학술논문
A Parallelizable Model for Analyzing Cancer Tissue Heterogeneity
Document Type
Periodical
Author
Source
IEEE/ACM Transactions on Computational Biology and Bioinformatics IEEE/ACM Trans. Comput. Biol. and Bioinf. Computational Biology and Bioinformatics, IEEE/ACM Transactions on. 19(4):2039-2048 Aug, 2022
Subject
Language
ISSN
1545-5963
1557-9964
2374-0043
1557-9964
2374-0043
Abstract
In a cancer study, the heterogeneous nature of a cell population creates a lot of challenges. Efficient determination of the compositional breakup of a cell population, from gene expression measurements, is critical to the success in a cancer study. This paper presents a new model for analyzing heterogeneity in cancer tissue using Markov chain Monte Carlo (MCMC) algorithms; we aim to compute the proportion wise breakup of the cell population on a GPU. We also show that the model computation time does not depend on the input data size, because the computation required to estimate the compositional breakup are parallelized. This model uses qPCR (quantitative polymerase chain reaction) gene expression data to determine compositional breakup in the heterogeneous cell population. We test this model on synthetic data and real-world data collected from fibroblasts. We also show how well this model scales to hundreds of gene expression data.