1. PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples
- Author
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Olli Carpén, Katja Kaipio, Kaisa Huhtinen, Antti Häkkinen, Erdogan Pekcan Erkan, Noora Andersson, Naziha Mansuri, Sampsa Hautaniemi, Anna Vähärautio, Johanna Hynninen, Tarja Lamminen, Amjad Alkodsi, Rainer Lehtonen, Kaiyang Zhang, Jun Dai, Sakari Hietanen, Sampsa Hautaniemi / Principal Investigator, Research Program in Systems Oncology, Research Programs Unit, HUSLAB, Department of Pathology, Precision Cancer Pathology, Olli Mikael Carpen / Principal Investigator, Biosciences, Faculty Common Matters (Faculty of Medicine), Bioinformatics, and Department of Biochemistry and Developmental Biology
- Subjects
Statistics and Probability ,AcademicSubjects/SCI01060 ,Cell type specific ,Gene Expression ,RNA-Seq ,In situ hybridization ,Computational biology ,Biology ,Patient response ,Biochemistry ,PATHWAY ,Transcriptome ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Molecular Biology ,SIGNATURES ,GENE-EXPRESSION ,030304 developmental biology ,0303 health sciences ,318 Medical biotechnology ,RNA ,Cancer ,medicine.disease ,CANCER ,Original Papers ,3. Good health ,Computer Science Applications ,Computational Mathematics ,Computational Theory and Mathematics ,030220 oncology & carcinogenesis ,1182 Biochemistry, cell and molecular biology ,3111 Biomedicine ,Prism - Abstract
Motivation A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. Results To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. Availabilityand implementation https://bitbucket.org/anthakki/prism. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2021
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