Prepare 3 tab format for RNAseq dataset uploading

1. load in count file countsFileName="count.txt"

   count = read.delim(sep="\t",countsFileName, header=T, check.names=F)
   
   names(count)[1] = "gene"
   rownames(count) = count$gene
   

2. generate the observation file

    options(repr.matrix.max.rows=50, repr.matrix.max.cols=200)
   
    meta = read.delim("phenotype.txt")
   
   
    	#re-name the columns, change gender to your variable of interest 
    	names(obs) = c("observations","genotype", "gender")
   

#keep replicate name the same #pipe the dataframe to the group_by function and send output to mutate function to count row numbers and assign them to the variable replicate

	obs <- obs %>% group_by(genotype, gender) %>% mutate(replicate = row_number())
	obs = data.frame(obs)
	write.table(obs, file= "observation.tab", sep="\t", row.names=F, quote=F)

3. annotation. check whether there is ensemble in the count file

    	temp=count[grepl("ENS",count[,1]),]
    	
    	if (nrow(temp)>0) {
    	 print('ensembl ID are detected')
   
    			} else {
    	print('Error: no ensemble detected')}
   

Here we are using the mouse annotation database, if you wish to use a different database run listDatasets(mart) to get a full list of available annotations.

		library(biomaRT)
                    mart = useMart( 'ensembl' )
		datasets <- listDatasets(mart)
		mart = useDataset( 'mmusculus_gene_ensembl' , mart = mart )
		ensembl = getBM( attributes = c('ensembl_gene_id','external_gene_name') , mart=mart)
		names(ensembl)[2] = "gene_symbol"
		head(ensembl)

#only keep the first ensemble ID for each gene

		ensembl.m=ensembl[!duplicated(ensembl$gene_symbol),]

#merge by gene_symbol or ensembl ID 

		count.ann = merge(ensembl, count, by.x="gene_symbol", by.y="gene_symbol")

		names(count.ann)[c(2,1)] = c("ensembl_ID", "gene_symbol")


		write.table(count.ann[-1], file= "expression.tab", sep="\t", quote=F, row.names=F)

		genes_to_use=count.ann[c(2,1)]

		write.table(genes_to_use,file= "genes.tab", sep="\t", quote=F,row.names=F)

if the count file is raw count, basic normalization need to be completed before uploading

		count_to_norm=count.ann[-c(1:2)]

		row.names(count_to_norm)=count.ann[,1]

		cpm=sweep(count_to_norm,2,colSums(count_to_norm),FUN="/")*10^6

##quantile normalization of cpm

	quantile_normalisation <- function(df){
	df_rank <- apply(df,2,rank,ties.method="min")
	df_sorted <- data.frame(apply(df, 2, sort))
	df_mean <- apply(df_sorted, 1, mean)
	index_to_mean <- function(my_index, my_mean){
	return(my_mean[my_index])}
	df_final <- apply(df_rank, 2, index_to_mean, my_mean=df_mean)
	rownames(df_final) <- rownames(df)
	return(df_final)}

	nq=quantile_normalisation(data.frame(cpm))
	write.table(nq, file=paste(outfile_prefix, "expression.tab", sep = ""), sep="\t", quote=F, row.names=T)

####compress files together for uploading

system( 'tar -czvf upload.tar.gz *.tab')