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Self-assembling manifolds in single-cell RNA sequencing data
Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795480/ https://www.ncbi.nlm.nih.gov/pubmed/31524596 http://dx.doi.org/10.7554/eLife.48994 |
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author | Tarashansky, Alexander J Xue, Yuan Li, Pengyang Quake, Stephen R Wang, Bo |
author_facet | Tarashansky, Alexander J Xue, Yuan Li, Pengyang Quake, Stephen R Wang, Bo |
author_sort | Tarashansky, Alexander J |
collection | PubMed |
description | Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks. |
format | Online Article Text |
id | pubmed-6795480 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-67954802019-10-17 Self-assembling manifolds in single-cell RNA sequencing data Tarashansky, Alexander J Xue, Yuan Li, Pengyang Quake, Stephen R Wang, Bo eLife Computational and Systems Biology Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks. eLife Sciences Publications, Ltd 2019-09-16 /pmc/articles/PMC6795480/ /pubmed/31524596 http://dx.doi.org/10.7554/eLife.48994 Text en © 2019, Tarashansky et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Tarashansky, Alexander J Xue, Yuan Li, Pengyang Quake, Stephen R Wang, Bo Self-assembling manifolds in single-cell RNA sequencing data |
title | Self-assembling manifolds in single-cell RNA sequencing data |
title_full | Self-assembling manifolds in single-cell RNA sequencing data |
title_fullStr | Self-assembling manifolds in single-cell RNA sequencing data |
title_full_unstemmed | Self-assembling manifolds in single-cell RNA sequencing data |
title_short | Self-assembling manifolds in single-cell RNA sequencing data |
title_sort | self-assembling manifolds in single-cell rna sequencing data |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795480/ https://www.ncbi.nlm.nih.gov/pubmed/31524596 http://dx.doi.org/10.7554/eLife.48994 |
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