StPipeline: tl#

Import Stereopy :

import stereo as st

A tool-integrated class for basic analysis of StereoExpData, which is compromised of basic preprocessing, embedding, clustering, and so on.

core.StPipeline(data)

core.StPipeline.cal_qc()

Calculate the key indicators of quality control.

core.StPipeline.filter_cells([min_gene, ...])

Filter cells based on counts or the numbers of genes expressed.

core.StPipeline.filter_genes([min_cell, ...])

Filter genes based on the numbers of cells or counts.

core.StPipeline.filter_coordinates([min_x, ...])

Filter cells based on coordinate information.

core.StPipeline.filter_by_clusters([...])

Filter cells based on clustering result.

core.StPipeline.filter_by_hvgs([...])

Filter genes based on the result of highly_variable_genes function.

core.StPipeline.raw_checkpoint()

Save current data to self.raw.

core.StPipeline.sctransform([n_cells, ...])

Normalization of scTransform, refering to Seurat [Hafemeister19].

core.StPipeline.normalize_total([...])

Normalize total counts over all genes per cell such that each cell has the same total count after normalization.

core.StPipeline.log1p([inplace, res_key])

Transform the express matrix logarithmically.

core.StPipeline.scale([zero_center, ...])

Scale express matrix to unit variance and zero mean.

core.StPipeline.highly_variable_genes([...])

Annotate highly variable genes, refering to Scanpy.

core.StPipeline.pca([use_highly_genes, ...])

Principal component analysis.

core.StPipeline.neighbors([pca_res_key, ...])

Compute a spatial neighborhood graph over all cells.

core.StPipeline.spatial_neighbors([...])

Create a graph from spatial coordinates using Squidpy.

core.StPipeline.umap([pca_res_key, ...])

Embed the neighborhood graph using UMAP [McInnes18].

core.StPipeline.leiden([neighbors_res_key, ...])

Cluster cells into subgroups by Leiden algorithm [Traag18].

core.StPipeline.louvain([neighbors_res_key, ...])

Cluster cells into subgroups by Louvain algorithm [Blondel08].

core.StPipeline.phenograph([phenograph_k, ...])

Cluster cells into subgroups by Phenograph.

core.StPipeline.find_marker_genes(...[, ...])

A tool to find maker genes.

core.StPipeline.spatial_hotspot([...])

Identify informative genes or gene modules.

core.StPipeline.gaussian_smooth([...])

Smooth the express matrix by the algorithm of Gaussian smoothing [Shen22].

core.StPipeline.annotation(...[, ...])

Set annotation to clusters.

core.StPipeline.adjusted_rand_score(...)

Calculate Adjusted Rand index between two cluster results.

core.StPipeline.silhouette_score(cluster_res_key)

Calculate the mean Silhouette Coefficient for a cluster result.

algorithm.single_r.SingleR.main(ref_exp_data)

Single-cell recognition is a tool to automatically annotate a test sample by a reference sample.

algorithm.batch_qc.BatchQc.main([...])

_summary_

algorithm.paste.Paste.main([method, ...])

Calculates and returns optimal alignment of two slices or computes center alignment of slices.

algorithm.paste.pairwise_align(sliceA, sliceB)

Calculates and returns optimal alignment of two slices.

algorithm.paste.center_align(initial_slice, ...)

Computes center alignment of slices.

algorithm.get_niche.GetNiche.main(...[, ...])

To ensure the accuracy and specificity of this juxtacrine signaling model, we extract cells bordering their niches and statistically calculate their CCC activity scores of L-R pairs under the assumption that intercellular L-R communications routinely exist among closely neighboring cells

algorithm.gen_ccc_micro_envs.GenCccMicroEnvs.main([...])

Generate the micro-environment used for the CCC analysis.

algorithm.cell_cell_communication.CellCellCommunication.main([...])

Cell-cell communication analysis main functon.

algorithm.regulatory_network_inference.RegulatoryNetworkInference.main([...])

Enables researchers to infer transcription factors (TFs) and gene regulatory networks.

algorithm.co_occurrence.CoOccurrence.main(...)

Co-occurence calculates the score or probability of two or more cell types in spatial.

algorithm.community_detection.CommunityDetection.main(...)

CCD divides the tissue using sliding windows by accommodating multiple window sizes, and enables the simultaneous analysis of multiple slices from the same tissue.

algorithm.time_series_analysis.TimeSeriesAnalysis.main([...])

type run_method:

algorithm.dendrogram.Dendrogram.main(...[, ...])

Computes a hierarchical clustering for the given cluster_res_key categories.