Differential expression across all donors
Davis J. McCarthy
Last updated: 2018-08-19
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 013c8bd | davismcc | 2018-08-19 | Tweaking plot sizes |
| Rmd | 5ab2fe7 | davismcc | 2018-08-19 | Adding plot for GoF vs n_mutations; changing “donor” to “line” in plot labels |
| html | 1489d32 | davismcc | 2018-08-17 | Add html files |
| Rmd | dac4720 | davismcc | 2018-08-17 | Tidying up output |
| Rmd | ef72f36 | davismcc | 2018-08-17 | Fixing little bug |
| Rmd | bc1dbc5 | davismcc | 2018-08-17 | Adding analysis of differential expression results |
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
Load the genewise differential expression results produced with the edgeR quasi-likelihood F test and gene set enrichment results produced with camera.
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.
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"))
}reading euts : 79 cells.
reading fawm : 53 cells.
reading feec : 75 cells.
reading fikt : 39 cells.
reading garx : 70 cells.
reading gesg : 105 cells.
reading heja : 50 cells.
reading hipn : 62 cells.
reading ieki : 58 cells.
reading joxm : 79 cells.
reading kuco : 48 cells.
reading laey : 55 cells.
reading lexy : 63 cells.
reading naju : 44 cells.
reading nusw : 60 cells.
reading oaaz : 38 cells.
reading oilg : 90 cells.
reading pipw : 107 cells.
reading puie : 41 cells.
reading qayj : 97 cells.
reading qolg : 36 cells.
reading qonc : 58 cells.
reading rozh : 91 cells.
reading sehl : 30 cells.
reading ualf : 89 cells.
reading vass : 37 cells.
reading vils : 37 cells.
reading vuna : 71 cells.
reading wahn : 82 cells.
reading wetu : 77 cells.
reading xugn : 35 cells.
reading zoxy : 88 cells.The starting point for differential expression analysis was a set of 32 donors, of which 31 donors had enough cells assigned to clones to conduct DE testing.
Summarise cell assignment information.
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)85% of cells from these donors are assigned with confidence to a clone.
Load donor info including evidence for selection dynamics in donors.
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.
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) %>%
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) %>%
summarise(n_donors = n()) %>% group_by(n_donors) %>%
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) %>%
summarise(n_donors = n()) %>% group_by(n_donors) %>%
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)p2 <- df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
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) %>%
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.
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) %>%
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) %>% summarise(min = min(n_genes),
median = median(n_genes),
mean = mean(n_genes),
max = max(n_genes))# A tibble: 11 x 5
n_donors min median mean max
<int> <dbl> <dbl> <dbl> <dbl>
1 1 3946 4120 4121. 4292
2 2 2356 2466 2468. 2578
3 3 821 897 896. 976
4 4 177 223 223. 269
5 5 23 40 40.3 59
6 6 0 5 5.63 14
7 7 0 0 0.595 4
8 8 0 0 0.062 2
9 9 0 0 0.007 1
10 10 0 0 0 0
11 11 0 0 0 0ppp <- df_de_sig_unst %>%
group_by(gene) %>%
dplyr::mutate(id = paste0(donor, gene)) %>% distinct(id, .keep_all = TRUE) %>%
summarise(n_donors = n()) %>% group_by(n_donors) %>%
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) %>%
summarise(n_donors = n()) %>% group_by(n_donors) %>%
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.
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) %>%
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.
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.
head(df_camera_sig_unst)# A tibble: 6 x 11
NGenes Direction PValue FDR collection geneset coeff donor stat
<dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 6 Down 6.43e-15 3.02e-11 c2 VALK_A… clon… euts signF
2 7 Down 2.29e-11 5.37e- 8 c2 WANG_R… clon… euts signF
3 5 Down 7.80e-11 1.22e- 7 c2 GUTIER… clon… euts signF
4 8 Down 1.35e- 9 1.58e- 6 c2 KUMAMO… clon… euts signF
5 11 Down 4.95e- 9 4.65e- 6 c2 VALK_A… clon… euts signF
6 4 Down 2.86e- 8 1.92e- 5 c2 REACTO… clon… euts signF
# ... with 2 more variables: contrast <chr>, msigdb_collection <chr>And a dataframe with all results.
head(df_camera_all_unst)# A tibble: 6 x 11
NGenes Direction PValue FDR collection geneset coeff donor stat
<dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 7 Down 2.88e-4 0.771 c2 WANG_R… clon… euts logFC
2 10 Down 4.73e-4 0.771 c2 BIOCAR… clon… euts logFC
3 15 Up 8.30e-4 0.771 c2 LI_CIS… clon… euts logFC
4 14 Down 1.07e-3 0.771 c2 MOROSE… clon… euts logFC
5 5 Down 1.12e-3 0.771 c2 IYENGA… clon… euts logFC
6 15 Up 1.19e-3 0.771 c2 PID_TC… clon… euts logFC
# ... with 2 more variables: contrast <chr>, msigdb_collection <chr>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.
