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{ "item_title" : "Omic Association Studies with R and Bioconductor", "item_author" : [" Juan R. González", "Alejandro Cáceres "], "item_description" : "After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data.Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic dataUses up-to-date methods to exploit omic dataPresents methods through specific examples and computing sessionsSupplemented by a website, including code, datasets, and solutions", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/13/834/056/1138340561_b.jpg", "price_data" : { "retail_price" : "165.00", "online_price" : "165.00", "our_price" : "165.00", "club_price" : "165.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Omic Association Studies with R and Bioconductor|Juan R. González

Omic Association Studies with R and Bioconductor

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Overview

After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data.

  • Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data
  • Uses up-to-date methods to exploit omic data
  • Presents methods through specific examples and computing sessions
  • Supplemented by a website, including code, datasets, and solutions

This item is Non-Returnable

Details

  • ISBN-13: 9781138340565
  • ISBN-10: 1138340561
  • Publisher: CRC Press
  • Publish Date: June 2019
  • Dimensions: 9.4 x 6.2 x 0.9 inches
  • Shipping Weight: 1.55 pounds
  • Page Count: 376

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