stereo.core.StPipeline#
- class stereo.core.StPipeline(data)[source]#
- __init__(data)[source]#
A analysis tool sets for StereoExpData. include preprocess, filter, cluster, plot and so on.
- Parameters:
data (
Union
[StereoExpData
,AnnBasedStereoExpData
]) – StereoExpData object.
Methods
__init__
(data)A analysis tool sets for StereoExpData.
adjusted_rand_score
(cluster_res_key_a, ...)Calculate Adjusted Rand index between two cluster results.
annotation
(annotation_information[, ...])Set annotation to clusters.
batches_integrate
([pca_res_key, res_key])integrate different experiments base on the pca result
cal_qc
()Calculate the key indicators of quality control.
disksmooth_zscore
([r, inplace, res_key])for each position, given a radius, calculate the z-score within this circle as final normalized value.
filter_by_clusters
([cluster_res_key, ...])Filter cells based on clustering result.
filter_by_hvgs
([hvg_res_key, filter_raw, ...])Filter genes based on the result of highly_variable_genes function.
filter_cells
([min_gene, max_gene, ...])Filter cells based on counts or the numbers of genes expressed.
filter_coordinates
([min_x, max_x, min_y, ...])Filter cells based on coordinate information.
filter_genes
([min_cell, max_cell, ...])Filter genes based on the numbers of cells or counts.
filter_marker_genes
([marker_genes_res_key, ...])Filters out genes based on log fold change and fraction of genes expressing the gene within and outside each group.
find_marker_genes
(cluster_res_key[, method, ...])A tool to find maker genes.
gaussian_smooth
([n_neighbors, ...])Smooth the express matrix by the algorithm of Gaussian smoothing [Shen22].
get_neighbors_res
(neighbors_res_key)get the neighbor result by the key.
highly_variable_genes
([groups, method, ...])Annotate highly variable genes, refering to Scanpy.
leiden
([neighbors_res_key, res_key, ...])Cluster cells into subgroups by Leiden algorithm [Traag18].
log1p
([inplace, res_key])Transform the express matrix logarithmically.
louvain
([neighbors_res_key, res_key, ...])Cluster cells into subgroups by Louvain algorithm [Blondel08].
lr_score
(lr_pairs[, distance, spot_comp, ...])calculate cci score for each LR pair and do permutation test
neighbors
([pca_res_key, method, metric, ...])Compute a spatial neighborhood graph over all cells.
normalize_total
([target_sum, inplace, res_key])Normalize total counts over all genes per cell such that each cell has the same total count after normalization.
pca
([use_highly_genes, n_pcs, svd_solver, ...])Principal component analysis.
phenograph
([phenograph_k, pca_res_key, ...])Cluster cells into subgroups by Phenograph.
quantile
([inplace, res_key])Normalize the columns of X to each have the same distribution.
Save current data to
self.raw
.reset_raw_data
()Reset
self.data
to the raw data saved inself.raw
when you want data get raw expression matrix.scale
([zero_center, max_value, inplace, res_key])Scale express matrix to unit variance and zero mean.
sctransform
([n_cells, n_genes, filter_hvgs, ...])Normalization of scTransform, refering to Seurat [Hafemeister19].
silhouette_score
(cluster_res_key[, metric, ...])Calculate the mean Silhouette Coefficient for a cluster result.
spatial_hotspot
([use_highly_genes, ...])Identify informative genes or gene modules.
spatial_neighbors
([neighbors_res_key, ...])Create a graph from spatial coordinates using Squidpy.
spatial_pattern_score
([use_raw, res_key])calculate the spatial pattern score.
subset_by_hvg
(hvg_res_key[, use_raw, inplace])get the subset by the result of highly variable genes.
umap
([pca_res_key, neighbors_res_key, ...])Embed the neighborhood graph using UMAP [McInnes18].
Attributes
raw
get the StereoExpData whose exp_matrix is raw count.