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Estimating cell type composition using isoform expression one gene at a time.

Hillary M Heiling1, Douglas R Wilson1, Naim U Rashid1,2

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

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|December 18, 2021
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Summary
This summary is machine-generated.

This study introduces IsoDeconvMM, a new computational method for estimating cell type proportions in human tissues using RNA isoform expression data. It offers accurate cell type deconvolution even with single genes, improving gene expression analysis.

Keywords:
RNA-seqalternative splicingbulk expressiondeconvolutionisoform

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Human tissue samples are complex mixtures of diverse cell types.
  • Heterogeneity in cell types can complicate gene expression data analysis.
  • Accurate cell type composition is crucial for interpreting differential gene expression.

Purpose of the Study:

  • To develop a novel computational method for estimating cell type proportions from isoform-level gene expression data.
  • To introduce IsoDeconvMM, a tool capable of cell type deconvolution using RNA isoforms.
  • To assess the utility of isoform-level expression for cell type origin determination.

Main Methods:

  • Proposed IsoDeconvMM, a computational method for cell type fraction estimation.
  • Utilized isoform-level gene expression data for deconvolution.
  • Validated the method using a unique dataset of cell type-specific RNA-seq data from over 135 individuals.
  • Complemented empirical validation with simulations.

Main Results:

  • IsoDeconvMM accurately estimates cell type fractions from isoform expression data.
  • The method demonstrates effectiveness even when analyzing single genes.
  • Aggregating estimates from multiple genes enhances accuracy.
  • Performance was evaluated against biological variation across individuals.

Conclusions:

  • Isoform-level expression data provides valuable information for cell type deconvolution.
  • IsoDeconvMM offers a novel and flexible approach to estimating cell type proportions.
  • The method has implications for improving the analysis of complex tissue transcriptomics data.