In cbg-ethz/slidr: Discovery of mutation-specific synthetic lethal partners from large pan-cancer perturbation screens

knitr::opts_chunk$set(echo = TRUE)

Creating data object

Loading all the relevant data and creating the data object with the mutation and viability scores for each type of cancer.

library(slidr)
library(dplyr)
library(reshape2)
library(biclust)

# Set path to all data
base_folder = "~/GitHub/CrispR/Data/"

# Loading mutations for all cell lines
all_cancers_mut_df <- read.delim(paste0(base_folder,
                                        "MutationFiles/MutationsCCLE/CCLE_maf_allcancers.txt"),
                                 stringsAsFactors = FALSE, header = TRUE)
# Filtering out silent mutations
all_cancers_mut_df <- all_cancers_mut_df %>%
                        dplyr::filter(Variant_Classification != "Silent" )

# Load annotation file and meta data
cellline_annot <-  read.csv(paste0(base_folder, "ProjectDRIVE/TableS2.csv"),
                            stringsAsFactors = F)
meta_data <- read.csv(paste0(base_folder, "MutationFiles/File_metadata.csv"),
                      header = TRUE, stringsAsFactors = FALSE)
Primary_sites <-   meta_data$Primary_site

# Load log2 copy number data
CN_df <- read.delim(paste0(base_folder,"/CopyNumber/CCLE_copynumber_byGene.txt"),
                    stringsAsFactors = F, header = T)

# Load Gistic copy number data
CN_df_gistic <- read.delim(paste0(base_folder,"/CopyNumber/CCLE_CNA_Integers.txt"),
                           stringsAsFactors = F, header=T)
CN_df_gistic <- CN_df_gistic[-which(duplicated(CN_df_gistic$Hugo_Symbol)),]

# Getting essential genes
data_rsa <- readRDS(paste0(base_folder, "ProjectDRIVE/DRIVE_RSA_data.RDS"))

# Remove the essential data from ATARiS normalised data
data_ataris <- readRDS(paste0(base_folder, "ProjectDRIVE/DRIVE_ATARiS_data.RDS"))

# Getting a list of cell lines for each cancer type
cellline_list <- lapply(Primary_sites,
                        function(x){slidr::getCelllines(x, cellline_annot, meta_data)})
names(cellline_list) <- Primary_sites

# Getting a list of essential genes for each cancer type
essen_gene_list <- lapply(names(cellline_list),
                          function(x){slidr::getEssentialGenes(x = x,
                                                               celllines = cellline_list[[x]],
                                                               data = data_rsa)})
names(essen_gene_list) <- Primary_sites

all_data = list()
fdr = 0.05
min_Nmut = 2

# Generate objects for each primary site
all_data <- lapply(Primary_sites,
                   function(x){ slidr::prepareDataObjects(data = data_ataris,
                                                          x = x,
                                                          fdr = fdr,
                                                          min_Nmut = min_Nmut,
                                                          all_cancers_mut_df = all_cancers_mut_df,
                                                          CN_df = CN_df, #CN_df_gistic
                                                          gistic = FALSE, #TRUE
                                                          celllines = cellline_list[[x]],
                                                          meta_data = meta_data,
                                                          essential_genes = essen_gene_list[[x]])})
names(all_data) <- Primary_sites

Identify SL hits

Once the data is processed in the correct format, use the identifySLHits function to get the mutation-specific SL partner for each cancer type.

path_results <- "~/Downloads/Slidr_Results_new/"

hits <- lapply(all_data,
               function(x){slidr::identifySLHits(canc_data = x, path_results = path_results, WT_pval_thresh = 0.1)})
names(hits) <- names(all_data)

save.image(paste(path_results, "ProcessedData.Rdata"))

Pan Cancer Analysis

Creating the data object for the pan-cancer analysis.

