Release Notes

v 1.0.0

• First public release.

v 1.1.7

bulk module:

• Added Deseq2, including pyDEseq functions: deseq2_normalize, estimateSizeFactors, estimateDispersions, Matrix_ID_mapping.

• Included TCGA with TCGA.

• Introduced Enrichment with functions geneset_enrichment, geneset_plot.

single module:

• Integrated scdrug with functions autoResolution, writeGEP, Drug_Response.

• Added cpdb with functions cpdb_network_cal, cpdb_plot_network, cpdb_plot_interaction, cpdb_interaction_filtered.

• Included scgsea with functions geneset_aucell, pathway_aucell, pathway_aucell_enrichment, pathway_enrichment, pathway_enrichment_plot.

v 1.1.8

single module:

• Addressed errors in cpdb, including import errors and color issues in cpdb_plot_network.

• Introduced cpdb_submeans_exacted in cpdb for easy sub-network extraction.

v 1.1.9

bulk2single module:

• Added the bulk2single module.

• Fixed model load error from bulk2space.

• Resolved early stop issues from bulk2space.

• Included more user-friendly input methods and visualizations.

• Added loss history visualization.

utils module:

• Introduced pyomic_palette in the plot module.

v 1.1.10

• Updated all code references.

single module:

• Fixed non-valid parameters in single.mofa.mofa_run function.

• Added layer raw count addition in single.scanpy_lazy function.

• Introduced utils.plot_boxplot for plotting box plots with jittered points.

• Added bulk.pyDEseq.plot_boxplot for plotting box plots with jittered points for specific genes.

v 1.2.0

bulk module:

• Fixed non-valid cutoff parameter in bulk.geneset_enrichment.

• Added modules: pyPPI, pyGSEA, pyWGCNA, pyTCGA, pyDEG.

bulk2single module:

• Introduced bulk2single.save for manual model saving.

v 1.2.1-4

single module:

• Added pySCSA module with functions: cell_anno, cell_anno_print, cell_auto_anno, get_model_tissue.

• Implemented doublet cell filtering in single.scanpy_lazy.

• Added single.scanpy_cellanno_from_dict for easier annotation.

• Updated SCSA database from CellMarker2.0.

• Fixed errors in SCSA database keys: Ensembl_HGNC and Ensembl_Mouse.

v 1.2.5

single module:

• Added pyVIA module with functions: run, plot_piechart_graph, plot_stream, plot_trajectory_gams, plot_lineage_probability, plot_gene_trend, plot_gene_trend_heatmap, plot_clustergraph.

• Fixed warning error in utils.pyomic_plot_set.

• Updated requirements, including pybind11, hnswlib, termcolor, pygam, pillow, gdown.

v 1.2.6

single module:

• Added pyVIA.get_piechart_dict and pyVIA.get_pseudotime.

v 1.2.7

bulk2single module:

• Added Single2Spatial module with functions: load, save, train, spot_assess.

• Fixed installation errors for packages in pip.

v 1.2.8

• Fixed pip installation errors.

bulk2single module:

• Replaced deep-forest in Single2Spatial with Neuron Network for classification tasks.

• Accelerated the entire Single2Spatial inference process using GPU and batch-level estimation by modifying the predicted_size setting.

v 1.2.9

bulk module:

• Fixed duplicates_index mapping in Matrix_ID_mapping.

• Resolved hub genes plot issues in pyWGCNA.plot_sub_network.

• Fixed backupgene in pyGSEA.geneset_enrichment to support rare species.

• Added matrix plot module in pyWGCNA.plot_matrix.

single module:

• Added rank_genes_groups check in pySCSA.

bulk2single module:

• Fixed import error of deepforest.

v 1.2.10

• Renamed the package to omicverse.

single module:

• Fixed argument error in pySCSA.

bulk2single module:

• Updated plot arguments in bulk2single.

v 1.2.11

bulk module:

• Fixed wilcoxon method in pyDEG.deg_analysis.

• Added parameter setting for treatment and control group names in pyDEG.plot_boxplot.

• Fixed figure display issues in pyWGCNA.plot_matrix.

• Fixed category correlation failed by one-hot in pyWGCNA.analysis_meta_correlation.

• Fixed network display issues in pyWGCNA.plot_sub_network and updated utils.plot_network to avoid errors.

v 1.3.0

bulk module:

• Added DEseq2 method to pyDEG.deg_analysis.

• Introduced pyGSEA module in bulk.

• Renamed raw pyGSEA to pyGSE in bulk.

• Added get_gene_annotation in utils for gene name transformation.

v 1.3.1

single module:

• Added get_celltype_marker method.

single module:

• Added GLUE_pair, pyMOFA, pyMOFAART module.

• Added tutorials for Multi omics analysis by MOFA and GLUE.

• Updated tutorial for Multi omics analysis by MOFA.

v 1.4.0

bulk2single module:

• Added BulkTrajBlend method.

single module:

• Fixed errors in scnocd model.

• Added save, load, and get_pair_dict in scnocd model.

utils module:

• Added mde method.

• Added gz format support for utils.read.

v 1.4.1

preprocess module:

• Added pp (preprocess) module with qc (quantity control), hvg (high variable feature), pca.

• Added data_files for cell cycle calculation from Cellula and pegasus.

v 1.4.3

preprocess module:

• Fixed sparse preprocess error in pp.

• Fixed trajectory import error in via.

• Added gene correlation analysis of trajectory.

v 1.4.4

single module:

• Added panglaodb database to pySCSA module.

• Fixed errors in pySCSA.cell_auto_anno when some cell types are not found in clusters.

• Fixed errors in pySCSA.cell_anno when rank_genes_groups are not consistent with clusters.

• Added pySIMBA module in single for batch correction.

preprocess module:

• Added store_layers and retrieve_layers in ov.utils.

• Added plot_embedding_celltype and plot_cellproportion in ov.utils.

v 1.4.5

single module:

• Added MetaTiME module to perform cell type annotation automatically in TME.

v 1.4.12

• Updated conda install omicverse -c conda-forge.

single module:

• Added pyTOSICA module to perform cell type migration from reference scRNA-seq in Transformer model.

• Added atac_concat_get_index, atac_concat_inner, atac_concat_outer functions to merge/concatenate scATAC data.

• Fixed MetaTime.predicted when Unknown cell type appears.

preprocess module:

• Added plot_embedding in ov.utils to plot UMAP in a special color dictionary.

v 1.4.13

bulk module:

• Added mad_filtered to filter robust genes when calculating the network in ov.bulk.pyWGCNA module.

• Fixed string_interaction in ov.bulk.pyPPI for string-db updates.

preprocess module:

• Changed mode argument of pp.preprocess to control preprocessing steps.

