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.

• Get quickly started by browsing Usage Principles, Tutorials or API.

• 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.

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 1.5.1

1.5.1 : 2024-12-26

Features:

1. st.io.stereo_to_anndata supports adding image information into the converted AnnData object.

2. h5ad2rds.R supports adding image information into the converted RDS file.

3. Optimized the visualization of the plotting scale for spatial scatter plot when inputting small data.

BUG Fixes:

1. Fixed the problem that the layers was lost when converting StereoExpData to AnnData by using st.io.stereo_to_anndata.

2. Fixed the problem that the result of st.tl.gen_ccc_micro_envs cannot be reproduced.

Version 1.5.0

1.5.0 : 2024-11-08

Features:

1. Addition of new algorithm SpaTrack for trajectory inference.

2. Addition of Layer for saving expression matrices at different analysis stages, the functions that can use expression matrices in Layer as following:

   • st.tl.cal_qc

   • st.tl.filter_cells

   • st.tl.filter_genes

   • st.tl.log1p

   • st.tl.normalize_total

   • st.tl.scale

   • st.tl.quantile

   • st.tl.disksmooth_zscore

   • st.tl.sctransform

   • st.tl.highly_variable_genes

   • st.tl.pca

   • st.tl.find_marker_genes

3. Merger of multiple samples can merge some analysis result in every single samples when data type is StereoExpData.

4. st.plt.spatial_scatter supports setting regist.tif as background to display simultaneously on the plot.

BUG Fixes:

1. Fixed the problem that the proportion of chondriogenes was calculated incorrectly when input data contains geneID.

2. Fixed the problem that saving MSData into h5mu was failed after running st.tl.highly_variable_genes.

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.