Main description:

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

Contents:

Preface.- Part I Single-cell Analysis.- Computational and statistical methods for single-cell RNA sequencing data.- Pre-processing, dimension reduction, and clustering for single-cell RNA-seq data.- Integrative analyses of single-cell multi-omics data: a review from a statistical perspective.- Approaches to marker gene identification from single-cell RNA-sequencing data.- Model-based clustering of single-cell omics data.- Deep learning methods for single cell omics data.- Part II Network Analysis.- Probabilistic Graphical Models for Gene Regulatory Networks.- Additive conditional independence for large and complex biological structures.- Integration of Boolean and Bayesian Networks.- Computational methods for identifying microRNA-gene regulatory modules.- Causal inference in biostatistics.- Bayesian Balance Mediation Analysis in Microbiome Studies.- Part III Systems Biology.- Identifying genetic loci associated with complex trait variability.- Cell Type Specific Analysis for Gene Expression and DNA Methylation.- Recent development of computational methods in the field of epitranscriptomics.- Estimation of Tumor Immune Signatures from Transcriptomics Data.- Cross-Linking Mass Spectrometry Data Analysis.- Cis-regulatory Element Frequency Modules and their Phase Transition across Hominidae.- Improving tip-dating and rooting a viral phylogeny by modeling evolutionary rate as a function of time.