/
query_tcga_group.R
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query_tcga_group.R
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#' Group TPC samples by build-in or custom phenotype and support filtering or merging operations
#'
#' @param cancer select cancer cohort(s)
#' @param custom upload custom phenotype data
#' @param group target group names
#' @param filter_by filter samples by one or multiple criterion
#' @param merge_by merge the target group for main categories
#' @param return_all return the all phenotype data
#' @param filter_id directly filter samples by provided sample ids
#' @param merge_quantile whether to merge numerical variable by percentiles
#' @param database one of c("toil","pcawg","ccle")
#'
#' @return a list object with grouping samples and statistics
#' @export
#'
#' @examples
#' \dontrun{
#' query_tcga_group(group = "Age")
#'
#' query_tcga_group(cancer="BRCA",
#' group = "Stage_ajcc"
#' )
#'
#' query_tcga_group(cancer="BRCA",
#' group = "Stage_ajcc",
#' filter_by = list(
#' c("Code",c("TP"),"+"),
#' c("Stage_ajcc",c(NA),"-"))
#' )
#'
#' query_tcga_group(cancer="BRCA",
#' group = "Stage_ajcc",
#' filter_by = list(
#' c("Age",c(0.5),"%>"))
#' )
#'
#' query_tcga_group(cancer="BRCA",
#' group = "Stage_ajcc",
#' filter_by = list(
#' c("Age",c(60),">"))
#' )
#'
#' query_tcga_group(cancer="BRCA",
#' group = "Stage_ajcc",
#' merge_by = list(
#' "Early"=c("Stage I"),
#' "Late" = c("Stage II","Stage III","Stage IV"))
#' )
#'
#' query_tcga_group(cancer="BRCA",
#' group = "Age",
#' merge_by = list(
#' "Young"= c(20, 60),
#' "Old"= c(60, NA)
#' )
#' )
#'
#' query_tcga_group(cancer="BRCA",
#' group = "Age",
#' merge_quantile = TRUE,
#' merge_by = list(
#' "Young"= c(0, 0.5),
#' "Old"= c(0.5, 1)
#' )
#' )
#' }
query_tcga_group = function(database = c("toil","pcawg","ccle"),
cancer=NULL,
custom = NULL,
group = "Gender",
filter_by = NULL,
filter_id = NULL,
merge_by = NULL,
merge_quantile = FALSE,
return_all = FALSE
){
# step1: load build-in data
database <- match.arg(database)
# data("tcga_clinical_fine")
# data("pcawg_info_fine")
# data("ccle_info_fine")
meta_data = switch(database,
"toil" = UCSCXenaShiny::tcga_clinical_fine,
"pcawg"= UCSCXenaShiny::pcawg_info_fine,
"ccle" = UCSCXenaShiny::ccle_info_fine)
colnames(meta_data)[1:2] = c("Sample","Cancer") # CCLE:c("CCLE_name","Primary_Site)
# Step2: add user-customized data
if(!is.null(custom)){
dup_names = intersect(colnames(meta_data)[-1:-2], colnames(custom)[-1])
if(length(dup_names)>0){
meta_data = meta_data %>%
dplyr::select(!all_of(dup_names))
} # 如果有重复列名，则去除原始metadata的重复列
colnames(custom)[1] = "Sample"
meta_data = dplyr::inner_join(meta_data, custom) # Note: Only consider the intersection
}
# step3: filter cancer(s)
if(is.null(cancer)){cancer = unique(meta_data$Cancer)}
meta_data_sub = meta_data %>% dplyr::filter(.data$Cancer %in% cancer)
# 检查选择的分组参数是否在现有的列名中
if(!any(colnames(meta_data_sub)==group)){
stop(paste0("Please input the right group names:\n",
paste(colnames(meta_data_sub)[-1:-2], collapse = " ")))
}
# step5: filter by specialized conditions
if(!is.null(filter_by)){
## 按list内条件顺序级联筛选
for (i in seq(filter_by)){
# i = 1
filter_by_L1 = trimws(filter_by[[i]][1])
filter_by_L3 = trimws(filter_by[[i]][3])
filter_by_L2 = sapply(strsplit(filter_by[[i]][2],"|",fixed = T)[[1]],
trimws,USE.NAMES = FALSE)
filter_by_L2[filter_by_L2 %in% "NA"] <- NA
# 6种过滤方式
if (filter_by_L3=="+"){ #保留
meta_data_sub <- meta_data_sub %>%
dplyr::filter(.data[[filter_by_L1]] %in% filter_by_L2)
} else if (filter_by_L3=="-"){ #剔除
meta_data_sub <- meta_data_sub %>%
