Abstract
Here we estimated the optimal parameters of the independent component analysis method regarding the tasks of identification of glioblastoma and pancreatic cancer subtypes, prediction of patient survival and characterization of active biological processes. Analysis of deconvolution results of bulk and single-cell data allows sharing annotation between highly correlated components and improving interpretability of the results
Original language | English |
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Title of host publication | Computer Technologies and Data Analysis (CTDA'2020) : Materials of II International Scientific-Practical Conference, Minsk, 23-24 April, 2020 |
Editors | V.A. Bishkek, V. Skakun |
Place of Publication | Minsk |
Publisher | BSU |
Pages | 170-173 |
Number of pages | 4 |
ISBN (Print) | 978-985-566-942-6. |
Publication status | Published - 9 Jul 2020 |
Keywords
- transcriptomics; RNA-seq; independent component analysis; single-cell; cancer; survival