Tissue-specific deconvolution of cell composition using miRNA sequencing data
Abstract
The expression of miRNAs is affected by different factors. This poses challenges when interpreting the results of the study. Pathological processes can influence both the functional activity of cells and the ratio of cell populations. A possible solution is to utilize the approaches of deconvolution. We have developed an algorithm to assess reference panel from the microRNA dataset on the example of artery walls. In this study, we have shown that existing deconvolution algorithms are suitable for miRNA data.
About the Authors
A. A. Zarubin
Research Institute of Medical Genetics, Tomsk National Research Medical Center, Russian Academy of Sciences
Russian Federation
Russian Federation
A. V. Markov
Research Institute of Medical Genetics, Tomsk National Research Medical Center, Russian Academy of Sciences
Russian Federation
Russian Federation
A. A. Sleptcov
Research Institute of Medical Genetics, Tomsk National Research Medical Center, Russian Academy of Sciences
Russian Federation
Russian Federation
M. S. Nazarenko
Research Institute of Medical Genetics, Tomsk National Research Medical Center, Russian Academy of Sciences
Russian Federation
Russian Federation
References
1. De Rie D. et al. An integrated expression atlas of miRNAs and their promoters in human and mouse. Nature biotechnology. 2017;35(9):872-878.
2. Gong T., Szustakowski J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics. 2013;29(8):1083-1085.
3. Newman A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nature methods. 2015;12(5):453-457.
Review
For citations:
Zarubin A.A., Markov A.V., Sleptcov A.A., Nazarenko M.S. Tissue-specific deconvolution of cell composition using miRNA sequencing data. Medical Genetics. 2020;19(12):64-65. (In Russ.)
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