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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...

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Detalles Bibliográficos
Autores principales: Tarashansky, Alexander J, Xue, Yuan, Li, Pengyang, Quake, Stephen R, Wang, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2019
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.
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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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