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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.

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

  • …

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

Title figure

Latest Additions

Version 1.4.0

1.4.0 : 2024-09-05

Features:

  1. Addition of new algorithm SpaSEG for multiple SRT analysis.

  2. Addition of colorbar or legend for st.plt.cells_plotting.

  3. st.plt.cells_plotting supports exporting plots as PNG, SVG or PDF.

  4. Addition of method st.io.write_h5mu and st.io.mudata_to_msdata for conversion between MSData and MuData.

BUG Fixes:

  1. Fixed the problem that CellCorrection is incompatible with small-size images (less than 2000px in any dimension) when using the EDM method.

  2. Fixed the problem that MSData.to_integrate is incompatible when the number of cells in the integrated sample is less than the total number of cells in all single samples.

  3. Fixed the problem that st.plt.time_series_tree_plot can not capture the result of PAGA, leading to an incorrect plot.

  4. Fixed other bugs.

Version 1.3.1

1.3.1 : 2024-06-28

Features:

  1. Addition of new method ‘adaptive’ for st.tl.get_niche (the original method is named ‘fixed’).

  2. Changed some parameter names of st.tl.filter_cells and st.tl.filter_genes for eliminating ambiguity(old parameter names are still compatible).

  3. Filter the results of PCA and UMAP simultaneously when running st.tl.filter_cells.

BUG Fixes:

  1. Fixed the problem that ms_data.to_isolated is incompatible with that there are duplicate cell names in different samples.

  2. Fixed the problem that st.io.read_gef is incompatible with those GEF files that contain gene names ending with ‘_{number}’ (like ‘ABC_123’).

  3. Upgraded gefpy to latest for fixing the error that gene names are lost after running CellCorrection.

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.