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RNAseq_function.R
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# function for RNA-seq data ---------------------------------------------------
# pre-def
CON <- c(1,2, 14,15, 27,28, 40,41)
AS <- c(1,2,9,5,10, 14,15,22,18,23, 27,28,35,31,36, 40,41,48,44,49)
only_AS <- c(9,5,10, 22,18,23, 35,31,36, 48,44,49)
CO <- c(1,2,9,6,11, 14,15,22,19,24, 27,28,35,32,37, 40,41,48,45,50)
only_CO <- c(9,6,11, 22,19,24, 35,32,37, 48,45,50)
LCD <- c(1,2,9,7,12, 14,15,22,20,25, 27,28,35,33,38, 40,41,48,46,51)
only_LCD <- c(9,7,12, 22,20,25, 35,33,38, 48,46,51)
HCD <- c(1,2,9,8,13, 14,15,22,21,26, 27,28,35,34,39, 40,41,48,47,52)
only_HCD <- c(9,8,13, 22,21,26, 35,34,39, 48,47,52)
CD <- c(1,2,9,7,8,12,13, 14,15,22,20,21,25,26, 27,28,35,33,34,38,39, 40,41,48,46,47,51,52)
only_CD <- c(9,7,8,12,13, 22,20,21,25,26, 35,33,34,38,39, 48,46,47,51,52)
BAP <- c(1,2,9,10,11,12,13, 14,15,22,23,24,25,26, 27,28,35,36,37,38,39, 40,41,48,49,50,51,52)
only_BAP <- c(1,2,9, 14,15,22, 27,28,35, 40,41,48)
# color code
col_W <- "grey40"
col_L <- "#5FA9C6"
col_D <- "#BAE18D"
col_Y <- "#5FA751"
col_CON <- "gainsboro"
col_DMS <- "darkgray"
col_AZA <- "#FFF0F5"
col_DAC <- "#E6E6FA"
col_AS <- "gold"
col_CO <- "#F4A9A1"
col_LCD <- "#ACCCFB"
col_HCD <- "#4B6DCA"
col_BAP <- "#AA7709"
col_AS_BAP <- "#F29E49"
col_CO_BAP <- "#D9573E"
col_LCD_BAP <- "#D8BFF8"
col_HCD_BAP <- "#6A5F9A"
# abb ---------------------------------------------------------------------
abb <- function(name,type="group"){
if(!(type%in%c("file","group"))){
stop("'type' must be 'file' or 'group'")
}
if(type=="file"){
df <- name_df %>% filter(file_name==name) %>% pull(abbreviate)
} else{
df <- name_df %>% filter(abbreviate==name) %>% pull(abbreviate)
}
return(df)
}
# force_list --------------------------------------------------------------
force_list <- function(x){
input_string <- x
output_list <- unlist(strsplit(input_string, split = "/"))
return(output_list)
}
# # filter_gene -------------------------------------------------------------
# filter_gene <- function(file_name){
# df <- readxl::read_xlsx(file.path(data_path, file_name))
# sum <- df[,c(1,10:11)] %>% column_to_rownames("ENSEMBL") %>%
# setNames(c("group1","group2")) %>%
# mutate("avg" = (group1 + group2)/2) %>%
# mutate("g1"=group1 > 1) %>%
# mutate("g2"=group2 > 1) %>%
# filter(avg > 2)
# df <- df %>% filter(ENSEMBL %in% rownames(sum))
# return(df)
# }
# draw_heatmap ------------------------------------------------------------
draw_heatmap <- function(file=NULL,
log_crit=3,
groups="ALL",
show_row_names = FALSE,
log_scale=T,
row_km=T,
km=0,
return_cluster=F,
title="",
list=NULL
){
if(!(groups%in%c("AS","CO","LCD","HCD","CD","BAP","only_AS","only_CO",
"only_LCD","only_HCD","only_CD","CON","ALL"))){
stop('"groups" must be "AS","CO","LCD","HCD","CD","BAP","only_AS","only_CO",
"only_LCD","only_HCD","only_CD","CON" or "ALL".')
