Abstract 2453: A new deconvolution algorithm for accurate assessing immune and stromal cell populations in mouse transcriptomic data

Abstract

Abstract Multiple deconvolution methods have been developed for investigating the heterogeneous immune and stromal (I/S) cell types in human cancer tissue by deciphering their relative abundances using transcriptomic data. However, there is a lack of a user-friendly software for mouse transcriptomic data deconvolution. Key challenges for developing such a tool include: (1) limited training data sets for available for deriving signature genes of I/S cell types in sporadic mouse data; (2) mouse models of diverse geno-/pheno-types may have varied expressions of I/S cell marker genes; (3) the transcriptomic data may be collected from mouse with certain levels of immuno-deficiency; and (4) the transcriptomic data may come from highly diverse experimental platforms. To solve these challenges, we (i) developed a novel non-parametric analysis method to derive potential I/S cell signature genes from a large collection of mouse data sets; (ii) implemented a low rank sub matrix identification method with a non-negative matrix factorization (NMF) based deconvolution method; and (iii) enabled the flexibility that certain I/S cell types may be absent if their respective cell markers do not form a significant low rank structure. The new method is applied to mouse prostate cancer data sets to infer the level of anti-cancer immune cell populations. Genes expressed by cancer cells that are negatively associated with the anti-cancer immune cells are further inferred, and compared with the results derived in human data. A user-friendly R package of the deconvolution method is released through GitHub. Citation Format: Xiaoyu Lu, Szu-wei Tu, Wennan Chang, Yan Huo, Pengcheng Wang, Yu Zhang, Chi Zhang, Sha Cao. A new deconvolution algorithm for accurate assessing immune and stromal cell populations in mouse transcriptomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2453.