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.
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.
df_camera_sig_unst %>%
dplyr::filter(stat == "logFC") %>%
dplyr::filter(FDR < 0.05) %>%
group_by(donor, msigdb_collection) %>%
summarise(n_sig = n()) %>% print(n = Inf)# A tibble: 67 x 3
# Groups: donor [?]
donor msigdb_collection n_sig
<chr> <chr> <int>
1 fawm MSigDB curated (c2) 348
2 fawm MSigDB Hallmark 7
3 fawm MSigDB oncogenic (c6) 3
4 feec MSigDB curated (c2) 256
5 feec MSigDB Hallmark 4
6 feec MSigDB oncogenic (c6) 3
7 fikt MSigDB curated (c2) 426
8 fikt MSigDB Hallmark 9
9 fikt MSigDB oncogenic (c6) 4
10 garx MSigDB curated (c2) 244
11 garx MSigDB Hallmark 3
12 garx MSigDB oncogenic (c6) 7
13 gesg MSigDB curated (c2) 4
14 heja MSigDB curated (c2) 7
15 hipn MSigDB curated (c2) 99
16 hipn MSigDB Hallmark 5
17 hipn MSigDB oncogenic (c6) 1
18 ieki MSigDB curated (c2) 480
19 ieki MSigDB Hallmark 9
20 ieki MSigDB oncogenic (c6) 10
21 joxm MSigDB curated (c2) 150
22 joxm MSigDB Hallmark 3
23 joxm MSigDB oncogenic (c6) 1
24 kuco MSigDB curated (c2) 1
25 laey MSigDB curated (c2) 502
26 laey MSigDB Hallmark 7
27 laey MSigDB oncogenic (c6) 19
28 lexy MSigDB curated (c2) 546
29 lexy MSigDB Hallmark 8
30 lexy MSigDB oncogenic (c6) 8
31 naju MSigDB curated (c2) 99
32 naju MSigDB Hallmark 2
33 naju MSigDB oncogenic (c6) 10
34 oilg MSigDB curated (c2) 102
35 oilg MSigDB Hallmark 1
36 oilg MSigDB oncogenic (c6) 1
37 pipw MSigDB curated (c2) 1
38 puie MSigDB curated (c2) 1
39 qayj MSigDB curated (c2) 152
40 qayj MSigDB Hallmark 5
41 qayj MSigDB oncogenic (c6) 4
42 qolg MSigDB curated (c2) 293
43 qolg MSigDB Hallmark 3
44 qolg MSigDB oncogenic (c6) 9
45 qonc MSigDB curated (c2) 382
46 qonc MSigDB Hallmark 5
47 qonc MSigDB oncogenic (c6) 2
48 rozh MSigDB curated (c2) 553
49 rozh MSigDB Hallmark 10
50 rozh MSigDB oncogenic (c6) 10
51 sehl MSigDB curated (c2) 59
52 sehl MSigDB Hallmark 2
53 sehl MSigDB oncogenic (c6) 2
54 ualf MSigDB curated (c2) 378
55 ualf MSigDB Hallmark 3
56 ualf MSigDB oncogenic (c6) 4
57 vass MSigDB curated (c2) 270
58 vass MSigDB Hallmark 8
59 vass MSigDB oncogenic (c6) 3
60 wahn MSigDB curated (c2) 129
61 wahn MSigDB Hallmark 2
62 wetu MSigDB curated (c2) 3
63 xugn MSigDB curated (c2) 120
64 xugn MSigDB Hallmark 4
65 zoxy MSigDB curated (c2) 488
66 zoxy MSigDB Hallmark 11
67 zoxy MSigDB oncogenic (c6) 19We can look at the number of significant gene sets for each donor.
## simpler version
df_camera_all_unst %>%
dplyr::filter(stat == "logFC") %>%
group_by(donor, msigdb_collection) %>%
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.
