R/select_cell_lines.R
In ytakemon/GINIR: Genetic inteRaction and EssenTiality neTwork mApper
Defines functions
select_cell_lines
Documented in
select_cell_lines
#' Select mutant groups based on input gene of interest
#'
#' @description
#' `select_cell_lines()` assigns cancer cell lines to either `Control` groups or one of the following mutant groups: `HomDel`,
#' `T-HetDel`, `HetDel`, `Amplified`, or `Others` (see details).
#'
#' @param input_gene string as Hugo Symbol
#' @param input_aa_change string Amino acid change (eg. "A387A"). input_gene must be specified
#' @param input_disease string Cancer type listed in `list_available_cancer_types()`
#' @param input_disease_subtype string Cancer subtype listed in `list_available_cancer_subtypes()`
#' @param data_dir string Path to GINIR_data
#'
#' @return Data frame containing a summary of mutations found in cell lines and their control and mutant group assignments.
#' @import rlang
#' @import dplyr
#' @import utils
#' @import stringr
#'
#' @export
#' @details
#' Mutant groups in more detail when only `input_gene` is defined:
#' * `Control` cell lines do not harbor any single nucleotide variations (SNVs) or insertions and deletions (InDels) with a neutral copy number (CN).
#' * `HomDel` cell lines harbor one or more homozygous deleterious SNVs or have deep CN loss.
#' * `T-HetDel` cell lines harbor two or more heterozygous deleterious SNVs/InDels with neutral or CN loss.
#' * `HetDel` cell lines harbor one heterozygous deleterious SNV/InDel with neutral CN, or no SNV/InDel with CN loss.
#' * `Amplified` cell lines harbor no SNVs/InDels with increased CN.
#' * `Others` cell lines harbor deleterious SNVs with increased CN.
#'
#'
#' If input_aa_change` is also defined:
#' * `Control` cell lines do not harbor any single nucleotide variations (SNVs) or insertions and deletions (InDels) with a neutral copy number (CN).
#' * `HomAlt` cell lines harbor a homozygous alteration for the specified mutation.
#' * `HetAlt` cell lines harbor a heterozygous alteration for the specified mutation.
#' * `_CNneutral`, `_CNamplified`, and `_CNloss` define copy number (CN) status of the above mutation states.
#' * `Others` cell lines that do not meet above criteria.
#'
#' @examples
#' gretta_data_dir <- './GRETTA_example/'
#' gretta_output_dir <- './GRETTA_example_output/'
#'
#' if(!dir.exists(gretta_data_dir)){
#' download_example_data(".")
#' }
#'
#' select_cell_lines(input_gene = "ARID1A",
#' input_disease = "Lung Cancer",
#' data_dir = gretta_data_dir)
#'
select_cell_lines <- function(input_gene = NULL, input_aa_change = NULL, input_disease = NULL, input_disease_subtype = NULL, data_dir = NULL){
# Print and check to see input
if(is.null(c(input_gene, input_disease, input_disease_subtype))){
stop("No input given. Please prvide a Hugo gene symbol and/or cancer type")
} else if(is.null(c(input_gene, input_disease)) & !is.null(input_disease_subtype)){
stop("No cancer context provided. Please define the `disease` argument.")
} else if(is.null(input_gene) & !is.null(input_aa_change)){
stop("AA change was provided, ", input_aa_change,",
but no gene was provided. Please define input_gene!")
} else if(is.null(c(input_disease, input_disease_subtype)) & !is.null(input_gene)){
message("Selecting mutant groups for: ", paste0(input_gene, collapse = ", "), " in all cancer cell lines")
} else if(!is.null(c(input_gene, input_disease, input_disease_subtype))){
message("Selecting mutant groups for: ", paste0(input_gene, collapse = ", "), " in ", input_disease,", ",
input_disease_subtype, " cell lines")
} else if(is.null(input_disease_subtype) & !is.null(c(input_gene, input_disease))){
message("Selecting mutant groups for: ", input_gene, " in ", input_disease, " cell lines")
} else if(is.null(input_gene) & !is.null(c(input_disease, input_disease_subtype))){
message("Selecting all ", input_disease, ", ", input_disease_subtype, " cancer cell lines")
} else if(is.null(c(input_gene, input_disease_subtype)) & !is.null(input_disease)){
message("Selecting all ", input_disease, " cancer cell lines")
} else {
stop("Issue with input.")
}
if(is.null(data_dir)){
stop("No directory to data was specified. Please provide path to DepMap data.")
}
if(!dir.exists(data_dir)){
stop("DepMap data directory does not exists.",
"Please check again and provide the full path to the DepMap data directory.")