}
if(log_scale==T){
CPM_df <- mylogCPM
} else {
CPM_df <- myCPM
}
mat <- CPM_df %>% as.data.frame() %>% rownames_to_column("ENSEMBL") %>%
left_join(.,gene_df, by="ENSEMBL") %>%
group_by(SYMBOL) %>%
summarize(across(where(is.numeric), sum)) %>%
column_to_rownames(., var = "SYMBOL") %>% na.omit()
if(!is.null(file)){
cat(c(" -> load data from",file.path(data_path, file),"\n"))
data <- readxl::read_xlsx(file.path(data_path, file)) # 讀檔
data <- data %>% filter(abs(M)>log_crit,
!(SYMBOL%in% c("havana","ensembl_havana","havana_tagene")))
cat(c(" -> log2FC criteria is", log_crit,"\n"))
deg <- data$SYMBOL
mat <- mat %>% filter(rownames(.)%in% deg) # 抓出差異ensembl id
if(!is.null(list)){
mat <- mat %>% filter(rownames(.)%in% list)
cat(" -> filter file DEGs in 'list'.\n")
}
} else{
if(!is.null(list)){
mat <- mat %>% filter(rownames(.)%in% list)
cat(" -> filter genes in 'list'.\n")
} else {
stop(" error: both 'file' and 'list' missing.")
}
}
# select groups
if(groups == "AS"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[AS]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP,
'AS' = col_AS, 'AS_BAP' = col_AS_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "CO"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[CO]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP,
'CO' = col_CO, 'CO_BAP' = col_CO_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "CD"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[CD]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP,
'LCD' = col_LCD, 'HCD' = col_HCD,
'LCD_BAP' = col_LCD_BAP, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "BAP"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[BAP]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP,
'AS_BAP' = col_AS_BAP, 'CO_BAP' = col_CO_BAP,
'LCD_BAP' = col_LCD_BAP, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "ALL"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'AZA' = col_AZA, 'DAC' = col_DAC,
'BAP' = col_BAP,'AS' = col_AS, 'CO' = col_CO,
'LCD' = col_LCD, 'HCD' = col_HCD,
'AS_BAP' = col_AS_BAP, 'CO_BAP' = col_CO_BAP,
'LCD_BAP' = col_LCD_BAP, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "only_CO"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_CO]))
ann <- list(agent = c('BAP' = col_BAP,'CO' = col_CO, 'CO_BAP' = col_CO_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "only_AS"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_AS]))
ann <- list(agent = c('BAP' = col_BAP, 'AS' = col_AS, 'AS_BAP' = col_AS_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "only_LCD"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_LCD]))
ann <- list(agent = c('BAP' = col_BAP, 'LCD' = col_LCD, 'LCD_BAP' = col_LCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "only_HCD"){
data_mat <- mat %>% dplyr::select(name_df$abbreviate[only_HCD])
ann <- list(agent = c('BAP' = col_BAP,'HCD' = col_HCD, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "only_CD"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_CD]))
ann <- list(agent = c('BAP' = col_BAP,'LCD' = col_LCD, 'HCD' = col_HCD,
'LCD_BAP' = col_LCD_BAP, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else if(groups == "CON"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[CON]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'YAP' = col_Y))
} else{
return(NULL)
}
mat_scale <- data_mat %>% t() %>% scale(scale = T) %>% t() %>% as.matrix() %>% na.omit()
### heat-map argument
col <- colnames(mat_scale)
# agent name
agent <- factor (
str_replace_all(col, c("^W_|^L_|^D_|^Y_"='')),
levels=c('CON','DMS',"AZA","DAC","BAP",'AS',"CO","LCD","HCD",
"AS_BAP","CO_BAP","LCD_BAP","HCD_BAP"))
# clone name
clone <- factor(
str_replace_all(str_sub(col, 1,1), c("W" = "WT", "L" = "L858R",
"D" = "Del19", "Y" = "YAP")),
levels=c('WT','L858R',"Del19","YAP"))
# draw hp
ha <- HeatmapAnnotation(clone = clone,
agent = agent,
col = ann)
heat <- ComplexHeatmap::Heatmap(mat_scale, top_annotation = ha, cluster_columns = F,
show_row_names = show_row_names,
show_column_names = F, row_title = title,
name = "Z-score",row_km = km)
cat(c( " ->", nrow(mat_scale), "genes"))
if(return_cluster==T){
group = kmeans((mat_scale), centers = km)$cluster
return(group)
} else{
heat
}
}
# draw_from_list ----------------------------------------------------------
draw_from_list <- function(list,
groups="ALL",
id="SYMBOL",
row_cluster=TRUE,
anno=TRUE,
title="",
show_row_names = TRUE, # ENSEMBL/SYMBOL
label_num=F,
log_scale=T,
col_cluster=FALSE,
row_km = 0,
filter=T,
row_title_rot = 90
){
if(!(groups%in%c("AS","CO","LCD","HCD","CD","BAP","only_AS","only_CO",
"only_LCD","only_HCD","only_CD","only_BAP","CON","ALL"))){
stop('"groups" must be "AS","CO","LCD","HCD","CD","BAP","only_AS","only_CO",
"only_LCD","only_HCD","only_CD","only_BAP,"CON" or "ALL".')