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) %>%
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.
## 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) %>%
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"]]) [1] "fawm" "feec" "fikt" "garx" "hipn" "ieki" "joxm" "laey" "lexy" "naju"
[11] "oilg" "qayj" "qolg" "qonc" "rozh" "sehl" "ualf" "vass" "wahn" "xugn"
[21] "zoxy"21 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.
df_camera_sig_unst %>% dplyr::filter(collection == "H") %>%
dplyr::filter(stat == "logFC") %>%
group_by(geneset, Direction) %>%
dplyr::mutate(id = paste0(donor, geneset)) %>%
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.
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) %>%
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_camera_sig_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
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.
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)Finally, we can add a panel to this figure showing the number of donors in which each of these gene sets is significantly enriched.
## 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) %>%
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)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.
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())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.
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) %>%
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$order])) %>%
ggplot(aes(y = n_donors, x = geneset)) +
geom_hline(yintercept = 0, colour = "gray50") +
geom_segment(aes(xend = geneset, yend = 0),
colour = "gray50") +
geom_point(size = 4, colour = "gray30", alpha = 1) +
ggthemes::scale_colour_tableau() +
coord_flip() +
xlab("Gene set") + ylab("Number of lines\nsignificant") +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank())
ggdraw() +
draw_plot(corrplot1 + theme(legend.position = "top"),
x = 0, y = 0, width = 0.8, scale = 1) +
draw_plot(pp_nsig,
x = 0.785, y = 0.213, width = 0.2, height = 0.685)
ggsave("figures/differential_expression/top_genesets_H_corrplot_with_nsig_donor.png",
height = 9, width = 11)
ggsave("figures/differential_expression/top_genesets_H_corrplot_with_nsig_donor.pdf",
height = 9, width = 11)Linking DE to selection
df_donor_info <- read.table("data/donor_info_070818.txt")
df_donor_info <- as_data_frame(df_donor_info)
df_donor_info$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) %>%
summarise(n_cells = n())
colnames(df_ncells_de)[1] <- "donor"
df_prop_assigned <- assignments %>%
dplyr::filter(donor_short_id %in% names(de_res$qlf_list)) %>%
group_by(donor_short_id) %>%
summarise(prop_assigned = mean(assigned != "unassigned"))
colnames(df_prop_assigned)[1] <- "donor"
df_nvars_by_cat <- readr::read_tsv("output/nvars_by_category_by_donor.tsv")
df_nvars_by_cat_wd <- tidyr::spread(
df_nvars_by_cat[, 1:3], consequence, n_vars_all_genes)
df_nvars_by_cat_wd <- left_join(
summarise(group_by(df_nvars_by_cat, donor), nvars_all = sum(n_vars_all_genes)),
df_nvars_by_cat_wd
)
df_nvars_by_cat_wd <- df_nvars_by_cat %>%
dplyr::filter(consequence %in% c("missense", "splicing", "nonsense")) %>%
group_by(donor) %>%
summarise(nvars_misnonspli = sum(n_vars_all_genes)) %>%
left_join(., df_nvars_by_cat_wd)
df_donor_info <- left_join(df_ncells_de, df_donor_info)
df_donor_info <- left_join(df_prop_assigned, df_donor_info)
df_donor_info <- left_join(df_donor_n_de, df_donor_info)
df_donor_info$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))
Call:
lm(formula = .resid ~ selection, data = df_nbglm_nde)
Residuals:
Min 1Q Median 3Q Max
-1.7896 -0.4823 -0.0568 0.4630 3.3122
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9100 0.4242 -2.145 0.0408 *
selectionselected 0.7766 0.5612 1.384 0.1773
selectionundetermined 0.5391 0.4934 1.093 0.2839
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.039 on 28 degrees of freedom
Multiple R-squared: 0.06604, Adjusted R-squared: -0.0006725
F-statistic: 0.9899 on 2 and 28 DF, p-value: 0.3842## 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) %>%
# summarise(count = n())Linking pathway results to selection
Not yet implemented
Compare pathway results to clone prevalence
Hallmark gene sets. Not yet implemented.
Write DE and pathway results to file
## 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"))
}
}Session information
devtools::session_info() setting value
version R version 3.5.1 (2018-07-02)
system x86_64, darwin15.6.0
ui X11
language (EN)
collate en_GB.UTF-8
tz Europe/London
date 2018-08-19
package * version date source
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