}
# If input_gene is provided look for mutations:
if(!is.null(input_gene)){
# Load necessary data
mut_calls <- copy_num_annot <- copy_num <- dep <- sample_annot <- NULL
# see: https://support.bioconductor.org/p/24756/
load(paste0(data_dir, "/mut_calls.rda"), envir = environment())
load(paste0(data_dir, "/copy_num_annot.rda"), envir = environment())
load(paste0(data_dir, "/copy_num.rda"), envir = environment())
load(paste0(data_dir, "/dep.rda"), envir = environment())
load(paste0(data_dir, "/sample_annot.rda"), envir = environment())
# Check if input gene mutations exist
if(!any(mut_calls$Hugo_Symbol %in% input_gene) &
!any(copy_num_annot$GeneNames %in% input_gene)){
stop("No mutations were found for: ", input_gene,
". Please check spelling and for valid Hugo Symbols")
}
# Convert to unique geneID
input_geneID <- get_GeneNameID(input_gene, data_dir = data_dir)
# Get copy number
# Fix names if needed
test <- grep("\\(", names(copy_num))
if(length(test) != 0){
# needs fixing
fix1 <- stringr::str_replace_all(string = names(copy_num), pattern = "\\ \\(", "_")
fix2 <- stringr::str_replace_all(string = fix1, pattern = "\\)", "")
names(copy_num) <- fix2
}
if(any(names(copy_num) %in% input_geneID)){
target_copy_num <- copy_num %>%
dplyr::select(.data$DepMap_ID, dplyr::all_of(input_geneID)) %>%
dplyr::filter(.data$DepMap_ID %in% dep$DepMap_ID) %>%
dplyr::arrange(.data$DepMap_ID) %>%
dplyr::mutate( Status = dplyr::case_when(
!!as.name(input_geneID) <= 0.25 ~ "Deep_del",
!!as.name(input_geneID) > 0.25 & !!as.name(input_geneID) < 0.75 ~ "Loss",
!!as.name(input_geneID) >= 0.75 & !!as.name(input_geneID) < 1.25 ~ "Neutral",
!!as.name(input_geneID) >= 1.25 ~ "Amplified",
TRUE ~ "Other"))
} else {
target_copy_num <- dep %>%
dplyr::select(.data$DepMap_ID) %>%
dplyr::arrange(.data$DepMap_ID) %>%
dplyr::mutate(!!as.name(input_geneID) := NA,
Status = "Unknown")
}
# Get ALL Mutations
target_mut <- mut_calls %>%
dplyr::filter((.data$DepMap_ID %in% dep$DepMap_ID) &
(.data$Hugo_Symbol %in% input_gene))
# Only some mut_calls contain a "SangerRecalibWES_AC" column
if(any(colnames(target_mut) %in% "SangerRecalibWES_AC")){
target_mut <- target_mut %>%
dplyr::mutate(AC_combined = dplyr::coalesce(.data$CGA_WES_AC, .data$SangerRecalibWES_AC,
.data$SangerWES_AC, .data$RNAseq_AC, .data$HC_AC,
.data$RD_AC, .data$WGS_AC), #(Alt:REF)
AC_ref_NULL = grepl(":0", .data$AC_combined)) %>%
dplyr::mutate(AC_Variant = dplyr::case_when(
.data$AC_ref_NULL == "TRUE" ~ "Hom_Mut",
TRUE ~ "Het_Mut"),
AC_Variant = paste0(.data$Variant_Classification," ", .data$AC_Variant)) %>% # are there any with 0 contribution from reference?
dplyr::select(.data$DepMap_ID, .data$Hugo_Symbol, .data$Chromosome, .data$Start_position,
.data$End_position, .data$Strand, .data$Variant_Classification, .data$Variant_Type,
.data$Reference_Allele, .data$Tumor_Seq_Allele1, .data$dbSNP_RS, .data$dbSNP_Val_Status,
.data$Genome_Change, .data$Annotation_Transcript, .data$cDNA_Change, .data$Codon_Change,
.data$Protein_Change, .data$isDeleterious, .data$isTCGAhotspot,
.data$TCGAhsCnt, .data$isCOSMIChotspot, .data$COSMIChsCnt, .data$Variant_annotation,
.data$AC_combined, .data$AC_ref_NULL, .data$AC_Variant) %>%
dplyr::arrange(.data$Start_position)
} else {
target_mut <- target_mut %>%
dplyr::mutate(AC_combined = dplyr::coalesce(.data$CGA_WES_AC, .data$SangerWES_AC, .data$RNAseq_AC,
.data$HC_AC, .data$RD_AC, .data$WGS_AC), #(Alt:REF)
AC_ref_NULL = grepl(":0", .data$AC_combined)) %>%
dplyr::mutate(AC_Variant = dplyr::case_when(
.data$AC_ref_NULL == "TRUE" ~ "Hom_Mut",
TRUE ~ "Het_Mut"),
AC_Variant = paste0(.data$Variant_Classification," ", .data$AC_Variant)) %>% # are there any with 0 contribution from reference?
dplyr::select(.data$DepMap_ID, .data$Hugo_Symbol, .data$Chromosome, .data$Start_position,
.data$End_position,
.data$Strand, .data$Variant_Classification, .data$Variant_Type, .data$Reference_Allele,
.data$Alternate_Allele, .data$dbSNP_RS, .data$dbSNP_Val_Status, .data$Genome_Change,
.data$Annotation_Transcript,
.data$cDNA_Change, .data$Codon_Change, .data$Protein_Change, .data$isDeleterious,
.data$isTCGAhotspot,
.data$TCGAhsCnt, .data$isCOSMIChotspot, .data$COSMIChsCnt, .data$Variant_annotation,
.data$AC_combined,
.data$AC_ref_NULL, .data$AC_Variant) %>%
dplyr::arrange(.data$Start_position)
}
# Count number of mutations found per sample
all_mutations_count_by_sample <- target_mut %>% dplyr::count(.data$DepMap_ID)
# If specific mutations are defined
if(!is.null(input_aa_change)){
select_muts <- target_mut %>%
dplyr::filter(.data$Protein_Change %in% paste0("p.",input_aa_change)) %>%
dplyr::select(.data$DepMap_ID:.data$Protein_Change, .data$Variant_annotation:.data$AC_Variant)
# Summarize mutations for all samples
summary <- sample_annot %>%
dplyr::filter(.data$DepMap_ID %in% dep$DepMap_ID) %>%
dplyr::select(.data$DepMap_ID, .data$stripped_cell_line_name, .data$disease, .data$disease_subtype,
.data$primary_or_metastasis) %>%
dplyr::left_join(all_mutations_count_by_sample, by = "DepMap_ID") %>%
dplyr::rename(Total_mutations = .data$n) %>%
dplyr::mutate(Total_mutations = dplyr::case_when(
is.na(.data$Total_mutations) ~ as.double(0),
TRUE ~ as.double(.data$Total_mutations))) %>%
dplyr::left_join(target_copy_num %>%
dplyr::select(.data$DepMap_ID, .data$Status), by = "DepMap_ID") %>%
dplyr::rename(CN_status = .data$Status) %>%
dplyr::left_join(select_muts)
# Annotate mutant group types based on conditions
if(!all(summary$CN_status == "Unknown")){
Groups <- summary %>%
dplyr::mutate(
Group = dplyr::case_when(
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status == "Neutral") &
(.data$AC_ref_NULL == TRUE)) ~ paste0(input_gene,"_", input_aa_change, "_HomAlt_CNneutral"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status == "Amplified") &
(.data$AC_ref_NULL == TRUE)) ~ paste0(input_gene,"_", input_aa_change, "_HomAlt_CNamplified"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status %in% c("Loss","Deep_del")) &
(.data$AC_ref_NULL == TRUE)) ~ paste0(input_gene,"_", input_aa_change, "_HomAlt_CNloss"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status == "Neutral") &
(.data$AC_ref_NULL == FALSE)) ~ paste0(input_gene,"_", input_aa_change, "_HetAlt_CNneutral"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status == "Amplified") &
(.data$AC_ref_NULL == FALSE)) ~ paste0(input_gene,"_", input_aa_change, "_HetAlt_CNamplified"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status %in% c("Loss","Deep_del")) &
(.data$AC_ref_NULL == FALSE)) ~ paste0(input_gene,"_", input_aa_change, "_HetAlt_CNloss"),
((.data$Total_mutations == 0) &
(.data$CN_status == "Neutral")) ~ paste0(input_gene,"_", input_aa_change, "_Control_CNneutral"),
((.data$Total_mutations == 0) &
(.data$CN_status %in% c("Loss","Deep_del"))) ~ paste0(input_gene,"_", input_aa_change,
"_Control_CNloss"),
TRUE ~ "Others")) %>%
dplyr::mutate(GeneNameID = input_geneID,
GeneName = input_gene)
} else {
Groups <- summary %>%
dplyr::mutate(
Group = dplyr::case_when(
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$AC_ref_NULL == TRUE)) ~ paste0(input_gene,"_", input_aa_change, "_HomAlt"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$AC_ref_NULL == FALSE)) ~ paste0(input_gene,"_", input_aa_change, "_HetAlt"),
(.data$Total_mutations == 0) ~ paste0(input_gene,"_", input_aa_change, "_Control"),
TRUE ~ "Others")) %>%
dplyr::mutate(GeneNameID = input_geneID,
GeneName = input_gene)
}
} else {
# If no mutations are defined, find default deleterious mutations
# Get deleterious/damaging mutations
mut_dels <- target_mut %>% dplyr::filter(.data$Variant_annotation == "damaging")
# Count number of deleterious mutations found per sample
del_mutations_count_by_sample <- mut_dels %>% dplyr::count(.data$DepMap_ID)
# Find samples with homozygous deleterious mutations (HomDels)
hom_del_muts_by_sample <- mut_dels %>% dplyr::select(.data$DepMap_ID, .data$AC_ref_NULL) %>%
dplyr::distinct() %>%
dplyr::filter(.data$AC_ref_NULL == TRUE)
