The aim of this textbook is to train new researchers in analyzing high-throughput omics data by building fundamental skills instead of focusing on technology or platform-specific features that change every few years. The authors seek a balance between breadth and depth in the broad field. The book contains many real examples to illustrate the methodological concept and biological relevance. Computer lab materials (data and hands-on programming code) are included along with homework exercises to provide real-world data analysis experiences.
High-Throughput Omics Data. Experimental Design and Data Preprocessing.
Differential and Association Analysis. Dimension Reduction. Robust
Nonparametric Methods. Unsupervised Machine Learning and Clustering.
Supervised Machine Learning I: Methods. Supervised Machine Learning II:
Concept and Principles. Regularization Method. Bayesian Methods and
Applications. Network Analysis. Enrichment Analysis. Meta-Analysis and
Integrative Analysis. Selected Computational Algorithms. Reproducible
Research and Critical Thinking in Bioinformatics. Appendix: Case Studies.
George C. Tseng is an associate professor in the Department of Biostatistics at the University of Pittsburgh, Pennsylvania, USA
Zhiguang Huo and Tianzhu Ma are PhD students at the University of Pittsburgh, Pennsylvania, USA
