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.

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