InstaPrism: an R package for fast implementation of BayesPrism

Abstract

Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness against model misspecifications by independent studies. However, given that BayesPrism relies on Gibbs sampling, it is orders of magnitude more computationally expensive than standard approaches. Here, we introduce the InstaPrism algorithm which re-implements BayesPrism in a derandomized framework by replacing the time-consuming Gibbs sampling steps in BayesPrism with a fixed-point algorithm. We demonstrate that the new algorithm is effectively equivalent to BayesPrism while providing a considerable speed advantage. InstaPrism is implemented as a standalone R package with C++ backend and can be accessed from GitHub at https://github.com/humengying0907/InstaPrism.