# Choosing cell lines with both CN and viability data and the driver genes
pc_celllines  <- intersect(colnames(data_ataris),tolower(colnames(CN_df)))

# driver_genes  <- read.delim(paste0(base_folder,
#                                        "MutationFiles/Cell2018_mutations.txt"),
#                                 stringsAsFactors = FALSE, header = FALSE)[[1]]
driver_genes  <- unique(all_cancers_mut_df$Hugo_Symbol)
# Getting the essential genes
pc_essen_gene <- slidr::getEssentialGenes(x = NULL, data = data_rsa, celllines = pc_celllines)
# Removing the essential genes
pc_data <- list(viabilities = NULL,
                mutations = NULL,
                CNalterations = NULL,
                mutation_annot = NULL,
                primary_site = "pan_cancer")

data_ataris <- as.data.frame(t(apply(data_ataris,
                                     1,
                                     function(x){
                                       x[which(is.na(x))] <- mean(x, na.rm = TRUE)
                                       x})))
pc_data$viabilities <- data_ataris[!rownames(data_ataris) %in% pc_essen_gene,pc_celllines]

# Threshold for removing drivers with fewer than 8% mutated samples
mut_pc     <- 8 #2
mut_thresh <- floor(mut_pc * length(pc_celllines) / 100)
CN_Thr     <- 1 # Threshold to choose only deep deletions
# Binary mutation data
mut_mat    <- slidr::prepareMutMat(x = NULL,
                                   driver_genes = driver_genes,
                                   samples = pc_celllines,
                                   all_cancers_mut_df = all_cancers_mut_df)

pc_data$CNalterations <- slidr::prepareCNMat(CN_df = CN_df,
                                             samples = pc_celllines,
                                             driver_genes = driver_genes,
                                             x = NULL)

# binarized copy number data with only deep deletions
CN_bin <- binarize(2 - pc_data$CNalterations, threshold = CN_Thr)
CN_bin[is.na(CN_bin)] <- 0

# Updating copy numbers in mutation matrix
pc_data$mutations <- binarize((CN_bin + mut_mat), threshold = 0)

# Removing genes with < 8% mutated samples
pc_data$mutations   <- pc_data$mutations[rowSums(pc_data$mutations) >= mut_thresh, ]

# Create a mutation annotation file
mut_mat_annot <- melt(pc_data$mutations)
mut_mat_annot <- mut_mat_annot %>% dplyr::filter(value != 0)
colnames(mut_mat_annot) <- c("Hugo_Symbol","Cell_Line","Mut_Status")
# Use copy number info in the mutation annotation file
all_cancers_mut_df$Tumor_Sample_Barcode <- tolower(all_cancers_mut_df$Tumor_Sample_Barcode)
CN_alterations_df <- melt(pc_data$CNalterations)
colnames(CN_alterations_df) <- c("Hugo_Symbol","Cell_Line","CN_Value")

# Annotating mutations and adding copy number information
pc_data$mutation_annot <- left_join(mut_mat_annot,all_cancers_mut_df,
                                    by = c("Hugo_Symbol" = "Hugo_Symbol","Cell_Line" = "Tumor_Sample_Barcode")) %>%
  dplyr::select(Hugo_Symbol, Cell_Line, Mut_Status, Variant_Classification) %>%
  dplyr::group_by(Hugo_Symbol,Cell_Line,Mut_Status) %>%
  dplyr::summarise(Variant_Classification = paste(Variant_Classification, collapse=";"))

pc_data$mutation_annot <- left_join(pc_data$mutation_annot, CN_alterations_df, by = c("Hugo_Symbol","Cell_Line"))

# Replacing all the na
pc_data$mutation_annot[is.na(pc_data$mutation_annot)] <- 0

save.image("~/Downloads/Slidr_Results_new/pancan.Rdata")

Identify Synthetic lethals from pan cancer data.

