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Related Experiment Video

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A User-friendly and Powerful R Analysis of Large-scale Datasets

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

Zhiyue Tom Hu¹, Yuting Ye, Patrick A Newbury

¹Department of Biostatistics, University of California, Berkeley, USA, zyhu95@berkeley.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|March 14, 2019

Summary

This study addresses inconsistencies in pharmacogenomics data, enhancing drug-wise correlations. The novel framework improves data consistency and biological relevance for biomarker discovery.

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Area of Science:

Genomics
Computational Biology
Pharmacology

Background:

Pharmacogenomics datasets from different studies exhibit inconsistencies, limiting their use in tasks like biomarker discovery.
While cell-line correlations across studies are strong, drug-wise correlations are weak, indicating noise despite underlying consistency.

Purpose of the Study:

To develop a novel framework to resolve inconsistencies in large-scale pharmacogenomics datasets.
To enhance drug-wise correlations and improve the usability of existing and future pharmacogenomics data.

Main Methods:

A novel framework was developed to address data inconsistency across multiple pharmacogenomics datasets.
The method was applied to re-summarized and normalized datasets, and its performance was evaluated using various criteria.

Main Results:

The proposed method significantly boosts drug-wise correlation in pharmacogenomics datasets.
The corrected datasets were demonstrated to be both consistent and biologically meaningful.

Conclusions:

The developed framework effectively resolves inconsistencies in pharmacogenomics data, enhancing its utility for research.
The framework is extendable for future datasets, aiming to provide a "ground-truth" reference for the field.