• Added ov.utils.embedding, ov.utils.neighbors, and ov.utils.stacking_vol.

v 1.4.14

preprocess module:

• Added batch_key in pp.preprocess and pp.qc.

utils module:

• Added plot_ConvexHull to visualize the boundary of clusters.

• Added weighted_knn_trainer and weighted_knn_transfer for multi-adata integration.

single module:

• Fixed import errors in mofa.

v 1.4.17

bulk module:

• Fixed compatibility issues with pydeseq2 version 0.4.0.

• Added bulk.batch_correction for multi-bulk RNA-seq/microarray samples.

single module:

• Added single.batch_correction for multi-single cell datasets.

preprocess module:

• Added parameter layers_add in pp.scale.

v 1.5.0

single module:

• Added cellfategenie to calculate timing-associated genes/genesets.

• Fixed the name error in atac_concat_outer.

• Added more kwargs for batch_correction.

utils module:

• Added plot_heatmap to visualize the heatmap of pseudotime.

• Fixed embedding when the version of mpl is larger than 3.7.0.

• Added geneset_wordcloud to visualize geneset heatmaps of pseudotime.

v 1.5.1

single module:

• Added scLTNN to infer cell trajectory.

bulk2single module:

• Updated cell fraction prediction with TAPE in bulk2single.

• Fixed group and normalization issues in bulk2single.

utils module:

• Added Ro/e calculation (by: Haihao Zhang).

• Added cal_paga and plot_paga to visualize the state transfer matrix.

• Fixed the read function.

v 1.5.2

bulk2single Module:

• Resolved a matrix error occurring when gene symbols are not unique.

• Addressed the interpolation issue in BulkTrajBlend when target cells do not exist.

• Corrected the generate function in BulkTrajBlend.

• Rectified the argument for vae_configure in BulkTrajBlend when cell_target_num is set to None.

• Introduced the parameter max_single_cells for input in BulkTrajBlend.

• Defaulted to using scaden for deconvolution in Bulk RNA-seq.

single Module:

• Fixed an error in pyVIA when the root is set to None.

• Added the TrajInfer module for inferring cell trajectories.

• Integrated Palantir and Diffusion_map into the TrajInfer module.

• Corrected the parameter error in batch_correction.

utils Module:

• Introduced plot_pca_variance_ratio for visualizing the ratio of PCA variance.

• Added the cluster and filtered module for clustering the cells

• Integrated MiRA to calculate the LDA topic

v 1.5.3

single Module:

• Added scVI and MIRA to remove batch effect

space Module:

• Added STAGATE to cluster and denoisy the spatial RNA-seq

pp Module:

• Added doublets argument of ov.pp.qc to control doublets(‘Default’=True)

v 1.5.4

bulk Module:

• Fixed an error in pyDEG.deg_analysis when n_cpus can not be set in pyDeseq2(v0.4.3)

single Module:

• Fixed an argument error in single.batch_correction of combat

utils Module:

• Added venn4 plot to visualize

• Fixed the label visualization of plot_network

• Added ondisk argument of LDA_topic

space Module:

• Added Tangram to mapping the scRNA-seq to stRNA-seq

v 1.5.5

pp Module:

• Added max_cells_ratio and max_genes_ratio to control the max threshold in qc of scRNA-seq

single Module:

• Added SEACells model to calculate the metacells from scRNA-seq

space Module:

• Added STAligner to integrate multi stRNA-seq

v 1.5.6

pp Module

• Added mt_startswith argument to control the qc in mouse or other species.

utils Module

• Added schist method to cluster the single cell RNA-seq

single Module

• Fixed the import error of palantir in SEACells

• Added CEFCON model to identify the driver regulators of cell fate decisions

bulk2single Module

• Added use_rep and neighbor_rep argument to configure the nocd

space Module

• Added SpaceFlow to identify the pseudo-spatial map

v 1.5.8

pp Module

• Added score_genes_cell_cycle function to calculate the cell cycle

bulk Module

• Fixed dds.plot_volcano text plot error when the version of adjustText larger than 0.9

single Module

• Optimised MetaCell.load model loading logic

• Fixed an error when loading the model usng MetaCell.load

• Added tutorials of Metacells

pl Module

Add pl as a unified drawing prefix for the next release, to replace the drawing functionality in the original utils, while retaining the drawing in the original utils.

• Added embedding to plot the embedding of scRNA-seq using ov.pl.embedding

• Added optim_palette to provide a spatially constrained approach that generates discriminate color assignments for visualizing single-cell spatial data in various scenarios

• Added cellproportion to plot the proportion of stack bar of scRNA-seq

• Added embedding_celltype to plot the figures both celltype proportion and embedding

• Added ConvexHull to plot the ConvexHull around the target cells

• Added embedding_adjust to adjust the text of celltype legend in embedding

• Added embedding_density to plot the category density in the cells

• Added bardotplot to plot the bardotplot between different groups.

• Added add_palue to plot the p-threshold between different groups.

• Added embedding_multi to support the mudata object

• Added purple_color to visualize the purple palette.

• Added venn to plot the venn from set 2 to set 4

• Added boxplot to visualize the boxdotplot

• Added volcano to visualzize the result of differential expressed genes

v 1.5.9

single Module

• Added slingshot in single.TrajInfer

• Fixed some error of scLTNN

• Added GPU mode to preprocess the data

• Added cNMF to calculate the nmf

space Module

• Added Spatrio to mapping the scRNA-seq to stRNA-seq

v 1.6.0

Move CEFCON,GNTD,mofapy2,spaceflow,spatrio,STAligner,tosica from root to external module.

space Module

• Added STT in omicverse.space to calculate the spatial transition tensor.

• Added scSLAT in omicverse.external to align of different spatial slices.

• Added PROST in omicverse.external and svg in omicverse.space to identify the spatially variable genes and domain.

single Module

• Added get_results_rfc in omicverse.single.cNMF to predict the precise cluster in complex scRNA-seq/stRNA-seq

• Added get_results_rfc in omicverse.utils.LDA_topic to predict the precise cluster in complex scRNA-seq/stRNA-seq

• Added gptcelltype in omicverse.single to annotate celltype using large language model #82.

pl Module

• Added plot_spatial in omicverse.pl to visual the spot proportion of cells when deconvolution

v 1.6.2

Support Raw Windows platform

• Added mde in omicverse.pp to accerate the umap calculation.

v 1.6.3

• Added ov.setting.cpu_init to change the environment to CPU.

• Move module tape,SEACells and palantir to external

Single Module

• Added CytoTrace2 to predict cellular potency categories and absolute developmental potential from single-cell RNA-sequencing data.

• Added cpdb_exact_target and cpdb_exact_source to exact the means of special ligand/receptor

• Added gptcelltype_local to identify the celltype using local LLM #96 #99

Bulk Module

• Added MaxBaseMean columns in dds.result to help people ignore the empty samples.

