]$full_tree$P),
        prevalence = cell_assign_list[[don]]$full_tree$P[, 1])
}
df_clone_prevalence <- do.call("bind_rows", df_clone_prevalence)
    
df_camera_all_unst <- dplyr::mutate(
    df_camera_all_unst, 
    id = paste(donor, collection, geneset, coeff, stat, sep = ".")
)

## plot clone prevalences across donors
df_clone_prevalence %>%
    ggplot(aes(x = prevalence, y = clone)) +
    geom_density_ridges() +
    theme_ridges()


## assign clone prevalences to camera results
df_camera_all_unst <- dplyr::mutate(
    df_camera_all_unst, 
    up_clone = gsub("(clone[0-9])_clone[0-9]", "\\1", coeff),
    dn_clone = gsub("clone[0-9]_(clone[0-9])", "\\1", coeff),
    donor_upclone = paste0(donor, up_clone),
    donor_dnclone = paste0(donor, dn_clone)) 
df_clone_prevalence1 <- dplyr::mutate(df_clone_prevalence,
                                      up_clone = clone,  
                                      clone = NULL,
                                      donor_upclone = paste0(donor, up_clone),
                                      prevalence_upclone = prevalence, 
                                      prevalence = NULL
)
df_clone_prevalence2 <- dplyr::mutate(df_clone_prevalence,
                                      dn_clone = clone,  
                                      clone = NULL,
                                      donor_dnclone = paste0(donor, dn_clone),
                                      prevalence_dnclone = prevalence, 
                                      prevalence = NULL
)
tmp1 <- dplyr::select(df_camera_all_unst, donor, up_clone, dn_clone, id) %>%
    left_join(., df_clone_prevalence1)
tmp2 <- dplyr::select(df_camera_all_unst, donor, up_clone, dn_clone, id) %>%
    left_join(., df_clone_prevalence2)

tmp3 <- dplyr::inner_join(df_camera_all_unst, tmp1, by = "id")
tmp4 <- dplyr::inner_join(tmp3, tmp2, by = "id")
df_camera_all_unst <- tmp4
df_camera_all_unst <- dplyr::mutate(
    df_camera_all_unst, 
    prevalence_relative_up_clone = prevalence_upclone / prevalence_dnclone)

df_camera_all_unst %>% dplyr::filter(collection == "H", FDR < 0.05) %>% 
    dplyr::filter(geneset %in% c("HALLMARK_E2F_TARGETS", "HALLMARK_G2M_CHECKPOINT",
                                 "HALLMARK_MYC_TARGETS_V1",
                                 "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION",
                                 "HALLMARK_MYC_TARGETS_V2", "HALLMARK_MITOTIC_SPINDLE")) %>%
    dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
    dplyr::mutate(geneset = factor(geneset, levels = 
                                       c("MITOTIC SPINDLE", "MYOGENESIS", "ANGIOGENESIS",
                                         "EPITHELIAL MESENCHYMAL TRANSITION", "MYC TARGETS V2",
                                        "MYC TARGETS V1", "E2F TARGETS", "G2M CHECKPOINT"))) %>%
    ggplot(aes(y = prevalence_upclone, x = geneset, fill = Direction)) +
    geom_boxplot(width = 0.5) +
    # ggthemes::scale_fill_canva(palette = "Surf and turf") +
    scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
    coord_flip() +
    theme_ridges(14) +
    guides(fill = guide_legend(reverse = TRUE)) +
    xlab("Gene set") + ylab("Clone prevalence")

ggsave("figures/differential_expression/top_genesets_H_by_direction_prevalence.png", height = 6, width = 10)
ggsave("figures/differential_expression/top_genesets_H_by_direction_prevalence.pdf", height = 6, width = 10)
ggsave("figures/differential_expression/top_genesets_H_by_direction_prevalence.svg", height = 6, width = 10)


