Biology subjects

Tai, A.-S.

Publications and source records attributed to Tai, A.-S..

3 recordsLinked to original sources

bioRxiv2019

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

Gene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to clarify the role of the immune system in the progression and development of cancer through analyzing the immune cell components of tumors. To that end, many deconvolution methods have been proposed for inferring cell subpopulations within tissues. Nevertheless, two problems limit the practicality of current approaches. First, all approaches use external purified data to preselect cell type-specific genes that contribute to deconvolution. However, some types of cells cannot be found in purified profiles and the genes specifically over- or under-expressed in them cannot be identified. This is particularly a problem in cancer studies. Hence, a preselection strategy that is independent from deconvolution is inappropriate. The second problem is that existing approaches do not recover the expression profiles of unknown cells present in bulk tissues, which results in biased estimation of unknown cell proportions. Furthermore, it causes the shift-invariant property of deconvolution to fail, which then affects the estimation performance. To address these two problems, we propose a novel deconvolution approach, BayICE, which employs hierarchical Bayesian modeling with stochastic search variable selection. We develop a comprehensive Markov chain Monte Carlo procedure through Gibbs sampling to estimate cell proportions, gene expression profiles, and signature genes. Simulation and validation studies illustrate that BayICE outperforms existing deconvolution approaches in estimating cell proportions. Subsequently, we demonstrate an application of BayICE in the RNA sequencing of patients with non-small cell lung cancer. The model is implemented in the R package \"BayICE\" and the algorithm is available for download.

genomics↗

bioRxiv2019

A sparse negative binomial classifier with covariateadjustment for RNA-seq data

Supervised machine learning methods have been increasingly used in biomedical research and in clinical practice. In transcriptomic applications, RNA-seq data have become dominating and have gradually replaced traditional microarray due to its reduced background noise and increased digital precision. Most existing machine learning methods are, however, designed for continuous intensities of microarray and are not suitable for RNA-seq count data. In this paper, we develop a negative binomial model via generalized linear model framework with double regularization for gene and covariate sparsity to accommodate three key elements: adequate modeling of count data with overdispersion, gene selection and adjustment for covariate effect. The proposed method is evaluated in simulations and two real applications using cervical tumor miRNA-seq data and schizophrenia post-mortem brain tissue RNA-seq data to demonstrate its superior performance in prediction accuracy and feature selection.

genomics↗

bioRxiv2018

Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations

Multistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.

bioinformatics↗