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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Improved gene co-expression network quality through expression dataset down-sampling and network aggregation.

Franziska Liesecke¹, Johan-Owen De Craene², Sébastien Besseau²

¹EA2106 BBV, Université de Tours, Tours, 37200, France. franzi.liesecke@gmail.com.

Scientific Reports

|October 10, 2019

Summary

This summary is machine-generated.

Integrating many samples into gene co-expression networks may not improve performance. Down-sampling and network aggregation methods were tested to optimize gene association discovery from expression data.

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Gene co-expression networks are crucial for identifying gene functions and associations.
Publicly available expression data (microarrays, RNA-seq) facilitates expression profile creation.
Optimal sample size for network construction with diverse conditions remains unclear.

Purpose of the Study:

To investigate the impact of sample size on gene co-expression network construction.
To evaluate down-sampling and network aggregation strategies for improving gene association recovery.
To compare network performance using different sample sizes and integration methods.

Main Methods:

Utilized microarray and RNA-seq data from three plant species.
Implemented various down-sampling techniques to create subsets of samples.
Constructed co-expression networks from full and down-sampled datasets.
Tested six different network aggregation methods.
Assessed network performance in recovering known gene associations.

Main Results:

Network performance showed saturation with increasing sample size, suggesting diminishing returns.
Down-sampling methods demonstrated varying effectiveness in preserving known gene associations.
Network aggregation strategies offered potential improvements in performance compared to individual networks.
The optimal approach depended on the specific dataset and biological context.

Conclusions:

Simply increasing sample size does not guarantee better gene co-expression network performance.
Strategic down-sampling and network aggregation are valuable approaches for optimizing gene association discovery.
Further research is needed to refine these methods for diverse biological applications.