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multi-omics-code.R
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# sourced by 'server.R'
# save as 'GTEx_exp_server.R'
# server elements 'GTEx_exp' sub tab of 'GTEx' tab
source(file.path(config$wd, "functions", "data_function.R"))
source(file.path(config$wd, "functions", "gtex_gexp_function.R"))
# welcome information -----------------------------------------------------
output$ui_gtex_exp_welcome <- shiny::renderUI({fn_gtex_exp_welcome()})
output$ui_gtex_exp_help <- shiny::renderUI({fn_gtex_exp_help()})
# generate eqtl result out ui -------------------------------------------------------
output$ui_gexp_result <- shiny::renderUI({fn_gexp_result(selected_analysis$gtex_exp)})
# get tissue type --------------------------------------------------------
#
# observeEvent(input$gtex_expr_submit, {
# status$gtex_expr_submit <- TRUE
# shinyjs::disable(id = "GTEx_tissue_submit")
# shinyjs::enable(id = "analysis_stop")
# })
# stop analysis -----------------------------------------------------------
# observeEvent(input$analysis_stop, {
# status$GTEx_tissue_submit <- FALSE
# shinyjs::enable(id = "GTEx_tissue_submit")
# GTEx_hide <- c("GTEx_exp", "GTEx_gsva")
# hidePic(GTEx_hide) # hide pic when stop clicked!
# })
# get gene set-----------------------
# GTEx_expr_gene_list <- eventReactive(
# eventExpr = status$analysis,
# ignoreNULL = TRUE,
# valueExpr = {
# # be sure the following code run after start analysis
# if (status$analysis == TRUE) {
# status$gtex_expr_submit <- TRUE
# shinyjs::disable(id = "GTEx_tissue_submit")
# shinyjs::enable(id = "analysis_stop")
# as.character(gene_set$match.gtex)
# }
# }
# )
# Selected tissue types ---------------------------------------------------
# GTEx_tissue_type <- callModule(GTEx_normal_Tissue, "GTEx_exp")
# output$GTEx_selected_tissues <- renderText(GTEx_tissue_type())
####### reset tissue type selection when click-----------
# observeEvent(input$GTEx_tissue_reset, {
# GTEx_tissue_type <- callModule(resetGTExTissueType, "GTEx_exp")
# })
# observeEvent(input$GTEx_tissue_submit, heatmap_gsva_4_geneset(gene_set = gene_set, gtex_expr = gtex_expr, tissue_set = tissue_set))
# analysis core ----------------------------
##### get gene expression profiles in GTEx dataset######
get_gene_exp_profile <- function(gene_set = gene_set, gtex_expr = gtex_expr, tissue_set = tissue_set, filter_gene = 0) {
gtex_expr <- gtex_expr[gtex_expr$SMTS %in% tissue_set, ]
if (filter_gene) {
gtex_expr %>%
dplyr::mutate(
expr = purrr::map(
.x = expr,
.f = function(.x) {
.x %>%
dplyr::filter(symbol %in% gene_set)
}
)
) -> gtex_gene_list_expr
} else {
gtex_gene_list_expr <- gtex_expr
}
return(gtex_gene_list_expr)
}
get_gene_mean_profile <- function(gene_set = gene_set, gtex_expr_mean = gtex_expr_mean, tissue_set = tissue_set, filter_gene = 0) {
gtex_expr_mean <- gtex_expr_mean[gtex_expr_mean$SMTS %in% tissue_set, ]
if (filter_gene) {
gtex_expr_mean %>%
dplyr::mutate(
Mean = purrr::map(
.x = Mean,
.f = function(.x) {
.x %>%
dplyr::filter(symbol %in% gene_set)
}
)
) -> gtex_gene_list_mean
} else {
gtex_gene_list_mean <- gtex_expr_mean
}
return(gtex_gene_list_mean)
}
###### do heatmap and gsva for gene set ####
heatmap_gsva_4_geneset <- eventReactive(
{
status$analysis == TRUE
},
ignoreNULL = TRUE,
valueExpr = {
if (status$analysis == TRUE) {
if (selected_analysis$gtex_exp == TRUE) {
# check box-------------------------
callModule(module = tissueTypesSelect, id = "GTEx_exp", .sctps = intersect(selected_ctyps(), gtex_data))
callModule(module = selectAndAnalysis, id = "GTEx_exp", .id = "GTEx_exp")
load_data_gexp()
print(glue::glue("{paste0(rep('-', 10), collapse = '')} start: expression profiles of gene set on GTEx dataset processing@ {Sys.time()} {paste0(rep('-', 10), collapse = '')}"))
# gene_set <- GTEx_expr_gene_list()
# tissue_set <- c("Heart", "Ovary", "Lung","Muscle","Blood","Uterus","Vagina","Breast","Skin","Testis","Colon","Stomach","Pancreas")
# print(tissue_set)
# print(gene_set)
##### start: draw heatmap for the gene set in GTEx dataset######
print("start: draw heatmap for the gene set in GTEx dataset")
gtex_gene_list_expr.mean <- get_gene_mean_profile(gene_set = gene_set$match.gtex, gtex_expr_mean = gtex_expr_mean, tissue_set = input$select_ctps, filter_gene = 1)
if(nrow(gtex_gene_list_expr.mean)>0){
gene_n <- nrow(gtex_gene_list_expr.mean$Mean[[1]])
display_matrix <- data.frame(round(matrix(unlist(lapply(gtex_gene_list_expr.mean$Mean,function(.x){.x[2]})), nrow = gene_n), 2))
colnames(display_matrix) <- gtex_gene_list_expr.mean$SMTS
display_matrix$GeneName <- gtex_gene_list_expr.mean$Mean[[1]]$symbol
display_matrix %>% tidyr::gather(Tissue, RPKM, -GeneName) %>% dplyr::rename("TPM"="RPKM")-> hm_4_p
callModule(heatmap_GTEX_Plot, "GTEx_exp", data = hm_4_p, status=status, downloadname = "heatmap_GTEX_Plot")
} else {
.msg <- glue::glue("No GTEx expression in gene set for your selected tissue.")
