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Modeling zero inflation is not necessary for spatial transcriptomics.

Peiyao Zhao1,2, Jiaqiang Zhu1,2, Ying Ma1,2

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.

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|May 18, 2022
PubMed
Summary
This summary is machine-generated.

Excessive zeros in spatial transcriptomics data are not due to zero inflation. Analysis of 20 datasets shows count models without zero inflation are suitable for spatial transcriptomics gene expression.

Keywords:
Negative binomial modelOverdispersionPoisson modelSpatial transcriptomicsZero inflation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics technologies profile gene expression with spatial localization.
  • Recent data often present as sparse counts with excessive zero values.

Purpose of the Study:

  • To characterize distributional properties of spatial transcriptomics count data.
  • To understand the statistical nature of zero values in this data.

Main Methods:

  • Comprehensive analysis of 20 spatial transcriptomics datasets from 11 distinct technologies.
  • Characterization of gene expression count data distribution and zero value properties.
  • Evaluation of Poisson and negative binomial models for gene expression fitting.

Main Results:

  • A substantial fraction of genes exhibit overdispersion and/or zero inflation beyond Poisson models.
  • Overdispersed genes significantly overlap with zero-inflated genes.
  • Poisson or negative binomial models adequately fit most genes across technologies.
  • Gene expression heterogeneity and cell type distribution are key sources of overdispersion/zero inflation.
  • Focusing on homogeneous tissue locations or controlling for cell types reduces detected overdispersion/zero inflation, often making Poisson models sufficient.

Conclusions:

  • Provides comprehensive evidence that excessive zeros in spatial transcriptomics are not caused by zero inflation.
  • Supports the use of count models lacking a zero inflation component for spatial transcriptomics data analysis.