Statistical and Computational Methods for Comparing High-Throughput Data from Two Conditions

The development of high-throughput biological technologies have enabled researchers to simultaneously perform analysis on thousands of features (e.g., genes, genomic regions, and proteins). The most common goal of analyzing high-throughput data is to contrast two conditions, to identify ``interesting’’ features, whose values differ between two conditions. How to contrast the features from two conditions to extract useful information from high-throughput data, and how to ensure the reliability of identified features are two increasingly pressing challenge to statistical and computational science. This dissertation aim to address these two problems regarding analysing high-throughput data from two conditions.My first project focuses on false discovery rate (FDR) control in high-throughput data analysis from two conditions. FDR is defined as the expected proportion of uninteresting features among the identified ones. It is the most widely-used criterion to ensure the reliability of the interesting features identified. Existing bioinformatics tools primarily control the FDR based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions, two requirements that are often unmet in biological studies. In Chapter \ref{chap:clipper}, we propose Clipper, a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper is applicable to identifying both enriched and differential features from high-throughput biological data of diverse types. In comprehensive simulation and real-data benchmarking, Clipper outperforms existing generic FDR control methods and specific bioinformatics tools designed for various tasks, including peak calling from ChIP-seq data, and differentially expressed gene identification from bulk or single-cell RNA-seq data. Our results demonstrate Clipper's flexibility and reliability for FDR control, as well as its broad applications in high-throughput data analysis. My second project focuses on alignment of multi-track epigenomic signals from different samples or conditions. The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign can also detect common chromatin state patterns across multiple epigenomes from conditions, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns