Deciphering gene expression patterns using large-scale transcriptomic data and its applications.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Gene expression variability is influenced by factors like gender, race, and health status.
• Understanding these stochastic patterns is vital for identifying informative genes, sample classification, and disease research, particularly in cancer.
• Bulk and single-cell RNA sequencing (RNA-seq) provide distinct views of gene expression.

Purpose of the Study:

• To analyze gene expression distributions across diverse sample types (normal, cancer, bulk, single-cell).
• To challenge the assumption of normal distribution in gene expression data.
• To develop novel methods for gene selection and sample classification based on expression patterns.

Main Methods:

• Analysis of 11,252 bulk RNA-seq and 4,884 single-cell RNA-seq samples.
• Statistical modeling using 16 probability distributions to characterize gene expression.
• Development of a skewness-based metric and an improved naive Bayes classifier.

Main Results:

• Normal samples exhibit broader distribution ranges than cancer samples, which favor asymmetric distributions.
• Over 95.5% of genes display non-normal expression distributions, contradicting traditional assumptions.
• Significant differences in gene expression distributions were observed between bulk and single-cell RNA-seq data.
• A novel skewness metric identified biologically relevant genes with significant distribution variations.

Conclusions:

• Gene expression distributions are complex and often non-normal, necessitating advanced analytical approaches.
• Distribution characteristics offer a powerful basis for gene selection and sample classification in omics studies.
• The proposed methods enhance the accuracy of gene selection and classification in cancer research.