}
if(log_scale==T){
CPM_df <- mylogCPM %>% as.data.frame()
} else {
CPM_df <- myCPM %>% as.data.frame()
}
cat(c(" -> input list with",length(list),"genes","\n"))
if(id=="ENSEMBL"){
list <- list
if(filter==F){
mat <- CPM_df %>%
mutate(.,ENSEMBL=rownames(CPM_df)) %>%
filter(.,rownames(CPM_df) %in% list)
} else{
mat <- CPM_df %>%
mutate(.,ENSEMBL=rownames(CPM_df)) %>%
left_join(.,gene_df, by="ENSEMBL") %>%
filter(.,rownames(CPM_df) %in% list,
!(SYMBOL%in% c("havana","ensembl_havana","havana_tagene"))) %>%
group_by(SYMBOL) %>%
summarize(across(where(is.numeric), sum)) %>%
column_to_rownames(., var = "SYMBOL")
}
}else if(id=="SYMBOL"){
list <- list
mat <- CPM_df %>%
mutate(.,ENSEMBL=rownames(CPM_df)) %>%
left_join(.,gene_df, by="ENSEMBL") %>%
group_by(SYMBOL) %>%
summarize(across(where(is.numeric), sum)) %>%
filter(.,SYMBOL %in% list,
!(SYMBOL %in% c("havana","ensembl_havana","havana_tagene"))) %>%
column_to_rownames(.,var="SYMBOL")
}else{
cat(c("<- error: check gene id", "\n"))
return(NULL)
}
cat(c(" -> get",length(rownames(mat)),"genes matrix. Scaling and plotting.","\n"))
# select groups
if(groups == "AS"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[AS]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP,
'AS' = col_AS, 'AS_BAP' = col_AS_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "CO"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[CO]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP,
'CO' = col_CO, 'CO_BAP' = col_CO_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "CD"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[CD]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP,
'LCD' = col_LCD, 'HCD' = col_HCD,
'LCD_BAP' = col_LCD_BAP, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "BAP"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[BAP]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP,
'AS_BAP' = col_AS_BAP, 'CO_BAP' = col_CO_BAP,
'LCD_BAP' = col_LCD_BAP, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "only_BAP"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_BAP]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'BAP' = col_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "ALL"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS, 'AZA' = col_AZA, 'DAC' = col_DAC,
'BAP' = col_BAP,'AS' = col_AS, 'CO' = col_CO,
'LCD' = col_LCD, 'HCD' = col_HCD,
'AS_BAP' = col_AS_BAP, 'CO_BAP' = col_CO_BAP,
'LCD_BAP' = col_LCD_BAP, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "only_CO"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_CO]))
ann <- list(agent = c('BAP' = col_BAP,'CO' = col_CO, 'CO_BAP' = col_CO_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "only_AS"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_AS]))
ann <- list(agent = c('BAP' = col_BAP, 'AS' = col_AS, 'AS_BAP' = col_AS_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "only_LCD"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_LCD]))
ann <- list(agent = c('BAP' = col_BAP, 'LCD' = col_LCD, 'LCD_BAP' = col_LCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "only_HCD"){
data_mat <- mat %>% dplyr::select(name_df$abbreviate[only_HCD])
ann <- list(agent = c('BAP' = col_BAP,'HCD' = col_HCD, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "only_CD"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[only_CD]))
ann <- list(agent = c('BAP' = col_BAP,'LCD' = col_LCD, 'HCD' = col_HCD,
'LCD_BAP' = col_LCD_BAP, 'HCD_BAP' = col_HCD_BAP),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else if(groups == "CON"){
data_mat <- mat %>% dplyr::select(all_of(name_df$abbreviate[CON]))
ann <- list(agent = c('CON' = col_CON, 'DMS' = col_DMS),
clone = c('WT' = col_W, 'L858R' = col_L,
'Del19' = col_D, 'Del19/YAP1' = col_Y))
} else{
return(NULL)
}
# scaling
mat_scale <- data_mat %>% t() %>% scale() %>% t() %>% as.matrix() %>% na.omit()
# color
col_fun = colorRamp2(c(-2, 0, 2), c("blue", "white", "red"))
# heat-map argument
col <- colnames(mat_scale)
# agent name
agent <- factor(
str_replace_all(col, c("^W_|^L_|^D_|^Y_"='')),
levels=c('CON','DMS',"AZA","DAC","BAP",'AS',"CO","LCD","HCD",
"AS_BAP","CO_BAP","LCD_BAP","HCD_BAP"))
# clone name
clone <- factor(
str_replace_all(str_sub(col, 1,1), c("W" = "WT", "L" = "L858R",
"D" = "Del19", "Y" = "Del19/YAP1")),
levels=c('WT','L858R',"Del19","Del19/YAP1"))
# draw hp
ha <- HeatmapAnnotation(clone = clone,
agent = agent,
col = ann)
if(anno==T){
if(label_num==T){
ComplexHeatmap::Heatmap(mat_scale, top_annotation = ha,
cluster_columns = col_cluster,
show_row_names = show_row_names,
show_column_names = F,cluster_rows = row_cluster,
col = col_fun, row_km = row_km ,
name = "Z-score", row_title = title, row_title_rot = row_title_rot,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.1f", mat_scale[i, j]), x, y, gp = gpar(fontsize = 10))
})
}else{
ComplexHeatmap::Heatmap(mat_scale, top_annotation = ha,
cluster_columns = col_cluster, row_title_rot = row_title_rot,
show_row_names = show_row_names,col = col_fun,
show_column_names = F,cluster_rows = row_cluster,
name = "Z-score", row_title = title,row_km = row_km
)
}
} else{
if(label_num==T){
ComplexHeatmap::Heatmap(mat_scale, cluster_columns = col_cluster, row_title_rot = row_title_rot,
show_row_names = show_row_names,col = col_fun,
show_column_names = F,cluster_rows = row_cluster,
name = "Z-score", row_title = title,row_km = row_km ,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.1f", mat_scale[i, j]), x, y, gp = gpar(fontsize = 10))
})
}else{
ComplexHeatmap::Heatmap(mat_scale, cluster_columns = col_cluster, row_title_rot = row_title_rot,
show_row_names = show_row_names,col = col_fun,
show_column_names = F,cluster_rows = row_cluster,
name = "Z-score", row_title = title,row_km = row_km
)
}
}
}
# get_deg --------------------------------------------------------
get_deg <- function(file=file,
log_crit = c(1,-1),
dir="all", # all/up/down
type="SYMBOL",
top="all"
){
# 檢查 type 是否有效
if (!(type %in% c("ENTREZID", "ENSEMBL", "SYMBOL"))) {
stop("Invalid type. Allowed values are 'ENTREZID','ENSEMBL'or 'SYMBOL'.")
}
cat(c(" -> load data from", file,"\n"))
raw_data <- readxl::read_xlsx(file.path(data_path, file)) # 讀檔
# 處理資料
data <- raw_data %>%
as.data.frame() %>%
filter(geneBiotype == "protein_coding") %>%
dplyr::select(!!sym(type), M) %>%
group_by(!!sym(type)) %>%
summarize(across(where(is.numeric), sum))
if(top=="all"){
if(dir=="all"){
data <- data %>% filter(M >log_crit[1]| M < log_crit[2])
cat(c(" -> abs(log2FC) larger than", log_crit[1],"->",nrow(data),"genes.\n"))
}else if(dir=="up"){
data <- data %>% filter(M > log_crit[1])
cat(c(" -> log2FC larger than", log_crit[1],"->",nrow(data),"genes.\n"))
}else if(dir=="down"){
data <- data %>% filter(M < log_crit[2])
cat(c(" -> log2FC smaller than", log_crit[2],"->",nrow(data),"genes.\n"))
}else{
cat(c("<- error, check direction", "\n"))
return(NULL)
}
} else{
if(dir=="all"){
data <- data %>% filter(M >log_crit[1]| M < log_crit[2]) %>%
arrange(desc(abs(M))) %>% .[1:top,]
cat(c(" -> abs(log2FC) larger than",log_crit[1],"->",nrow(data),"genes.\n"))
}else if(dir=="up"){
data <- data %>% filter(M > log_crit[1]) %>%
arrange(desc(abs(M))) %>% .[1:top,]
cat(c(" -> log2FC larger than", log_crit[1],"->",nrow(data),"genes.\n"))
}else if(dir=="down"){
data <- data %>% filter(M < log_crit[2]) %>%
arrange(desc(abs(M))) %>% .[1:top,]
cat(c(" -> log2FC smaller than", log_crit[2],"->",nrow(data),"genes.\n"))
}else{
cat(c("<- error, check direction", "\n"))
return(NULL)
}
}
# 抓出差異gene id
if(type=="ENTREZID"){
list <- data$ENTREZID %>% na.omit()
}else if(type=="ENSEMBL"){
list <- data$ENSEMBL
}else{
list <- data$SYMBOL
}
return(list)
}
# get_list ------------------------------------------------------
get_list <- function(file_name,
type = "ENTREZID"
) {
# 檢查 type 是否有效
if (!(type %in% c("ENTREZID", "ENSEMBL","SYMBOL"))) {
stop("Invalid type. Allowed values are 'ENTREZID', 'ENSEMBL' or 'SYMBOL'.")