shinyBS::createAlert(
session = session, anchorId = "GTEx_expr-no_gene_set", title = "Oops", style = "danger",
content = .msg, append = FALSE
)
}
print("end: draw heatmap for the gene set in GTEx dataset")
######## calculate and draw GSVA profiles for gene set in selected tissues in GTEx dataset############
print(glue::glue("{paste0(rep('-', 10), collapse = '')} start: calculating gene set on GTEx dataset@ {Sys.time()} {paste0(rep('-', 10), collapse = '')}"))
}
# print(GTEx_expr_gene_list(), "2")
# print("2")
## can not get the tissue info!!!!!#
# print(GTEx_tissue_type())
# gtex_expr <- get_gene_exp_profile(gene_set = gene_set, gtex_expr = gtex_expr, tissue_set = tissue_set,filter_gene = 0)
#
# gene_set.lst <- list(atg_lys = gene_set)
# fn_gsva <- function(.y, gene_set.lst = gene_set.lst){
# .y %>%
# tidyr::drop_na() %>%
# dplyr::select( -ensembl_gene_id) -> .d
#
# .d_mat <- as.matrix(.d[,-1])
# rownames(.d_mat) <- .d$symbol
# .es_dif <- gsva(.d_mat, gset.idx.list = gene_set.lst, method = "gsva", mx.diff = TRUE, verbose = FALSE, parallel.sz = 1)
# .es_dif %>%
# as.data.frame() %>%
# tibble::as_tibble() %>%
# tibble::add_column(set = "atg_lys", .before = 1) -> .d_es
# }
# print("run_gsva")
# gtex_expr %>%
# dplyr::mutate(
# gsva = purrr::map(
# .x = expr,
# .f = function(.x) {
# fn_gsva(.x, gene_set.lst = gene_set.lst)
# }
# )
# ) -> gtex_expr_gsva
# print("end_gsva")
# gtex_expr_gsva %>%
# dplyr::select(SMTS, gsva) %>%
# dplyr::mutate(
# gsva = purrr::map(
# .x = gsva,
# .f = function(.x) {
# .x %>%
# dplyr::select(-set) %>%
# tidyr::gather(key = barcode, value = gsva)
# }
# )
# ) %>% tidyr::unnest() -> plot_ready
#
# print("call box plot for gsva")
# callModule(box_GTEx_GSVA_Plot, "GTEx_gsva", data=plot_ready)
}
}
)
# observe(GTEx_expr_gene_list())
observe(heatmap_gsva_4_geneset())
# source by "server.R"
source(file.path(config$wd, "functions", "data_function.R"))
source(file.path(config$wd, "functions", "tcga_expr_function.R"))
expr_clean <- NULL
survival_clean <- NULL
# toga expr welcome -------------------------------------------------------
output$ui_expr_welcome <- shiny::renderUI({fn_expr_welcome()})
output$ui_expr_help <- shiny::renderUI({fn_expr_help()})
# expr analysis result ----------------------------------------------------
output$ui_expr_result <- shiny::renderUI({fn_expr_result(selected_analysis$expr)})
# Start analysis ----------------------------------------------------------
expr_start_analysis <- function(input, output, session, .expr_clean, .survival_clean, .subtype_clean, .msg, .msg_no_result) {
output$expr_dt_comparison <- DT::renderDataTable({expr_clean_datatable(.expr_clean)})
if(nrow(.expr_clean)>0){
exp_plot_height <- .expr_clean$symbol %>% unique() %>% length()*20
output$expr_bubble_plot <- renderPlot({
.expr_clean %>% expr_buble_plot()
} ,height = function(){ifelse(exp_plot_height<200,200,exp_plot_height)})
output$`de-picdownload` <- downloadHandler(
filename = function() {
paste("Differential_Expression", ".", input$`de-pictype`, sep = "")
},
content = function(file){
print(file)
ggsave(file,expr_buble_plot(.expr_clean),device = input$`de-pictype`,width = input$`de-d_width`,height = input$`de-d_height`)
}
)
.msg_de_massage <- NULL
} else {
.msg_de_massage <- .msg
output$expr_bubble_plot <- renderPlot({NULL})
output$`de-picdownload` <- downloadHandler(NULL)
}
# survival
if(nrow(.survival_clean)>0){
sur_plot_height <- .survival_clean$symbol %>% unique() %>% length()*20
output$survival <- renderPlot({.survival_clean %>% survival_bubble_plot()},height = function(){ifelse(sur_plot_height<200,200,sur_plot_height)})
output$`sur-picdownload` <- downloadHandler(
filename = function() {
paste("Expression_Survival", ".", input$`sur-pictype`, sep = "")
},
content = function(file){
