Statistical Genomics

This volume provides a collection of protocols from researchers in the statistical genomics field. Chapters focus on integrating genomics with other omics data, such as transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and thorough, Statistical Genomics hopes that by covering these diverse and timely topics researchers are provided insights into future directions and priorities of pan-omics and the precision medicine era.

Includes cutting-edge methods and protocols Provides step-by-step detail essential for reproducible results Contains key notes and implementation advice from the experts

Contenu
Multi-omics data deconvolution and integration: new methods, insights and translational implications.- Multi-omics data deconvolution and integration: new methods, insights and translational implications.- Cell-type deconvolution of bulk DNA methylation data with EpiSCORE.- Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper.- Statistical methods for integrative clustering of multi-omics data.- Analysis of Single-Cell RNA-seq Data.- A Primer On Pre-Processing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data.- Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.- Statistical Analysis in ChIP-seq Related Applications.- Bioinformatics and Statistical Analysis of Microbiome Data.- Statistical and Computational Methods for Microbial Strain Analysis.- Statistics and machine learning in mass spectrometry-based metabolomics analysis.- Statistical and Computational Methods for Proteogenomic Data Analysis.- Pharmacogenomics and Statistical Analysis.- Statistical methods for disease risk prediction with genotype data.- Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-Omics.