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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution.

Hananeh Aliee¹, Fabian J Theis²

¹Institute of Computational Biology, Helmholtz Centre, Munich, Bayern 85764, Germany.

|July 22, 2021

Summary

This summary is machine-generated.

AutoGeneS is a new tool that automatically finds the best genes for analyzing cell types in bulk RNA samples. It accurately reveals cellular heterogeneity without needing prior marker gene knowledge.

Keywords:

bulk RNA-seq bulk deconvolution feature selection, marker genes multi-objective optimization single-cell RNA-seq

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Accurate cell-type proportions in tissues are crucial for understanding disease mechanisms.
Existing deconvolution methods for bulk RNA samples struggle with noisy data and similar cell types.
Gene selection significantly impacts the performance of deconvolution algorithms.

Purpose of the Study:

To introduce AutoGeneS, a novel platform for automated gene selection in bulk RNA deconvolution.
To reveal cellular heterogeneity by identifying discriminative genes without prior marker knowledge.
To improve the accuracy of cell-type proportion inference from bulk RNA sequencing data.

Main Methods:

AutoGeneS employs an automated approach to extract discriminative genes.
It optimizes gene selection by simultaneously minimizing cell-type correlation and maximizing cell-type distance.
The platform is applicable to diverse reference profiles, including single-cell and sorted cell data.

Main Results:

AutoGeneS accurately identifies cell-type proportions in bulk RNA samples.
Validation using flow cytometry confirmed the precision of AutoGeneS's inferred proportions.
The method effectively handles noisy reference profiles and closely correlated cell types.

Conclusions:

AutoGeneS provides a robust and automated solution for gene selection in deconvolution.
It enhances the understanding of cellular heterogeneity in complex biological samples.
The tool is accessible as a standalone Python package for broader research application.