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.