print(file)
ggsave(file,survival_bubble_plot(.survival_clean),device = input$`sur-pictype`,width = input$`sur-d_width`,height = input$`sur-d_height`)
}
)
.msg_sur_massage <- NULL
} else {
.msg_sur_massage <- .msg_no_result
output$survival <- renderPlot({NULL})
output$`sur-picdownload` <- downloadHandler(NULL)
}
# subtype
if(nrow(.subtype_clean)>0){
sub_plot_height <- .subtype_clean$symbol %>% unique() %>% length()*20
output$subtype <- renderPlot({.subtype_clean %>% subtype_bubble_plot()},height = function(){ifelse(sub_plot_height<200,200,sub_plot_height)})
output$`sub-picdownload` <- downloadHandler(
filename = function() {
paste("Subtype", ".", input$`sub-pictype`, sep = "")
},
content = function(file){
print(file)
ggsave(file,subtype_bubble_plot(.subtype_clean),device = input$`sub-pictype`,width = input$`sub-d_width`,height = input$`sub-d_height`)
}
)
.msg_sub_massage <- NULL
} else {
.msg_sub_massage <- .msg_no_result
output$subtype <- renderPlot({NULL})
output$`sub-picdownload` <- downloadHandler(NULL)
}
# message output ----
output[["de_massage"]] <- renderUI({
tagList(
shiny::tags$p(.msg_de_massage, style = "color:#CD3700")
)
})
output[["sur_massage"]] <- renderUI({
tagList(
shiny::tags$p(.msg_sur_massage, style = "color:#CD3700")
)
})
output[["sub_massage"]] <- renderUI({
tagList(
shiny::tags$p(.msg_sub_massage, style = "color:#CD3700")
)
})
}
# From start analysis -----------------------------------------------------
expr_analysis <- eventReactive(
eventExpr = status$analysis,
ignoreNULL = TRUE,
valueExpr = {
# be sure the following code run after start analysis
if (status$analysis == TRUE) {
# load data expr ----
processing$start_loading_start <- TRUE
# Cancer types value box selection ----------------------------------------
callModule(module = cancerTypesSelect, id = "expr", .sctps = intersect(selected_ctyps(), tcga_data))
# Check box ---------------------------------------------------------------
callModule(module = selectAndAnalysis, id = "expr", .id = "expr")
updateProgressBar(session = session, id = "progressbar", value = 40, status = "danger")
session$onFlushed(function() {progress$expr_loading <- TRUE})
observeEvent(eventExpr = progress$expr_loading, handlerExpr = {
if (progress$expr_loading == TRUE) {
# load data
load_data_expr()
processing$start_loading_end <- TRUE
}
})
observeEvent(processing$start_loading_end, {
if (processing$start_loading_end == TRUE) {
updateProgressBar(session = session, id = "progressbar", value = 70, status = "warning")
session$onFlushed(function() {
progress$expr_calc <- TRUE
})
}
})
observeEvent(eventExpr = progress$expr_calc, handlerExpr = {
if (progress$expr_calc == TRUE) {
.valid_ctps <- intersect(paired_cancer_types, selected_ctyps())
.invalid_ctps <- setdiff(selected_ctyps(), paired_cancer_types)
.msg_valid_ctps <- ""
.msg_invalid_ctps <- ""
if (length(.valid_ctps) > 1) {
.msg_valid_ctps <- glue::glue("only {paste0(.valid_ctps, collapse = ', ')} have paired samples")
} else if (length(.valid_ctps) == 1) {
.msg_valid_ctps <- glue::glue("only {.valid_ctps} has paired samples")
} else{
.msg_valid_ctps <- "no cancer types have paired samples"
}
if (length(.invalid_ctps) > 1) {
.msg_invalid_ctps <- glue::glue("The cancer types {paste0(.invalid_ctps, collapse = ', ')} don't have paired samples.")
} else if (length(.invalid_ctps) == 1) {
.msg_invalid_ctps <- glue::glue("The cancer type {.invalid_ctps} doesn't have paired samples.")
} else {
.msg_invalid_ctps <- ""
}
.msg <- glue::glue("In Tumor vs. Normal module, the analysis is based on paired samples in each cancer types. In your selected cancer types, {.msg_valid_ctps}. {.msg_invalid_ctps}")
.msg_no_result <- glue::glue("No significant result of gene set in your selected cancer types: {selected_ctyps()}.")
