scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

Abstract Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state...

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Main Authors: Jose Alquicira-Hernandez, Anuja Sathe, Hanlee P. Ji, Quan Nguyen, Joseph E. Powell
Format: Article
Language:English
Published: BMC 2019-12-01
Series:Genome Biology
Online Access:https://doi.org/10.1186/s13059-019-1862-5
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spelling doaj-a5a91b84bca4496fada8fd7eb43c008c2020-12-13T12:39:39ZengBMCGenome Biology1474-760X2019-12-0120111710.1186/s13059-019-1862-5scPred: accurate supervised method for cell-type classification from single-cell RNA-seq dataJose Alquicira-Hernandez0Anuja Sathe1Hanlee P. Ji2Quan Nguyen3Joseph E. Powell4Garvan Institute of Medical ResearchDivision of Oncology, Department of Medicine, Stanford University School of MedicineDivision of Oncology, Department of Medicine, Stanford University School of MedicineInstitute for Molecular Bioscience, University of QueenslandGarvan Institute of Medical ResearchAbstract Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.https://doi.org/10.1186/s13059-019-1862-5
collection DOAJ
language English
format Article
sources DOAJ
author Jose Alquicira-Hernandez
Anuja Sathe
Hanlee P. Ji
Quan Nguyen
Joseph E. Powell
spellingShingle Jose Alquicira-Hernandez
Anuja Sathe
Hanlee P. Ji
Quan Nguyen
Joseph E. Powell
scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
Genome Biology
author_facet Jose Alquicira-Hernandez
Anuja Sathe
Hanlee P. Ji
Quan Nguyen
Joseph E. Powell
author_sort Jose Alquicira-Hernandez
title scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
title_short scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
title_full scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
title_fullStr scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
title_full_unstemmed scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
title_sort scpred: accurate supervised method for cell-type classification from single-cell rna-seq data
publisher BMC
series Genome Biology
issn 1474-760X
publishDate 2019-12-01
description Abstract Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.
url https://doi.org/10.1186/s13059-019-1862-5
work_keys_str_mv AT josealquicirahernandez scpredaccuratesupervisedmethodforcelltypeclassificationfromsinglecellrnaseqdata
AT anujasathe scpredaccuratesupervisedmethodforcelltypeclassificationfromsinglecellrnaseqdata
AT hanleepji scpredaccuratesupervisedmethodforcelltypeclassificationfromsinglecellrnaseqdata
AT quannguyen scpredaccuratesupervisedmethodforcelltypeclassificationfromsinglecellrnaseqdata
AT josephepowell scpredaccuratesupervisedmethodforcelltypeclassificationfromsinglecellrnaseqdata
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