Year: 2017
Type: article
Abstract: BACKGROUND: Deconvolution is a mathematical process of resolving an observed function into its constituent elements. In the field of biomedical research, deconvolution analysis is applied to obtain single cell-type or tissue specific signatures from a mixed signal and most of them follow the linearity assumption. Although recent development of next generation sequencing technology suggests RNA-seq as a fast and accurate method for obtaining transcriptomic profiles, few studies have been conducted to investigate best RNA-seq quantification methods that yield the optimum linear space for deconvolution analysis. RESULTS: Using a benchmark RNA-seq dataset, we investigated the linearity of abundance estimated from seven most popular RNA-seq quantification methods both at the gene and isoform levels. Linearity is evaluated through parameter estimation, concordance analysis and residual analysis based on a multiple linear regression model. Results show that count data gives poor parameter estimations, large intercepts and high inter-sample variability; while TPM value from Kallisto and Salmon shows high linearity in all analyses. CONCLUSIONS: Salmon and Kallisto TPM data gives the best fit to the linear model studied. This suggests that TPM values estimated from Salmon and Kallisto are the ideal RNA-seq measurements for deconvolution studies.
Source: BMC Bioinformatics
Authors: Haijing Jin, Ying-Wooi Wan, Zhandong Liu
Institution: Baylor College of Medicine
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W2599073119