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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

A multi-index ROC-based methodology for high throughput experiments in gene discovery.

Dimitri Kagaris¹, Constantin T Yiannoutsos

¹Electrical and Computer Engineering Department, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USA. kagaris@engr.siu.edu

International Journal of Data Mining and Bioinformatics

|July 20, 2013

Summary

This study introduces a novel method for ranking differentially expressed genes in high-throughput experiments using Receiver Operating Characteristic (ROC) curves. The approach enhances gene discovery by identifying significant genes missed by traditional Area Under the Curve (AUC) analysis.

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Area of Science:

Bioinformatics
Genomics
Statistical Analysis

Background:

High-throughput experiments generate vast amounts of gene expression data.
Ranking differentially expressed genes is crucial for biological insights.
Traditional methods like Area Under the Curve (AUC) may miss important genes.

Purpose of the Study:

To develop an improved method for ranking differentially expressed genes.
To identify genes missed by standard AUC analysis in high-throughput experiments.
To leverage Receiver Operating Characteristic (ROC) curves for enhanced gene discovery.

Main Methods:

Generated four ROC curves per gene to account for unknown group labels.
Utilized classification indices based on ROC curves.
Identified genes ranked low by AUC but high by alternative indices.

Main Results:

Discovered genes that would be missed by AUC analysis alone.
Demonstrated the utility of alternative ROC-based indices for gene ranking.
Provided a more comprehensive approach to identifying differentially expressed genes.

Conclusions:

The proposed method offers a more sensitive approach to identifying differentially expressed genes.
Utilizing multiple ROC curves and alternative indices improves gene discovery.
This technique enhances the interpretation of high-throughput gene expression data.