mosaicmpi 1.9.0

mosaicMPI: Mosaic Multi-resolution Program Integration

mosaicMPI: mosaic multi-resolution program integration

Authors: Ted Verhey, Heewon Seo, Sorana Morrissy

mosaicMPI is a Python package enabling mosaic integration of bulk, single-cell, and spatial expression data through program-level integration. Programs are first discovered using consensus non-negative matrix factorization and then integrated using a flexible network-based approach to group similar programs together across resolutions and datasets. Program communities are then interpreted using sample/cell metadata and classical gene set analyses. Integrative program communities enable metadata transfer across datasets.

⚡Main Features

Here are just a few of the things that mosaicMPI does well:

• Identifies interpretable, non-negative programs at multiple resolutions
• Mosaic integration does not require subsetting features/genes to a shared or overdispersed subset
• Multi-omics integration does not require shared sample IDs
• Ideal for incremental integration (adding datasets one at a time) since deconvolution is performed independently on each dataset
• Integration performs well even when the datasets have mismatched features (eg. Microarray, RNA-Seq, Proteomics) or sparsity (eg single-cell vs bulk RNA-Seq and ATAC-Seq)
• Metadata transfer across datasets
• Command-line interface for rapid data exploration and python interface for extensibility and flexibility

🔧 Install

✨ Latest Release

Install the package with conda (in an isolated conda environment):

conda create -n mosaicmpi -c conda-forge mosaicmpi
conda activate mosaicmpi

📖 Documentation

🗐 Data guidelines

mosaicMPI can factorize a wide variety of datasets, but will work optimally in these conditions:

• Use untransformed, raw data data where possible, and avoid log-transformed data
• For single-cell, spatial, or bulk RNA-Seq data, the best data to use is feature counts, then TPM-normalized values, then RPKM/FPKM-normalized values.

📓 Python interface

To get started, sample datasets and a Jupyter notebook tutorial is available here.

Detailed API reference can be found on ReadTheDocs.

⌨️ Command line interface

See the command line interface documentation.

💭 Getting Help

For errors arising during use of mosaicMPI, create and browse issues in the GitHub "issues" tab.