vivs 0.1.1

Calibrated Variational Inference for single-cell omics.

VIVS

VIVS (Variational Inference for Variable Selection) is a Python package to identify molecular dependencies in omics data. Please refer to the preprint and to the project repository here for more details.

Installation

Currently, VIVS is only available on GitHub. To install the package, on Python >= 3.9, you can do the following:

1. install jax with GPU support (see here for instructions)
2. install the package using pip:

pip install vivs

Alternatively, you can clone the repository and install the package in editable mode:

pip install -e .

Basic usage

Data format

To use VIVS in your project, import the data of intest in a scanpy AnnData object. Right now, VIVS only supports assays where $X$ corresponds to gene expression counts. In particular, make sure than the anndata contains raw counts and not normalized data.

The response(s) $Y$ of interest are expected to be stored in the obsm attribute of the anndata object, either as an array or as a dataframe.

VIVS may not scale to many thousands of genes. In such a case, it is recommended to filter the genes before running VIVS, which can be done in the following way:

from vivs import select_genes

adata = select_genes(adata, n_top_genes=2000)

Model fitting and inference

VI-VS can be initialized and trained as follows:

from vivs import VIVS

model = VIVS(
    adata,
    feature_obsm_key="my_obsm_key",
    xy_linear=False,
    xy_model_kwargs={"n_hidden": 8}
)
model.train_all()

Once the model is trained, feature significance can be computed as follows:

res = model.get_hier_importance(n_clusters_list=[100, 200])

Here, n_clusters_list is a list of the number of clusters to consider for the hierarchical clustering of the features.

These results can be visualized using plot_hier_importance:

model.plot_hier_importance(res, theme_kwargs=dict(figure_size=(15, 2))