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MerQuaCo: a computational tool for quality control in image-based spatial transcriptomics.

Naomi Martin1, Paul Olsen1, Jacob Quon1

  • 1Allen Institute for Brain Science, 615 Westlake Ave N, Seattle WA.

Biorxiv : the Preprint Server for Biology
|December 16, 2024
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics data quality is often imperfect. We identified common issues and developed MerQuaCo, an open-source tool to automatically detect and quantify these imperfections for more accurate cell analysis.

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Area of Science:

  • Spatial transcriptomics
  • Genomics
  • Bioinformatics

Background:

  • Image-based spatial transcriptomics enables cell population identification and gene expression analysis in intact tissues.
  • Analysis of large, high-dimension spatial datasets is facilitated by open-source software.
  • Current methods for assessing spatial transcriptomics data quality are manual, subjective, and labor-intensive.

Purpose of the Study:

  • To characterize common imperfections in spatial transcriptomics datasets.
  • To assess the impact of these imperfections on downstream analyses, such as cell type labeling.
  • To develop an automated tool for quantifying data quality in spatial transcriptomics.

Main Methods:

  • Analysis of 641 fresh-frozen adult mouse brain sections from the Vizgen MERSCOPE platform.
  • Identification and quantification of common data imperfections including tissue loss, detachment, dropped images, and variable detection probability.
  • Development of MerQuaCo, an open-source computational tool for automated detection and quantification of spatial transcriptomics data imperfections.

Main Results:

  • Common imperfections identified include local tissue loss, detachment from coverslip, dropped images, spatial variation in detection probability, and inter-experiment differences.
  • These imperfections can significantly impact the accuracy of cell type labels derived from spatial transcriptomics data.
  • MerQuaCo successfully detects and quantifies these imperfections without user input, enabling objective data quality assessment.

Conclusions:

  • Spatial transcriptomics data quality assessment is crucial for accurate biological interpretation.
  • MerQuaCo provides a rigorous and objective method for evaluating spatial transcriptomics data quality.
  • This tool facilitates the selection of high-quality sections for further analysis, improving the reliability of spatial transcriptomics studies.