Space Module

• Added **kwargs in STT.compute_pathway

• Added GraphST to identify the spatial domain

pl Module

• Added cpdb_network, cpdb_chord, cpdb_heatmap, cpdb_interacting_network,cpdb_interacting_heatmap and cpdb_group_heatmap to visualize the result of CellPhoneDB

utils Module

• Added mclust_py to identify the Gaussian Mixture cluster

• Added mclust methdo in cluster function

v 1.6.4

Bulk Module

• Optimised pyGSEA’s geneset_plot visualisation of coordinate effects

• Fixed an error of pyTCGA.survival_analysis when the matrix is sparse. #62, #68, #95

• Added tqdm to visualize the process of pyTCGA.survial_analysis_all

• Fixed an error of data_drop_duplicates_index with remove duplicate indexes to retain only the highest expressed genes #45

• Added geneset_plot_multi in ov.bulk to visualize the multi results of enrichment. #103

Single Module

• Added mellon_density to calculate the cell density. #103

PP Module

• Fixed an error of ov.pp.pca when pcs smaller than 13. #102

• Added COMPOSITE in ov.pp.qc’s method to predicted doublet cells. #103

• Added species argument in score_genes_cell_cycle to calculate the cell phase without gene manual input

v 1.6.6

Pl Module

• Fixed the ‘celltyep_key’ error of ov.pl.cpdb_group_heatmap #109

• Fixed an error in ov.utils.roe when some expected frequencies are less than expected value.

• Added cellstackarea to visual the Percent stacked area chart of celltype in samples.

Single Module

• Fixed the bug of ov.single.cytotrace2 when adata.X is not sparse data. #115, #116

• Fixed the groupby error in ov.single.get_obs_value of SEACells.

• Fixed the error of cNMF #107, #85

• Fixed the plot error when Pycomplexheatmap version > 1.7 #136

Bulk Module

• Fixed an key error in ov.bulk.Matrix_ID_mapping

• Added enrichment_multi_concat in ov.bulk to concat the result of enrichment.

• Fixed the pandas version error in gseapy #137

Bulk2Single Module

• Added adata.var_names_make_unique() to avoid mat shape error if gene not unique. #100

Space Module

• Fixed an error in construct_landscape of ov.space.STT

• Fixed an error of get_image_idx_1D in ov.space.svg #117

• Added COMMOT to calculate the cell-cell interaction of spatial RNA-seq.

• Added starfysh to deconvolute spatial transcriptomic without scRNA-seq (#108)

PP Module

• Updated constraint error of ov.pp.mde #129

• Fixed type error of float128 #134

v 1.6.7

Space Module

• Added n_jobs argument to adjust thread in extenel.STT.pl.plot_tensor_single

• Fixed an error in extenel.STT.tl.construct_landscape

• Updated the tutorial of COMMOT and Flowsig

Pl Module

• Added legend_awargs to adjust the legend set in pl.cellstackarea and pl.cellproportion

Single Module

• Fixed the error of get_results and get_results_rfc in cNMF module. (#143) (#139)

• Added sccaf to obtain the best clusters.

• Fixed the .str error in cytotrace2 (#146)

Bulk Module

• Fixed the import error of gseapy in bulk.geneset_enrichment

• Optimized code logic for offline enrichment analysis, added background parameter

• Added pyWGCNA package replace the raw calculation of pyWGCNA (#162)

Bulk2Single Module

• Remove _stat_axis in bulk2single_data_prepare and use index instead of it (#160).

PP Module

• Fixed a return bugs in pp.regress_and_scale (#156)

• Fixed a scanpy version error when using ov.pp.pca (#154)

v 1.6.8

Bulk Module

• Fixed the error of log_init in gsea_obj.enrichment (#184)

• Added ax argument to visualize the geneset_plot

Space Module

• Added CAST to integrate multi slice

• Added crop_space_visium in omicverse.tl to crop the sub area of space data

Pl Module

• Added legend argument to visualize the cpdb_heatmap

• Added text_show argument to visualize the cellstackarea

• Added ForbiddenCity color system

v 1.6.9

PP Module

• Added recover_counts to recover counts after ov.pp.preprocess

• removed the lognorm layers added in ov.pp.pca

Single Module

• Added MultiMap module to integrate multi species

• Added CellVote to vote the best cells

• Added CellANOVA to integrate samples and correct the batch effect

• Added StaVia to calculate the pseudotime and infer trajectory.

Space Module

• Added ov.space.cluster to identify the spatial domain

• Added Binary for spatial cluster

• Added Spateo to calculate the SVG

v 1.7.0

Added cpu-gpu-mixed to accelerate the analysis of scrna-seq using GPU. Changed the logo presentation of Omicverse to ov.plot_set

Bulk Module

• Added limma, edgeR in different expression gene analysis. (#238)

• Fixed the version error of DEseq2 analysis.

Single Module

• Added lazy function to calculate all function of scrna-seq (#291)

• Added generate_scRNA_report and generate_reference_table to generate the report and reference (#291) (#292)

• Fixed geneset_prepare not being able to read gmt not split by \t\t (#235) (#238)

• Added geneset_aucell_tmp,pathway_aucell_tmp,pathway_aucell_enrichment_tmp to test the chunk_size (#238)

• Added data enhancement of Fate

• Added plot_atlas_view_ov in VIA

• Fixed an error when the matrix is too large in recover_counts.

• Added forceatlas2 to calculate the X_force_directed.

• Added milo and scCODA to analysis different celltype abundance.

• Added memento to analysis different gene expression.

Space Module

• Added GASTON to learn a topographic map of a tissue slice from spatially resolved transcriptomics (SRT) data (#238)

• Added super kwargs in plot_tensor_single of STT.

• Updated COMMOT using GPU-accerlate

Plot Module

• Added dotplot_doublegroup to visual the genes in doublegroup.

• Added transpose argument of cpdb_interacting_heatmap to transpose the figure.

• Added calculate_gene_density to plot the gene’s density.

v 1.7.1

Single Module

• Fixed some error of ov.single.lazy.

• Fixed the format of ov.single.generate_scRNA_report

• Updated some functions of palantir

• Added CellOntologyMapper to map cell name.

v 1.7.2

Pl Module

• Optimated the plot effect of ov.pl.box_plot

• Optimated the plot effect of ov.pl.volcano Optimated the plot effect of ov.pl.violin

• Added beautiful dotplot than scanpy (#318)

• Added the similar visualization function of CellChat. (#313)

Space Module

• Added 3D cell-cell interaction analysis in COMMOT (#315)

Single Module

• Fixed the error of pathway_enrichment. (#184)

• Added SCENIC module with GPU-accerlate. (#331)

utils Module

• Added scICE to calculate the best cluster (#329)

v 1.7.6

LLM Module

• Added GeneFromer, scGPT, scFoundation, UCE, CellPLM to call directly in OmicVerse.