# Find samples with multiple heterozygous deleterious mutations. Trans-heterozygous mutants (T-HetDels)
multi_mut_dels <- mut_dels %>%
dplyr::filter(.data$AC_ref_NULL == FALSE) %>%
dplyr::add_count(.data$DepMap_ID) %>%
dplyr::filter(.data$n > 1)
# Summarize mutations for all samples
summary <- sample_annot %>%
dplyr::filter(.data$DepMap_ID %in% dep$DepMap_ID) %>%
dplyr::select(.data$DepMap_ID, .data$stripped_cell_line_name, .data$disease, .data$disease_subtype,
.data$primary_or_metastasis) %>%
dplyr::left_join(all_mutations_count_by_sample, by = "DepMap_ID") %>%
dplyr::rename(Total_mutations = .data$n) %>%
dplyr::mutate(Total_mutations = dplyr::case_when(
is.na(.data$Total_mutations) ~ as.double(0),
TRUE ~ as.double(.data$Total_mutations))) %>%
dplyr::left_join(del_mutations_count_by_sample, by = "DepMap_ID") %>%
dplyr::rename(Del_mutations = .data$n) %>%
dplyr::mutate(Del_mutations = dplyr::case_when(
is.na(.data$Del_mutations) ~ as.double(0),
TRUE ~ as.double(.data$Del_mutations)),
Del_hom_mut = dplyr::case_when(
.data$DepMap_ID %in% hom_del_muts_by_sample$DepMap_ID ~ TRUE,
TRUE ~ FALSE)) %>%
dplyr::left_join(target_copy_num %>%
dplyr::select(.data$DepMap_ID, .data$Status), by = "DepMap_ID") %>%
dplyr::rename(CN_status = .data$Status) %>%
dplyr::arrange(-.data$Del_mutations, -.data$Total_mutations, .data$CN_status)
# Annotate mutant group types based on conditions
if(!all(summary$CN_status == "Unknown")){
Groups <- summary %>%
dplyr::mutate(
Group = dplyr::case_when(
((.data$CN_status == "Deep_del") | (.data$Del_hom_mut == TRUE)) ~ paste0(input_gene,"_HomDel"),
((.data$Del_mutations > 1) & (.data$CN_status == "Neutral")) |
((.data$Del_mutations == 1) & (.data$CN_status == "Loss")) ~ paste0(input_gene,"_T-HetDel"),
((.data$Del_mutations == 1) & (.data$CN_status == "Neutral")) |
((.data$Del_mutations == 0) & (.data$CN_status == "Loss")) ~ paste0(input_gene,"_HetDel"),
((.data$Total_mutations == 0) & (.data$CN_status == "Neutral")) ~ "Control",
((.data$Total_mutations == 0) & (.data$CN_status == "Amplified")) ~ "Amplified",
((.data$Del_mutations == 1) & (.data$CN_status == "Amplified")) ~ "Others",
TRUE ~ "Others")) %>%
dplyr::mutate(GeneNameID = input_geneID,
GeneName = input_gene)
} else {
Groups <- summary %>%
dplyr::mutate(
Group = dplyr::case_when(
(.data$Del_hom_mut == TRUE) ~ paste0(input_gene,"_HomDel"),
(.data$Del_mutations > 1) ~ paste0(input_gene,"_T-HetDel"),
(.data$Del_mutations == 1) ~ paste0(input_gene,"_HetDel"),
(.data$Total_mutations == 0) ~ "Control",
TRUE ~ "Others")) %>%
dplyr::mutate(GeneNameID = input_geneID,
GeneName = input_gene)
}
}
} else {
# Load necessary data
sample_annot <- NULL # see: https://support.bioconductor.org/p/24756/
data(list = list("sample_annot"), envir = environment())
}
# Output based on input conditions
if(is.null(c(input_disease, input_disease_subtype)) & !is.null(input_gene)){
output <- Groups
} else if(is.null(input_disease_subtype) & !is.null(c(input_gene, input_disease))){
output <- Groups %>%
dplyr::filter(.data$disease %in% input_disease)
} else if(is.null(input_gene) & !is.null(c(input_disease, input_disease_subtype))){
output <- sample_annot %>%
dplyr::select(.data$DepMap_ID, .data$stripped_cell_line_name, .data$disease, .data$disease_subtype,
.data$primary_or_metastasis) %>%
dplyr::filter(.data$disease %in% input_disease &
.data$disease_subtype %in% input_disease_subtype)
} else if(is.null(c(input_gene, input_disease_subtype)) & !is.null(input_disease)){
output <- sample_annot %>%
dplyr::select(.data$DepMap_ID, .data$stripped_cell_line_name, .data$disease, .data$disease_subtype,
.data$primary_or_metastasis) %>%
dplyr::filter(.data$disease %in% input_disease)
} else if(!is.null(c(input_gene, input_disease, input_disease_subtype))){
output <- Groups %>%
dplyr::filter(.data$disease %in% input_disease &
.data$disease_subtype %in% input_disease_subtype)
} else {
stop("Issue with input")
}
# Check if output has both control and mutants
# Print quick summary if no mutants were found
if(!any(stringr::str_detect(output$Group, "Del|Alt")) | !any(stringr::str_detect(output$Group, "Control"))){
message("No mutants or controls found! \n",
"Check results and consider using different criteria")
}
output <- output %>% dplyr::arrange(.data$DepMap_ID)
return(output)
}
ytakemon/GINIR documentation built on Feb. 27, 2024, 1:33 p.m.
