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Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF.

08:00 EDT 24th March 2020 | BioPortfolio

Summary of "Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF."

The rapid proliferation of single-cell RNA-Sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity has increased, most require significant user-tuning, are heavily reliant on dimension reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface.

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This article was published in the following journal.

Name: Bioinformatics (Oxford, England)
ISSN: 1367-4811
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Medical and Biotech [MESH] Definitions

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

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