milopy.core

Module Contents

Functions

make_nhoods(adata[, neighbors_key, prop, seed])

This function randomly samples vertcies on a graph to define neighbourhoods.

count_nhoods(adata, sample_col)

  • adata

DA_nhoods(adata, design[, model_contrasts, ...])

This will perform differential neighbourhood abundance testing (using edgeR under the hood)

_graph_spatialFDR(adata)

FDR correction weighted on inverse of connectivity of neighbourhoods.

_try_import_bioc_library(name)

milopy.core.make_nhoods(adata: anndata.AnnData, neighbors_key: str = None, prop=0.1, seed: int = 42)

This function randomly samples vertcies on a graph to define neighbourhoods. These are then refined by computing the median profile for the neighbourhood in reduced dimensional space and selecting the nearest vertex to this position. Thus, multiple neighbourhoods may be collapsed down together to prevent over-sampling the graph space.

  • adata: AnnData object. Should contain a knn graph in adata.obsp

  • neighbors_key: string indicating the key in adata.obsp to use as KNN graph. If not specified,

make_nhoods looks .obsp[‘connectivities’] for connectivities (default storage places for scanpy.pp.neighbors). If specified, it looks .obsp[.uns[neighbors_key][‘connectivities_key’]] for connectivities. - prop: fraction of cells to sample for neighbourhood index search (default: 0.1) - seed: random seed for cell sampling (default: 42)

milopy.core.count_nhoods(adata: anndata.AnnData, sample_col: str)

  • adata

  • sample_col: string, column in adata.obs that contains sample information

(what should be in the columns of the nhoodCount matrix)

Returns: None Updated adata.uns slot to contain adata.uns[“nhood_adata”], where: - adata.uns[“nhood_adata”].obs_names are neighbourhoods - adata.uns[“nhood_adata”].var_names are samples - adata.uns[“nhood_adata”].X is the matrix counting the number of cells from each sample in each neighbourhood

milopy.core.DA_nhoods(adata, design, model_contrasts=None, subset_samples=None, add_intercept=True)

This will perform differential neighbourhood abundance testing (using edgeR under the hood) - adata - design: formula (terms should be columns in adata.uns[“nhood_adata”].var) - model_contrasts: A string vector that defines the contrasts used to perform DA testing - subset_samples: subset of samples (columns in adata.uns[“nhood_adata”].X) to use for the test - add_intercept: whether to include an intercept in the model. If False, this is equivalent to adding + 0 in the design formula. When model_contrasts is specified, this is set to False by default.

milopy.core._graph_spatialFDR(adata)

FDR correction weighted on inverse of connectivity of neighbourhoods. The distance to the k-th nearest neighbor is used as a measure of connectivity.

milopy.core._try_import_bioc_library(name)