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Defines functions select_cell_lines
Documented in select_cell_lines
#' Select mutant groups based on input gene of interest
#'
#' @description
#' `select_cell_lines()` assigns cancer cell lines to either `Control` groups or one of the following mutant groups: `HomDel`,
#' `T-HetDel`, `HetDel`, `Amplified`, or `Others` (see details).
#'
#' @param input_gene string as Hugo Symbol
#' @param input_aa_change string Amino acid change (eg. "A387A"). input_gene must be specified
#' @param input_disease string Cancer type listed in `list_available_cancer_types()`
#' @param input_disease_subtype string Cancer subtype listed in `list_available_cancer_subtypes()`
#' @param data_dir string Path to GINIR_data
#'
#' @return Data frame containing a summary of mutations found in cell lines and their control and mutant group assignments.
#' @import rlang
#' @import dplyr
#' @import utils
#' @import stringr
#'
#' @export
#' @details
#' Mutant groups in more detail when only `input_gene` is defined:
#' * `Control` cell lines do not harbor any single nucleotide variations (SNVs) or insertions and deletions (InDels) with a neutral copy number (CN).
#' * `HomDel` cell lines harbor one or more homozygous deleterious SNVs or have deep CN loss.
#' * `T-HetDel` cell lines harbor two or more heterozygous deleterious SNVs/InDels with neutral or CN loss.
#' * `HetDel` cell lines harbor one heterozygous deleterious SNV/InDel with neutral CN, or no SNV/InDel with CN loss.
#' * `Amplified` cell lines harbor no SNVs/InDels with increased CN.
#' * `Others` cell lines harbor deleterious SNVs with increased CN.
#'
#'
#' If input_aa_change` is also defined:
#' * `Control` cell lines do not harbor any single nucleotide variations (SNVs) or insertions and deletions (InDels) with a neutral copy number (CN).
#' * `HomAlt` cell lines harbor a homozygous alteration for the specified mutation.
#' * `HetAlt` cell lines harbor a heterozygous alteration for the specified mutation.
#' * `_CNneutral`, `_CNamplified`, and `_CNloss` define copy number (CN) status of the above mutation states.
#' * `Others` cell lines that do not meet above criteria.
#'
#' @examples
#' gretta_data_dir <- './GRETTA_example/'
#' gretta_output_dir <- './GRETTA_example_output/'
#'
#' if(!dir.exists(gretta_data_dir)){
#' download_example_data(".")
#' }
#'
#' select_cell_lines(input_gene = "ARID1A",
#' input_disease = "Lung Cancer",
#' data_dir = gretta_data_dir)
#'
select_cell_lines <- function(input_gene = NULL, input_aa_change = NULL, input_disease = NULL, input_disease_subtype = NULL, data_dir = NULL){
# Print and check to see input
if(is.null(c(input_gene, input_disease, input_disease_subtype))){
stop("No input given. Please prvide a Hugo gene symbol and/or cancer type")
} else if(is.null(c(input_gene, input_disease)) & !is.null(input_disease_subtype)){
stop("No cancer context provided. Please define the `disease` argument.")
} else if(is.null(input_gene) & !is.null(input_aa_change)){
stop("AA change was provided, ", input_aa_change,",
but no gene was provided. Please define input_gene!")
} else if(is.null(c(input_disease, input_disease_subtype)) & !is.null(input_gene)){
message("Selecting mutant groups for: ", paste0(input_gene, collapse = ", "), " in all cancer cell lines")
} else if(!is.null(c(input_gene, input_disease, input_disease_subtype))){
message("Selecting mutant groups for: ", paste0(input_gene, collapse = ", "), " in ", input_disease,", ",
input_disease_subtype, " cell lines")
} else if(is.null(input_disease_subtype) & !is.null(c(input_gene, input_disease))){
message("Selecting mutant groups for: ", input_gene, " in ", input_disease, " cell lines")
} else if(is.null(input_gene) & !is.null(c(input_disease, input_disease_subtype))){
message("Selecting all ", input_disease, ", ", input_disease_subtype, " cancer cell lines")
} else if(is.null(c(input_gene, input_disease_subtype)) & !is.null(input_disease)){
message("Selecting all ", input_disease, " cancer cell lines")
} else {
stop("Issue with input.")
}
if(is.null(data_dir)){
stop("No directory to data was specified. Please provide path to DepMap data.")
}
if(!dir.exists(data_dir)){
stop("DepMap data directory does not exists.",
"Please check again and provide the full path to the DepMap data directory.")
