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Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention
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 interpretabl...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312486/ https://www.ncbi.nlm.nih.gov/pubmed/37398136 http://dx.doi.org/10.1101/2023.05.29.542760 |
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author | Davalos, Oscar A. Heydari, A. Ali Fertig, Elana J. Sindi, Suzanne S. Hoyer, Katrina K. |
author_facet | Davalos, Oscar A. Heydari, A. Ali Fertig, Elana J. Sindi, Suzanne S. Hoyer, Katrina K. |
author_sort | Davalos, Oscar A. |
collection | PubMed |
description | 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 scRNAseq 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. |
format | Online Article Text |
id | pubmed-10312486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-103124862023-07-01 Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention Davalos, Oscar A. Heydari, A. Ali Fertig, Elana J. Sindi, Suzanne S. Hoyer, Katrina K. bioRxiv Article 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 scRNAseq 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. Cold Spring Harbor Laboratory 2023-06-01 /pmc/articles/PMC10312486/ /pubmed/37398136 http://dx.doi.org/10.1101/2023.05.29.542760 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Davalos, Oscar A. Heydari, A. Ali Fertig, Elana J. Sindi, Suzanne S. Hoyer, Katrina K. Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention |
title | Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention |
title_full | Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention |
title_fullStr | Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention |
title_full_unstemmed | Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention |
title_short | Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention |
title_sort | boosting single-cell rna sequencing analysis with simple neural attention |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312486/ https://www.ncbi.nlm.nih.gov/pubmed/37398136 http://dx.doi.org/10.1101/2023.05.29.542760 |
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