ACTINN: automated identification of cell types in single cell RNA sequencing

Ma, Feiyang; Pellegrini, Matteo; Robinson, ed., Mark

doi:10.1093/bioinformatics/btz592

ACTINN: automated identification of cell types in single cell RNA sequencing

Journal Article · Mon Jul 29 00:00:00 EDT 2019 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btz592· OSTI ID:1922564

; Pellegrini, Matteo; Robinson, ed., Mark

Abstract Motivation

Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters.

Results

We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.

Availability and implementation

The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python.

Supplementary information

Supplementary data are available at Bioinformatics online.

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: FC02-02ER63421

OSTI ID:: 1922564

Alternate ID(s):: OSTI ID: 1799618

Journal Information:: Bioinformatics, Journal Name: Bioinformatics Journal Issue: 2 Vol. 36; ISSN 1367-4803

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (11)

Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks Cho, Hyunghoon; Berger, Bonnie; Peng, Jian Cell Systems, Vol. 7, Issue 2 https://doi.org/10.1016/j.cels.2018.05.017	journal	August 2018
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors Haghverdi, Laleh; Lun, Aaron T. L.; Morgan, Michael D. Nature Biotechnology, Vol. 36, Issue 5 https://doi.org/10.1038/nbt.4091	journal	April 2018
Integrating single-cell transcriptomic data across different conditions, technologies, and species Butler, Andrew; Hoffman, Paul; Smibert, Peter Nature Biotechnology, Vol. 36, Issue 5 https://doi.org/10.1038/nbt.4096	journal	April 2018
Massively parallel digital transcriptional profiling of single cells Zheng, Grace X. Y.; Terry, Jessica M.; Belgrader, Phillip Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms14049	journal	January 2017
Single-cell RNA sequencing technologies and bioinformatics pipelines Hwang, Byungjin; Lee, Ji Hyun; Bang, Duhee Experimental & Molecular Medicine, Vol. 50, Issue 8 https://doi.org/10.1038/s12276-018-0071-8	journal	August 2018
Deep generative modeling for single-cell transcriptomics Lopez, Romain; Regier, Jeffrey; Cole, Michael B. Nature Methods, Vol. 15, Issue 12 https://doi.org/10.1038/s41592-018-0229-2	journal	November 2018
Removal of batch effects using distribution-matching residual networks Shaham, Uri; Stanton, Kelly P.; Zhao, Jun Bioinformatics, Vol. 33, Issue 16 https://doi.org/10.1093/bioinformatics/btx196	journal	April 2017
Using neural networks for reducing the dimensions of single-cell RNA-Seq data Lin, Chieh; Jain, Siddhartha; Kim, Hannah Nucleic Acids Research, Vol. 45, Issue 17 https://doi.org/10.1093/nar/gkx681	journal	July 2017
SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles Xie, Peng; Gao, Mingxuan; Wang, Chunming Nucleic Acids Research, Vol. 47, Issue 8 https://doi.org/10.1093/nar/gkz116	journal	February 2019
Atlas of the Immune Cell Repertoire in Mouse Atherosclerosis Defined by Single-Cell RNA-Sequencing and Mass Cytometry Winkels, Holger; Ehinger, Erik; Vassallo, Melanie Circulation Research, Vol. 122, Issue 12 https://doi.org/10.1161/CIRCRESAHA.117.312513	journal	June 2018
CaSTLe – Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments Lieberman, Yuval; Rokach, Lior; Shay, Tal PLOS ONE, Vol. 13, Issue 10 https://doi.org/10.1371/journal.pone.0205499	journal	October 2018

Similar Records

ACTINN: automated identification of cell types in single cell RNA sequencing

Journal Article · Mon Jul 29 00:00:00 EDT 2019 · Bioinformatics · OSTI ID:1799618

Asc-Seurat: analytical single-cell Seurat-based web application

Journal Article · Wed Nov 17 19:00:00 EST 2021 · BMC Bioinformatics · OSTI ID:1831282

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network

Journal Article · Tue Feb 15 19:00:00 EST 2022 · Briefings in Bioinformatics · OSTI ID:1845371

ACTINN: automated identification of cell types in single cell RNA sequencing

Citation Formats

References (11)

Similar Records

Related Subjects