scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
By José Alquicira-Hernández, Anuja Sathe, Hanlee P. Ji, Quan Nguyen, Joseph E. Powell. Genome Biology, 2019
401 citations recorded by the source corpus.
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/.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/b53dc4b9dde4d7a7d0fa068c2b07d92a505be605
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In Molecular Biology
Citations in the corpus
- scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network (2021, methodology): “Several methods including Sin-gleR (8), CHETAH (9), scMap (10), scID (11), scPred (12), ACTINN (13), CellAssign (14), Garnett (15), SCINA (16), singleCellNet (17) and support vector machine (SVM) belong to this category as described in the recent review (18).”
- WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics (2024, methodology): “Therefore, for small input gene lists, we recommend users to verify W ebGestalt’ s outputs with machine learning-based approaches such as scPred ( 24 ) and SingleCellNet ( 25 ).”