}
# 讀取資料
raw_df <- readxl::read_xlsx(file.path(data_path, file_name)) %>% as.data.frame()
# 根據不同的類型選擇相應的欄位名稱
if (type == "ENTREZID") {
id_col <- "ENTREZID"
} else if (type == "ENSEMBL"){
id_col <- "ENSEMBL"
} else {
id_col <- "SYMBOL"
}
# 處理資料
df <- raw_df %>% as.data.frame() %>%
filter(geneBiotype=="protein_coding") %>%
dplyr::select(all_of(id_col),M) %>%
group_by(across(all_of(id_col))) %>%
summarize(across(where(is.numeric), mean)) %>%
arrange(desc(M)) %>%
dplyr::rename(.,logFC=M) # 確保將 'M' 列重命名為 'logFC'
# 創建列表
enrich_list <- df$logFC
names(enrich_list) <- df[[id_col]]
enrich_list <- na.omit(enrich_list)
return(enrich_list)
}
# gsea_run ----------------------------------------------------------------
gsea_run <- function(file,
method="GSEA",
list,
list_id="ensembl", # ensembl/symbol
gsea_term="ALL" # "BP","MF","CC"
){
# 檢查 type 是否有效
if (!(list_id %in% c("ensembl", "symbol"))) {
stop("Invalid type. Allowed values are 'ensembl' or 'symbol'")
}
if (!(method %in% c("GSEA", "ORA"))) {
stop("Invalid type. Allowed methods are 'GSEA' or 'ORA'")
}
cat(c(" -> load data from",file.path(data_path, file),"\n"))
if(method=="GSEA"){
df <- readxl::read_xlsx(file.path(data_path, file))
cat(c(" >- input all gene and run GSEA","\n"))
} else {
if(list_id=="ensembl"){
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
filter(ENSEMBL %in% list)
} else {
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
filter(SYMBOL %in% list)
}
cat(c(" -> input selected gene and run ORA","\n"))
}
# set organism for human
organism <- "org.Hs.eg.db"
cat(c(" -> organism type is", organism,"\n"))
# $csv file's colume namm of log2 fold change
original_gene_list <- df$M
# $csv file's colume namm of ENSEMBL ID
names(original_gene_list) <- df$ENSEMBL
# omit any NA values and sort the list in decreasing order
gsea_gene_list <- na.omit(original_gene_list) %>%
sort(., decreasing = TRUE)
cat(c(" -> length of the gene list is",length(gsea_gene_list),"\n"))
#run GSEA
cat(c(" -> running GSEA in", gsea_term ,"GSEA-term\n"))
gse <- gseGO(geneList = gsea_gene_list,
ont = gsea_term,
keyType = "ENSEMBL",
nPerm = 10000,
minGSSize = 3,
maxGSSize = 800,
pvalueCutoff = 0.05,
verbose = TRUE,
OrgDb = organism,
pAdjustMethod = "none")
return(gse)
}
# kegg_run ----------------------------------------------------------------
kegg_run <- function(file,
method="GSEA",
list,
list_id="ensembl", # ensembl/symbol
return_gene_list=F,
pvalueCutoff=0.05){
# 檢查 type 是否有效
if (!(list_id %in% c("ensembl", "symbol"))) {
stop("Invalid type. Allowed values are 'ensembl' or 'symbol'")
}
if (!(method %in% c("GSEA", "ORA"))) {
stop("Invalid type. Allowed methods are 'GSEA' or 'ORA'")
}
# select gene in list
cat(c(" -> load data from",file.path(data_path, file),"\n"))
if(method=="GSEA"){
df <- readxl::read_xlsx(file.path(data_path, file))
cat(c(" >- input all gene and run KEGG","\n"))
} else {
if(list_id=="ensembl"){
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
filter(ENSEMBL %in% list)
} else {
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
filter(SYMBOL %in% list)
}
cat(c(" >- input selected gene and run ORA","\n"))
}
# change KEGG Organism Code from https://www.genome.jp/kegg/catalog/org_list.html
kegg_organism <- "hsa" # human is "hsa"
cat(c(" -> organism type is", kegg_organism,"\n"))
# selcet log2FC value
kegg_gene_list <- df$M
# Name vector with ENTREZ ids
names(kegg_gene_list) <- df$ENTREZID
# omit any NA values and sort the list in decreasing order
kegg_gene_list<- na.omit(kegg_gene_list) %>%
sort(., decreasing = TRUE)
