![]() Generally, the first part of the analysis includes quality control (QC) of raw base call (BCL) files and alignment of reads with the reference genome, followed by their quantification at the gene-level. Obtaining biological insights from single-cell data, however, requires complex computational analysis. Several platforms have emerged over the last decade for high-throughput RNA sequencing at the single-cell resolution ( Zheng et al., 2020). Supplemental Materials are available at PeerJ online. Source code is also available for download on Zenodo: DOI 10.5281/zenodo.2533377. The software can be deployed using the Python import function following installation. The DCS toolkit is available for download and installation through the Python Package Index (PyPI). We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. ![]() This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We present a suite of software elements for the analysis of scRNA-seq data. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis. Here, we introduce: (i) two methods for automatic cell type identification (i.e., without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. ![]() PeerJ 9: e10670 Īnalysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics. Cite this article Domanskyi S, Hakansson A, Bertus TJ, Paternostro G, Piermarocchi C. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. ![]() 2 Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA DOI 10.7717/peerj.10670 Published Accepted Received Academic Editor Zhaohui Qin Subject Areas Bioinformatics, Computational Biology, Molecular Biology, Computational Science Keywords Transcriptome analysis software, Single cell RNA sequencing, Hopfield classifier, Hopfield landscapes visualization, Automatic cell type identification, Consensus annotation, Anomaly detection Copyright © 2021 Domanskyi et al.
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