shinyBS::createAlert(
session = session, anchorId = "expr-no_gene_set", title = "Information", style = "info",
content = .msg, append = FALSE
)
expr %>%
dplyr::filter(cancer_types %in% paired_cancer_types) %>%
dplyr::filter(cancer_types %in% selected_ctyps()) %>%
dplyr::filter(symbol %in% gene_set$match) ->> expr_clean
expr_survival %>%
dplyr::filter(cancer_types %in% selected_ctyps()) %>%
dplyr::filter(symbol %in% gene_set$match) ->> survival_clean
expr_subtype %>%
dplyr::filter(cancer_types %in% selected_ctyps()) %>%
dplyr::filter(symbol %in% gene_set$match) ->> subtype_clean
print(glue::glue("{paste0(rep('-', 10), collapse = '')} clean data complete @ {Sys.time()} {paste0(rep('-', 10), collapse = '')}"))
# call module
callModule(module = expr_start_analysis, id = "expr", .expr_clean = expr_clean, .survival_clean = survival_clean, .subtype_clean = subtype_clean, .msg = .msg, .msg_no_result=.msg_no_result)
print(glue::glue("{paste0(rep('-', 10), collapse = '')} expr bubble plot complete @ {Sys.time()} {paste0(rep('-', 10), collapse = '')}"))
processing$expr_calc_end <- TRUE
}
})
observeEvent(processing$expr_calc_end, {
if (processing$expr_calc_end == TRUE) {
updateProgressBar(session = session, id = "progressbar", value = 100, status = "info")
progress$progress_end <- TRUE
}
})
}
}
)
observe(expr_analysis())
# source by server.R
# saved as functions_server.R
# Check input gene set ----------------------------------------------------
check_gene_set <- function(.s, status = status, error = error) {
.s %>%
stringr::str_split(pattern = "[^[:alnum:]]+", simplify = TRUE) %>%
.[1, ] %>%
stringr::str_trim(side = "both") -> .ss
#print(.ss)
if (!dplyr::between(length(.ss), 5, 100)) {
error$gene_set <- "The number of genes should be between 5 and 100."
status$trigger <- if (status$trigger == TRUE) FALSE else TRUE
status$gene_set <- FALSE
}
.ss
}
# Validate gene with TCGA gene symbol -------------------------------------
# can get the old version from github
validate_gene_set <- function(.v, user_dir = user_dir, user_logs = user_logs, total_gene_symbol = total_gene_symbol, status = status, error = error, gene_set = gene_set) {
.log_file <- user_logs$gene_set
total_gene_symbol %>%
dplyr::mutate(alias = toupper(alias)) %>%
dplyr::mutate(NCBI_sym = toupper(NCBI_sym))-> total_gene_symbol
.v_dedup <- tibble::tibble(input = .v[.v != ""]) %>% unique() %>%
dplyr::mutate(Up = toupper(input))
.v_dedup %>% dplyr::rename("TCGA_sym" = "Up") %>%
dplyr::inner_join(total_gene_symbol, by ="TCGA_sym") %>%
# dplyr::filter(TCGA_sym %in% .v_dedup$Up) %>%
# .$TCGA_sym %>%
unique() -> .v_tcga
.v_dedup %>% dplyr::rename("GTEX_sym" = "Up") %>%
dplyr::inner_join(total_gene_symbol, by ="GTEX_sym") %>%
#dplyr::filter(GTEX_sym %in% .v_dedup$Up) %>%
#.$NCBI_sym %>%
unique() -> .v_gtex
# gene id not match in gtex data
setdiff(.v_dedup$input,unique(.v_gtex$input)) -> gtex.diff
.v_dedup %>% dplyr::rename("alias" = "Up") %>%
dplyr::filter(input %in% gtex.diff) %>%
dplyr::inner_join(total_gene_symbol, by ="alias") %>%
unique() -> .v_alias.gtex.1
.v_dedup %>% dplyr::rename("NCBI_sym" = "Up") %>%
dplyr::filter(input %in% gtex.diff) %>%
dplyr::inner_join(total_gene_symbol, by ="NCBI_sym") %>%
unique() -> .v_alias.gtex.2
rbind(.v_alias.gtex.1,.v_alias.gtex.2) %>% unique() -> .v_alias.ncbi
# gene id not match in TCGA data
setdiff(.v_dedup$input,unique(.v_tcga$input)) -> tcga.diff
.v_dedup %>% dplyr::rename("alias" = "Up") %>%
dplyr::filter(input %in% tcga.diff) %>%
dplyr::inner_join(total_gene_symbol, by ="alias") %>%
unique() -> .v_alias.tcga.1
.v_dedup %>% dplyr::rename("NCBI_sym" = "Up") %>%
dplyr::filter(input %in% tcga.diff) %>%
dplyr::inner_join(total_gene_symbol, by ="NCBI_sym") %>%
unique() -> .v_alias.tcga.2
rbind(.v_alias.tcga.1,.v_alias.tcga.2) %>% unique() -> .v_alias.tcga
.v_tcga %>%
rbind(.v_gtex) %>%
rbind(.v_alias.tcga) %>%
rbind(.v_alias.ncbi) %>%
unique() -> match_all
gene_set$match <- match_all %>% dplyr::filter(!is.na(TCGA_sym)) %>% .$TCGA_sym %>% unique()
gene_set$match.gtex <- match_all %>% dplyr::filter(!is.na(GTEX_sym)) %>% .$GTEX_sym %>% unique()
.non_match <- match_all %>%
dplyr::select(input) %>%
unique() %>%
tidyr::drop_na() %>%
dplyr::mutate(match = "TRUE") %>%
dplyr::right_join(.v_dedup,by = "input") %>%
dplyr::filter(is.na(match)) %>%
.$input
gene_set$non_match <- .non_match
gene_set$n_match <- length(match_all %>%
dplyr::select(input) %>%
unique() %>% .$input)
gene_set$n_non_match <- length(.non_match)
gene_set$n_total <- gene_set$n_match + gene_set$n_non_match
if (length(gene_set$match) < 5) {
error$gene_set <- "Please input at least five valid gene symbol."
