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Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical

Brian B Nadel1,2, Meritxell Oliva3, Benjamin L Shou4

  • 1Department of Molecular Cellular and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA.

Briefings in Bioinformatics
|August 4, 2021
PubMed
Summary
This summary is machine-generated.

Estimating cell type proportions from gene expression data is crucial. This study compared 12 computational tools across diverse datasets, finding that reference profile selection significantly impacts accuracy for cell type deconvolution.

Keywords:
benchmarkingcell type deconvolutioncell type quantificationgene expression

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Accurate estimation of cell type composition is vital for laboratory research and clinical diagnostics.
  • Numerous computational tools have emerged to infer cell type abundance from gene expression data.
  • These tools rely on reference profiles of purified cell types for deconvolution.

Purpose of the Study:

  • To comprehensively compare the performance of 12 different cell type quantification tools.
  • To evaluate tool performance across 10 distinct reference profiles.
  • To assess accuracy using synthetic and clinical datasets.

Main Methods:

  • Evaluated 12 cell type deconvolution tools on over 4000 samples.
  • Utilized in vitro, in silico, and clinical (Framingham cohort) datasets with known cell proportions.
  • Quantified cell populations using electrical impedance cell counting for clinical samples.

Main Results:

  • Performance varied across tools and datasets; Estimating the Proportions of Immune and Cancer cells (EPIC) showed highest correlation on Framingham data, while Gene Expression Deconvolution Interactive Tool (GEDIT) had lowest error.
  • CIBERSORT and GEDIT demonstrated consistent accuracy across multiple datasets.
  • Optimal reference profile selection is tool-dependent, and suggested references are provided.

Conclusions:

  • Cell type deconvolution methods can yield high-quality results for estimating cell composition.
  • The choice of reference profile is a critical factor influencing the accuracy of deconvolution.
  • While most tools are rapid, some, like CIBERSORT, can have long runtimes on large datasets.