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Statistical Method for Integrative Platform Analysis: Application to Integration of Proteomic and Microarray Data.

¹Department of Mathematics and Statistics, York University, Toronto, ON, Canada, M3J 1P3. xingao@mathstat.yorku.ca.

Methods in Molecular Biology (Clifton, N.J.)

|November 1, 2015

Summary

This study introduces a novel Fisher's combined probability test for integrating multiple genomic data sources. The method consolidates p-values for robust, accurate, and convenient consolidated genomic inference, even with small sample sizes.

Keywords:

Data integration Fisher’s combined probability test Microarray Permutation Proteomics

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Area of Science:

Genomics
Biostatistics
Bioinformatics

Background:

Integrative analysis of multiple genomic datasets is crucial for robust biological insights.
Combining evidence from diverse data sources presents statistical challenges, particularly concerning data dependency and sample size limitations.

Purpose of the Study:

To develop a robust statistical method for integrative genomic data analysis.
To enhance Fisher's combined probability test for improved consolidated inference across varied genomic data sources.

Main Methods:

Fisher's combined probability test is adapted to combine individual p-values from multiple genomic data sources.
A chi-square test statistic is constructed for overall significance assessment.
The method is extended to handle dependent data sources and small sample sizes, overcoming limitations of exact distributions.

Main Results:

The proposed method provides a valid approach for combining results from independent genomic data sources.
The enhanced method demonstrates robustness to distributional assumptions and small sample sizes.
Consolidated inference is achieved through a convenient and practical statistical framework.

Conclusions:

The adapted Fisher's combined probability test offers a powerful and flexible tool for integrative genomic analysis.
This approach facilitates more reliable biological discoveries by effectively leveraging multiple genomic datasets.
The method's robustness makes it suitable for a wide range of genomic research scenarios.