status$trigger <- if (status$trigger == TRUE) FALSE else TRUE
status$gene_set <- FALSE
}
.log <- c(
glue::glue("{paste0(rep('-', 10), collapse = '')} Notice: Input total gene set number is {length(gene_set$n_total)} {paste0(rep('-', 10), collapse = '')}"),
glue::glue("{paste0(rep('-', 10), collapse = '')} Notice: Unique gene set number is {length(.v_dedup)} {paste0(rep('-', 10), collapse = '')}"),
glue::glue("#Total input gene set: {paste0(.v, collapse = ', ')}"),
glue::glue("#Validated genes: {paste0(gene_set$match, collapse = ', ')}"),
glue::glue("#Invalidated genes: {paste0(gene_set$non_match, collapse = ', ')}")
)
write(x = .log, file = .log_file, append = TRUE)
}
# older version of match gene set
# validate_gene_set <- function(.v, user_dir = user_dir, user_logs = user_logs, total_gene_symbol = total_gene_symbol, status = status, error = error, gene_set = gene_set) {
# .log_file <- user_logs$gene_set
#
#
# .v_dedup <- .v[.v != ""] %>% unique() %>% sapply(FUN = tolower, USE.NAMES = FALSE)
# .v_dedup %in% names(total_gene_symbol) -> .inter
#
#
# gene_set$match <- total_gene_symbol[.v_dedup[.inter]]
# gene_set$non_match <- .v[!.inter] #.v_dedup[!.inter]
# gene_set$n_match <- length(total_gene_symbol[.v_dedup[.inter]])
# gene_set$n_non_match <- length(.v_dedup[!.inter])
# gene_set$n_total <- length(total_gene_symbol[.v_dedup[.inter]]) + length(.v_dedup[!.inter])
#
# if (length(gene_set$match) < 5) {
# error$gene_set <- "Please input at least five valid gene symbol."
# status$trigger <- if (status$trigger == TRUE) FALSE else TRUE
# status$gene_set <- FALSE
# }
#
# .log <- c(
# glue::glue("{paste0(rep('-', 10), collapse = '')} Notice: Input total gene set number is {length(gene_set$n_total)} {paste0(rep('-', 10), collapse = '')}"),
# glue::glue("{paste0(rep('-', 10), collapse = '')} Notice: Unique gene set number is {length(.v_dedup)} {paste0(rep('-', 10), collapse = '')}"),
# glue::glue("#Total input gene set: {paste0(.v, collapse = ', ')}"),
# glue::glue("#Validated genes: {paste0(gene_set$match, collapse = ', ')}"),
# glue::glue("#Invalidated genes: {paste0(gene_set$non_match, collapse = ', ')}")
# )
# write(x = .log, file = .log_file, append = TRUE)
# }
# cnv bar data prepare ----------------------------------------------------
# threshold cnv -----------------------------------------------------------
fn_get_amplitue_threshold <- function(.x) {
tibble::tibble(
a_total = sum(.x > 0) / length(.x),
d_total = sum(.x < 0) / length(.x),
a_homo = sum(.x == 2) / length(.x),
d_homo = sum(.x == -2) / length(.x),
a_hete = sum(.x == 1) / length(.x),
d_hete = sum(.x == -1) / length(.x)
)
}
# get cnv percent ---------------------------------------------------------
fn_get_ad <- function(.d) {
.d %>%
unlist(use.name = F) %>%
fn_get_amplitue_threshold()
}
fn_get_percent <- function(cancer_types, filter_cnv) {
filter_cnv %>%
tidyr::nest(-symbol) %>%
dplyr::mutate(ad = purrr::map(data, .f = fn_get_ad)) %>%
dplyr::select(-data) %>%
tidyr::unnest(ad) %>%
tibble::add_column(cancer_types = cancer_types, .before = 1)
}
fn_cnv_percecnt <- function(data) {
data %>%
dplyr::mutate(rs = purrr::map2(cancer_types, filter_cnv, fn_get_percent)) %>%
dplyr::collect() %>%
dplyr::as_tibble() %>%
dplyr::ungroup() %>%
dplyr::select(-cancer_types, -filter_cnv) %>%
tidyr::unnest(rs) -> gene_list_cnv_per
}
fn_gen_combined_core_atg <- function(cancer_types, filter_cnv, g_list, n) {
# cancer_types <- "KIRC"
# filter_cnv <- gene_list_cancer_cnv$filter_cnv[[1]]
# g_list <-gene_list
# n=1
filter_cnv %>%
dplyr::filter(symbol %in% g_list) %>%
tidyr::drop_na() %>%
tidyr::gather(key = barcode, value = gistic, -symbol) %>%
tidyr::spread(key = symbol, value = gistic) %>%
dplyr::select(-barcode) -> .d
n_sample <- nrow(.d)
.d %>%
dplyr::filter_all(.vars_predicate = dplyr::any_vars(. == -n)) %>%
nrow() -> .del
.d %>%
dplyr::filter_all(.vars_predicate = dplyr::any_vars(. == -n)) %>%
dplyr::filter_all(.vars_predicate = dplyr::any_vars(. == n)) %>%
nrow() -> .sub_d
.d %>%
dplyr::filter_all(.vars_predicate = dplyr::any_vars(. == n)) %>%
nrow() -> .amp
.d %>%