}
# If input_gene is provided look for mutations:
if(!is.null(input_gene)){
# Load necessary data
mut_calls <- copy_num_annot <- copy_num <- dep <- sample_annot <- NULL
# see: https://support.bioconductor.org/p/24756/
load(paste0(data_dir, "/mut_calls.rda"), envir = environment())
load(paste0(data_dir, "/copy_num_annot.rda"), envir = environment())
load(paste0(data_dir, "/copy_num.rda"), envir = environment())
load(paste0(data_dir, "/dep.rda"), envir = environment())
load(paste0(data_dir, "/sample_annot.rda"), envir = environment())
# Check if input gene mutations exist
if(!any(mut_calls$Hugo_Symbol %in% input_gene) &
!any(copy_num_annot$GeneNames %in% input_gene)){
stop("No mutations were found for: ", input_gene,
". Please check spelling and for valid Hugo Symbols")
}
# Convert to unique geneID
input_geneID <- get_GeneNameID(input_gene, data_dir = data_dir)
# Get copy number
# Fix names if needed
test <- grep("\\(", names(copy_num))
if(length(test) != 0){
# needs fixing
fix1 <- stringr::str_replace_all(string = names(copy_num), pattern = "\\ \\(", "_")
fix2 <- stringr::str_replace_all(string = fix1, pattern = "\\)", "")
names(copy_num) <- fix2
}
if(any(names(copy_num) %in% input_geneID)){
target_copy_num <- copy_num %>%
dplyr::select(.data$DepMap_ID, dplyr::all_of(input_geneID)) %>%
dplyr::filter(.data$DepMap_ID %in% dep$DepMap_ID) %>%
dplyr::arrange(.data$DepMap_ID) %>%
dplyr::mutate( Status = dplyr::case_when(
!!as.name(input_geneID) <= 0.25 ~ "Deep_del",
!!as.name(input_geneID) > 0.25 & !!as.name(input_geneID) < 0.75 ~ "Loss",
!!as.name(input_geneID) >= 0.75 & !!as.name(input_geneID) < 1.25 ~ "Neutral",
!!as.name(input_geneID) >= 1.25 ~ "Amplified",
TRUE ~ "Other"))
} else {
target_copy_num <- dep %>%
dplyr::select(.data$DepMap_ID) %>%
dplyr::arrange(.data$DepMap_ID) %>%
dplyr::mutate(!!as.name(input_geneID) := NA,
Status = "Unknown")
}
# Get ALL Mutations
target_mut <- mut_calls %>%
dplyr::filter((.data$DepMap_ID %in% dep$DepMap_ID) &
(.data$Hugo_Symbol %in% input_gene))
# Only some mut_calls contain a "SangerRecalibWES_AC" column
if(any(colnames(target_mut) %in% "SangerRecalibWES_AC")){
target_mut <- target_mut %>%
dplyr::mutate(AC_combined = dplyr::coalesce(.data$CGA_WES_AC, .data$SangerRecalibWES_AC,
.data$SangerWES_AC, .data$RNAseq_AC, .data$HC_AC,
.data$RD_AC, .data$WGS_AC), #(Alt:REF)
AC_ref_NULL = grepl(":0", .data$AC_combined)) %>%
dplyr::mutate(AC_Variant = dplyr::case_when(
.data$AC_ref_NULL == "TRUE" ~ "Hom_Mut",
TRUE ~ "Het_Mut"),
AC_Variant = paste0(.data$Variant_Classification," ", .data$AC_Variant)) %>% # are there any with 0 contribution from reference?
dplyr::select(.data$DepMap_ID, .data$Hugo_Symbol, .data$Chromosome, .data$Start_position,
.data$End_position, .data$Strand, .data$Variant_Classification, .data$Variant_Type,
.data$Reference_Allele, .data$Tumor_Seq_Allele1, .data$dbSNP_RS, .data$dbSNP_Val_Status,
.data$Genome_Change, .data$Annotation_Transcript, .data$cDNA_Change, .data$Codon_Change,
.data$Protein_Change, .data$isDeleterious, .data$isTCGAhotspot,
.data$TCGAhsCnt, .data$isCOSMIChotspot, .data$COSMIChsCnt, .data$Variant_annotation,
.data$AC_combined, .data$AC_ref_NULL, .data$AC_Variant) %>%
dplyr::arrange(.data$Start_position)
} else {
target_mut <- target_mut %>%
dplyr::mutate(AC_combined = dplyr::coalesce(.data$CGA_WES_AC, .data$SangerWES_AC, .data$RNAseq_AC,
.data$HC_AC, .data$RD_AC, .data$WGS_AC), #(Alt:REF)
AC_ref_NULL = grepl(":0", .data$AC_combined)) %>%
dplyr::mutate(AC_Variant = dplyr::case_when(
.data$AC_ref_NULL == "TRUE" ~ "Hom_Mut",
TRUE ~ "Het_Mut"),
AC_Variant = paste0(.data$Variant_Classification," ", .data$AC_Variant)) %>% # are there any with 0 contribution from reference?
