Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). The reference recommends using zFPKM > -3 to select expressed genes. Validated with ENCODE open/closed promoter chromatin structure epigenetic data on six of the ENCODE cell lines. It works well for gene level data using FPKM or TPM, but does not appear to calibrate well for transcript level data.

The zFPKM package is now part of Bioconductor, and can be installed by typing the following into an R console.

source("https://bioconductor.org/biocLite.R")
biocLite("zFPKM")

Alternatively, to install the newest version directly from the GitHub repository.

devtools::install_github("ronammar/zFPKM")

We calculate zFPKM for existing normalized FPKM from GSE94802.

library(dplyr)
gse94802 <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94802/suppl/GSE94802_Minkina_etal_normalized_FPKM.csv.gz"
temp <- tempfile()
download.file(gse94802, temp)
fpkm <- read.csv(gzfile(temp), row.names=1)
fpkm <- select(fpkm, -MGI_Symbol)

library(zFPKM)
zfpkm <- zFPKM(fpkm)

The zFPKM function also optionally plots the Guassian fit to the FPKM data for which the z-scores are based.

To determine which genes are active across all samples, we use rowMeans() and a zFPKM cutoff of -3, as suggested by the authors.

activeGenes <- which(rowMeans(zfpkm) > -3)

Hart T, Komori HK, LaMere S, Podshivalova K, Salomon DR. Finding the active genes in deep RNA-seq gene expression studies. BMC Genomics. 2013 Nov 11;14:778. doi: 10.1186/1471-2164-14-778.