dplyr::filter_all(.vars_predicate = dplyr::any_vars(. == n)) %>%
dplyr::filter_all(.vars_predicate = dplyr::any_vars(. == -n)) %>%
nrow() -> .sub_a
tibble::tibble(del_a = .del / n_sample, del_s = (.del - .sub_d) / n_sample, amp_a = .amp / n_sample, amp_s = (.amp - .sub_a) / n_sample)
}
# extract gene set from TCGA data -----------------------------------------
filter_gene_list <- function(.x, gene_list) {
.x %>%
dplyr::filter(symbol %in% gene_list)
# gene_list %>%
# dplyr::select(symbol) %>%
# dplyr::left_join(.x, by = "symbol")
}
# get exclusive cnv -------------------------------------------------------
fn_cnv_exclusive <- function(V1, V2, .data, cancer_types) {
# .data <- filter_cnv
# V1 <- 'TP53'
# V2 <- 'EZH2'
.data %>%
dplyr::filter(symbol %in% c(V1, V2)) %>%
tidyr::gather(key = barcode, value = gistic, -symbol) %>%
tidyr::spread(key = symbol, value = gistic) %>%
dplyr::select(-barcode) -> .d
.g_name <- colnames(.d)
# colnames(.d) <- c("A", "B")
name <- paste(c(cancer_types, .g_name), collapse = "_")
.d %>%
dplyr::filter_all(.vars_predicate = dplyr::all_vars(. == 0)) %>%
nrow() -> nn
.d %>%
dplyr::filter_all(.vars_predicate = dplyr::all_vars(. != 0)) %>%
nrow() -> aa
.d %>%
dplyr::filter_all(.vars_predicate = dplyr::any_vars(. != 0)) %>%
dplyr::filter_all(.vars_predicate = dplyr::any_vars(. == 0)) -> .d_an
sum(.d_an %>% dplyr::pull(1) != 0) -> an
sum(.d_an %>% dplyr::pull(2) != 0) -> na
c(nn = nn, an = an, na = na, aa = aa) %>%
cometExactTest::comet_exact_test(mutmatplot = F) -> p_val
tibble::tibble(te = name, nn = nn, an = an, na = na, aa = aa, p_val = p_val)
}
fn_cnv_mutal_exclusive <- function(cancer_types, filter_cnv, cluster) {
# cancer_types <- te$cancer_types
# filter_cnv <- te$filter_cnv[[1]]
filter_cnv %>%
dplyr::pull(symbol) %>%
combn(m = 2) %>%
t() %>%
dplyr::as_data_frame() -> .gene_pairs
.gene_pairs %>%
multidplyr::partition(cluster = cluster) %>%
multidplyr::cluster_library("magrittr") %>%
multidplyr::cluster_assign_value("fn_cnv_exclusive", fn_cnv_exclusive) %>%
multidplyr::cluster_assign_value("filter_cnv", filter_cnv) %>%
multidplyr::cluster_assign_value("cancer_types", cancer_types) %>%
dplyr::mutate(rs = purrr::map2(V1, V2, .f = fn_cnv_exclusive, .data = filter_cnv, cancer_types = cancer_types)) %>%
dplyr::collect() %>%
dplyr::as_tibble() %>%
dplyr::ungroup() %>%
dplyr::select(-PARTITION_ID) %>%
dplyr::select(rs) %>%
tidyr::unnest() %>%
tidyr::separate(col = te, into = c("cancer_types", "g1", "g2")) -> .gene_pairs_pval
.gene_pairs_pval %>%
dplyr::mutate(fdr = p.adjust(p_val, method = "fdr"))
}
# rppa line contact faction -----------------------------------------------
get_rppa_text <- function(data) {
data %>%
dplyr::pull(symbol) %>%
unique() -> gene.text
data %>%
dplyr::pull(cancer_types) %>%
unique() -> cancer.text
data %>%
dplyr::pull(pathway) %>%
unique() -> pathway.text
c.text <- data.frame(x = 0.5, y = 1, text = "test", type = "test")
g.l <- data$symbol %>% unique() %>% length()
c.l <- data$cancer_types %>% unique() %>% length()
p.l <- data$pathway %>% unique() %>% length()
# condition 1: gene is more -----------------------------------------------
if (g.l >= c.l & g.l >= p.l) {
for (i in 1:length(gene.text)) {
data.frame(x = 4, y = 2 * i - 1, text = gene.text[i], type = "gene") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
c.text %>%
dplyr::filter(type == "gene") %>%
dplyr::select(y) %>%
max() -> g.m
g.m / c.l -> c.i
for (i in 1:length(cancer.text)) {
data.frame(x = 1, y = i * c.i - 1, text = cancer.text[i], type = "cancer") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
g.m / 10 -> p.i
for (i in 1:length(pathway.text)) {
data.frame(x = 7, y = i * p.i - 1, text = pathway.text[i], type = "pathway") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
}
# condition 2: cancer is more ---------------------------------------------
if (c.l >= g.l & c.l >= p.l) {
for (i in 1:length(cancer.text)) {
data.frame(x = 1, y = 2 * i - 1, text = cancer.text[i], type = "cancer") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
c.text %>%
dplyr::filter(type == "cancer") %>%
dplyr::select(y) %>%
max() -> c.m
