TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository

Abstract

Background In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. Methods In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. Results Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. Conclusion We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.

Functional Haplotypes and Evolutionary Insight into the Granule-Bound Starch Synthase II (GBSSII) Gene in Korean Rice Accessions (KRICE_CORE)

“…High-throughput RNA sequencing (RNA-Seq) provides insight into the transcriptome [60], and careful selection of the RNA-Seq quantification measure is critical for inter-sample comparisons, such as different gene expression levels between two or more conditions [61]. In this study, the measures of fragments per kilobase of exon per million (FPKM) were quantified from the RNA-Seq data of 353 rice accessions (300 cultivated and 53 wild) within the GBSSII genomic region for an expression analysis based on the identified haplotypes.…”

Comprehensive Analysis of Pyroptosis-Related Genes and Tumor Microenvironment Infiltration Characterization in Papillary Renal Cell Carcinoma

“…The Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ) platform was used to collect the GEO cohort (GSE2748) KIRP samples. Incomplete clinical information was deleted from samples, and FPKM values in TCGA-KIRP were transformed to Transcripts Per Kilobase Million (TPM) values and utilized for copy number variation (CNV) analysis ( Zhao et al, 2021 ). The transcriptome RNA sequences from the TCGA-KIRP and GSE2748 datasets were combined after correction.…”

Plant Endophytic Fungus Extract ZNC Improved Potato Immunity, Yield, and Quality

“…After obtaining the new transcript, the new transcripts with protein coding potential were added to the reference gene sequence to form a complete reference sequence, and then the gene expression level was calculated by RNA-Seq by Expectation-Maximization (RSEM) software (version 1.2.31). Finally, the differentially expressed genes between different samples for multiple samples were detected and based on fragments per kilobase of transcript per million fragments mapped (FPKM; Zhao et al, 2021 ). The values of relative gene expression were represented by log10(FPKM+1), and differential expression analysis adopted log2FoldChange.…”