dplyr::select(.data$DepMap_ID, .data$Hugo_Symbol, .data$Chromosome, .data$Start_position,
.data$End_position,
.data$Strand, .data$Variant_Classification, .data$Variant_Type, .data$Reference_Allele,
.data$Alternate_Allele, .data$dbSNP_RS, .data$dbSNP_Val_Status, .data$Genome_Change,
.data$Annotation_Transcript,
.data$cDNA_Change, .data$Codon_Change, .data$Protein_Change, .data$isDeleterious,
.data$isTCGAhotspot,
.data$TCGAhsCnt, .data$isCOSMIChotspot, .data$COSMIChsCnt, .data$Variant_annotation,
.data$AC_combined,
.data$AC_ref_NULL, .data$AC_Variant) %>%
dplyr::arrange(.data$Start_position)
}
# Count number of mutations found per sample
all_mutations_count_by_sample <- target_mut %>% dplyr::count(.data$DepMap_ID)
# If specific mutations are defined
if(!is.null(input_aa_change)){
select_muts <- target_mut %>%
dplyr::filter(.data$Protein_Change %in% paste0("p.",input_aa_change)) %>%
dplyr::select(.data$DepMap_ID:.data$Protein_Change, .data$Variant_annotation:.data$AC_Variant)
# Summarize mutations for all samples
summary <- sample_annot %>%
dplyr::filter(.data$DepMap_ID %in% dep$DepMap_ID) %>%
dplyr::select(.data$DepMap_ID, .data$stripped_cell_line_name, .data$disease, .data$disease_subtype,
.data$primary_or_metastasis) %>%
dplyr::left_join(all_mutations_count_by_sample, by = "DepMap_ID") %>%
dplyr::rename(Total_mutations = .data$n) %>%
dplyr::mutate(Total_mutations = dplyr::case_when(
is.na(.data$Total_mutations) ~ as.double(0),
TRUE ~ as.double(.data$Total_mutations))) %>%
dplyr::left_join(target_copy_num %>%
dplyr::select(.data$DepMap_ID, .data$Status), by = "DepMap_ID") %>%
dplyr::rename(CN_status = .data$Status) %>%
dplyr::left_join(select_muts)
# Annotate mutant group types based on conditions
if(!all(summary$CN_status == "Unknown")){
Groups <- summary %>%
dplyr::mutate(
Group = dplyr::case_when(
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status == "Neutral") &
(.data$AC_ref_NULL == TRUE)) ~ paste0(input_gene,"_", input_aa_change, "_HomAlt_CNneutral"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status == "Amplified") &
(.data$AC_ref_NULL == TRUE)) ~ paste0(input_gene,"_", input_aa_change, "_HomAlt_CNamplified"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status %in% c("Loss","Deep_del")) &
(.data$AC_ref_NULL == TRUE)) ~ paste0(input_gene,"_", input_aa_change, "_HomAlt_CNloss"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status == "Neutral") &
(.data$AC_ref_NULL == FALSE)) ~ paste0(input_gene,"_", input_aa_change, "_HetAlt_CNneutral"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status == "Amplified") &
(.data$AC_ref_NULL == FALSE)) ~ paste0(input_gene,"_", input_aa_change, "_HetAlt_CNamplified"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$CN_status %in% c("Loss","Deep_del")) &
(.data$AC_ref_NULL == FALSE)) ~ paste0(input_gene,"_", input_aa_change, "_HetAlt_CNloss"),
((.data$Total_mutations == 0) &
(.data$CN_status == "Neutral")) ~ paste0(input_gene,"_", input_aa_change, "_Control_CNneutral"),
((.data$Total_mutations == 0) &
(.data$CN_status %in% c("Loss","Deep_del"))) ~ paste0(input_gene,"_", input_aa_change,
"_Control_CNloss"),
TRUE ~ "Others")) %>%
dplyr::mutate(GeneNameID = input_geneID,
GeneName = input_gene)
} else {
Groups <- summary %>%
dplyr::mutate(
Group = dplyr::case_when(
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$AC_ref_NULL == TRUE)) ~ paste0(input_gene,"_", input_aa_change, "_HomAlt"),
(.data$Protein_Change %in% paste0("p.",input_aa_change) &
(.data$AC_ref_NULL == FALSE)) ~ paste0(input_gene,"_", input_aa_change, "_HetAlt"),
(.data$Total_mutations == 0) ~ paste0(input_gene,"_", input_aa_change, "_Control"),
TRUE ~ "Others")) %>%
dplyr::mutate(GeneNameID = input_geneID,
GeneName = input_gene)
}
} else {
# If no mutations are defined, find default deleterious mutations
# Get deleterious/damaging mutations
mut_dels <- target_mut %>% dplyr::filter(.data$Variant_annotation == "damaging")
# Count number of deleterious mutations found per sample
del_mutations_count_by_sample <- mut_dels %>% dplyr::count(.data$DepMap_ID)
# Find samples with homozygous deleterious mutations (HomDels)
hom_del_muts_by_sample <- mut_dels %>% dplyr::select(.data$DepMap_ID, .data$AC_ref_NULL) %>%
dplyr::distinct() %>%
dplyr::filter(.data$AC_ref_NULL == TRUE)
