mira.tl.get_chromatin_differential

mira.tl.get_chromatin_differential(adata, *, add_layer='chromatin_differential', sparse=True)

The per-cell difference in predictions between LITE and NITE models of gene is called “chromatin differential”, and reflects the over or under- estimation of expression levels by local chromatin. Positive chromatin differential means local chromatin over-estimates expression in that cell, negative chromatin differential means lcoal chromatin under-estimates expression.

Parameters

adataanndata.AnnData

Adata of expression features per cell. This data must first be annotated with “LITE_prediction” and “NITE_prediction” using LITE and NITE RP models’ predict function.

Returns

adataanndata.AnnData

.layers[“chromatin_differential”]scipy.spmatrix of shape (n_cells, n_genes)

Chromatin differential matrix. Genes that were not modeled are left empty.

Raises

KeyErrorif adata is missing “LITE_prediction” or “NITE_prediction”.

Examples

>>> rp_args = dict(expr_adata = atac_data, expr_adata = rna_data)
>>> litemodel.predict(**rp_args)
>>> nitemodel.predict(**rp_args)
>>> mira.tl.get_chromatin_differential(rna_data)