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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Algebraic comparison of partial lists in bioinformatics.

Giuseppe Jurman¹, Samantha Riccadonna, Roberto Visintainer

¹Fondazione Bruno Kessler, Trento, Italy. jurman@fbk.eu

|May 23, 2012

Summary

This study introduces a permutation-based method to assess the stability of genomic feature lists, even when they are of unequal length. This approach helps understand variability in functional genomics findings, crucial for robust biological insights.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Functional genomics pipelines often yield ranked lists of genomic features for phenotype modeling.
Resampling or meta-analysis can result in multiple, potentially unequal-length feature lists.

Purpose of the Study:

To introduce a permutation-based method for evaluating "list stability" among unequal-length genomic feature lists.
To provide algorithms for assessing stability within the full feature set or restricted to identified features.

Main Methods:

Development of a permutation-based statistical approach.
Implementation of algorithms to evaluate list stability for embedded and partial lists.
Application to a large prostate cancer dataset with two cohorts (455 samples total).

Main Results:

The method successfully evaluates the variability and stability of genomic feature lists of differing lengths.
Gene profiles were identified and compared on a substantial prostate cancer dataset, demonstrating the method's utility.

Conclusions:

The introduced method offers a robust way to quantify list stability in functional genomics.
This facilitates more reliable interpretation of genomic feature selection across different datasets and analytical pipelines.