icat-sc 0.1.4

Identify cell states across treatments in single-cell RNA sequencing experiments

icat

Identifying Cell-states Across Treatments

ICAT is a tool developed to better identify cell states in scRNAseq experiments where perturbations or some other biologic heterogeneity is present, such as gene knock-outs.

The method works by first identifying a set of conrol-defined cell states by performing unsupervised clustering. These identified cell states are then fed into a sparse gene weighting algorithm, Neighborhood Component Feature Selection (NCFS), to highly weight the most predictive genes, while also removing variance from non-explanatory genes. We then transform the data matrix using this weight vector, and perform semi-supervised clustering such that the originally identified control labels remain constant, but cells from experimental conditions

Installation

ICAT can be installed using pip and can be installed using the following command:

pip install icat-sc

How to use

ICAT makes heavy use of the excellent scanpy library along with the associated AnnData data structure.

An example code block walks through running icat on a simulated dataset. The final clustering is stored in the sslouvain column of the returned AnnData object.

    from icat import simulate
    from icat import models
    import scanpy as sc
    import numpy as np
    data_model = simulate.SingleCellDataset(
        populations=2,
        genes=1000,
        dispersion=np.random.choice([1, 2, 3], 1000)
    )
    controls = data_model.simulate()
    controls.obs['treatment'] = 'control'
    perturbed = simulate.perturb(controls)
    perturbed.obs['treatment'] = 'perturbed'
    adata = controls.concatenate([perturbed])
    sc.pp.log1p(adata)

visualizing dataset

    # specify model parameters -- see documentation for more information
    model = models.icat(
        ctrl_value="control",
        ncfs_kws={'reg': 1, 'sigma': 3},
        neighbor_kws={'n_neighbors': 15}, 
        cluster_kws={'resolution': 0.75},
    )
    # cluster cells by providing treatment information
    out = model.cluster(adata, adata.obs['treatment'])
    print(out.obs['sslouvain'].unique())

visualizing results