c.m / g.l -> g.i
for (i in 1:length(gene.text)) {
data.frame(x = 4, y = i * g.i - 1, text = gene.text[i], type = "gene") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
c.m / 10 -> p.i
for (i in 1:length(pathway.text)) {
data.frame(x = 7, y = i * p.i - 1, text = pathway.text[i], type = "pathway") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
}
# condition 3: pathway is more --------------------------------------------
if (p.l >= c.l & p.l >= g.l) {
for (i in 1:length(pathway.text)) {
data.frame(x = 7, y = i * 2 - 1, text = pathway.text[i], type = "pathway") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
c.text %>%
dplyr::filter(type == "pathway") %>%
dplyr::select(y) %>%
max() -> p.m
p.m / c.l -> c.i
for (i in 1:length(cancer.text)) {
data.frame(x = 1, y = i * c.i - 1, text = cancer.text[i], type = "cancer") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
p.m / g.l -> g.i
for (i in 1:length(gene.text)) {
data.frame(x = 4, y = i * g.i - 1, text = gene.text[i], type = "gene") -> tmp.text
rbind(c.text, tmp.text) -> c.text
}
}
return(c.text[-1, ])
}
get_rppa_seg <- function(data,cancer_text) {
# name <- c("x1","y1","x2","y2","Cancer","Regulation")
# print(n)
data[1,1] %>% as.character() -> cancer
data[1,2] %>% as.character() -> gene
data[1,3] %>% as.character() -> pathway
data[1,4] %>% as.numeric() -> diff
if (diff > 0) {
line_type <- "Activate"
} else {
line_type <- "Inhibit"
}
cancer_text %>%
dplyr::filter(text %in% gene) %>%
dplyr::select(x, y) %>%
dplyr::mutate(x = x - 0.5) -> g1.pos
cancer_text %>%
dplyr::filter(text %in% gene) %>%
dplyr::select(x, y) %>%
dplyr::mutate(x = x + 0.5) -> g2.pos
cancer_text %>%
dplyr::filter(text %in% cancer) %>%
dplyr::select(x, y) -> c.pos
cancer_text %>%
dplyr::filter(text %in% pathway) %>%
dplyr::select(x, y) -> p.pos
.d_seq_tmp1 <- data.frame(x1 = c.pos$x, y1 = c.pos$y, x2 = g1.pos$x, y2 = g1.pos$y, Cancer = cancer, Regulation = "Activate")
.d_seq_tmp2 <- data.frame(x1 = g2.pos$x, y1 = g2.pos$y, x2 = p.pos$x, y2 = p.pos$y, Cancer = cancer, Regulation = line_type)
rbind(.d_seq_tmp1,.d_seq_tmp2) -> .d_seg
.d_seg$Cancer <- .d_seg$Cancer %>% as.character()
.d_seg$Regulation <- .d_seg$Regulation %>% as.character()
tibble::as_tibble(.d_seg)
}
get_rppa_seg1 <- function(cancer_text, data) {
.d_seg <- data.frame(x1 = 0, y1 = 0, x2 = 0, y2 = 0, Cancer = "test", Regulation = "test")
nrow(data) -> n
for (i in 1:n) {
data[i, 1] -> cancer
data[i, 2] -> gene
data[i, 3] -> pathway
data[i, 4] -> diff
if (diff > 0) {
line_type <- "Activate"
} else {
line_type <- "Inhibit"
}
cancer_text %>%
dplyr::filter(text %in% gene) %>%
dplyr::select(x, y) %>%
dplyr::mutate(x = x - 0.5) -> g1.pos
cancer_text %>%
dplyr::filter(text %in% gene) %>%
dplyr::select(x, y) %>%
dplyr::mutate(x = x + 0.5) -> g2.pos
cancer_text %>%
dplyr::filter(text %in% cancer) %>%
dplyr::select(x, y) -> c.pos
cancer_text %>%
dplyr::filter(text %in% pathway) %>%
dplyr::select(x, y) -> p.pos
.d_seq_tmp1 <- data.frame(x1 = c.pos$x, y1 = c.pos$y, x2 = g1.pos$x, y2 = g1.pos$y, Cancer = cancer$cancer_types, Regulation = "Activate")
.d_seq_tmp2 <- data.frame(x1 = g2.pos$x, y1 = g2.pos$y, x2 = p.pos$x, y2 = p.pos$y, Cancer = cancer$cancer_types, Regulation = line_type)
rbind(.d_seg, .d_seq_tmp1) %>% rbind(.d_seq_tmp2) -> .d_seg
}
.d_seg[-1, ] %>%
unique() -> .d_seg
return(.d_seg)
}
# maftools subsetMaf edit to suit our addition. ---------------------------
my_subsetMaf <- function (maf, tsb = NULL, genes = NULL, fields = NULL, cancer = NULL,
mafObj = FALSE, includeSyn = TRUE, isTCGA = FALSE)
{
maf.silent <- [email protected]
maf.dat <- maf@data
maf.anno <- [email protected]
if (!is.null(tsb)) {
if (isTCGA) {
tsb = substr(x = tsb, start = 1, stop = 12)
}
maf.dat = maf.dat[Tumor_Sample_Barcode %in% tsb, ]
maf.silent = maf.silent[Tumor_Sample_Barcode %in% tsb,
]
}
if (!is.null(genes)) {
maf.dat = maf.dat[Hugo_Symbol %in% genes, ]
maf.silent = maf.silent[Hugo_Symbol %in% genes, ]
}
if (!is.null(cancer)) {
# maf.dat = maf.dat[eval(parse(text = query))]
maf.dat = maf.dat[Cancer_Types %in% cancer,]