cat(c(" -> length of the gene list is",length(kegg_gene_list),"\n"))
if(return_gene_list==T){
return(kegg_gene_list)
}
# Run KEGG
cat(c(" -> running KEGG", "\n"))
kegg <- gseKEGG(geneList = kegg_gene_list,
organism = kegg_organism,
nPerm = 10000,
minGSSize = 3,
maxGSSize = 800,
pvalueCutoff = pvalueCutoff,
pAdjustMethod = "none",
keyType = "ncbi-geneid")
return(kegg)
}
# get_df ------------------------------------------------------------------
get_df <- function(file,
de=F,
list=c(),
dir="all",
log_crit=c(1,-1),
with_D=FALSE,
all=FALSE
){
if(all==TRUE){
df <- readxl::read_xlsx(file.path(data_path, file)) %>% as.data.frame()
cat(c(" -> raw data from ",file,"\n"))
return(df)
}
if(de==TRUE){
if(dir=="all"){
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
as.data.frame() %>%
filter(geneBiotype=="protein_coding") %>%
filter(abs(M) > log_crit[1]) %>%
dplyr::select(M, SYMBOL) %>%
setNames(c("logFC","gene"))
}else if(dir=="up"){
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
as.data.frame() %>%
filter(geneBiotype=="protein_coding") %>%
filter(M > log_crit[1]) %>%
dplyr::select(M, SYMBOL) %>%
setNames(c("logFC","gene"))
}else if(dir=="down"){
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
as.data.frame() %>%
filter(geneBiotype=="protein_coding") %>%
filter(M < log_crit[2]) %>%
dplyr::select(M, SYMBOL) %>%
setNames(c("logFC","gene"))
}else{
cat(c("<- error, check direction", "\n"))
return(NULL)
}
df <- df %>% arrange(desc(abs(logFC)))
return(df)
} else {
if(with_D==T){
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
as.data.frame() %>%
filter(geneBiotype=="protein_coding") %>%
filter(ENSEMBL %in% list) %>%
dplyr::select(M,D, SYMBOL) %>%
setNames(c("logFC","D","gene"))
} else{
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
as.data.frame() %>%
filter(geneBiotype=="protein_coding") %>%
filter(ENSEMBL %in% list) %>%
dplyr::select(M, SYMBOL) %>%
setNames(c("logFC","gene"))
}
return(df)
}
}
# venn_to_excel -----------------------------------------------------------
# 將venn diagram結果輸出成excel
venn_to_excel <- function(venn_list, name) {
# list中資料依序加入data frame
max_length <- max(sapply(venn_list$item, length))
data_df <- data.frame(matrix(NA, ncol = length(venn_list$item), nrow = max_length))
for (i in 1:length(venn_list$item)) {
sublist <- venn_list$item[[i]]
data_df[, i] <- c(sublist, rep(NA, max_length - length(sublist)))
colnames(data_df) <- venn_list$name
}
# 設置欄寬
wb <- openxlsx::createWorkbook()
addWorksheet(wb, "Sheet1")
writeData(wb, "Sheet1", data_df)
for (i in 1:ncol(data_df)) {
setColWidths(wb, sheet = 1, cols = i, widths = "auto")
}
saveWorkbook(wb, paste0(name, ".xlsx"))
cat(paste("-> Output completed. File name is ", paste0(name, ".xlsx")))
}
# get_kegg_list -----------------------------------------------------------
get_kegg_list <- function(path_ID, return_list=T
){
# 获取指定通路的基因列表
pathway_df <- KEGGREST::keggGet(path_ID)[[1]]
pathway_genes <- pathway_df$GENE
# 提取基因名称
gene_names <- sapply(strsplit(pathway_genes, ";"), `[`, 1)
# 删除奇数索引的元素
gene_list_even <- gene_names[seq_along(gene_names) %% 2 == 0]
cat(c(" ->",length(gene_list_even),"genes involved.\n"))
if(return_list==T){
cat(c(" -> pathway name:", pathway_df$NAME,"\n"))
return(gene_list_even)
} else {
return(pathway_df$NAME)
}
}
# get_anno ----------------------------------------------------------------
get_anno <- function(list,
file,
id_type){
# 檢查 type 是否有效
if (!(id_type %in% c("ENSEMBL","SYMBOL"))) {
stop("Invalid type. Allowed values are 'ENSEMBL','SYMBOL'.")
