add rnaseq123_multi

	}

}

rnaseq123_multi <- function(

				dge_list = NULL,

				count_files,

				group,

				compare,

				organism = "human",

				count_type = "star"

){

	if(is.null(dge_list)){

		if(tolower(count_type) == "star"){

			cols <- c(1, 2)

			header_str <- FALSE

			skip_rows <- 4

		}else if(tolower(count_type) == "featurecounts"){

			cols <- c(1, 7)

			header_str <- FALSE

			skip_rows <- 1

		}else if(tolower(count_type) == "salmon"){

			cols <- c(1,5)

			header_str <- TRUE

			skip_rows <- 0

		}

		x <-	readDGE(

					count_files,

					columns = cols,

					header = header_str,

					skip = skip_rows

				)

	}else if(class(dge_list) == "DGEList"){

		x <- dge_list

	}

	x$samples$group <- group

	if(count_type %in% c("star","featurecounts")){

		gtf_info <- gtf(organism,"gene") %>% 

					as.data.table() %>% 

					.[,.(gene_id,gene_name,type = gene_type,chr = seqnames,start,end,strand)] |>

					setkey(gene_id,gene_name)

	}else if(count_type == "salmon"){

		gtf_info <- gtf(organism,"transcript") %>% 

					as.data.table() %>% 

					.[,.(gene_id = transcript_id,gene_name = transcript_name,type = transcript_type,chr = seqnames,start,end,strand)] |>

					setkey(gene_id,gene_name)

	}

	## it will take few minutes to load the gtf_file

	## filter the unneeded rows

	x$genes <-	gtf_info[,.(gene_id,gene_name)] %>%

				unique()

	#get the correspondece between gene_id (Ensembl ID) and gene_name

	x <-	x[

				filterByExpr(x, group = group), 

				keep.lib.sizes = F

			] %>%

			calcNormFactors(method = "TMM")

	design <- model.matrix(~0+group)

	colnames(design) <- gsub("group", "", colnames(design))

	contr.matrix <- makeContrasts(

						contrasts = compare,

						levels = colnames(design)

					)

	x %>%

	voomWithQualityWeights(design, plot = F) %>%

	lmFit(design) %>%

	contrasts.fit(contrasts = contr.matrix) %>%

	eBayes() %>%

	topTable(n = Inf) %>%

	as.data.table() %>%

	.[,log10P := -log10(adj.P.Val)] %>%

	merge(gtf_info[,.(gene_id,gene_name,type)])

}

## the input data for venn_plot should come from rnaseq123_multi()

venn_plot <-	function(

					.data,

					regulation = "up",

					list = FALSE,

					group = NULL

){

	other_cols <-  c("gene_id","gene_name","AveExpr","F","P.Value","adj.P.Val")

	other_cols <-  c("gene_id","gene_name","AveExpr","F","P.Value","adj.P.Val","log10P","type")

	samples <-	colnames(.data) %>% 

				.[! . %in% other_cols]