Matilda: Multi-task learning from single-cell multimodal omics

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.

For more details, please check out our publication.

This repository packages Matilda as a Python package (pip install matilda-sc, import matilda) and an R package, wrapping the unchanged published engine, with documentation and runnable Colab tutorials.

• Documentation: https://pyanglab.github.io/Matilda/
• Python tutorial (Colab):
• R tutorial (Colab):
• R package: https://github.com/PYangLab/Matilda/tree/main/matilda-r (SingleCellExperiment-based, Bioconductor-style)

Install

Python (import name matilda; PyPI distribution matilda-sc):

pip install matilda-sc

R (Python is provisioned automatically by basilisk, so you never install or manage it):

remotes::install_github("PYangLab/Matilda", subdir = "matilda-r")

Two interfaces, one engine

Matilda ships as a Python package (import matilda) and an R package (matilda, operating on a SingleCellExperiment). Both call the same engine, so on the same hardware they give the same result. Pick the interface that matches your workflow; see the documentation for both APIs and full tutorials.

Quickstart (Python)

Work with in-memory AnnData (or arrays, or file paths) and get results back as objects. After train, there is one verb per task: classify / reduce / markers / simulate.

import matilda

# rna/adt/atac: AnnData | ndarray | scipy.sparse | path | None
# labels: a vector, an `.obs` column name, or a .csv path (string or numeric labels)
fit = matilda.train(rna, adt=adt, atac=atac, labels="cell_type")

res = matilda.classify({"rna": q_rna, "adt": q_adt, "atac": q_atac},
                       model=fit, query_labels=q_labels)
res.predictions        # DataFrame: cell_id, real, predicted, probability
res.celltype_accuracy  # DataFrame: celltype, accuracy, n

lat = matilda.reduce({"rna": rna, "adt": adt, "atac": atac}, model=fit)                        # lat.latent
mk  = matilda.markers({"rna": rna, "adt": adt, "atac": atac}, model=fit, labels="cell_type")   # mk.markers
sim = matilda.simulate({"rna": rna, "adt": adt, "atac": atac}, model=fit, celltype="B.Naive", n=200, labels="cell_type")  # sim.simulated

The modality combination is inferred automatically (RNA only, RNA+ADT for CITE-seq, RNA+ATAC for SHARE-seq, all three for TEA-seq). classify reconciles features automatically: if the query is missing some of the model's features it takes the per-modality reference intersection (real values, no zero-padding), retrains, and classifies; res.retrained / res.common_features report what happened. See the Quickstart and API reference for the full surface.

Citation

If you use Matilda, please cite the Matilda paper (see the Citation page).