Pl Module

• Optimized the visualization effect of embedding.

• Added ov.pl.umap, ov.pl.pca, ov.pl.mde, and ov.pl.tsne

v 1.7.8

Implemented lazy loading system that reduces import omicverse time by 40% (from ~7.8s to ~4.7s). Added GPU-accelerated PCA support for Apple Silicon (MLX) and CUDA (TorchDR) devices. Introduced Smart Agent System with natural language processing for 50+ AI models from 8 providers. Added and fixed the anndata-rs to support million size’s datasets (#336)

PP Module

• Added GPU-accelerated PCA in ov.pp.pca() with MLX support for Apple Silicon MPS devices

• Added TorchDR-based PCA acceleration in ov.pp.pca() for NVIDIA CUDA devices

• Added smart device detection and automatic backend selection in init_pca() and pca() functions

• Added graceful fallback to CPU implementation when GPU acceleration fails

• Added enhanced verbose output with device selection information and emoji indicators

• Added optimal component determination based on variance contribution thresholds in init_pca()

• Added GPU-accelerated SUDE dimensionality reduction in ov.pp.sude() with MLX/CUDA support

• Optimize the ov.pp.qc and added ribosome and hb-genes to know more information of data quantity.

Datasets Module

• Complete elimination of scanpy dependencies for faster loading

• Added dynamo-style dataset framework with comprehensive collection

• Added robust download system with progress tracking and caching

• Added enhanced mock data generation with realistic structure

• Added support for h5ad, loom, xlsx, and compressed formats

Agent Module

• Added multi-provider LLM support (OpenAI, Anthropic, Google, DeepSeek, Qwen, Moonshot, Grok, Zhipu AI)

• Added natural language processing for both English and Chinese

• Added code generation architecture with local execution

• Added function registry system with multi-language aliases

• Added smart API key management and provider-specific configuration

Bulk Module

• Added BayesPrime and Scaden to deconvoluted Bulk RNA-seq’s celltype proportion.

• Added alignment to alignment the fastq to counts.

Single Module

• Added ov.single.Annotation and ov.single.AnnotationRef to annotate the cell type automatically.

• Added ov.alignment.single to alignment the scRNA-seq to counts directly.

v 1.7.9

Implemented smart lazy loading system that dramatically reduces import omicverse time by 85.6x (from ~16.57s to ~0.19s). Enhanced RNA-seq alignment workflow with comprehensive toolkit for FASTQ processing and counting. Optimized dataset management with nested directory creation for better organization.

Performance Optimization

Lazy Loading System:

• Implemented module-level lazy loading using __getattr__ mechanism for all major modules

• Added attribute-level lazy loading for frequently-used functions (read, palette, Agent, etc.)

• Introduced intelligent caching system to ensure instant access after first load

• Reduced initial import time from 16.57 seconds to 0.19 seconds (85.6x speedup)

• Maintained full backward compatibility - all existing code works without modification

• Preserved complete IDE support with tab completion via __dir__() implementation

• Fixed circular import issues by delaying settings module initialization

• MkDocs API documentation generation fully compatible with lazy loading

Benefits for Users:

• ⚡ Instant startup for Jupyter notebooks and scripts

• 🎯 Load only what you use - modules imported on first access

• 💾 Reduced memory footprint for simple tasks

• 🔄 Second access is cached and instant (< 0.001s)

Alignment Module

New Comprehensive RNA-seq Alignment Toolkit:

Added complete end-to-end workflow for processing raw sequencing data:

• ov.alignment.prefetch: Download SRA datasets from NCBI with built-in retry logic

• ov.alignment.fqdump: Convert SRA to FASTQ format with parallel processing support

• ov.alignment.parallel_fastq_dump: High-performance parallel FASTQ extraction

• ov.alignment.fastp: Quality control and adapter trimming for FASTQ files

• ov.alignment.STAR: RNA-seq alignment using STAR aligner with customizable parameters

• ov.alignment.featureCount: Gene-level read counting (renamed from count to avoid conflicts)

• ov.alignment.single: One-command scRNA-seq alignment with kb-python (kallisto|bustools)

• ov.alignment.ref: Build kallisto|bustools reference index for alignment

• ov.alignment.count: Quantify gene expression from aligned reads

Key Features:

• Unified API for both bulk RNA-seq (STAR + featureCount) and scRNA-seq (kb-python) workflows

• Built-in support for RNA velocity analysis with kb-python

• Parallel processing capabilities for faster data conversion

• Automatic handling of paired-end and single-end reads

• Technology-specific filtering for bulk vs single-cell data

• Integration with SRA toolkit for seamless data download

Example Workflow:

# Download and process bulk RNA-seq
ov.alignment.prefetch('SRR1234567', output_dir='./data')
ov.alignment.fqdump('SRR1234567', output_dir='./fastq')
ov.alignment.fastp('sample_1.fastq.gz', 'sample_2.fastq.gz', output_prefix='clean')
ov.alignment.STAR(fastq1='clean_1.fastq.gz', fastq2='clean_2.fastq.gz',
                  genome_dir='./genome', output_prefix='aligned')
ov.alignment.featureCount(bam='aligned.bam', annotation='genes.gtf', output='counts.txt')

# Or use one-command scRNA-seq alignment
ov.alignment.single(
    fastq=['read1.fastq.gz', 'read2.fastq.gz'],
    index='./kb_index',
    output_dir='./kb_output',
    technology='10xv3'
)

PP Module

• Fixed an HVG (Highly Variable Genes) selection issue in ov.pp.preprocess

• Improved preprocessing pipeline stability and accuracy

• Refactored PCA implementation to utilize torch_pca for GPU acceleration (replacing TorchDR)

• Enhanced support for sparse matrices in PCA computation

• Updated PCA embedding basis from X_pca to PCA for clarity and consistency

• Improved error handling with try-except blocks in PCA computation

• Fixed PCA GPU mode support with sparse matrices to avoid memory errors

Single Module

• Added CONCORD method to ov.single.batch_correction for single-cell data integration

• Enhanced batch correction capabilities with state-of-the-art algorithm

• Fixed critical performance issue in pySCENIC: Reverted inefficient correlation calculation optimization that caused memory issues and slowdowns in scRNA-seq data

• Removed misleading warnings about dropout genes in SCENIC correlation calculations

• Restored memory-efficient pairwise correlation computation (prevents OOM with >20k genes)

• SCENIC now uses original approach: calculate correlations only for specific TF-target pairs instead of creating full gene×gene matrices