}
# 讀檔獲取df
if(id_type=="ENSEMBL"){
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
as.data.frame() %>%
filter(ENSEMBL %in% list)
} else if(id_type=="SYMBOL"){
df <- readxl::read_xlsx(file.path(data_path, file)) %>%
as.data.frame() %>%
filter(SYMBOL %in% list)
}
}
# ensembl_to_symbol -------------------------------------------------------
ensembl_to_symbol <- function(list){
df <- gene_df %>% filter(ENSEMBL %in% list) %>% pull(SYMBOL)
}
# negative ----------------------------------------------------------------
negative <- function(x) {
return(-x)
}
# run_keg_path -----------------------------------------------------------
run_keg_path <- function(file_name, dir="up", log_crit=1){
if (!(dir %in% c("up","down"))) {
stop("Invalid type. Allowed values are 'up' or 'down'.")
}
genelist <- get_list(file_name)
if(dir=="up"){
gene <- names(genelist)[genelist >= log_crit]
} else{
gene <- names(genelist)[genelist <= negative(log_crit)]
}
# run kegg ORA
message("Running KEGG analysis")
kk <- enrichKEGG(gene = gene,
organism = 'hsa',
pvalueCutoff = 0.1)
# ENTREZID to SYMBOL
net <- DOSE::setReadable(kk, 'org.Hs.eg.db', 'ENTREZID')
return(net)
}
# run_reactome ------------------------------------------------------------
run_reactome <- function(file_name, dir="up",log_crit=1,
return_df = F){
if (!(dir %in% c("up","down"))) {
stop("Invalid type. Allowed values are 'up' or 'down'.")
}
genelist <- get_list(file_name)
if(dir=="up"){
gene <- names(genelist)[genelist >= log_crit]
} else{
gene <- names(genelist)[genelist <= negative(log_crit)]
}
# Reactome pathway over-representation analysis
message("Running reactome analysis")
de_pathway <- enrichPathway(gene=gene, pvalueCutoff = 0.1, readable=TRUE)
return(de_pathway)
}
# get_p -------------------------------------------------------------------
get_p <- function(x, abbreviate, top=0, p_value_cutoff = 0.01){
pvalue <- x@result %>% .[,c("Description","pvalue")] %>%
filter(pvalue < p_value_cutoff) %>%
setNames(c("Description",abbreviate))
if(top != 0){
pvalue <- pvalue %>% head(top)
}
return(pvalue)
}
# plot_heatmap ------------------------------------------------------------
plot_heatmap <- function(file_list, abbreviates, analysis, dir="up",
col_title="", row_title="",top=20) {
# 檢查參數是否匹配
if (length(file_list) != length(abbreviates)) {
stop("file_list and abbreviates must have the same length")
}
if (!(analysis %in% c("kegg", "reactome"))) {
stop("Invalid type. Allowed values are 'kegg' or 'reactome'.")
}
# 讀取並處理每個文件
if(analysis=="kegg"){
dfs <- lapply(file_list, function(file) run_keg_path(file, log_crit = 1, dir = dir))
} else{
dfs <- lapply(file_list, function(file) run_reactome(file, log_crit = 1, dir = dir))
}
processed_dfs <- mapply(get_p, dfs, abbreviate = abbreviates, SIMPLIFY = FALSE)
# 合併所有數據框
combined_df <- Reduce(function(x, y) full_join(x, y, by = "Description"), processed_dfs) %>%
column_to_rownames("Description") %>% as.matrix()
# 計算na值
non_NA_count <- rowSums(!is.na(combined_df)) %>% sort(.,decreasing = T)
# 計算 -log10(P) 值
mat <- log10(combined_df) %>% -.