# Path for results
path_results <- "~/Downloads/Slidr_Results_new/"

hits_pancan <- slidr::identifySLHits(canc_data = pc_data, path_results = path_results, WT_pval_thresh = 0, fp_thresh = 1)

Processing data for the result plots

load("~/Downloads/Slidr_Results_new/PanCan8pc/causal_res.Rdata")

# create data frame for each pair with frequency of different celllines for all original 151 hits
orig_canc_type_df <- do.call(rbind.data.frame, lapply(1:nrow(hits_pancan), function(x){
                                                  mutations     <- pc_data$mutations
                                                  mut_celllines <- names(which(mutations[hits_pancan$driver_gene[x],] == 1))
                                                  cancer_types  <- sapply(mut_celllines, 
                                                                        function(x){paste(strsplit(x,"_")[[1]][-1], collapse = "_")})
                                                  cancer_types  <- as.data.frame(table(cancer_types))
                                                  data.frame(hits_pancan$driver_gene[x], hits_pancan$sl_partner_gene[x], cancer_types)
                                                #}

}))

colnames(orig_canc_type_df) <- c("driver_gene", "sl_partner_gene", "canc_type", "n_samples")
orig_canc_type_df$tot_samples <- rowSums(pc_data$mutations[as.character(orig_canc_type_df$driver_gene),])


# modifying for the filtered hits after causal inference
original_hits_pancan <- hits_pancan
hits_pancan          <- filt_hits_pancan

# create data frame for each pair with frequency of different celllines
canc_type_df <- do.call(rbind.data.frame, lapply(1:nrow(hits_pancan), function(x){
                                                  mutations     <- pc_data$mutations
                                                  mut_celllines <- names(which(mutations[hits_pancan$driver_gene[x],] == 1))
                                                  cancer_types  <- sapply(mut_celllines, 
                                                                        function(x){paste(strsplit(x,"_")[[1]][-1], collapse = "_")})
                                                  cancer_types  <- as.data.frame(table(cancer_types))
                                                  data.frame(hits_pancan$driver_gene[x], hits_pancan$sl_partner_gene[x], cancer_types)
                                                #}

}))

colnames(canc_type_df) <- c("driver_gene", "sl_partner_gene", "canc_type", "n_samples")

min_Nmut = 2

# get the p-value for each pair and type of cancer
pval_df <- do.call(rbind.data.frame, lapply(1:nrow(canc_type_df), function(x){
                                          canc_type        <- as.character(canc_type_df$canc_type[x])
                                          driver           <- as.character(canc_type_df$driver_gene[x])
                                          sl_partner       <- as.character(canc_type_df$sl_partner_gene[x])
                                          celllines        <- grep(canc_type,colnames(pc_data$mutations))
                                          mut_samples      <- sum(pc_data$mutations[driver,celllines])
                                          if(mut_samples >= min_Nmut & (length(celllines) - mut_samples) >= min_Nmut){
                                            temp             <- NULL
                                            temp$mutations   <- pc_data$mutations[,celllines]
                                            temp$viabilities <- pc_data$viabilities[,celllines]
                                            c(canc_type,slidr::getPval(temp,driver,sl_partner))
                                          }else
                                            c(canc_type,driver,sl_partner,1,1)

}))

colnames(pval_df)          <- c("canc_type","driver_gene", "sl_partner_gene", "WT_pvalue", "mut_pvalue")
summary_df                 <- left_join(canc_type_df, pval_df, by = c("driver_gene","sl_partner_gene","canc_type"))
summary_df$sl_partner_gene <- sapply(summary_df$sl_partner_gene, function(x){strsplit(x, ",")[[1]][1]})
summary_df                 <- cbind(paste(summary_df$driver_gene,summary_df$sl_partner_gene, sep = "_"), 
                                    summary_df,
                                    rowSums(pc_data$mutations[summary_df$driver_gene,]))
colnames(summary_df)[c(1,8)]  <- c("sl_pairs","tot_samples")
summary_df                    <- summary_df %>% dplyr::filter(mut_pvalue != 1 & WT_pvalue != 1)

save(list = c("orig_canc_type_df", "summary_df"), file = "~/Downloads/Slidr_Results_new/PanCan8pc/summary_data.Rdata")

cbg-ethz/slidr documentation built on Feb. 8, 2023, 11:15 p.m.