• Added ov.single.find_markers for unified marker gene identification supporting five methods: cosg, t-test, t-test_overestim_var, wilcoxon, and logreg; statistical methods are natively ported from scanpy with no scanpy runtime dependency and numerically consistent results (rtol=1e-4)

• Added ov.single.get_markers to extract top marker genes from results as a DataFrame or dict, with support for single/multiple cluster filtering and optional filtering by min_logfoldchange, min_score, and min_pval_adj; output includes pct_group and pct_rest columns showing cell expression proportions within and outside each cluster

Space Module

• Added FlashDeconv for fast, GPU-free deconvolution in Visium spatial transcriptomics

• Added Banksy clustering method for spatial domain identification

• Updated spatial analysis documentation with new clustering approaches

Web Module

• Launched Omicverse-Notebook for browser-based interactive analysis without local installation

• Launched Omicverse-Web for web-based data analysis without coding requirements

• Democratized bioinformatics analysis for researchers without programming background

Agent Module

• Enhanced ov.Agent with improved natural language processing for data analysis

• Expanded LLM provider support and model selection

• Optimized code generation and execution pipeline

Pl Module

• Enhanced categorical legend handling for scatterplot embeddings

• Added legend_loc='on data' option for direct annotation on plots

• Improved visualization clarity for complex datasets

• Added ov.pl.markers_dotplot as a cleaner drop-in for rank_genes_groups_dotplot with improved defaults (standard_scale='var', cmap='Spectral_r', dendrogram=False)

• Fixed KeyError in rank_genes_groups_df when cluster names are numeric strings (e.g., leiden '0', '1'); now correctly handles structured arrays, DataFrames, and plain 2D arrays from all marker methods

Datasets Module

• Added comprehensive dataset URLs for easier data access

• Expanded data downloading utilities with progress tracking

• Fixed dataset download to create nested target directories automatically

• Improved dataset utilities with better error handling

• Refreshed download behaviors for more reliable data fetching

Docs

• Strengthened data handling documentation in dotplot and DEG analysis tutorials

• Updated the scTour clustering tutorial with latest best practices

• Added comprehensive release notes for v1.7.9

• Enhanced alignment module documentation with end-to-end workflows

Bug Fixes

• Resolved circular import issues between _settings and utils modules

• Fixed compatibility issues with latest package versions (zarr, pandas, etc.)

• Improved error handling in parallel processing functions

Single Module

Enhanced DEG Analysis with Expression Percentages: Added cell expression percentage information to differential expression results

• Added pct_ctrl column showing percentage of cells expressing each gene in control group (0-100%)

• Added pct_test column showing percentage of cells expressing each gene in test group (0-100%)

• Added pct_diff column showing the difference in expression percentage (pct_test - pct_ctrl)

• Works with all DEG methods: wilcoxon, t-test, and memento-de

• Enables better marker gene identification by filtering genes based on expression prevalence

• Similar to dotplot circle size information, helps identify genes with widespread vs. sparse expression patterns

Example Usage:

deg_obj = ov.single.DEG(adata, condition='condition',
                        ctrl_group='Control', test_group='Treatment')
deg_obj.run(celltype_key='cell_label', celltype_group=['T_cells'])
results = deg_obj.get_results()
# Now includes pct_ctrl, pct_test, pct_diff columns

Compatibility

NumPy 2.0 Compatibility: Fixed all NPY201 compatibility issues to ensure seamless support for both NumPy 1.x and 2.x

Fixed Issues (31 total):

1. np.in1d → np.isin (9 instances)

   • omicverse/bulk/_dynamicTree.py: 3 instances (lines 697, 741)

   • omicverse/single/_cosg.py: 1 instance (line 77)

   • omicverse/external/GNTD/_preprocessing.py: 2 instances

   • omicverse/external/scdiffusion/guided_diffusion/cell_datasets_WOT.py: 1 instance

   • Other external modules: 2 instances

2. np.row_stack → np.vstack (13 instances)

   • omicverse/external/CAST/CAST_Projection.py: 2 instances

   • omicverse/external/CAST/visualize.py: 2 instances

   • omicverse/external/scSLAT/viz/multi_dataset.py: multiple instances

   • omicverse/single/_mdic3.py: 1 instance

3. np.product → np.prod (4 instances)

   • omicverse/external/umap_pytorch/model.py: 2 instances

   • omicverse/external/umap_pytorch/modules.py: 2 instances

4. np.trapz compatibility wrapper (2 instances)

   • Added compatibility wrapper in:

     • omicverse/external/VIA/plotting_via.py

     • omicverse/external/VIA/plotting_via_ov.py

   • Uses numpy.trapezoid (NumPy 2.0+) with fallback to numpy.trapz (NumPy 1.x)

Backward Compatibility:

• ✅ All changes maintain full backward compatibility with NumPy 1.x (1.13+)

• ✅ np.isin available since NumPy 1.13

• ✅ np.vstack available in all NumPy versions

• ✅ np.prod available in all NumPy versions

• ✅ Custom compatibility wrapper handles trapz/trapezoid transition

v 1.7.10

Scope

• This release note summarizes changes from commit cd3d151 (version set to 1.7.10rc1) to current HEAD.

• Total code delta in this window: 252 files changed, +46,992 / -9,752.

Agent & Runtime

• Upgraded ov.Agent architecture to modern agentic tool-calling workflows with subagent delegation (v4/v5 evolution).

• Improved GPT-5.2 robustness, response parsing, and backend error handling.

• Added harness runtime components for execution contracts, tool catalog, runtime state, tracing, and cleanup policies.

• Strengthened sandbox behavior with restricted import controls for internal modules.

• Added web bridge and session-level execution improvements for agent workflows.

New Modules

• Added omicverse.biocontext for biomedical knowledge queries via BioContext MCP tooling.

• Added omicverse.fm (foundation-model adapters, routing, registry, and API).

• Added structured omicverse.io namespaces for general/single/bulk/spatial I/O paths.

• Added omicverse.jarvis multi-channel bot framework (Feishu/QQ/Telegram) with bridge support.

Core OmicVerse Improvements

• Continued enhancements across pp, pl, single, space, and utils modules.

• Fixed circular import between preprocessing utility internals (_utils.py and _scale.py path).

• Added/updated function-level metadata and documentation quality in key analysis modules (preprocessing, annotation, trajectory, spatial, datasets, bulk).

• Extended dataset utilities with new signature resources and improved loading pathways.

Registry & Help System

• Improved registry behavior and module import exposure in package entrypoints.

• Enhanced function/class registration metadata coverage for agent discoverability.

• Registry help generation now better aligns with class constructor documentation in class-based tools.

Web & UI

• Single-cell analysis UI received iterative upgrades:

  • Better code cell management and undo behavior

  • Improved AnnData slot detail retrieval and display

  • Better DataFrame rendering and integration

  • Plot density/point style control refinements

  • i18n and UX polish for analysis panels

• omicverse_web service layer expanded with session-oriented agent service support.

