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Robust partial reference-free cell composition estimation from tissue expression.

Ziyi Li1, Zhenxing Guo1, Ying Cheng2

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.

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|March 14, 2020
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Summary
This summary is machine-generated.

Accurately estimating cell composition in heterogeneous tissues is crucial for omics data analysis. New computational methods, TOAST/-P and TOAST/+P, offer improved accuracy without requiring extensive reference panels.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Estimating cell composition is vital for analyzing high-throughput omics data from tissue samples.
  • Current cell composition quantification methods face limitations such as high cost, labor intensity, and technical constraints.
  • Existing computational approaches often rely on reference panels or exhibit low accuracy.

Purpose of the Study:

  • To develop novel computational algorithms for estimating cell composition in heterogeneous tissues.
  • To introduce TOols for the Analysis of heterogeneouS Tissues (TOAST/-P and TOAST/+P) as partial reference-free methods.
  • To improve the accuracy and robustness of cell composition estimation from gene expression profiles.

Main Methods:

  • Developed TOAST/-P and TOAST/+P, partial reference-free algorithms for cell composition estimation.
  • Incorporated cell-type-specific markers and prior knowledge into the estimation algorithms.
  • Validated methods through extensive simulation studies and real-world data analyses.

Main Results:

  • TOAST/-P and TOAST/+P demonstrate superior accuracy and robustness compared to existing methods.
  • The algorithms effectively estimate cell composition using gene expression profiles.
  • Successful application to real biological datasets confirmed the methods' utility.

Conclusions:

  • TOAST/-P and TOAST/+P provide accurate and reliable cell composition estimation for heterogeneous tissues.
  • These methods overcome limitations of traditional approaches, offering a valuable tool for omics data analysis.
  • The TOAST package is available as an R/Bioconductor package for broader accessibility.