Relative effect size-based profiles as an alternative to differentiation analysis in multi-species single-cell...

Area of Science:

• Genomics
• Immunology
• Computational Biology

Background:

• Multispecies studies are crucial for understanding cross-species pathogen dynamics and disease mechanisms.
• Analyzing multispecies gene expression via single-cell RNA sequencing (scRNA-seq) faces challenges like annotation inconsistencies and batch effects, especially with small sample sizes.

Purpose of the Study:

• To demonstrate the suitability of an effect size profiling approach for fusing multispecies scRNA-seq data from small samples.
• To establish effect size profiles as a tool for linking cell type clusters across different organisms and identifying differentially regulated genes.

Main Methods:

• Developed an analysis pipeline based on effect size metric profiles within cell clusters, substituting traditional p-value-based differential expression analysis.
• Tested and validated the algorithms on existing and newly generated scRNA-seq data from human and bovine peripheral blood mononuclear cells stimulated with Mycobacterium tuberculosis.

Main Results:

• Effect size profiles successfully linked human and bovine cell types across datasets.
• Effect size ratios identified differentially regulated genes, which were experimentally confirmed using qPCR.
• Demonstrated that effect size profiling is a valid alternative to traditional methods when batch effects are prominent in multispecies scRNA-seq data.

Conclusions:

• Effect size profiling provides a robust method for multispecies gene expression analysis, particularly for small sample sizes and complex datasets.
• This approach effectively overcomes common challenges in scRNA-seq data integration, enabling cross-species cell type identification and gene discovery.