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Updated: Aug 16, 2025

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A comparative performance evaluation of imputation methods in spatially resolved transcriptomics data.

Gülben Avşar1, Pınar Pir1

  • 1Department of Bioengineering, Gebze Technical University, 41400 Kocaeli, Turkey. g.avsar@gtu.edu.tr.

Molecular Omics
|December 23, 2022
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Summary

Spatially resolved transcriptomics data often has missing values due to low capture rates. Our study evaluated five imputation methods, finding stPlus and gimVI performed best, though overall performance needs improvement for accurate dropout correction.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Spatially resolved transcriptomics provides RNA expression patterns with spatial context.
  • Low capture rates in these technologies lead to significant data dropout, hindering analysis.
  • Data imputation is a crucial strategy to address missing values in transcriptomic datasets.

Purpose of the Study:

  • To evaluate the imputation performance of five available methods for spatially resolved transcriptomics data.
  • To identify the most effective computational tools for predicting and correcting dropout events.

Main Methods:

  • Qualitative evaluation through visualization of imputation predictions against original data.
  • Quantitative assessment using metrics: Pearson's correlation coefficient, cosine similarity, root mean squared log-error, Silhouette Index, and Calinski Harabasz Index.
  • Comparison of five imputation methods: SpaGE, stPlus, gimVI, Tangram, and stLearn.

Main Results:

  • stPlus and gimVI demonstrated superior imputation performance compared to SpaGE, Tangram, and stLearn.
  • Despite improvements, the overall performance of all evaluated methods was below expectations.
  • A notable gap exists in current imputation tools for effectively handling dropout events in this data type.

Conclusions:

  • While stPlus and gimVI show promise, current imputation methods for spatially resolved transcriptomics require further development.
  • Addressing dropout events remains a critical challenge for accurate whole-transcriptome profiling in spatial transcriptomics.