Developer Experience & Testing

• Added FM test suite and multiple harness/ovagent test modules.

• Removed obsolete legacy-priority and complexity-classifier test paths.

• Added workflow and harness documentation pages for runtime contracts and operational guidance.

Documentation

• Updated and expanded agent architecture and streaming API docs.

• Updated t_preprocess_cpu.ipynb to match latest GPU/version detection behavior.

• Added bilingual and deployment-oriented guidance for Jarvis and agent-related workflows.

v 2.1.x

Scope

• Summarises every change between v2.0.0 (tagged 2026-03-18) and the current dev tree (master + the in-flight feat/metabol and feat/alignment-16s-amplicon branches that land in v2.1.x).

• Window stats on master: 462 commits, 429 files changed, +98,799 / −17,461 lines. Plus the two pending feature branches add ov.metabol (12 commits) and ov.micro + ov.alignment (16S amplicon — 5 commits).

• Three top-level themes drove the release:

  1. New bio modules — ov.metabol (metabolomics), ov.micro + ov.alignment (16S amplicon → microbiome), ov.single.Monocle, ov.io.read_xenium, ov.utils.cluster(method='pymclustR'), CellSAM, CellCharter, Harmony v0.2, Marsilea heatmaps, CCC plotting, Nanostring spatial, dynamic trajectories, anndataoom OOM Rust backend.

  2. Spatial-platform support — Xenium In Situ end-to-end (read + segmentation + viz), Visium HD SpaceRanger v4 round-trip (cellpose + write_visium_hd_cellseg), Nanostring CosMx FOV-aware plotting.

  3. OVAgent / Jarvis runtime overhaul — ~60 task-* commits across PRs #596–#605 (facade slimming, multi-channel migration, P0/P1 security closures, Codex OAuth).

---

Single-cell trajectory inference

• ov.single.Monocle — pure-Python Monocle 2 reimplementation: a from-scratch port of the R monocle2 pipeline (DDRTree, MST, branch-state assignment, BEAM differential expression). No rpy2, no R install. Includes:

  • method='fast' DDRTree update (default): caches X X.T, solves in (K × D) instead of (K × N), truncates the soft-assignment R to the top-K/5 entries per row → ~3× faster per iteration with bounded numerical drift. method='exact' retained for bitwise R-parity.

  • Delaunay-based Euclidean MST in _project_cells_to_mst — replaces the O(N²) dense pairwise distance matrix (164 GB for a 143 k-cell atlas in R) with an O(N·d) Delaunay + sparse MST that is provably exact.

  • Robust HSMM tutorial output matching the R Monocle 2 reference direction.

  • Pseudotime correlation with R Monocle 2 ≥ 0.99 on every benchmark dataset; 30-100× faster.

  • Also published as a standalone PyPI package (monocle2-py) for users who want trajectory inference without the full omicverse stack.

• Dynamic-trajectory utilities: new feature-fitting + lineage-aware trend plotting; better Palantir trend visualisation; cleanup of Slingshot debug plots and sctour input stabilisation.

Spatial omics

10x Xenium In Situ — end-to-end support (PR #629)

• ov.io.read_xenium — full reader for the standard Xenium outs/ layout:

  • cell_feature_matrix.h5 (or .h5ad) → adata.X

  • cells.csv.gz / cells.parquet → adata.obs, with x_centroid / y_centroid → adata.obsm['spatial']

  • experiment.xenium JSON → adata.uns['spatial'][library_id]['metadata']

  • Auto-resolves library_id from experiment.xenium (region_name → run_name).

  • Exposed as both ov.io.spatial.read_xenium and ov.io.read_xenium.

  • Verified against the Xenium_FFPE_Human_Breast_Cancer_Rep1 public sample.

• load_boundaries=True parameter loads cell_boundaries.parquet / .csv.gz (Xenium’s long-form per-vertex table) into per-cell WKT POLYGON strings — sets uns['omicverse_io']['type'] = 'xenium_seg' so downstream code (matching nanostring_seg) can render cell boundaries directly via ov.pl.spatialseg.

• cache_file + smart pyramid-level image loading — pick the right resolution from the multi-resolution morphology TIFF without loading the full pyramid.

• New tutorial t_xenium_preprocess.ipynb showing read → preprocess → spatialseg overlay (verified on KRT7 in the Breast Cancer sample).

10x Atera (WTA Preview) — end-to-end support (PR #700, omicverse-tutorials#30)

• ov.io.read_atera — reader for the 10x Atera (whole-transcriptome) outs/ bundle. Atera ships a Xenium-format core (cell_feature_matrix.h5, cells.parquet, cell_boundaries.parquet, experiment.xenium) plus four Atera-only additions, all handled by the reader:

  • nucleus_boundaries.parquet → obs['nucleus_geometry'] (WKT POLYGON).

  • morphology_focus/ch####_<tag>.ome.tif — content-named multi-stain pyramid TIFFs. Channel selector accepts a semantic tag ('dapi' / 'boundary' / 'rna' / 'stroma'), a filename substring ('cd45', '18s'), or an integer-as-string index. tifffile’s OME multi-file series is bypassed (is_ome=False) so each channel’s pyramid IFDs are walked standalone.

  • Optional vendor cell_groups.csv merge → obs['cell_group'] and obs['cell_group_color'], NaN-preserving for cells absent from the CSV.

  • Optional H&E OME-TIFF + 3×3 affine CSV → uns['spatial'][lib]['images']['he'] and scalefactors['he_affine'] / 'he_downsample'.

  • Verified on the public WTA_Preview_FFPE_Breast_Cancer sample (170,057 cells × 18,028 genes after dropping 9,076 control probes/codewords).

• New helpers extracted from the tutorial — ov.pl.to_rgb_grayscale (percentile-clipped RGB stack so morphology overlays bypass matplotlib’s default viridis colormap), ov.pl.sync_categorical_palette (wires a per-cell colour column into uns[<key>_colors]), ov.space.subset_window (rectangular spatial-window subset preserving uns).

• New tutorial t_atera_preprocess.ipynb — load the bundle, render the four morphology channels in a 2 × 2 grid, plot the vendor cell-group classifier with ov.pl.spatial, render polygon zooms via ov.pl.spatialseg(img_key=...) against each of the four channels, run a standard preprocessing → HVG → PCA pipeline, and map canonical breast-cancer markers (KRT8 / PTPRC / COL1A1 / PECAM1).

10x Visium HD — SpaceRanger v4 compat (PRs #620, #622)

• ov.io.write_visium_hd_cellseg — export cell-level AnnData back to a SpaceRanger v4 directory structure so downstream tools (Loupe Browser, spaceranger-aware pipelines) can consume the cellpose / CellSAM output as if it came straight out of SpaceRanger v4.

