This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology.
Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling and statistical network analysis.
Chapter 1: Road to Statistical Bioinformatics
Chapter 2: Probability concepts and distributions for analyzing large biological data
Chapter 3: Quality control of high throughput biological data
Chapter 4: Statistical testing and significance for large biological data analysis
Chapter 5: Advance statistical modeling and inference on large biological data
Chapter 6: Clustering: unsupervised learning in large screening biological data
Chapter 7: Classification: supervised learning in large screening biological data
Chapter 8: Multi-dimensional analysis and visualization on large biological data
Chapter 9: Experimental designs on high throughput biological experiments
Chapter 10: Statistical resampling techniques for large biological data analysis
Chapter 11: Statistical network analysis for biological systems and pathways
Chapter 12: Advances in current statistical genetics and association studies
Chatper 13: R and Bioconductor packages in bioinformatics
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