MULTISCALE POISSON PROCESS APPROACHES FOR DETECTING AND ESTIMATING DIFFERENCES FROM HIGH-THROUGHPUT SEQUENCING ASSAYS

• 成果类型: Article
• 署名作者: Shim, Heejung; Xing, Zhengrong; Pantaleo, Ester; Luca, Francesca; Pique-Regi, Roger; Stephens, Matthew
• 署名单位: University of Melbourne; University of Chicago; Wayne State University; Wayne State University; Wayne State University; University of Chicago
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1828
• 发表日期: 2024
• 页码: 1773-1788
• 关键词: rna-seq; expression analysis; open chromatin; in-vivo; association

摘要

Estimating and testing for differences in molecular phenotypes (e.g., gene expression, chromatin accessibility, transcription factor binding) across conditions is an important part of understanding the molecular basis of gene regulation. These phenotypes are commonly measured using high-throughput high-resolution count data that reflect how the phenotypes vary along the genome. Multiple methods have been proposed to help exploit these highresolution measurements for differential expression analysis. However, they ignore the count nature of the data, instead using normal distributions that work well only for data with large sample sizes or high counts. Here we develop count-based methods to address this problem. We model the data for each sample using an inhomogeneous Poisson process with spatially structured underlying intensity function and then, building on multiscale models for the Poisson process, estimate and test for differences in the underlying intensity function across samples (or groups of samples). Using both simulation and real ATAC-seq data, we show that our method outperforms previous normal-based methods, especially in situations with small sample sizes or low counts.