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Short description: Matilda is a multi-task framework for learning from single-cell multimodal omics data. Matilda leverages the information from the multi-modality of such data and trains a neural network model to simultaneously learn multiple tasks including data simulation, dimension reduction, visualization, classification, and feature selection.
Thank you for submitting a method package to the HCA DCP Analysis Tools Registry!
To expedite your package's addition to https://data.humancellatlas.org/analyze/methods, please provide the following package metadata. You can easily edit this information later by clicking "Improve this page" at the bottom of your package's detail page (example).
Required:
Tool title: Matilda
Contact name: PYangLab
Contact email: cliu@cmri.org.au
Who to attribute: PYangLab
Method-ready Docker image URL: https://github.com/PYangLab/Matilda/tree/main/img/main.jpg
Code repository URL: https://github.com/PYangLab/Matilda
Affirm code is free and open source with license available in code repository: [x] (fill in as [x] to affirm)
Language: English
Upstream registry URL: https://github.com/PYangLab/Matilda/tree/main/img/main.jpg
Test data location: https://github.com/PYangLab/Matilda/tree/main/data/TEAseq
Basic command line usage:
Command to validate installation:
Short description: Matilda is a multi-task framework for learning from single-cell multimodal omics data. Matilda leverages the information from the multi-modality of such data and trains a neural network model to simultaneously learn multiple tasks including data simulation, dimension reduction, visualization, classification, and feature selection.
Optional: