MultiVelo - Velocity Inference from Single-Cell Multi-Omic Data
===============================================================
Single-cell multi-omic datasets, in which multiple molecular modalities are profiled
within the same cell, provide a unique opportunity to discover the interplay between
cellular epigenomic and transcriptomic changes. To realize this potential, we developed
**MultiVelo**, a mechanistic model of gene expression that extends the popular RNA velocity
framework by incorporating epigenomic data.
MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate
parameters of gene regulation, providing a quantitative summary of the temporal relationship
between epigenomic and transcriptomic changes. Fitting MultiVelo on single-cell multi-omic
datasets revealed two distinct mechanisms of regulation by chromatin accessibility, quantified
the degree of concordance or discordance between transcriptomic and epigenomic states within
each cell, and inferred the lengths of time lags between transcriptomic and epigenomic changes.
Installation
------------
Install through PyPI:
``pip install multivelo``
The package is also available on Bioconda. Install with:
``conda install -c bioconda multivelo`` or ``mamba install -c bioconda multivelo``
Documentation
-------------
We have a `ReadTheDocs <https://multivelo.readthedocs.io/en/latest/>`_ page.
Tutorial
--------
*New*: we have added Jupyter notebooks showing how to reproduce the main figure panels, along with all required processed data files. These can be found under the `Examples <https://github.com/welch-lab/MultiVelo/tree/main/Examples>`_ folder in this repository or on our `ReadTheDocs <https://multivelo.readthedocs.io/en/latest/>`_ page.
A tutorial showing how to run MultiVelo can be found here: (`jupyter notebook <https://github.com/welch-lab/MultiVelo/blob/main/Examples/MultiVelo_Demo.ipynb>`_)
The tutorial uses the embryonic E18 mouse brain from 10X Multiome as an example.
CellRanger output files can be downloaded from
`10X website <https://www.10xgenomics.com/resources/datasets/fresh-embryonic-e-18-mouse-brain-5-k-1-standard-1-0-0>`_.
Crucially, the filtered feature barcode matrix folder, ATAC peak annotations TSV, and the feature
linkage BEDPE file in the secondary analysis outputs folder will be needed in this demo.
You can download the processed data that we used for this analysis if you want to run the example yourself.
Unspliced and spliced counts, as well as cell type annotations can be downloaded from the MultiVelo GitHub page.
We provide the cell annotations for this dataset in "cell_annotations.tsv".
We also provide the nearest neighbor graph used to smooth chromatin accessibility values in the GitHub folder "seurat_wnn",
which contains a zip file of three files: "nn_cells.txt", "nn_dist.txt", and "nn_idx.txt". Please unzip the archive after downloading.
The R script used to generate these files can also be found in the same folder.
Citation
--------
| Li, C., Virgilio, M.C., Collins, K.L. & Welch J.D. Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction. *Nat Biotechnol* **41**, 387-398 (2023).
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"description": "MultiVelo - Velocity Inference from Single-Cell Multi-Omic Data\n===============================================================\n\nSingle-cell multi-omic datasets, in which multiple molecular modalities are profiled \nwithin the same cell, provide a unique opportunity to discover the interplay between \ncellular epigenomic and transcriptomic changes. To realize this potential, we developed \n**MultiVelo**, a mechanistic model of gene expression that extends the popular RNA velocity \nframework by incorporating epigenomic data.\n\nMultiVelo uses a probabilistic latent variable model to estimate the switch time and rate \nparameters of gene regulation, providing a quantitative summary of the temporal relationship \nbetween epigenomic and transcriptomic changes. Fitting MultiVelo on single-cell multi-omic \ndatasets revealed two distinct mechanisms of regulation by chromatin accessibility, quantified \nthe degree of concordance or discordance between transcriptomic and epigenomic states within \neach cell, and inferred the lengths of time lags between transcriptomic and epigenomic changes.\n\nInstallation\n------------\n\nInstall through PyPI: \n\n``pip install multivelo``\n\nThe package is also available on Bioconda. Install with:\n\n``conda install -c bioconda multivelo`` or ``mamba install -c bioconda multivelo``\n\nDocumentation\n-------------\n\nWe have a `ReadTheDocs <https://multivelo.readthedocs.io/en/latest/>`_ page.\n\nTutorial\n--------\n\n*New*: we have added Jupyter notebooks showing how to reproduce the main figure panels, along with all required processed data files. These can be found under the `Examples <https://github.com/welch-lab/MultiVelo/tree/main/Examples>`_ folder in this repository or on our `ReadTheDocs <https://multivelo.readthedocs.io/en/latest/>`_ page.\n\nA tutorial showing how to run MultiVelo can be found here: (`jupyter notebook <https://github.com/welch-lab/MultiVelo/blob/main/Examples/MultiVelo_Demo.ipynb>`_)\n\nThe tutorial uses the embryonic E18 mouse brain from 10X Multiome as an example.\nCellRanger output files can be downloaded from \n`10X website <https://www.10xgenomics.com/resources/datasets/fresh-embryonic-e-18-mouse-brain-5-k-1-standard-1-0-0>`_. \nCrucially, the filtered feature barcode matrix folder, ATAC peak annotations TSV, and the feature \nlinkage BEDPE file in the secondary analysis outputs folder will be needed in this demo.\n\nYou can download the processed data that we used for this analysis if you want to run the example yourself. \nUnspliced and spliced counts, as well as cell type annotations can be downloaded from the MultiVelo GitHub page. \nWe provide the cell annotations for this dataset in \"cell_annotations.tsv\". \nWe also provide the nearest neighbor graph used to smooth chromatin accessibility values in the GitHub folder \"seurat_wnn\", \nwhich contains a zip file of three files: \"nn_cells.txt\", \"nn_dist.txt\", and \"nn_idx.txt\". Please unzip the archive after downloading. \nThe R script used to generate these files can also be found in the same folder.\n\nCitation\n--------\n\n| Li, C., Virgilio, M.C., Collins, K.L. & Welch J.D. Multi-omic single-cell velocity models epigenome\u2013transcriptome interactions and improves cell fate prediction. *Nat Biotechnol* **41**, 387-398 (2023).\n",
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