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.
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Calculate the key indicators of quality control. |
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Filter cells based on counts or the numbers of genes expressed. |
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Filter genes based on the numbers of cells or counts. |
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Filter cells based on coordinate information. |
Filter cells based on clustering result. |
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Filter genes based on the result of highly_variable_genes function. |
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Save current data to |
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Normalization of scTransform, refering to Seurat [Hafemeister19]. |
Normalize total counts over all genes per cell such that each cell has the same total count after normalization. |
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Transform the express matrix logarithmically. |
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Scale express matrix to unit variance and zero mean. |
Annotate highly variable genes, refering to Scanpy. |
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Principal component analysis. |
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Compute a spatial neighborhood graph over all cells. |
Create a graph from spatial coordinates using Squidpy. |
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Embed the neighborhood graph using UMAP [McInnes18]. |
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Cluster cells into subgroups by Leiden algorithm [Traag18]. |
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Cluster cells into subgroups by Louvain algorithm [Blondel08]. |
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Cluster cells into subgroups by Phenograph. |
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A tool to find maker genes. |
Identify informative genes or gene modules. |
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Smooth the express matrix by the algorithm of Gaussian smoothing [Shen22]. |
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Set annotation to clusters. |
Calculate Adjusted Rand index between two cluster results. |
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Calculate the mean Silhouette Coefficient for a cluster result. |
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Single-cell recognition is a tool to automatically annotate a test sample by a reference sample. |
_summary_ |
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Calculates and returns optimal alignment of two slices or computes center alignment of slices. |
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Calculates and returns optimal alignment of two slices. |
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Computes center alignment of slices. |
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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 |
Generate the micro-environment used for the CCC analysis. |
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Cell-cell communication analysis main functon. |
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Enables researchers to infer transcription factors (TFs) and gene regulatory networks. |
Co-occurence calculates the score or probability of two or more cell types in spatial. |
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CCD divides the tissue using sliding windows by accommodating multiple window sizes, and enables the simultaneous analysis of multiple slices from the same tissue. |
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Computes a hierarchical clustering for the given |