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scReClassify: post hoc cell type classification of single-cell rNA-seq data
BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929456/ https://www.ncbi.nlm.nih.gov/pubmed/31874628 http://dx.doi.org/10.1186/s12864-019-6305-x |
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author | Kim, Taiyun Lo, Kitty Geddes, Thomas A. Kim, Hani Jieun Yang, Jean Yee Hwa Yang, Pengyi |
author_facet | Kim, Taiyun Lo, Kitty Geddes, Thomas A. Kim, Hani Jieun Yang, Jean Yee Hwa Yang, Pengyi |
author_sort | Kim, Taiyun |
collection | PubMed |
description | BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. RESULTS: Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. CONCLUSIONS: scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify |
format | Online Article Text |
id | pubmed-6929456 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69294562019-12-30 scReClassify: post hoc cell type classification of single-cell rNA-seq data Kim, Taiyun Lo, Kitty Geddes, Thomas A. Kim, Hani Jieun Yang, Jean Yee Hwa Yang, Pengyi BMC Genomics Research BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. RESULTS: Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. CONCLUSIONS: scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify BioMed Central 2019-12-24 /pmc/articles/PMC6929456/ /pubmed/31874628 http://dx.doi.org/10.1186/s12864-019-6305-x Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Kim, Taiyun Lo, Kitty Geddes, Thomas A. Kim, Hani Jieun Yang, Jean Yee Hwa Yang, Pengyi scReClassify: post hoc cell type classification of single-cell rNA-seq data |
title | scReClassify: post hoc cell type classification of single-cell rNA-seq data |
title_full | scReClassify: post hoc cell type classification of single-cell rNA-seq data |
title_fullStr | scReClassify: post hoc cell type classification of single-cell rNA-seq data |
title_full_unstemmed | scReClassify: post hoc cell type classification of single-cell rNA-seq data |
title_short | scReClassify: post hoc cell type classification of single-cell rNA-seq data |
title_sort | screclassify: post hoc cell type classification of single-cell rna-seq data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929456/ https://www.ncbi.nlm.nih.gov/pubmed/31874628 http://dx.doi.org/10.1186/s12864-019-6305-x |
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