Titlebook: Statistical Genomics; Brooke Fridley,Xuefeng Wang Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to Sp

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Statistical and Computational Methods for Microbial Strain Analysis,cessfully retrieved with culture-independent techniques using metagenomic sequencing. Such a strain variability has been increasingly shown to display additional phenotypic heterogeneities that affect host health, such as virulence, transmissibility, and antibiotics resistance. New statistical and c

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Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis,ses in mass spectrometry metabolomics studies, with illustration by example datasets. The missing peak recovery includes simple imputation by zero or limit of detection, regression-based or distribution-based imputation, and prediction by random forest. The batch effect can be removed by data-driven

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Pharmacogenomic and Statistical Analysis, many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical ge

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Statistical Methods for Disease Risk Prediction with Genotype Data, SNPs is to construct prediction models for assessing disease risk. Here, we introduce prediction methods for human traits using SNPs data, including the polygenic risk score (PRS), linear mixed models (LMMs), penalized regressions, and methods for controlling population stratification.

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Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics,s for the study of extremely rare diseases; this unique dynamic creates a research environment in which problems with high-dimension and low sample size are commonplace. Here, we present a few statistical methods that we have developed for our research setting and believe will prove valuable in othe

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1064-3745 ation advice from the experts.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 suc

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Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper,atially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.

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