==== Front
bioRxiv
BIORXIV
bioRxiv
Cold Spring Harbor Laboratory

10.1101/2023.05.29.542760
preprint
1
Article
Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention
Davalos Oscar A. http://orcid.org/0000-0002-9608-5701

Heydari A. Ali http://orcid.org/0000-0002-9141-3809

Fertig Elana J. http://orcid.org/0000-0003-3204-342X

Sindi Suzanne S. http://orcid.org/0000-0003-2742-4332

Hoyer Katrina K. http://orcid.org/0000-0001-5443-0733

01 6 2023
2023.05.29.542760http://biorxiv.org/lookup/doi/10.1101/2023.05.29.542760
nihpp-2023.05.29.542760.pdf
A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scR-NAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNA’s performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.
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