dplyr::filter(!.data[[filter_by_L1]] %in% filter_by_L2)
} else if (filter_by_L3==">"){ #大于 绝对值
filter_by_L2 = as.numeric(filter_by_L2)
meta_data_sub <- meta_data_sub %>%
dplyr::filter(.data[[filter_by_L1]] > filter_by_L2)
} else if (filter_by_L3=="%>"){ #大于 分位数
filter_by_L2 = as.numeric(filter_by_L2)
meta_data_sub <- meta_data_sub %>%
dplyr::group_by("Cancer") %>%
dplyr::filter(.data[[filter_by_L1]] >
quantile(.data[[filter_by_L1]],filter_by_L2,na.rm=T))
} else if (filter_by_L3=="<"){ #小于 绝对值
filter_by_L2 = as.numeric(filter_by_L2)
meta_data_sub <- meta_data_sub %>%
dplyr::filter(.data[[filter_by_L1]] < filter_by_L2)
} else if (filter_by_L3=="%<"){#小于 分位数
filter_by_L2 = as.numeric(filter_by_L2)
meta_data_sub <- meta_data_sub %>%
dplyr::group_by("Cancer") %>%
dplyr::filter(.data[[filter_by_L1]] <
quantile(.data[[filter_by_L1]],filter_by_L2,na.rm=T))
}
}
}
# step3-2: filter by sample id
if(!is.null(filter_id)){
meta_data_sub = meta_data_sub %>%
dplyr::filter(.data$Sample %in% filter_id)
}
dim(meta_data_sub)
# step3: merge group
if(!is.null(merge_by)){
# if(class(meta_data_sub[,group,drop=T])=="numeric"){
if(inherits(meta_data_sub[, group, drop = T], "numeric")){
# 分位数:每种肿瘤单独的分位数
if(merge_quantile){
for (i in seq(merge_by)){
if(is.na(merge_by[[i]][1])){
merge_by[[i]][1] = 0
}
if(is.na(merge_by [[i]][2])){
merge_by[[i]][2] = 1
}
}
meta_data_sub_split = split(meta_data_sub, meta_data_sub$Cancer)
meta_data_sub = lapply(meta_data_sub_split, function(meta_data_one){
# meta_data_one = meta_data_sub_split[["CESC"]]
dat_num = meta_data_one[,group,drop=T]
meta_data_one[,group,drop=T] = NA
merge_by_one = merge_by
for (i in seq(merge_by_one)){
merge_by_one[[i]] = quantile(dat_num, merge_by_one[[i]], na.rm = T)
}
# 对于较大值组，左右均闭，这样可能导致大组的数目偏多（中位数）
meta_data_one[,group,drop=T][which(findInterval(dat_num, merge_by_one[[1]])==1)] = names(merge_by_one)[1]
meta_data_one[,group,drop=T][which(findInterval(dat_num, merge_by_one[[2]], rightmost.closed = TRUE)==1)] = names(merge_by_one)[2]
meta_data_one
}) %>% do.call(rbind, .)
} else {
dat_num = meta_data_sub[,group,drop=T]
meta_data_sub[,group,drop=T] = NA
for (i in seq(merge_by )){
if(is.na(merge_by[[i]][1])){
merge_by[[i]][1] = min(dat_num,na.rm = T)
}
if(is.na(merge_by [[i]][2])){
merge_by[[i]][2] = max(dat_num,na.rm = T)
}
}
if(merge_by[[1]][1] < merge_by[[2]][2] && merge_by[[1]][2] > merge_by[[2]][1]){
stop("Please provide two independent grouping range")
}
meta_data_sub[,group,drop=T][which(findInterval(dat_num, merge_by[[1]])==1)] = names(merge_by)[1]
meta_data_sub[,group,drop=T][which(findInterval(dat_num, merge_by[[2]], rightmost.closed = TRUE)==1)] = names(merge_by)[2]
}
# } else if (class(meta_data_sub[,group,drop=T])=="character"){
} else if (inherits(meta_data_sub[, group, drop = T], "character")){
dat_chr = meta_data_sub[,group,drop=T]
meta_data_sub[,group,drop=T] = NA
if(length(intersect(merge_by[[1]], merge_by[[2]]))){
stop("Please provide two independent grouping range")
}
meta_data_sub[,group,drop=T][dat_chr %in% merge_by[[1]]] = names(merge_by)[1]
meta_data_sub[,group,drop=T][dat_chr %in% merge_by[[2]]] = names(merge_by)[2]
}
meta_data_sub = meta_data_sub[!is.na(meta_data_sub[,group,drop=T]),]
}
# 对否返回全部值
if(return_all){
return(meta_data_sub)
}
meta_data_sub2 = meta_data_sub[,c("Sample","Cancer",group)]
meta_data_sub2 = meta_data_sub2[, !duplicated(colnames(meta_data_sub2))]
sub2_group_stat = meta_data_sub2 %>%
dplyr::select(last_col()) %>%
dplyr::mutate(across(where(is.character), as.factor)) %>%
summary()
list(data = meta_data_sub2, stat = sub2_group_stat)
}