• Cell IDs use the SpaceRanger v4 convention — cellid_000000001-1 (zero-padded, suffixed with the slice index) rather than the older spot-id format.

• Image / scalefactors handling simplified; uses existing shapely polygon generation instead of redoing convex hulls.

• Cellpose tutorial rewritten end-to-end with executed outputs (0 errors, 7–16 baked-in figures across iterations) showing both Cellpose vs CellSAM segmentation comparison on a 1/16 crop of the HD sample, and the SpaceRanger v4-compatible export round-trip.

Nanostring CosMx — FOV-aware plotting

• New ov.pl.spatial_* family additions for FOV-aware plotting (multi-FOV layout, per-FOV background image overlay, rasterisation options for very dense slides).

• FOV image processing pipeline picks up cropping / rasterisation hints from uns.

Cell segmentation backends

• CellSAM backend added (alongside the existing Cellpose backend). Uses cropped images for the standard 10x HD flow.

• stardist() → cellseg() rename with backward-compatible alias — clearer name now that there are multiple backends behind it.

Other spatial features

• CellCharter integration: new method='cellcharter' in ov.utils.cluster plus enhanced spatial_neighbors graph construction. Includes AutoK export, Banksy Jupyter compat, and pickle cross-version loading fixes.

• ov.pl.create_custom_colormap: generic palette helper, registered for agent discoverability.

• Spatial-segmentation overlay fixes: cmap alpha now honoured in outline_only; FOV image processing supports rasterization options.

• Spatial background image scaling: fixed bug where the H&E background was rendered tiny (coords were not scaled). Affected all sc.pl.spatial-style overlays.

• Starfysh tutorial compatibility restored against modern numpy/pandas/scipy/pytorch (also pins s3fs ≥ 2023.1.0 to avoid Python 3.12 build failures).

Preprocessing & QC

• ov.pp.qc(adata, doublets='pydoubletfinder') — pure-Python DoubletFinder backend alongside the existing scrublet backend. No R install needed; matches the R DoubletFinder package within published-tutorial AUC range.

• Auto-detect mitochondrial gene prefix in pp.qc — no more hand-setting 'mt-' vs 'MT-' per species. Explicit override still respected.

• Harmony upgrade to upstream harmonypy v0.2.0 with three backends:

  • GPU: existing PyTorch path (unchanged).

  • CPU NumPy: pure-NumPy fallback for environments without torch/MLX.

  • MLX: native Apple-Silicon (MPS) backend rewritten to use pure MLX operations (no numpy round-trip). Restored emoji/colour/tqdm output, n_init=1 to match upstream, and reproducibility seeds. Multiple review rounds for MLX correctness (lambda double-insert, slice-assignment crash, dead self._W).

• torch_pca sparse + covariance_eigh fallback with dynamic memory limits — high-density sparse matrices now convert to dense before CPU PCA (instead of OOM-ing); float64 estimate, OSError handling, and dedup’d memory/threshold constants.

• scale() default: to_sparse=False (no surprise sparse → dense round trips).

• Removed: ov.pp.scrublet legacy module (use ov.pp.qc(doublets='scrublet') or 'pydoubletfinder').

I/O & out-of-memory backend

• anndataoom Rust backend (optional, opt-in): out-of-memory AnnData reads via Rust. New helper omicverse.utils.convert_adata_for_rust and a dedicated t_preprocess_rust tutorial. Multiple review rounds (1st through 7th) for stability, lazy-loading semantics, and clear error paths (no_cc=True is now refused on the OOM backend; unsorted CSR h5ad gets a precise diagnostic; spatial viz works on lazy AnnData).

• ov.io.read_xenium added (also covered above under Spatial).

• ov.read docstring refreshed for the Rust backend’s behaviour.

Plotting

• Marsilea-based heatmap plotting APIs (new family): clean, declarative heatmaps with regression coverage in the test suite.

• Cell-cell communication (CCC) plotting APIs (ov.pl.ccc_*): arrow / sigmoid / flow / scatter / chord-style ligand-receptor plots, with empty-interaction-palette guards and refined flow layouts. Aligned with the CellPhoneDB registry metadata.

• ov.pl.create_custom_colormap with white-ramp duplicate de-duplication.

• Half-violin boxplot function introduced; old equivalent deprecated.

• Subset plotting now drops unused categories so legends are accurate; legendkit show_at accepts 0–1 percentiles (not raw data values).

• Iterative refactor of plotting imports (lazy loading, optional torch dependency handling, deprecated old utilities in favour of the new embedding helpers).

ov.utils.cluster — pymclustR backend (PR #638)

• New method='pymclustR': pure-Python re-implementation of CRAN mclust covering all 14 Banfield-Raftery / Celeux-Govaert covariance parameterisations. Drop-in replacement for the legacy 'mclust_R' rpy2 backend, which is now removed (calling it raises a ValueError pointing at the replacement).

• Validation against CRAN mclust 6.1.1 across 222 records: 12 of 14 models bitwise-identical, overall mean z-correlation 0.9935.

• Published as pymclustR on PyPI.

Datasets & utilities

• Gene ID conversion functions added to ov.utils (with conflict-column handling on merge), plus a database-validation helper.

• Function search uses a global registry fallback for better discoverability across modules.

• Removed pymde dependency; tightened scipy pin and FOV image processing.

• Removed deprecated data files; cleaned up biocontext module exports.

ov.Agent / Jarvis / OVAgent runtime

A multi-month decomposition + hardening of the agent runtime stack. ~60 numbered task-* commits across PRs #596 (codex OAuth), #600 (runtime upgrade), #601 (decomposition follow-up), #602 (PR-602 reviewer follow-ups), #603 (security + runtime hardening), #604/#605 (orchestration waves 3 & 4).

Highlights:

• Smart-agent facade slimming: smart_agent.py collapsed; bootstrap, auth, runtime setup, codegen/review/reflection pipeline, session/context/history services, and tool facade extracted into focused modules.

• OVAgent runtime decomposition: analysis_executor.py, tool_runtime.py, agent_backend.py, turn_controller.py each split into transformer/diagnostic/sandbox/handler helpers.

• Tool dispatch facade collapse: the ~45-wrapper CodegenToolDispatchFacadeMixin reduced to 3 concrete delegations.

• Subagent infrastructure: configurable subagent profile schema, override plumbing through runtime bootstrap, and end-to-end coverage.

• Jarvis multi-channel migration onto a single MessageRuntime: Discord, Telegram, QQ, WeChat, Feishu, iMessage all now use the shared runtime / presenter / command / media abstractions. Channel-core session/request abstractions were also extracted (channel_media).

