Deconvolution of “big data” in cancer genomics: from pancancer level to single cells

M. Chepeleva, Y. Wang, A. Kakoichankava, A. Muller, Tony Kaoma, P. V. Nazarov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Large genomics pan-cancer datasets that were made publically available in the last decade are now complemented with measurements at single cell level and may include up to a billion data points. Here we show how deconvolution method based on independent component analysis can process transcriptomes measured for bulk samples at pan-cancer level and for single-cell measurements from normal tissues and neoplasia
Original languageEnglish
Title of host publicationComputer Technologies and Data Analysis (CTDA'2020) : Materials of II International Scientific-Practical Conference, Minsk, 23-24 April, 2020
EditorsV.A. Bishkek, V. Skakun
Place of PublicationMinsk
Number of pages5
ISBN (Print)978-985-566-942-6.
Publication statusPublished - 9 Jul 2020


  • transcriptomics; RNA-seq; independent component analysis; pan-cancer; single-cell


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