Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics.
Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics.
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DOI：
10.7717/peerj.10670
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发表时间：
2021
期刊：
影响因子：
2.7
通讯作者：
Piermarocchi C
中科院分区：
文献类型：
--
作者：
Domanskyi S;Hakansson A;Bertus TJ;Paternostro G;Piermarocchi C
单细胞RNA测序分析(scRNA-seq)通常包括不同的步骤，包括质量控制、批次校正、聚类、细胞鉴定和表征以及可视化。ScRNA-seq数据量正在以极快的速度增长，改进这些步骤的新算法方法是提取更多生物信息的关键。在这里，我们介绍了两种基于投票算法和Hopfield分类器的细胞类型自动识别方法(即无需专家管理员)，(Ii)基于隔离森林的细胞异常量化方法，以及(Iii)基于Hopfield类能量函数的细胞表型景观可视化工具。这些新方法被整合到一个软件平台中，该平台包括许多其他最先进的方法，并为scRNA-seq分析提供了一个独立的工具包。我们提出了一套用于分析scRNA-seq数据的软件元素。这款基于Python的开源软件Digital Cell Sorter(DCS)包含了一套广泛的scRNA-seq分析方法工具包。我们使用来自外周血单个核细胞(PBMC)的大型数据集以及来自健康捐赠者和多发性骨髓瘤患者的骨髓样本的血浆细胞的数据来说明该软件的能力。我们通过评估新算法去卷积细胞混合物和检测PBMC数据中少量异常细胞的能力来测试新算法。可以通过PythonPackage Index(PyPI)下载和安装DCS工具包。该软件可以在安装后使用Python导入功能进行部署。源代码也可以在Zenodo：上下载。补充材料可在PeerJ Online上找到。
Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. 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. 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. We present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. 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. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data. The DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: . Supplemental Materials are available at PeerJ online.