Release Notes#

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.

Version 0.14.0b1 (Beta)#

0.14.0b1 : 2023-9-15#

Notice: this Beta version is specifically developed for multi-sample analysis.

Features:

  1. Addition of Cell Community Detection (CCD) analysis.

  2. Addition of Cell Co-occurrence analysis.

  3. Addition of Cellpose in cell segmentation, especially for cell cytoplasm using model_type='cyto2'.

  4. Addition of circos (st.plt.ccc_circos_plot) and sankey (st.plt.ccc_sankey_plot) plots in Cell-cell Communication analysis.

  5. Addition of volcano (st.plt.TVG_volcano_plot) and tree (st.plt.time_series_tree_plot) plots in Time Series analysis.

  6. Addition of PAGA tree plot, st.plt.paga_plot.

  7. Addition of visuallization of st.tl.dendrogram.

  8. Addition of version check using st.__version__.

  9. Supported obtain subset from a data object, using clustering output, by st.tl.filter_by_clusters.

  10. Supported filtering data using hvgs, by st.tl.filter_by_hvgs.

  11. Supported mapping the clustering result of SquareBin analysis to the same data but in CellBin.

  12. Supported writing annotation information into CellBin GEF file, only clustering result available before.

  13. Supported saving images of PNG and PDF formats, in interactive interface.

  14. Optimized the function of st.tl.find_marker_genes.

  15. Optimized the modification of titles in horizontal axis, vertical axis and plot.

BUG Fixes:

  1. Fixed the issue that SingleR calculating did not add filtration to the column field when traversing expression matrix, resulting in the subsequent absence of the column index.

  2. Fixed the issue that output Seurat h5ad could not be transformed into R format.

  3. Fixed the issue that clustering output of Leiden was in wrong data type under the scene of GPU acceleration, leading to errors in subsequent analysis which work on the clustering result.

  4. Fixed the issue that clustering result could not be written into GEF file, using st.io.update_gef, caused by data type error. From v0.12.1 on, date.cells.cell_name has changed from int to string.

Version 0.13.0b1 (Beta)#

0.13.0b1 : 2023-07-11#

Notice: this Beta version is specifically developed for multi-sample analysis. Major update points are listed below.

  1. Addition of 3D Cell-cell Communication.

  2. Addition of 3D Gene Regulatory Network.

  3. Addition of Trajectory Inference, including PAGA and DPT algorithms.

  4. Addition of Batch QC function for evaluation on batch effect.

  5. Addition of st.io.read_h5ad for improved compatibility with AnnData H5ad, we highly recommend that instead of st.io.read_ann_h5ad.

  6. Addition of analysis workflow tutorial based on multi-sample data, with assistant parameters scope and mode.

  7. Addition of resetting the image order of multi-sample analysis results.

  8. Addition of 3D mesh visualization.

  9. Improved the performance of Gaussian Smoothing.

Version 0.12.1#

0.12.1 : 2023-06-21#

  1. Addition of the pretreatment of calculating quality control metrics at the start of st.tl.filter_genes and st.tl.filter_cells.

  2. Fixed the bug that loaded data from GEF file had the same expression matrix but in different row order, through updating gefpy package to v0.6.24.

  3. Fixed the bug that scale.data had np.nan value in st.tl.sctransform , caused by data type limitation.

  4. Fixed the bug that dot symbol ( ‘.’ ) caused identification error of cluster name in .csv output, when doing st.tl.find_marker_genes.

Version 0.12.0#

0.12.0 : 2023-04-27#

  1. Addition of the algorithm of Cell Segmentation V3.0.

  2. Addition of method='hotspot' to st.tl.regulatory_network_inference, which takes spatial coordinate information into account to calculate the relative importance between TFs and their target genes.

  3. Addition of dpi and width/height setting for visualization, and addition of plotting scale for displaying static plot.

  4. Optimized required memory while plotting UMAP embedding via data.plt.umap and cell distribution via data.plt.cells_plotting.

  5. Fixed bug that input parameter of var_features_n was invalid, in data.tl.scTransform.

  6. Updated requirements.txt.

Version 0.11.0#

0.11.0 : 2023-04-04#

  1. Addition of Cell-cell Communication analysis.

  2. Addition of Gene Regulatory Network analysis.

  3. Addition of SingleR function for automatic annotation.

  4. Addition of v2 algorithm fast cell correction.

  5. Addition of dot plot to display gene-level results.

  6. Addition of the sorting function and the limitation of output genes in data.tl.find_marker_genes.

  7. Added pct and pct_rest to the output files of marker genes.

  8. Addition of the parameter mean_uni_gt in data.tl.filter_genes to filter genes on average expression.

  9. Fixed the bug that adata.X to output AnnData was the raw matrix.

  10. Fixed the failed compatibility to analysis results from .h5ad (version <= 0.9.0).

  11. Updated the tissue segmentation algorithm in the module of cell segmentation to avoid the lack of tissue.

  12. Reconstructed the manual of Stereopy.

  13. Updated requirements.txt.

Version 0.10.0#

0.10.0 :2023-02-22#

  1. Supported installation on Windows.

  2. Addition of displaying basic information of StereoExpData object when simply typing it.

  3. Addition of saving static results plots.

  4. Addition of marker gene proportion (optional), in-group and out-of-group, in data.tl.find_marker_genes. Otherwise, supported filtering marker genes via data.tl.filter_marker_genes.

