Stereopy - Spatial Transcriptomics Analysis in Python

Stereopy is a fundamental and comprehensive tool for mining and visualization based on spatial transcriptomics data, such as Stereo-seq (spatial enhanced resolution omics sequencing) data. More analysis will be added here, either from other popular tools or developed by ourselves, to meet diverse requirements. Meanwhile, we are still working on the improvement of performance and calculation efficiency.

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• Open to discuss and provide feedback on Github.

• Follow changes in Release Notes.

News

The paper of Stereopy has been pre-printed on bioRxiv!

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics.

Upcoming functions

• Batch Effect removal funciton

• Lasso expression matrix and image simultaneously

• …

Highlights

• More suitable for performing downstream analysis of Stereo-seq data.

• Support efficient reading and writing (IO), pre-processing, and standardization of multiple spatial transcriptomics data formats.

• Self-developed Gaussian smoothing model, tissue and cell segmentation algorithm models, and cell correction algorithm.

• Integrate various functions of dimensionality reduction, spatiotemporal clustering, cell clustering, spatial expression pattern analysis, etc.

• Develop interactive visualization functions based on features of Stereo-seq workflow.

Workflow

Latest Additions

Version 1.3.0

1.3.0 : 2024-05-31

Features:

1. Addition of MSData.tl.st_gears for spatial alignment of Multi-sample.

2. High Resolution Matrix Export can support both GEF and GEM files.

3. Addition of parameters min_count and max_count for st.tl.filter_genes.

4. MSData.integrate can be compatible with sparse matrix when MSData.var_type is union.

5. Addition of MSData.tl.set_scope_and_mode to set scope and mode globally on Multi-sample analysis.

6. Addition of MSData.plt.ms_spatial_scatter to plot spatial scatter plot for each sample in Multi-sample separately.

BUG Fixes:

1. Fixed the problem that st.io.read_gem is incompatible with GEM files containing geneID.

2. Fixed the bug of losing part of metadata when writing StereoExpData / MSData into Stereo-h5ad or h5ms file.

3. Fixed the incompatibility problem with AnnData when performing st.tl.sctransform.

Version 1.2.0

1.2.0 : 2024-03-30

Features:

1. st.io.read_gem and st.io.read_gef support expression matrix files with geneID information.

2. Analysis results of find_marker_genes will be saved into the output AnnData h5ad.

3. Upgraded tissue segmentation algorithm.

4. Addition of st.tl.adjusted_rand_score to calculate the adjusted Rand coefficient between two clusters.

5. Addition of st.tl.silhouette_score to calculate the average silhouette coefficient of a cluster.

6. h5ad2rds.R is compatible with AnnData version > 0.7.5, to convert from h5ad to rds files.

7. Addition of the clustering category labels to the graph of st.plt.paga_compare.

BUG Fixes:

1. Fixed the error of high memory consumption when converting X.raw into AnnData.