# Find samples with multiple heterozygous deleterious mutations. Trans-heterozygous mutants (T-HetDels)
multi_mut_dels <- mut_dels %>%
dplyr::filter(.data$AC_ref_NULL == FALSE) %>%
dplyr::add_count(.data$DepMap_ID) %>%
dplyr::filter(.data$n > 1)
# Summarize mutations for all samples
summary <- sample_annot %>%
dplyr::filter(.data$DepMap_ID %in% dep$DepMap_ID) %>%
dplyr::select(.data$DepMap_ID, .data$stripped_cell_line_name, .data$disease, .data$disease_subtype,
.data$primary_or_metastasis) %>%
dplyr::left_join(all_mutations_count_by_sample, by = "DepMap_ID") %>%
dplyr::rename(Total_mutations = .data$n) %>%
dplyr::mutate(Total_mutations = dplyr::case_when(
is.na(.data$Total_mutations) ~ as.double(0),
TRUE ~ as.double(.data$Total_mutations))) %>%
dplyr::left_join(del_mutations_count_by_sample, by = "DepMap_ID") %>%
dplyr::rename(Del_mutations = .data$n) %>%
dplyr::mutate(Del_mutations = dplyr::case_when(
is.na(.data$Del_mutations) ~ as.double(0),
TRUE ~ as.double(.data$Del_mutations)),
Del_hom_mut = dplyr::case_when(
.data$DepMap_ID %in% hom_del_muts_by_sample$DepMap_ID ~ TRUE,
TRUE ~ FALSE)) %>%
dplyr::left_join(target_copy_num %>%
dplyr::select(.data$DepMap_ID, .data$Status), by = "DepMap_ID") %>%
dplyr::rename(CN_status = .data$Status) %>%
dplyr::arrange(-.data$Del_mutations, -.data$Total_mutations, .data$CN_status)
# Annotate mutant group types based on conditions
if(!all(summary$CN_status == "Unknown")){
Groups <- summary %>%
dplyr::mutate(
Group = dplyr::case_when(
((.data$CN_status == "Deep_del") | (.data$Del_hom_mut == TRUE)) ~ paste0(input_gene,"_HomDel"),
((.data$Del_mutations > 1) & (.data$CN_status == "Neutral")) |
((.data$Del_mutations == 1) & (.data$CN_status == "Loss")) ~ paste0(input_gene,"_T-HetDel"),
((.data$Del_mutations == 1) & (.data$CN_status == "Neutral")) |
((.data$Del_mutations == 0) & (.data$CN_status == "Loss")) ~ paste0(input_gene,"_HetDel"),
((.data$Total_mutations == 0) & (.data$CN_status == "Neutral")) ~ "Control",
((.data$Total_mutations == 0) & (.data$CN_status == "Amplified")) ~ "Amplified",
((.data$Del_mutations == 1) & (.data$CN_status == "Amplified")) ~ "Others",
TRUE ~ "Others")) %>%
dplyr::mutate(GeneNameID = input_geneID,
GeneName = input_gene)
} else {
Groups <- summary %>%
dplyr::mutate(
Group = dplyr::case_when(
(.data$Del_hom_mut == TRUE) ~ paste0(input_gene,"_HomDel"),
(.data$Del_mutations > 1) ~ paste0(input_gene,"_T-HetDel"),
(.data$Del_mutations == 1) ~ paste0(input_gene,"_HetDel"),
(.data$Total_mutations == 0) ~ "Control",
TRUE ~ "Others")) %>%
dplyr::mutate(GeneNameID = input_geneID,
GeneName = input_gene)
}
}
} else {
# Load necessary data
sample_annot <- NULL # see: https://support.bioconductor.org/p/24756/
data(list = list("sample_annot"), envir = environment())
}
# Output based on input conditions
if(is.null(c(input_disease, input_disease_subtype)) & !is.null(input_gene)){
output <- Groups
} else if(is.null(input_disease_subtype) & !is.null(c(input_gene, input_disease))){
output <- Groups %>%
dplyr::filter(.data$disease %in% input_disease)
} else if(is.null(input_gene) & !is.null(c(input_disease, input_disease_subtype))){
output <- sample_annot %>%
dplyr::select(.data$DepMap_ID, .data$stripped_cell_line_name, .data$disease, .data$disease_subtype,
.data$primary_or_metastasis) %>%
dplyr::filter(.data$disease %in% input_disease &
.data$disease_subtype %in% input_disease_subtype)
} else if(is.null(c(input_gene, input_disease_subtype)) & !is.null(input_disease)){
output <- sample_annot %>%
dplyr::select(.data$DepMap_ID, .data$stripped_cell_line_name, .data$disease, .data$disease_subtype,
.data$primary_or_metastasis) %>%
dplyr::filter(.data$disease %in% input_disease)
} else if(!is.null(c(input_gene, input_disease, input_disease_subtype))){
output <- Groups %>%
dplyr::filter(.data$disease %in% input_disease &
.data$disease_subtype %in% input_disease_subtype)
} else {
stop("Issue with input")
}
# Check if output has both control and mutants
# Print quick summary if no mutants were found
if(!any(stringr::str_detect(output$Group, "Del|Alt")) | !any(stringr::str_detect(output$Group, "Control"))){
message("No mutants or controls found! \n",
"Check results and consider using different criteria")
}
output <- output %>% dplyr::arrange(.data$DepMap_ID)
return(output)
}
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