Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets

Zhiyue Tom Hu, Yuting Ye, Patrick A. Newbury, Haiyan Huang, Bin Chen
doi: https://doi.org/10.1101/386896
Zhiyue Tom Hu
†,2Department of Biostatistics, University of California, Berkeley
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yuting Ye
†,2Department of Biostatistics, University of California, Berkeley
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Patrick A. Newbury
3,5Department of Pediatrics and Human Development, Michigan State University
3,5Department of Pediatrics and Human Development, Michigan State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Haiyan Huang
4Department of Statistics, University of California, Berkeley E-mails: , ,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zyhu95@berkeley.edu yeyt@berkeley.edu kyh0110@berkeley.edu
Bin Chen
3,5Department of Pediatrics and Human Development, Michigan State University
3,5Department of Pediatrics and Human Development, Michigan State University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

The inconsistency of open pharmacogenomics datasets produced by different studies limits the usage of pharmacogenomics in biomarker discovery. Investigation of multiple pharmacogenomics datasets confirmed that the pairwise sensitivity data correlation between drugs, or rows, across different studies (drug-wise) is relatively low, while the pairwise sensitivity data correlation between cell-lines, or columns, across different studies (cell-wise) is considerably strong. This common interesting observation across multiple pharmacogenomics datasets suggests the existence of subtle consistency among the different studies (i.e., strong cell-wise correlation). However, significant noises are also shown (i.e., weak drug-wise correlation) and have prevented researchers from comfortably using the data directly. Motivated by this observation, we propose a novel framework for addressing the inconsistency between large-scale pharmacogenomics data sets. Our method can significantly boost the drug-wise correlation and can be easily applied to re-summarized and normalized datasets proposed by others. We also investigate our algorithm based on many different criteria to demonstrate that the corrected datasets are not only consistent, but also biologically meaningful. Eventually, we propose to extend our main algorithm into a framework, so that in the future when more data-sets become publicly available, our framework can hopefully offer a “ground-truth” guidance for references.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted August 07, 2018.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets
Zhiyue Tom Hu, Yuting Ye, Patrick A. Newbury, Haiyan Huang, Bin Chen
bioRxiv 386896; doi: https://doi.org/10.1101/386896
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets
Zhiyue Tom Hu, Yuting Ye, Patrick A. Newbury, Haiyan Huang, Bin Chen
bioRxiv 386896; doi: https://doi.org/10.1101/386896

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3579)
  • Biochemistry (7523)
  • Bioengineering (5486)
  • Bioinformatics (20699)
  • Biophysics (10260)
  • Cancer Biology (7939)
  • Cell Biology (11584)
  • Clinical Trials (138)
  • Developmental Biology (6573)
  • Ecology (10144)
  • Epidemiology (2065)
  • Evolutionary Biology (13551)
  • Genetics (9502)
  • Genomics (12793)
  • Immunology (7887)
  • Microbiology (19456)
  • Molecular Biology (7618)
  • Neuroscience (41913)
  • Paleontology (307)
  • Pathology (1253)
  • Pharmacology and Toxicology (2181)
  • Physiology (3253)
  • Plant Biology (7008)
  • Scientific Communication and Education (1291)
  • Synthetic Biology (1942)
  • Systems Biology (5410)
  • Zoology (1108)