https://github.com/saezlab/dot

DOT
https://github.com/saezlab/dot

biology cell-type-deconvolution frank-wolfe gene-enrichment multi-objective-optimization optimization spatial spatial-transcriptomics

Last synced: 3 months ago
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DOT

• Host: GitHub
• URL: https://github.com/saezlab/dot
• Owner: saezlab
• License: gpl-3.0
• Created: 2023-05-09T04:02:57.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-15T23:04:15.000Z (about 1 year ago)
• Last Synced: 2025-03-28T05:51:03.602Z (3 months ago)
• Topics: biology, cell-type-deconvolution, frank-wolfe, gene-enrichment, multi-objective-optimization, optimization, spatial, spatial-transcriptomics
• Language: R
• Homepage: https://saezlab.github.io/DOT/
• Size: 27.5 MB
• Stars: 8
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# DOT: Flexible Feature Transfer to Spatial Omics 

  [![R-CMD-check](https://github.com/saezlab/DOT/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/saezlab/DOT/actions/workflows/R-CMD-check.yaml)

[![GitHub issues](https://img.shields.io/github/issues/saezlab/DOT)](https://github.com/saezlab/DOT/issues)

  

  ## Overview

  

  `DOT` is a method for transferring cell features from a reference single-cell RNA-seq data to spots/cells in spatial omics. It operates by optimizing a combination of multiple objectives using a Frank-Wolfe algorithm to produce a high quality transfer. Apart from transferring cell types/states to spatial omics, `DOT` can be used for transferring other relevant categorical or continuous features from one set of omics to another, such as estimating the expression of missinng genes or transferring transcription factor/pathway activities.



  



    

For more information about how this package has been used with real data and expected outputs, please check the following link:

    

- [DOT's general usage](https://saezlab.github.io/DOT/articles/general.html)


## Installation

`DOT` is available under the R package `DOTr` which you can install from [GitHub](https://github.com/) with:

```r

devtools::install_github("saezlab/DOT")

```

## Dependencies

-   R (>= 4.0)

-   R packages: fields, ggplot2, Matrix, methods, Seurat, stats, stringr

For optimal performance on moderately sized instances, we recommend at least 4 GB of RAM. For large reference scRNA-seq data or very large spatial instances higher memory may be required.

Installation takes less than five minutes. The sample dataset provided can be run in less than a minute on a "normal" desktop computer. DOT takes approximately 7 minutes to process a MERFISH MOp dataset with approximately 250 genes, 100 cell types and 4,000 spots.

Operating system tested on: macOS Monterey 12.4

## Citation

If you use **DOT** for your research please cite the [following article](https://doi.org/10.1038/s41467-024-48868-z): 

> Rahimi, A., Vale-Silva, L.A., Fälth Savitski, M. et al. DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics. Nat Commun 15, 4994 (2024). https://doi.org/10.1038/s41467-024-48868-z.