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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
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An information-theoretic approach to single cell sequencing analysis.

Michael J Casey1,2, Jörg Fliege1, Rubén J Sánchez-García3,4,5

  • 1Mathematical Sciences, University of Southampton, Southampton, UK.

BMC Bioinformatics
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new method to measure gene expression heterogeneity in single-cell sequencing data. This approach effectively identifies distinct cell types and improves data analysis for biological insights.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell sequencing (sc-Seq) experiments generate vast datasets.
  • Large sc-Seq data volumes do not always correlate with high information content.
  • Quantifying information in sc-Seq data is crucial for biological discovery.

Purpose of the Study:

  • To formally quantify information derived from sc-Seq experiments.
  • To establish a link between gene expression heterogeneity and cell types.
  • To develop an unsupervised clustering method for sc-Seq data.

Main Methods:

  • Formal quantification of information in sc-Seq experiments.
  • Decomposition of gene expression heterogeneity into inter- and intra-cluster components.
  • Utilizing heterogeneity as an objective function for clustering algorithms.

Main Results:

  • Demonstrated a direct correspondence between quantified information and gene expression heterogeneity.
  • Showed that heterogeneity can be decomposed into cell type-specific (inter-cluster) and within-cell type (intra-cluster) components.
  • Validated heterogeneity as a descriptor for cell type-associated gene expression patterns.

Conclusions:

  • Gene heterogeneity provides a biologically meaningful definition of cell types.
  • The developed measure of heterogeneity is non-parametric, intrinsic, and unbiased.
  • An efficient, unsupervised method for sc-Seq data clustering was developed and implemented in an R package.