Tutorials (original) (raw)

Precorded talks

MOFA overview: precorded talk for the VIB workshop (Belgium, 2021), includes the model overview, intuition and a brief discussion of the CLL application.
Overview of single-cell multi-omics data integration: precorded talk for a webinar, includes brief discussion on CITE-seq and 10x Multiome applications.

Training a MOFA model in R: using simple simulated data
Downstream analysis in R: using simple simulated data
Gene set enrichment analysis: demonstrates how to do gene set enrichment analysis in R.
Demonstration of the stochastic inference algorithm: this is only useful for very large data sets and when having access to GPUs.

(authors’ favourite) Analysis of chronic lymphocytic leukaemia cohort for personalised medicine: a bulk multi-omics data set. Figure 2 and 3 of the [MOFA paper (https://www.embopress.org/doi/full/10.15252/msb.20178124#msb178124-fig-0002).
Integrative analysis of the Chromium Single Cell Multiome ATAC + Gene Expression assay: this is the result of a collaboration between the MOFA team and the 10x Genomics R&D team to provide a downstream analysis pipeline for the 10x Multiome kit.
Analysis of multi-modal microbiome data: we demonstrate how to systematically integrate viral, fungal and bacterial sequence data. Manuscript published in mSystems
Analysis of a time course scRNA-seq data set using the multi-group framework: Figure 2 of the MOFA+ paper. Demonstrates the multi-group functionality and how to train a MOFA model from a Seurat object.
Integration of scNMT-seq data (single-cell multi-omics): Figure 4 of the MOFA+ paper. Demonstrates the simultaneous multi-view and multi-group functionality using the multi-modal mouse gastrulation atlas.
Integration of SNARE-seq data (single-cell multi-omics). Demonstrates how MOFA can be used for the analysis of paired scRNA+scATAC data (from the same cell) from a Seurat object. This data set is very noisy and the results are not fantastic, we suggest you have a look at the 10x Multiome vignette instead.

All .Rmd files for the tutorials can be found here

MOFA is interfaced through muon:

Training MOFA with muon, CLL: the multimodal CLL dataset in the MuData format.
Integration of multimodal single-cell data: shows how muon can be used to train and explore the model together with the mofax package.
Training a model with mofapy2: a Jupyter notebook demonstrating how to train a MOFA model using simple simulated data. It uses stand-alone mofapy2 to train the model and mofax for downstream analysis.
Training a model on AnnData: demonstrates how to train a MOFA model on scRNA-seq data stored in AnnData format.
Downstream analysis in Python: demonstrates how to perform the downstream analysis of a MOFA model trained on scRNA-seq data, using mofax.

We provide template scripts to train your model: