Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples

By Günter P. Wagner, Koryu Kin, Vincent J. Lynch. Theory in biosciences, 2012

1,925 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/214a30e9789734868f26218644291b8fe2754ddf

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Citations in the corpus

Revealing the vectors of cellular identity with single-cell genomics (2016, background): “This approach was used for scRNA-seq with a detection threshold (in log-space TPM) 2 or as a mixture model for read counts in which the first component is a negative binomial and the second is a low-magnitude Poisson, rather than a fixed zero, to account for background noise 87 .”
Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms (2013, methodology): “The RPKM estimate is the most commonly used, and is ideally 109 times the rate at which reads are observed at a given position, but the TPM estimate has also become somewhat common [5, 17].”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, methodology): “Because of this imperfect coverage, the commonly used unit of normalized transcript levels used for bulk RNAseq, expressed as ‘reads per kilobase per million’ (RPKM), is biased on a single-cell level, and instead the related unit ‘transcripts per million’ (TPM) should be used for scRNA-seq [66].”
TP53 copy number expansion is associated with the evolution of increased body size and an enhanced DNA damage response in elephants (2015, methodology): “Finally we transformed FPKM estimates into transcripts per million (TPM), TPM=(FPKM per gene/sum FPKM all genes)x106, and defined genes with TPM≥2 as expressed (Li et al., 2010; Wagner et al., 2012; 2013).”
Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data (2017, methodology): “For Tophat-Cufflinks and Tophat-HTSeq, the FPKM values were converted to TPM 21 .”
Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols (2020, background): “Unfortunately, RPKMdoes not respect this invariance property and thus cannot be an accurate measure of rmc (Wagner et al. 2012).”
Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures (2019, methodology): “For TCGA dataset, RNA-sequencing data (FPKM values) were transformed into transcripts per kilobase million (TPM) values, which are more similar to those resulting from microarrays and more comparable between samples (24).”
Impact of genetic polymorphisms on human immune cell gene expression (2018, neutral): “Sequencing read coverage per gene was counted using HTSeq (https://htseq.readthedocs.io) (Anders et al., 2015) and transcript abundance expressed as transcripts per million (TPM) units (Wagner et al., 2012).”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, neutral): “The expression of each gene is quanti ﬁ ed as log 2 (TPM/10 + 1), where “ TPM ” standards for “ transcripts per million ” 60 .”