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    Area of Science:

    • Computational biology
    • Genomics
    • Cancer research

    Background:

    • Cancer studies face challenges due to the heterogeneous nature of cell populations.
    • Accurate determination of cell population composition from gene expression data is crucial for cancer research success.

    Purpose of the Study:

    • To present a novel computational model for analyzing heterogeneity in cancer tissues.
    • To compute the proportion-wise breakup of cell populations using Markov chain Monte Carlo (MCMC) algorithms on a GPU.
    • To assess the model's scalability and efficiency with gene expression data.

    Main Methods:

    • Development of a new computational model utilizing Markov chain Monte Carlo (MCMC) algorithms.
    • Implementation of GPU computing for parallelized computation of cell population compositional breakup.
    • Analysis of quantitative polymerase chain reaction (qPCR) gene expression data.
    • Validation using both synthetic and real-world fibroblast gene expression datasets.

    Main Results:

    • The proposed model effectively analyzes cancer cell heterogeneity.
    • Computation time is independent of input data size due to parallelized computations.
    • The model demonstrates scalability with hundreds of gene expression datasets.
    • Accurate compositional breakup of cell populations was achieved.

    Conclusions:

    • The novel MCMC-based model provides an efficient and scalable solution for analyzing cancer cell heterogeneity.
    • GPU parallelization ensures computational efficiency, making the model suitable for large-scale gene expression analysis.
    • This approach enhances the understanding of complex cancer cell populations.