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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

  • 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#

Title figure

Latest Additions#

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.

Version 1.1.0#

1.1.0 : 2024-01-17

Features:

  1. Reconstructed st.plt.violin visualizing function which is now not only applied to display QC indicators;

  2. ins.export_high_res_area can handle expression matrix and image simultaneously, to lasso region of interest and corresponding sub-image.

  3. Interactive visualizing st.plt.cells_plotting supported displaying expression heatmap and spatial distribution of a single gene.

  4. When input GEF and GEM at cell level, information of DNB count and cell area would be added into cells / obs, and cell border would be added into cells_matrix / obsm.

BUG Fixes:

  1. slideio package removed historical versions, resulting in an installation failure.

  2. Calculating error when performing ms_data.tl.batch_qc, due to abnormal os.getlogin.

  3. st.plt.paga_time_series_plot indicated that the image was too large to draw, due to unprocessed boundary values when computing median.

Version 1.0.0#

1.0.0 : 2023-12-04

Features:

  1. Addition of GPU acceleration on SinlgeR for large-volume data, and optimized calculating based on CPU version.

  2. Addition of st.plt.elbow to visualize PCA result, for appropriate number of pcs.

  3. Addition of color, max, min setting for colorbar, when plotting heatmap.

  4. Addition of cell segmentation of Deep Learning Model V1_Pro, which is improved based on V1.

  5. Supplemented parameters of st.plt.auc_heatmap and st.plt.auc_heatmap_by_group, full access to seaborn.clustermap;

  6. Addition of thread and seed setting in st.tl.umap, of which the default method have been changed to single thread with the sacrifice of computational efficiency to ensure reproducibility of results. More in https://umap-learn.readthedocs.io/en/latest/reproducibility.html.

  7. Modification of computing method of bin coordinates when reading GEM, consistent with GEF.

  8. Optimized st.io.stereo_to_anndata for efficient format conversion.

  9. Renamed st.tl.spatial_alignment function as st.tl.paste.

  10. export_high_res_area removed parameter cgef.

BUG Fixes:

  1. Occasional square-hollowing area in Deep Learning Model V3 of cell segmentation processing.

  2. st.tl.annotation could not set two or more clusters as a same name.

  3. The data object ins.selected_exp_data obtained from st.plt.interact_spatial_scatter could not be used for subsequent analysis.

  4. Part of data was missing when performed st.plt.interact_spatial_scatter to output high-resolution matrix in GEF format.

  5. Some files met reading error, led by no default setting of bin_type and bin_size in st.io.read_h5ms.

  6. Error in Batch QC calculation due to data type problem.

  7. There is NaN in Cell Community Detection output after threshold filtering, resulting in a calculating error when performed Find marker genes based on it.

  8. st.plt.paga_time_series_plot indicated the image is too large to draw, leading to graph overlap, due to the limitation of matplotlib package.