# maf.silent = maf.silent[eval(parse(text = query))]
maf.silent = maf.silent[Cancer_Types %in% cancer,]
}
default.fields = c("Hugo_Symbol", "Chromosome", "Start_Position",
"End_Position", "Reference_Allele", "Tumor_Seq_Allele2",
"Variant_Classification", "Variant_Type", "Tumor_Sample_Barcode")
if (!is.null(fields)) {
default.fields = unique(c(default.fields, fields))
if (length(default.fields[!default.fields %in% colnames(maf.dat)]) >
0) {
message("Missing fields. Ignoring them.. ")
print(default.fields[!default.fields %in% colnames(maf.dat)])
default.fields = default.fields[default.fields %in%
colnames(maf.dat)]
}
maf.dat = maf.dat[, default.fields, with = FALSE]
maf.silent = maf.silent[, default.fields, with = FALSE]
}
if (mafObj) {
maf.silent = droplevels.data.frame(maf.silent)
maf.dat = droplevels.data.frame(maf.dat)
maf.anno = droplevels.data.frame(maf.anno)
mafSummary = my_summarizeMaf(maf.dat, chatty = FALSE, anno = maf.anno)
m = my_MAF(data = maf.dat, variants.per.sample = mafSummary$variants.per.sample,
variant.type.summary = mafSummary$variant.type.summary,
variant.classification.summary = mafSummary$variant.classification.summary,
gene.summary = mafSummary$gene.summary, summary = mafSummary$summary,
maf.silent = maf.silent, clinical.data = mafSummary$sample.anno)
return(m)
}
else {
if (includeSyn) {
return(rbind(maf.dat, maf.silent, use.names = TRUE,
fill = TRUE))
}
else {
return(maf.dat)
}
}
}
my_summarizeMaf = function(maf, anno = NULL, chatty = TRUE){
if('NCBI_Build' %in% colnames(maf)){
NCBI_Build = unique(maf[!Variant_Type %in% 'CNV', NCBI_Build])
NCBI_Build = NCBI_Build[!is.na(NCBI_Build)]
if(chatty){
if(length(NCBI_Build) > 1){
message('NOTE: Mutiple reference builds found!')
NCBI_Build = do.call(paste, c(as.list(NCBI_Build), sep=";"))
message(NCBI_Build)
}
}
}else{
NCBI_Build = NA
}
if('Center' %in% colnames(maf)){
Center = unique(maf[!Variant_Type %in% 'CNV', Center])
#Center = Center[is.na(Center)]
if(length(Center) > 1){
Center = do.call(paste, c(as.list(Center), sep=";"))
if(chatty){
message('Mutiple centers found.')
print(Center)
}
}
}else{
Center = NA
}
#nGenes
nGenes = length(unique(maf[,Hugo_Symbol]))
#Top 20 FLAGS - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267152/
flags = c("TTN", "MUC16", "OBSCN", "AHNAK2", "SYNE1", "FLG", "MUC5B",
"DNAH17", "PLEC", "DST", "SYNE2", "NEB", "HSPG2", "LAMA5", "AHNAK",
"HMCN1", "USH2A", "DNAH11", "MACF1", "MUC17")
#Variants per TSB
tsb = maf[,.N, Tumor_Sample_Barcode]
colnames(tsb)[2] = 'Variants'
tsb = tsb[order(tsb$Variants, decreasing = TRUE),]
#summarise and casting by 'Variant_Classification'
vc = maf[,.N, .(Tumor_Sample_Barcode, Variant_Classification )]
vc.cast = data.table::dcast(data = vc, formula = Tumor_Sample_Barcode ~ Variant_Classification, fill = 0, value.var = 'N')
if(any(colnames(vc.cast) %in% c('Amp', 'Del'))){
vc.cast.cnv = vc.cast[,c('Tumor_Sample_Barcode', colnames(vc.cast)[colnames(vc.cast) %in% c('Amp', 'Del')]), with =FALSE]
vc.cast.cnv$CNV_total = rowSums(vc.cast.cnv[,2:ncol(vc.cast.cnv)], na.rm = TRUE)
vc.cast = vc.cast[,!colnames(vc.cast)[colnames(vc.cast) %in% c('Amp', 'Del')], with =FALSE]
vc.cast[,total:=rowSums(vc.cast[,2:ncol(vc.cast), with = FALSE])]
vc.cast = merge(vc.cast, vc.cast.cnv, by = 'Tumor_Sample_Barcode', all = TRUE)[order(total, CNV_total, decreasing = TRUE)]
vc.mean = as.numeric(as.character(c(NA, NA, NA, NA, apply(vc.cast[,2:ncol(vc.cast), with = FALSE], 2, mean))))
vc.median = as.numeric(as.character(c(NA, NA, NA, NA, apply(vc.cast[,2:ncol(vc.cast), with = FALSE], 2, median))))
}else{
vc.cast = vc.cast[,total:=rowSums(vc.cast[,2:ncol(vc.cast), with = FALSE])][order(total, decreasing = TRUE)]
vc.mean = round(as.numeric(as.character(c(NA, NA, NA, NA, apply(vc.cast[,2:ncol(vc.cast), with = FALSE], 2, mean)))), 3)
vc.median = round(as.numeric(as.character(c(NA, NA, NA, NA, apply(vc.cast[,2:ncol(vc.cast), with = FALSE], 2, median)))), 3)
}
#summarise and casting by 'Variant_Type'
vt = maf[,.N, .(Tumor_Sample_Barcode, Variant_Type )]
vt.cast = data.table::dcast(data = vt, formula = Tumor_Sample_Barcode ~ Variant_Type, value.var = 'N', fill = 0)