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E-raamat: Statistical Genomics

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  • Formaat: PDF+DRM
  • Sari: Methods in Molecular Biology 2629
  • Ilmumisaeg: 16-Mar-2023
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9781071629864
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Statistical GenomicsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Methods in Molecular Biology 2629
  • Ilmumisaeg: 16-Mar-2023
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9781071629864
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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.
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.
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