spateo.tools.architype#

Gene Expression Cartography M Nitzan*, N Karaiskos*, N Friedman†, N Rajewsky† Nature (2019)

code adapted from: https://github.com/rajewsky-lab/novosparc

Module Contents#

Functions#

find_spatial_archetypes(→ Tuple[numpy.ndarray, ...)

Clusters the expression data and finds gene archetypes. Current implementation is based on hierarchical

get_genes_from_spatial_archetype(...)

Get a list of genes which are the best representatives of the archetype.

find_spatially_related_genes(exp_mat, gene_names, ...)

Given a gene, find other genes which correlate well spatially.

archetypes(→ numpy.ndarray)

Identify archetypes from the anndata object.

archetypes_genes(→ dict)

Identify genes that belong to each expression archetype.
spateo.tools.architype.find_spatial_archetypes(num_clusters: int, exp_mat: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]#

Clusters the expression data and finds gene archetypes. Current implementation is based on hierarchical clustering with the Ward method. The archetypes are simply the average of genes belong to the same cell cluster.

Parameters:
num_clusters

number of gene clusters or archetypes.

exp_mat

expression matrix. Rows are genes and columns are buckets.

Returns:

Returns the archetypes, the gene sets (clusters) and the Pearson correlations of every gene with respect to each archetype.

spateo.tools.architype.get_genes_from_spatial_archetype(exp_mat: numpy.ndarray, gene_names: numpy.ndarray | list, archetypes: numpy.ndarray, archetype: int, pval_threshold: float = 0) → numpy.ndarray | list[source]#

Get a list of genes which are the best representatives of the archetype.

Parameters:
exp_mat

expression matrix.

gene_names

the gene names list that associates with the rows of expression matrix

archetypes

the archetypes output of find_spatial_archetypes

archetype

a number denoting the archetype

pval_threshold

the pvalue returned from the pearsonr function

Returns:

a list of genes which are the best representatives of the archetype

Given a gene, find other genes which correlate well spatially.

Parameters:
exp_mat

expression matrix.

gene_names

gene name list that associates with the rows of expression matrix.

archetypes

the archetypes output of find_spatial_archetypes

gene

the index of the gene to be queried

pval_threshold

the pvalue returned from the pearsonr function

Returns:

a list of genes which are the best representatives of the archetype

spateo.tools.architype.archetypes(adata: anndata.AnnData, moran_i_genes: numpy.ndarray | list, num_clusters: int = 5, layer: str | None = None) → numpy.ndarray[source]#

Identify archetypes from the anndata object.

Parameters:
adata

Anndata object of interests.

moran_i_genes

genes that are identified as singificant autocorrelation genes in space based on Moran’s I.

num_clusters

number of archetypes.

layers

the layer for the gene expression, can be None which corresponds to adata.X.

Returns:

the archetypes within the genes with high moran I scores.

Return type:

archetypes

Examples

>>> archetypes = st.tl.archetypes(adata)
>>> adata.obs = pd.concat((adata.obs, df), 1)
>> arch_cols = adata.obs.columns
>>> st.pl.space(adata, basis="spatial", color=arch_cols, pointsize=0.1, alpha=1)
spateo.tools.architype.archetypes_genes(adata: anndata.AnnData, archetypes: numpy.ndarray, num_clusters: int, moran_i_genes: numpy.ndarray | list, layer: str | None = None) → dict[source]#

Identify genes that belong to each expression archetype.

Parameters:
adata

Anndata object of interests.

archetypes

the archetypes output of find_spatial_archetypes

num_clusters

number of archetypes.

moran_i_genes

genes that are identified as singificant autocorrelation genes in space based on Moran’s I.

layer

the layer for the gene expression, can be None which corresponds to adata.X.

Returns:

a dictionary where the key is the index of the archetype and the values are the top genes for that particular archetype.

Return type:

archetypes_dict

Examples

>>> st.tl.archetypes_genes(adata)
>>> dyn.pl.scatters(subset_adata,
>>>     basis="spatial",
>>>     color=['archetype %d'% i] + typical_genes.to_list(),
>>>     pointsize=0.03,
>>>     alpha=1,
>>>     figsize=(3, ptp_vec[1]/ptp_vec[0] * 3)
>>> )