PRISM: Recovering cell type specific expression profiles from individual composite RNA-seq samples

Antti Häkkinen, Kaiyang Zhang, Amjad Alkodsi, Noora Andersson, Erdogan Pekcan Erkan, Jun Dai, Katja Kaipio, Tarja Lamminen, Naziha Mansuri, Kaisa Huhtinen, Anna Vähärautio, Olli Carpén (+4 others)
2021 Bioinformatics  
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. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample
more » ... 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. https://bitbucket.org/anthakki/prism. Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btab178 pmid:33720334 pmcid:PMC8479664 fatcat:jpann3s6fjctplrzvn4txm5zeq