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SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics

Xiaoyu Lu1, Szu-Wei Tu1, Wennan Chang2

  • 1Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis.

Briefings in Bioinformatics
|November 24, 2020
PubMed
Summary

Semi-Supervised Mouse data Deconvolution (SSMD) accurately identifies cell types and proportions in mouse tissues, overcoming challenges from genetic variations and diverse platforms. This method enhances the study of the mouse tissue microenvironment.

Keywords:
cancer microenvironmentmouse omics datasemi-supervised learningtissue data deconvolution

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Mouse models are crucial for biological research, but transcriptomic data deconvolution is complicated by genetic and physiological variations.
  • Existing methods struggle with dataset-specific cell types and marker genes, limiting accuracy in diverse experimental scenarios.

Purpose of the Study:

  • To develop a robust method for deconvoluting mouse transcriptomic data, addressing challenges posed by genetic diversity and varied experimental platforms.
  • To accurately identify cell types and estimate their proportions within the mouse tissue microenvironment.

Main Methods:

  • Developed Semi-Supervised Mouse data Deconvolution (SSMD), a novel nonparametric method for discovering dataset-specific cell type signature genes.
  • Employed a community detection approach to define cell types and marker genes, coupled with constrained matrix decomposition for robust proportion estimation.
  • Designed SSMD to be resilient to diverse experimental platforms and handle small sample sizes.

Main Results:

  • SSMD effectively addresses challenges including varied cell types, diverse platforms, and limited training data in mouse transcriptomics.
  • The method accurately estimates proportions for 35 cell types across blood, inflammatory, central nervous, and hematopoietic systems.
  • In silico and experimental validations confirm SSMD's high sensitivity and accuracy in cell type identification and proportion prediction compared to existing methods.

Conclusions:

  • SSMD provides a sensitive and accurate solution for deconvoluting mouse transcriptomic data, enhancing the study of complex tissue microenvironments.
  • The method's ability to adapt to dataset-specific variations and diverse platforms makes it a valuable tool for researchers.
  • A user-friendly R package and web server are available, facilitating broader adoption and application of SSMD.