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.
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