mat <- mat[names(non_NA_count), ] %>% head(top)
rownames(mat) <- factor(rownames(mat), levels = names(non_NA_count))
# 繪製熱圖
if(dir=="up"){
col_fun = colorRamp2(c(1, 5), c("white", "red"))
} else{
col_fun = colorRamp2(c(1, 5), c("white", "blue"))
}
ComplexHeatmap::Heatmap(mat,
na_col = "grey",
cluster_rows = FALSE,
cluster_columns = FALSE,
col = col_fun,
name = "-log10(P)",
row_names_gp = gpar(fontsize = 7),
column_names_gp = gpar(fontsize = 8),
column_title_gp = gpar(fontsize = 10),
column_names_rot = 45,
column_title = col_title, row_title = row_title,
heatmap_legend_param = list( # 控制color bar在0~5之間
at = c(1, 2, 3, 4, 5),
labels = c("1", "2", "3", "4", "5") ))
}
# run_path_heatmap --------------------------------------------------------
run_path_heatmap <- function(count){
count <- count %>% as.matrix()
message("running MLM analysis")
sample_acts <- run_mlm(mat=count, net=net, .source='source', .target='target',
.mor='weight', minsize = 5)
# Transform to wide matrix
sample_acts_mat <- sample_acts %>%
pivot_wider(id_cols = 'condition', names_from = 'source',
values_from = 'score') %>%
column_to_rownames('condition') %>%
as.matrix()
# Scale per feature
sample_acts_mat <- scale(sample_acts_mat)
# Choose color palette
palette_length = 100
my_color = colorRampPalette(c("darkblue", "white","red"))(palette_length)
my_breaks <- c(seq(-3, 0, length.out=ceiling(palette_length/2) + 1),
seq(0.05, 3, length.out=floor(palette_length/2)))
### heatmap
p <- pheatmap(sample_acts_mat, border_color = NA, color=my_color, breaks = my_breaks)
return(p)
}
# run_TF_heatmap ----------------------------------------------------------
run_TF_heatmap <- function(count){
count <- count %>% as.matrix()
message("running ULM analysis")
sample_TF <- run_ulm(mat=count, net=net_TF, .source='source', .target='target',
.mor='mor', minsize = 5)
# plot
n_tfs <- 25
# Transform to wide matrix
sample_TF_mat <- sample_TF %>%
pivot_wider(id_cols = 'condition', names_from = 'source',
values_from = 'score') %>%
column_to_rownames('condition') %>%
as.matrix()
# Get top tfs with more variable means across clusters
tfs <- sample_TF %>%
group_by(source) %>%
summarise(std = sd(score)) %>%
arrange(-abs(std)) %>%
head(n_tfs) %>%
pull(source)
sample_TF_mat <- sample_TF_mat[,tfs]
# Scale per sample
sample_TF_mat <- scale(sample_TF_mat)
# Choose color palette
palette_length = 100
my_color = colorRampPalette(c("Darkblue", "white","red"))(palette_length)
my_breaks <- c(seq(-3, 0, length.out=ceiling(palette_length/2) + 1),
seq(0.05, 3, length.out=floor(palette_length/2)))
### heatmap
pheatmap(sample_TF_mat, border_color = NA, color=my_color, breaks = my_breaks)
}
# run_pathway -------------------------------------------------------------
run_pathway <- function(file_name,title=""){
deg_FC <- get_df(file_name ,de=T,log_crit = c(1,-1)) %>%
group_by(gene) %>%
summarize(across(where(is.numeric), sum)) %>% na.omit() %>%
column_to_rownames(., var = "gene")
### run MLM analysis
message("running MLM analysis")
contrast_acts <- run_mlm(mat=deg_FC, net=net, .source='source', .target='target',
.mor='weight', minsize = 5)
### pathway enrich plot
ggplot(contrast_acts, aes(x = reorder(source, score), y = score)) +
geom_bar(aes(fill = score), stat = "identity") +
scale_fill_gradient2(low = "darkblue", high = "indianred",
mid = "whitesmoke", midpoint = 0) +
theme_minimal() +
theme(axis.title = element_text(face = "bold", size = 12),
axis.text.x =
element_text(angle = 45, hjust = 1, size =10, face= "bold"),
axis.text.y = element_text(size =10, face= "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
xlab("Pathways") + ggtitle(title) # change title
}
# plot_pathway ----------------------------------------------------------
plot_pathway <- function(file_name, pathway, title="",logFC_criteria = 1){
deg_df <- get_df(file_name ,all = T) %>% group_by(SYMBOL) %>%
summarize(across(where(is.numeric), sum)) %>% na.omit() %>%
column_to_rownames(., var = "SYMBOL") %>%
rownames_to_column("ID") %>%
.[,c(1,5,6,7,8)] %>%
setNames(c("ID","treat","control","M","D"))%>%
mutate(Average_expression =(treat+control)/2) %>%
.[,c(1,4,6)] %>%
setNames(c("ID","logFC","Average_expression"))
### filter pathway specific genes
path <- net %>%
filter(source == pathway) %>%
arrange(target) %>%
mutate(ID = target, color = "3") %>%
column_to_rownames('target') %>%
left_join(deg_df,"ID") %>%
mutate(color = if_else(logFC > logFC_criteria, '1', color)) %>%
mutate(color = if_else(logFC < logFC_criteria & logFC > negative(logFC_criteria), '2', color)) %>%
mutate(color = if_else(logFC < negative(logFC_criteria), '3', color))
### MD plot
p <- ggplot(path, aes(x = log(Average_expression), y = logFC, color = color)) + geom_point() +
scale_colour_manual(values = c("red","grey","royalblue3")) +
geom_label_repel(aes(label = ID)) +
theme_minimal() +
theme(legend.position = "none") +
geom_vline(xintercept = 0, linetype = 'dotted') +