Single‐Cell Transcriptomic Analysis

Zheng, Zhihong; Chen, Enguo; Lü, Weiguo; Mouradian, Gary; Hodges, Matthew R.; Liang, Mingyu; Liu, Pengyuan; Lu, Yan

doi:10.1002/cphy.c190037

Cited by 12 publications

(10 citation statements)

References 122 publications

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“…The analysis of sequencing data is the last part of SCS. Uneven coverage in the data and chimeric reads with great regularity are surprisingly common ( 18 ). Standardizing the nucleic acid library and performing hybrid analysis for sequencing data with high genetic similarity using SmashCell, Velvet-SC, and SPAdes software are strategies to provide more reliable results ( 19 ).…”

Section: Brief Glance Of Scs Launchingmentioning

confidence: 99%

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

et al. 2021

Front. Oncol.

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Tumorigenesis refers to the process of clonal dysplasia that occurs due to the collapse of normal growth regulation in cells caused by the action of various carcinogenic factors. These “successful” tumor cells pass on the genetic templates to their generations in evolutionary terms, but they also constantly adapt to ever-changing host environments. A unique peculiarity known as intratumor heterogeneity (ITH) is extensively involved in tumor development, metastasis, chemoresistance, and immune escape. An understanding of ITH is urgently required to identify the diversity and complexity of the tumor microenvironment (TME), but achieving this understanding has been a challenge. Single-cell sequencing (SCS) is a powerful tool that can gauge the distribution of genomic sequences in a single cell and the genetic variability among tumor cells, which can improve the understanding of ITH. SCS provides fundamental ideas about existing diversity in specific TMEs, thus improving cancer diagnosis and prognosis prediction, as well as improving the monitoring of therapeutic response. Herein, we will discuss advances in SCS and review SCS application in tumors based on current evidence.

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Section: Brief Glance Of Scs Launchingmentioning

confidence: 99%

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

et al. 2021

Front. Oncol.

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“…Several platforms have emerged over the last decade for high-throughput RNA sequencing at the single-cell resolution (Zheng et al, 2020). Obtaining biological insights from single-cell data, however, requires complex computational analysis.…”

Section: Introductionmentioning

confidence: 99%

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics

Domanskyi

Hakansson

Bertus

et al. 2021

PeerJ

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Motivation 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. Results 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. Availability 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: DOI 10.5281/zenodo.2533377. Supplementary information Supplemental Materials are available at PeerJ online.

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“…Several platforms have emerged over the last decade for highthroughput RNA sequencing at the single-cell resolution (32). Obtaining biological insights from single-cell data, however, requires complex computational analysis.…”

Section: Introductionmentioning

confidence: 99%

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics

Domanskyi

Hakansson

Bertus

et al. 2020

Preprint

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Motivation: 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. Results: 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. Availability: 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: doi.org/10.5281/zenodo.2533377.

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Single‐Cell Transcriptomic Analysis

Cited by 12 publications

References 122 publications

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

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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