massiR: a method for predicting the sex of samples in gene expression microarray datasets.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Gene expression microarrays are vital for transcriptome-wide analyses.
• Publicly available microarray datasets often lack crucial sample sex metadata.
• This data gap limits secondary analyses, especially those considering sex-based gene expression differences.

Purpose of the Study:

• To introduce the massiR R package for predicting sample sex from microarray data.
• To enable researchers to identify or confirm sample sex in mammalian datasets.
• To facilitate the reuse of public gene expression data in sex-specific research.

Main Methods:

• Utilizes microarray probes targeting Y chromosome genes.
• Implements unsupervised clustering algorithms for sample classification.
• Classifies samples into distinct male and female groups.

Main Results:

• Successfully predicts sample sex in microarray datasets.
• Provides an efficient method for sex determination.
• Enhances the utility of existing gene expression data.

Conclusions:

• massiR offers a robust solution for inferring sample sex from microarray data.
• The package improves the quality and scope of meta-analyses.
• Facilitates more comprehensive transcriptome-wide expression studies.