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spaTransfer: transfer learning for single-cell and spatial transcriptomics data using non-negative matrix factorization

This repository contains the processed data for the analyses associated with the manuscript "spaTransfer: transfer learning for single-cell and spatial transcriptomics data using non-negative matrix factorization".  Once unzipped, the processed-data folder contains the following folde

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CreatorFang, Cindy
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Published2025-12-09
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DOI10.5281/zenodo.17872293
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Downloads29
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Licensecc-by-4.0
File Size11.6 GB
Data TypeDataset
Published2025
Licensecc-by-4.0
Total Views93
Total Downloads29

This repository contains the processed data for the analyses associated with the manuscript “spaTransfer: transfer learning for single-cell and spatial transcriptomics data using non-negative matrix factorization”. 

Once unzipped, the processed-data folder contains the following folders:

  • dlpfc:
    • One folder for each of the dlpfc samples used in the manuscript (6 total), containing:
      • *-raw-SPE.RDS: SpatialExperiment objects containing the pre-QC Xenium raw counts matrices for each sample
      • *-filt-SPE.RDS: SpatialExperiment objects containing the post-QC Xenium counts matrices for each sample
      • *-filt-predLabels-SPE-bayesspace-logcounts.RDS: SpatialExperiment objects containing the post-QC Xenium counts matrices with spaTransfer domain predictions stored in the colData.
    • traam-outputs, a folder containing the spaTransfer outputs for the analyses in Figure 2.
    • 04_methodComparisons:
      • unsupervised_xenium_comparisons: 
        • One folder for each of the 5 unsupervised clustering methods benchmarked in Figure 4b. Each folder contains either SpatialExperiment or AnnData objects with the clustering method’s labels stored in the colData. 
        • *_labels.csv and *_metrics.csv: Labels and internal metrics for each of the clustering methods, respectively.
      • visium_comparisons:
        • One folder for each of the 3 label transfer methods benchmarked in Figure 4a. Eaach folder contains SpatialExperiment objects with the label transfer method’s labels stored in the colData.
        • *ARI*.csv: files contain ARI and NMI values for the label transfer methods.
  • hpc:
    • One folder for each of the hpc samples used in the manuscript (6 total), containing:
      • *-raw-SPE.RDS: SpatialExperiment objects containing the pre-QC Xenium raw counts matrices for each sample
      • *-filt-SPE.RDS: SpatialExperiment objects containing the post-QC Xenium counts matrices for each sample
      • *-filt-predLabels-SPE-bayesspace-logcounts.RDS: SpatialExperiment objects containing the post-QC Xenium counts matrices with spaTransfer domain predictions stored in the colData.
    • traam-outputs, a folder containing the spaTransfer outputs for the analyses in Figure 3.
    • custom-panel-pseudobulkedByDomain-SPE.RDS: pseudobulked SpatialExperiment object used Figure S14-S16.

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spaTransfer: transfer learning for single-cell and spatial transcriptomics… (Full Dataset)11.6 GB
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Files are hosted on the source repository. Click download to access the full dataset.

Fang, Cindy (2025). spaTransfer: transfer learning for single-cell and spatial transcriptomics data using non-negative matrix factorization. https://doi.org/10.5281/zenodo.17872293