R/fpkm_count_conversion.r

In GeoTcgaData: Processing various types of data on GEO and TCGA

countToTpm <- function(counts, effLen)
{
    rate <- log(counts) - log(effLen)
    denom <- log(sum(exp(rate)))
    exp(rate - denom + log(1e6))
}
countToFpkm <- function(counts, effLen)
{
    N <- sum(counts)
    exp( log(counts) + log(1e9) - log(effLen) - log(N) )
}

fpkmToTpm <- function(fpkm)
{
    exp(log(fpkm) - log(sum(fpkm)) + log(1e6))
}

countToEffCounts <- function(counts, len, effLen)
{
    counts * (len / effLen)
}

# if we have fpkm, then we can easily get the rate of counts/sum(counts).
# we can't get the real count value.
fpkmToCount <- function(fpkm, effLen, N=1e9)
{
    #rate <- (fpkm * effLen)/10^9
    rate <- exp(log(fpkm) + log(effLen) - log(1e9))
    counts <- rate * N
}




#' Convert count to FPKM
#'
#' @param counts_matrix a matrix, colnames of counts_matrix are sample name,
#' rownames of counts_matrix are gene symbols
#' @param keyType keyType
#' @param gene_cov gene_cov
#'
#' @return  a matrix
#' @export
#'
#' @examples
#' lung_squ_count2 <- matrix(c(1,2,3,4,5,6,7,8,9),ncol=3)
#' rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3")
#' colnames(lung_squ_count2) <- c("sample1","sample2","sample3")
#' jieguo <- countToFpkm_matrix(lung_squ_count2, keyType = "SYMBOL", 
#'     gene_cov = gene_cov)
countToFpkm_matrix <- function(counts_matrix, keyType = "SYMBOL", gene_cov) {
    gene_cov2 <- gene_cov
    if (keyType != "ENTREZID") {
        genes_bitr <- clusterProfiler::bitr(rownames(gene_cov), fromType = "ENTREZID", toType = keyType, 
            OrgDb = org.Hs.eg.db::org.Hs.eg.db, drop = TRUE)
        genes_bitr <- genes_bitr[!duplicated(genes_bitr[,2]), ]
        gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ]
        rownames(gene_cov2) <- genes_bitr[, 2]
    }
    genes_count <- intersect(rownames(counts_matrix), rownames(gene_cov2))
    counts_matrix_new <- counts_matrix[genes_count,]
    gene_loc_len_new <-gene_cov2[genes_count,]
    genes_length <- as.numeric(gene_loc_len_new[,1])
    counts_matrix_new2 <- counts_matrix_new
    for(i in seq_len(dim(counts_matrix_new2)[2])) {
        counts_matrix_new2[,i] <- countToFpkm(as.numeric(counts_matrix_new2[,i]),genes_length)
    }
    return(counts_matrix_new2)
}


#' Convert count to Tpm
#'
#' @param counts_matrix a matrix, colnames of counts_matrix are sample name,
#' rownames of counts_matrix are gene symbols
#' @param keyType keyType
#' @param gene_cov gene_cov
#'
#' @return a matrix
#' @export
#'
#' @examples
#' lung_squ_count2 <- matrix(c(1,2,3,4,5,6,7,8,9),ncol=3)
#' rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3")
#' colnames(lung_squ_count2) <- c("sample1","sample2","sample3")
#' result <- countToTpm_matrix(lung_squ_count2, keyType = "SYMBOL", 
#'     gene_cov = gene_cov)
countToTpm_matrix <- function(counts_matrix, keyType = "SYMBOL", gene_cov) {
    gene_cov2 <- gene_cov
    if (keyType != "ENTREZID") {
        genes_bitr <- clusterProfiler::bitr(rownames(gene_cov), fromType = "ENTREZID", toType = keyType, 
            OrgDb = org.Hs.eg.db::org.Hs.eg.db, drop = TRUE)
        genes_bitr <- genes_bitr[!duplicated(genes_bitr[,2]), ]
        gene_cov2 <- gene_cov[genes_bitr$ENTREZID, ]
        rownames(gene_cov2) <- genes_bitr[, 2]
    }
    genes_count <- intersect(rownames(counts_matrix), rownames(gene_cov2))
    counts_matrix_new <- counts_matrix[genes_count,]
    gene_loc_len_new <-gene_cov2[genes_count,]
    genes_length <- as.numeric(gene_loc_len_new[,1])
    counts_matrix_new2 <- counts_matrix_new
    for(i in seq_len(dim(counts_matrix_new2)[2])) {
        counts_matrix_new2[,i] <- countToTpm(as.numeric(counts_matrix_new2[,i]),genes_length)
    }
    return(counts_matrix_new2)
}


#' Convert fpkm to Tpm
#'
#' @param fpkm_matrix a matrix, colnames of fpkm_matrix are sample name,
#' rownames of fpkm_matrix are genes
#'
#' @return a matrix
#' @export
#'
#' @examples
#' lung_squ_count2 <- matrix(c(0.11,0.22,0.43,0.14,0.875,0.66,0.77,0.18,0.29),ncol=3)
#' rownames(lung_squ_count2) <- c("DISC1","TCOF1","SPPL3")
#' colnames(lung_squ_count2) <- c("sample1","sample2","sample3")
#' result <- fpkmToTpm_matrix(lung_squ_count2)
fpkmToTpm_matrix <- function(fpkm_matrix) {
    fpkm_matrix_new <- apply(fpkm_matrix, 2, fpkmToTpm)  
}