df_camera_all_unst %>% dplyr::filter(collection == "H", stat == "logFC", FDR < 0.05) %>% 
    dplyr::filter(geneset %in% c("HALLMARK_E2F_TARGETS", "HALLMARK_G2M_CHECKPOINT",
                                 "HALLMARK_MYC_TARGETS_V1",
                                 "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION",
                                 "HALLMARK_MYC_TARGETS_V2", "HALLMARK_MITOTIC_SPINDLE")) %>%
    dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
    dplyr::mutate(geneset = factor(geneset, levels = 
                                       c("MITOTIC SPINDLE", "MYOGENESIS", "ANGIOGENESIS",
                                         "EPITHELIAL MESENCHYMAL TRANSITION", "MYC TARGETS V2",
                                        "MYC TARGETS V1", "E2F TARGETS", "G2M CHECKPOINT"))) %>%
    ggplot(aes(y = prevalence_relative_up_clone, x = geneset, fill = Direction)) +
    geom_hline(yintercept = 1) +
    geom_boxplot(width = 0.5) +
    # ggthemes::scale_fill_canva(palette = "Surf and turf") +
    scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
    scale_y_log10() +
    coord_flip() +
    theme_ridges(14) +
    guides(fill = guide_legend(reverse = TRUE)) +
    xlab("Gene set") + ylab("Clone prevalence relative to base clone")


df_camera_all_unst %>% dplyr::filter(collection == "H", stat == "logFC", FDR < 0.05) %>% 
    dplyr::filter(geneset %in% c("HALLMARK_E2F_TARGETS", "HALLMARK_G2M_CHECKPOINT",
                                 "HALLMARK_MYC_TARGETS_V1",
                                 "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION",
                                 "HALLMARK_ANGIOGENESIS", "HALLMARK_MYOGENESIS",
                                 "HALLMARK_MYC_TARGETS_V2", "HALLMARK_MITOTIC_SPINDLE")) %>%
    dplyr::mutate(geneset = gsub("_", " ", gsub("HALLMARK_", "", geneset))) %>%
    dplyr::mutate(geneset = factor(geneset, levels = 
                                       c("MITOTIC SPINDLE", "MYOGENESIS", "ANGIOGENESIS",
                                         "EPITHELIAL MESENCHYMAL TRANSITION", "MYC TARGETS V2",
                                        "MYC TARGETS V1", "E2F TARGETS", "G2M CHECKPOINT"))) %>%
    ggplot(aes(y = prevalence_relative_up_clone, x = geneset, fill = Direction)) +
    geom_hline(yintercept = 1) +
    ggbeeswarm::geom_quasirandom(pch = 21, size = 5) +
    # ggthemes::scale_fill_canva(palette = "Surf and turf") +
    scale_fill_manual(values = c("lightgoldenrod1", "sienna1")) +
    scale_y_log10() +
    coord_flip() +
    theme_ridges(14) +
    guides(fill = guide_legend(reverse = TRUE)) +
    xlab("Gene set") + ylab("Clone prevalence relative to base clone")

df_camera_all_unst %>% dplyr::filter(collection == "H", stat == "logFC", FDR < 0.1) %>% 
    dplyr::filter(geneset %in% c("HALLMARK_E2F_TARGETS", "HALLMARK_G2M_CHECKPOINT",
                                 "HALLMARK_MITOTIC_SPINDLE")) %>%
    group_by(geneset) %>%
  do(broom::tidy(t.test(prevalence_upclone ~ Direction, data = .)))


```



## Write DE and pathway results to file

```{r write-results}
## Write DE results to file:
for (don in names(de_res$qlf_list)) {
  de_res$qlf_list[[don]]$table %>% 
    dplyr::mutate(gene = rownames(.), FDR = IHW::adj_pvalues(IHW::ihw(PValue, logCPM, alpha = 0.1))) %>%
    dplyr::arrange(FDR) %>% write_tsv(
      paste0("output/differential_expression/", don, "_qlf_de_results.tsv"))
}

for (don in names(de_res$camera$H)) {
  for (cntrst in names(de_res$camera$H[[don]])) {
    de_res$camera$H[[don]][[cntrst]]$logFC %>% 
      dplyr::mutate(geneset = rownames(.)) %>%
      dplyr::arrange(FDR) %>% write_tsv(
        paste0("output/differential_expression/", don, "_camera_hallmark_geneset_results_", cntrst, ".tsv"))    
  }
}

```