Comprehensive Evaluation of RNA-seq Quantification Methods for Linearity
| العنوان: | Comprehensive Evaluation of RNA-seq Quantification Methods for Linearity |
|---|---|
| المؤلفون: | Haijing Jin, Zhandong Liu, Ying-Wooi Wan |
| المصدر: | BCB BMC Bioinformatics |
| بيانات النشر: | ACM, 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | 0301 basic medicine, Computer science, Deconvolution, Bioinformatics, Residual, Biochemistry, 03 medical and health sciences, 0302 clinical medicine, Linearity, Structural Biology, Statistics, Humans, Protein Isoforms, Computer Simulation, Molecular Biology, Genome, Human, Sequence Analysis, RNA, Estimation theory, Research, Gene Expression Profiling, Applied Mathematics, Linear space, Linear model, High-Throughput Nucleotide Sequencing, Function (mathematics), Computer Science Applications, 030104 developmental biology, Linear Models, Benchmark (computing), RNA, RNA-seq, Transcriptome, Biological system, 030217 neurology & neurosurgery, Count data |
| الوصف: | 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1526-y) contains supplementary material, which is available to authorized users. |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36ef6574fe949ee0b153434e869f5331Test https://doi.org/10.1145/2975167.2985678Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....36ef6574fe949ee0b153434e869f5331 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |