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iScience. 2020 Mar 27;23(3):100914. doi: 10.1016/j.isci.2020.100914. Epub 2020 Feb 14.

scID Uses Discriminant Analysis to Identify Transcriptionally Equivalent Cell Types across Single-Cell RNA-Seq Data with Batch Effect.

Author information

1
Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK.
2
School of Informatics, University of Edinburgh, Edinburgh Eh8 9AB, UK.
3
Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK. Electronic address: nizar.batada@gmail.com.

Abstract

The power of single-cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell type-dependent phenotypes, which rests on the accuracy of cell type identification. However, resolving cell types within and, thus, comparison of scRNA-seq data across conditions is challenging owing to technical factors such as sparsity, low number of cells, and batch effect. To address these challenges, we developed scID (Single Cell IDentification), which uses the Fisher's Linear Discriminant Analysis-like framework to identify transcriptionally related cell types between scRNA-seq datasets. We demonstrate the accuracy and performance of scID relative to existing methods on several published datasets. By increasing power to identify transcriptionally similar cell types across datasets with batch effect, scID enhances investigator's ability to integrate and uncover development-, disease-, and perturbation-associated changes in scRNA-seq data.

KEYWORDS:

Bioinformatics; Biological Sciences; Mathematical Biosciences; Omics; Transcriptomics

Conflict of interest statement

Declaration of Interests The authors declare that they have no competing interest.

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