DoRC: Discovery of rare cells from ultra-large scRNA-seq data

Xiang Chen; Fang-Xiang Wu; Jin Chen; Min Li

doi:10.1109/BIBM47256.2019.8983250

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

DoRC: Discovery of rare cells from ultra-large scRNA-seq data

Year: 2019, Pages: 111-116

DOI Bookmark: 10.1109/BIBM47256.2019.8983250

Authors

Xiang Chen, School of Computer Science and Engineering, Central South University,Changsha,China
Fang-Xiang Wu, University of Saskatchewan,Division of Biomedical Engineering,Department of Mechanical Engineering,Saskatoon,Canada
Jin Chen, Institute of Biomedical Informatics, University of Kentucky,Lexington,USA
Min Li, School of Computer Science and Engineering, Central South University,Changsha,China

Abstract

The advent of droplet-based transcriptomics platforms has enabled parallel screening over thousands or millions of cells. One of the challenging issues is to identify the rare cells from the ultra-large scRNA-seq data. Existing algorithms to find rare cells are time consuming or memory-exhausting. We propose an efficient and accurate method, Discovery of Rare Cells (DoRC). The rareness scores generated by DoRC can help biologists focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. We also demonstrate the efficacy of DoRC in delineating human blood dendritic cell sub-types using ~68k single-cell expression profiles of human blood cells. DoRC can recover artificially planted rare cells and is sensitive to cell type identities as well.

Like what you’re reading?

Already a member?

Get this article FREE with a new membership!

Integration of Multiple scRNA-Seq Datasets on the Autoencoder Latent Space
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Multi-View Clustering With Graph Learning for scRNA-Seq Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics
ESR: Optimizing Gene Feature Selection for scRNA-seq Data
2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom)
A deep graph convolution network with attention for clustering scRNA-seq data
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Computational Methods for scRNA-seq Analysis at Cell Level
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
An Effective Biclustering-Based Framework for Identifying Cell Subpopulations From scRNA-seq Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
sigRGCN: A Robust Residual Graph Convolutional Network for scRNA-Seq Data Clustering
IEEE Transactions on Computational Biology and Bioinformatics
Enhancing Single-Cell RNA-Seq Data Completeness With a Graph Learning Framework
IEEE Transactions on Computational Biology and Bioinformatics
scGANCL: Bidirectional Generative Adversarial Network for Imputing scRNA-Seq Data With Contrastive Learning
IEEE Transactions on Computational Biology and Bioinformatics

DoRC: Discovery of rare cells from ultra-large scRNA-seq data

Authors

Abstract

Related Articles