Integrative Strelka and Salmon Analysis

patients <- colnames(salmon$clean$counts)

mut_status <- 
  maf@data %>% 
  filter(
    grepl("p[.][^=]", HGVSp_Short), 
    Consequence != "synonymous_variant",
    Hugo_Symbol %in% smgs,
    patient %in% patients) %>% 
  select(patient, Hugo_Symbol) %>% 
  distinct() %>% 
  mutate(Status = "Mutated") %>% 
  bind_rows(tibble(patient = patients, Hugo_Symbol = NA)) %>% 
  spread(Hugo_Symbol, Status, fill = "Unmutated") %>% 
  select(-`<NA>`) %>% 
  as.data.frame() %>%
  remove_rownames() %>% 
  column_to_rownames("patient")

mut_colours <- list()
for (gene in names(mut_status)) mut_colours[[gene]] <- c(Unmutated = "grey75", Mutated = "grey40")

plot_heatmap(assay(most_var(salmon$clean$cvst, ntop)), mut_colours, metadata = mut_status)

maf_gr <- 
  maf@data %>% 
  makeGRangesFromDataFrame(keep.extra.columns = TRUE, seqnames.field = "Chromosome", 
                           start.field = "Start_Position", end.field = "End_Position")

hits <- findOverlaps(maf_gr, GRanges("chr9", IRanges(37371000, 37372000)))

affected_patients <- maf_gr[queryHits(hits)] %$% unique(patient)

patients <- unique(maf@data$patient)

patient_order <- c(affected_patients, patients[!patients %in% affected_patients])

plot_region_expr("9", "35645518", "39092517", biomart, salmon, 
                 patient_order, gaps_row = length(affected_patients), scale = "column")

maf@data %>% 
  filter(is_nonsynonymous(Consequence)) %>% 
  select(patient, Hugo_Symbol) %>% 
  distinct() %>%
  mutate(status = "mutated") %>% 
  spread(Hugo_Symbol, status, fill = "unmutated") %>% 
  arrange(match(maf_patients, rownames(colData(salmon$muts$dds))))