Statistical Genomics

ISBN-13: 9781071629857 / Angielski / Twarda / 2023 / 302 str.

Brooke Fridley; Xuefeng Wang

Opis

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.

Spis treści

1. Multi-omics data deconvolution and integration: new methods, insights and translational implications

Xuefeng Wang and Brooke L Fridley

 

2. Multi-omics data deconvolution and integration: new methods, insights and translational implications

Xuefeng Wang and Brooke L Fridley

 

3. Cell-type deconvolution of bulk DNA methylation data with EpiSCORE

Tianyu Zhu and Andrew E. Teschendorff

4. Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper

Chloé B. Steen, Bogdan A. Luca, Ash A. Alizadeh, Andrew J. Gentles, and Aaron M. Newman

 

5. Statistical methods for integrative clustering of multi-omics data

Prabhakar Chalise, Deukwoo Kwon, Brooke L. Fridley, and Qianxing Mo

 

6. Analysis of Single-Cell RNA-seq Data

Xiaoru Dong

 

7. A Primer On Pre-Processing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data

Oscar Ospina, Alex Soupir, and Brooke L. Fridley

 

8. Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Julia Wrobel, Coleman Harris, and Simon Vandekar

 

9. Statistical Analysis in ChIP-seq Related Applications

Mingxiang Teng

 

10. Bioinformatics and Statistical Analysis of Microbiome Data

Youngchul Kim

 

11. Statistical and Computational Methods for Microbial Strain Analysis

Siyuan Ma and Hongzhe Li

 

12. Statistics and machine learning in mass spectrometry-based metabolomics analysis

Sili Fan, Christopher M. Wilson, Brooke L. Fridley, and Qian Li

 

13. Statistical and Computational Methods for Proteogenomic Data Analysis

Xiaoyu Song

 

14. Pharmacogenomics and Statistical Analysis

Haimeng Bai, Xueyi Zhang, and William S. Bush

 

15. Statistical methods for disease risk prediction with genotype data

Xiaoxuan Xia, Yexian Zhang, Yingying Wei, and Maggie Haitian Wang

 

16. Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-Omics

Xueyuan Cao, Abdelraham H. Elsayed, and Stanley B. Pounds