  5. Supported adapting to AnnData, to directly use data and results stored in AnnData for subsequent analysis.

  6. Addition of the matrix of gene count among clusters so that transformed output .rds file could be used for annotation by SingleR directly.

  7. Initial release of Stereopy development solution.

  8. Updated requirements.txt.

Version 0.9.0#

0.9.0 : 2023-01-10#

  1. Resolved cell boundary overlapping issues during cell correction visualization.

  2. Addition of manually annotating cells and clusters via command lines or interactive visualization features.

  3. Addition of GPU version of UMAP, Neighbors, Leiden, and Louvian.

  4. Updated requirements.txt.

Version 0.8.0#

0.8.0 : 2022-12-02#

  1. Reconstructed scTransform normalization in Stereopy.

  2. Optimized the efficiency of fast-cell-correction.

  3. Enabled to read Seurat output .h5ad file for further analysis.

Version 0.7.0#

0.7.0 : 2022-11-15#

  1. Supported acquiring the cell expression matrix (cellbin) from GEM file.

  2. Updated hotspot to the latest version. Allow to output gene lists for every module.

  3. Allowed to merge and arrange more than two matrices in a row.

  4. Speeded up Stereopy installation and allowed installing heavy frameworks, such as, TensorFlow and PyTorch later before using.

  5. Updated requirements.txt.

Version 0.6.0#

0.6.0 : 2022-09-30#

  1. Added ‘Remove Batch Effect’ algorithm.

  2. Added RNA velocity analysis.

  3. Added export_high_res_area method to export high resolution matrix file(cell bin GEF) after lasso operation.

  4. Updated algorithm of scale.

  5. Optimized the efficiency of cell correction.

  6. Increased multi-chip fusion analysis.

  7. Updated requirements.txt.

Version 0.5.1#

0.5.1 : 2022-09-4#

  1. Fixed bug when using GEM file to run fast-cell-correction algorithm.

Version 0.5.0#

0.5.0 : 2022-09-2#

  1. Added fast-cell-correction algorithm.

  2. Updated gmm-cell-correction algorithm(slower version), and fixed bug that genes in the same position(bin) were assigned to different cells.

  3. Added data.plt.cells_plotting method to display cell details.

  4. Added data.tl.export_high_res_area method to export high resolution matrix file(GEF) after lasso.

  5. Increased tissue_extraction_to_bgef method to extract the tissue area.

  6. Updated algorithm of highly_variable_genes, umap and normalization.

  7. Updated requirements.txt.

Version 0.4.0#

0.4.0 : 2022-07-30#

  1. Updated tissue segmentation algorithm.

  2. Added the n_jobs parameter in st.tl.neighbors and st.tl.phenograph.

  3. Added st.io.read_gef function filtered by the list of gene region.

  4. Updated requirements.txt.

Version 0.3.1#

0.3.1 : 2022-06-30#

  1. Added gaussian smooth function.

  2. Added the svd_solver parameter in data.tl.pca.

  3. Added the output parameter in st.io.write_h5ad.

  4. Updated requirements.txt.

Version 0.3.0#

0.3.0 : 2022-06-10#

  1. Added cell bin correction function.

  2. Added data.tl.scale function in normalization.

  3. Supported writing StereoExpData object into a GEF file.

  4. Fixed bug of scTransform, reading the GEF/GEM file and annh5ad2rds.R.

  5. Updated default cluster groups to start at 1.

  6. Supported writing StereoExpData to stereo .h5ad function.

  7. Updated requirements.txt.

Version 0.2.4#

0.2.4 : 2022-01-19#

  1. Fixed bug of tar package.

Version 0.2.3#

0.2.3 : 2022-01-17#

  1. Added cell segmentation and tissue segmentation function.

  2. Updated stereo_to_anndata function and supported output to .h5ad file.

  3. Added the Rscript supporting h5ad file(with anndata object) to rds file.

  4. Supported differentially expressed gene (DEG) output to the .csv file.

Version 0.2.2#

0.2.2 : 2021-11-17#

  1. Optimized the performance of finding marker genes.

  2. Added Cython setup_build function and optimized IO performance of GEF.

  3. Added hotspot pipeline for spatial data and Squidpy for spatial_neighbor function.

  4. Added polygon selection for interactive scatter plot and simplify the visualization part of the code.

Version 0.2.1#

0.2.1 : 2021-10-15#

  1. Fixed the bug of marker_genes_heatmap IndexError and sorted the text of heatmap plot.

  2. Inverted yaxis on the top for spatial_scatter and cluster_scatter plot funcs.

  3. Solved the problem that multiple results of sctransform run were inconsistent.

  4. Updated requirements.txt.

Version 0.2.0#

0.2.0 : 2021-09-16#

Stereopy provides the analysis process based on spatial omics, including reading, preprocessing, clustering, differential expression testing and visualization, etc. There are the updates we made in this version.

  1. We proposed StereoExpData, which is a data format specially adapted to spatial omics analysis.

  2. Supported reading the GEF file, which is faster than reading GEM file.

  3. Supported the conversion between StereoExpData and AnnData.

  4. Added the interactive visualization function for selected data, you can dynamically select the area of interest, and then perform the next step of analysis.

  5. Supported dynamically displaying clustering scatter plots, you can modify the color and point size.

  6. Updated clustering related methods, such as leiden, louvain, which are comparable to the original algorithms.

  7. Added some analysis, such as the method of logres for find marker genes, highly variable genes analysis, sctransform method of normalization like Seruat.

0.1.0 : 2021-05-30#

  • Initial release