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Influence of Cell-type Ratio on Spatially Resolved Single-cell Transcriptomes using the Tangram Algorithm: Based on

Can Cui1, Shunxing Bao1, Jia Li2

  • 1Vanderbilt University, Nashville TN 37215, USA.

Proceedings of Spie--The International Society for Optical Engineering
|June 16, 2023
PubMed
Summary

The Tangram algorithm aligns single-cell RNA sequencing data to spatial data. Mismatched cell-type ratios between datasets negatively impact Tangram

Keywords:
Cell classificationMulti-modality mappingSingle-cell transcriptomesSpatial data

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Area of Science:

  • Computational biology
  • Spatial transcriptomics
  • Single-cell genomics

Background:

  • The Tangram algorithm aligns single-cell (sc/snRNA-seq) data to spatial data for annotation projection.
  • Cell composition differences between sc/snRNA-seq and spatial data due to heterogeneous cell distribution are common.
  • Previous studies have not addressed Tangram's performance with mismatched cell-type ratios.

Purpose of the Study:

  • To investigate the impact of differing cell-type ratios on Tangram algorithm performance.
  • To evaluate Tangram's accuracy when mapping single-cell data to spatial data with varying cell compositions.
  • To provide quantitative insights into the robustness of Tangram under cell-type ratio discrepancies.

Main Methods:

  • Utilized simulation studies to model scenarios with varying cell-type ratios.
  • Performed empirical validation using Multiplex immunofluorescence (MxIF) spatial data.
  • Quantitatively assessed the influence of cell-type ratio mismatches on Tangram mapping accuracy.

Main Results:

  • Cell-type ratio differences between single-cell and spatial data were observed even in adjacent tissue areas.
  • Simulation and empirical results demonstrated a negative correlation between cell-type ratio mismatch and classification accuracy.
  • The study quantifies the detrimental effect of mismatched cell compositions on Tangram's mapping capabilities.

Conclusions:

  • Mismatched cell-type ratios significantly impair the accuracy of Tangram algorithm-based data projection.
  • The findings highlight the importance of considering cell composition when applying Tangram for sc/snRNA-seq to spatial data integration.
  • Further research may be needed to develop strategies for mitigating the effects of cell-type ratio differences in spatial mapping.