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bioRxiv · 10.1101/2024.09.01.610665

Interpretable scRNA-seq Analysis with Intelligent Gene Selection

Ni, T.·Zhang, X.·Jin, K.·Pei, G.·Xue, N.·Yan, G.·Li, T.·Li, B.
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Abstract

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA sequencing (scRNA-seq) data analysis faces multiple challenges, including high dimensionality, significant noise, and data loss. To effectively address these issues, we introduce AIGS, a robust and transparent single-cell analysis framework. AIGS utilizes an intelligent gene selection method that systematically identifies the most informative genes for clustering based on the normalized mutual information between pre-learned pseudo-labels and quantified genes. Additionally, AIGS incorporates a scale-invariant distance metric to assess cell-to-cell similarity, enhancing connections between homogenous cells and ensuring more accurate and robust results. Through comprehensive comparisons with state-of-the-art techniques, AIGS demonstrates superior performance in both clustering accuracy and multi-resolution visualization quality. Our in-depth analysis of clustering and visualization results further reveals that AIGS can uncover complex, stage-specific gene expression patterns during the same developmental cell stage.

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Ni, T., Zhang, X., Jin, K., Pei, G., Xue, N., Yan, G., Li, T., Li, B.. 2024-09-03. Interpretable scRNA-seq Analysis with Intelligent Gene Selection. https://doi.org/10.1101/2024.09.01.610665

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