• Tool runtime hardening: bounded sync bridge, fail-closed bash roots, explicit stdout guard, sandbox runtime enforcement, pandas.eval classification, repair-loop retry boundedness, LLM timeout, and dependency-safe parallel tool scheduler.

• Security closures (P0/P1 from PR #601 review): SafeOsProxy metadata escape closed via allowlist, strip_local_paths ReDoS-hardened, URL substring assertions replaced with parsed-hostname validation (CodeQL clean), CodeQL sensitive-data alerts cleared in real-provider E2E test, session-facade lock-test portability fixed.

• Backend hardening: Gemini and OpenAI adapter exception handling, credential resolution + factory consolidation, context budget model-window registry sync, bounded silent-fallback debug logging.

• Web bridge & sessions: prior-history retrieval per session, .h5ad channel handling, AgentBridge ↔ messaging-channel reply-text plumbing, shared kernel + AnnData support.

• Codex OAuth support added to ov.Agent (PR #596).

• CLI / install: omicclaw entrypoint replaces omicverseweb; install.sh rewritten as a guided installer with optional package menus (web, Jarvis, full bio suite); MCP startup-timeout troubleshooting documented for Windows.

Documentation & infrastructure

• Sphinx + Furo migration (later switched to sphinx_book_theme): the docs site moved from MkDocs to Sphinx. Bilingual docs/ + docs_zh/ checkouts, .readthedocs.yaml config with repo-root-relative paths, deploy-docs CI workflow migrated, gh-pages history accumulation prevented. Pinned Pygments < 2.20.

• Multi-Omics docs reorganisation: Tutorials-bulk2single/ folder moved under Tutorials-Multi-Omics/bulk-single/ with an overview page, in line with how Multi-Omics is now framed as a top-level domain alongside Bulk / Single / Spatial.

• Spatial-clustering tutorials: original combined t_cluster_space.ipynb split into 5 self-contained per-method notebooks under docs/Tutorials-space/cluster/ (GraphST, BINARY, STAGATE, CAST, BANKSY), all clustered with method='pymclustR' (no rpy2). New parent cluster/index.md with embedder paper DOIs and a recommendation table.

• Cellpose tutorial rewritten with executed CellSAM vs Cellpose comparison and SpaceRanger v4-compatible export.

• CCC plotting API docstrings expanded; OmicVerse Agent skill guidance updated for CellPhoneDB.

• Dependencies: pinned s3fs ≥ 2023.1.0 (Python 3.12), updated scipy, removed pymde, removed socksio from default deps.

• CI: dev PRs now run package + MCP workflows; flake8 excludes virtualenvs; lint regressions cleaned up across namespace exports and bootstrap tests.

ov.metabol — new metabolomics module (PR #636, branch feat/metabol)

A from-scratch metabolomics analysis namespace mirroring the structure of ov.bulk and ov.single. Twelve commits, several shipped sub-releases (v0.1 → v0.3).

v0.1 — base

• ov.metabol namespace registered; LC-MS reader; vendored gseapy for environments without it.

• ID mapping between HMDB / KEGG / LIPID MAPS / PubChem / ChEBI identifiers.

• Lipidomics class-level summarisation.

v0.2 — pathway analysis

• pyMSEA (metabolite set enrichment) with KEGG / LION / HMDB pathway sources.

• pyMummichog (peak-list-based pathway inference for unannotated LC-MS features).

• pathway_bar, pathway_dot plots; volcano gains use_pvalue + clip_log2fc options.

v0.3 — multi-factor + biomarker

• SERRF — QC-RF drift correction for batch / acquisition-order systematic bias.

• DGCA — differential gene/metabolite co-correlation analysis.

• ASCA + MixedLM — multi-factor analysis (e.g. treatment × time, repeated measures).

• ROC / biomarker panel — single + multi-feature ROC, AUC bootstrap CIs, panel selection.

Cross-cutting

• Every public API registered with @register_function so ov.Agent can dispatch metabolomics workflows.

• Lazy-loaded submodules — top-level import omicverse cost cut 3200× by deferring metabol’s heavy R-style stats imports until first call.

• Fetcher-based data migration — KEGG / LION / HMDB pathway tables fetched on demand instead of shipped (drops 3 data files from the wheel).

ov.micro + ov.alignment — microbiome / 16S amplicon (PR #637, branch feat/alignment-16s-amplicon)

End-to-end 16S rRNA amplicon → microbiome AnnData pipeline.

ov.alignment — upstream sequence processing

• cutadapt primer trimming + vsearch UNOISE3 ASV calling + SINTAX taxonomic classification.

• Phylogeny step — multiple sequence alignment via MAFFT + tree construction via FastTree, attached to the AnnData via ov.micro.attach_tree(adata, tree_path).

ov.micro — downstream microbiome analysis

• New downstream namespace covering alpha / beta diversity, differential abundance, ordination, taxonomic-level summaries.

• Compatible with scikit-bio 0.6 (PR #637 round-2 review fix bumps from the deprecated 0.5 API for the phylogenetic metrics).

• Class names follow the no-py-prefix convention (e.g. MicroBiome, Diversity).

Removed / deprecated

• ov.pp.scrublet legacy module.

• ov.utils.cluster(method='mclust_R') (rpy2 bridge) — replaced by 'pymclustR'.

• Old plotting utilities deprecated in favour of new embedding helpers + half_violin_boxplot.

• pymde runtime dependency.

• omicverse_web Git submodule (functionality folded into omicclaw).

• Legacy ReadTheDocs MkDocs config.

• Three shipped metabolomics data files (KEGG / LION / HMDB pathway subsets) — now fetched on demand.

v 2.2.0

Scope

• Summarises every change between v2.1.2 and the v2.2.0 release tip: 208 commits, 212 files changed, +34,878 / −8,888 lines, ~50 merged PRs (#652 → #727).

• Three top-level themes drove this release:

  1. New analysis modules — ov.es (vendored decoupler kernels with GPU acceleration), ov.single.NMF (Rust-backed), ov.single.CNV (copykat / infercnv), Geneformer in-silico perturbation, ov.pp.champ, ov.report (one-call HTML provenance report), ov.space.RCTD, ov.space.nmf_tissue_zones.

  2. GPU-first preprocessing — pure-PyTorch Leiden (74×), full-GPU parametric UMAP (21×), GPU KNN by default in pp.neighbors, native torch t-SNE, plus a ~20-method GPU sweep in ov.es (gsva 23–28×, mdt/udt 83–158×, viper 20×, gsea 13–16×, ora 48×).

  3. Maturation of v2.1.x modules — Atera (WTA Preview) reader, Xenium V2 / Prime morphology fix, paired microbe↔metabolite + cross-cohort 16S meta-analysis in ov.micro, MTBLS1 case study + plot helpers in ov.metabol, Seurat-style CCA in single.batch_correction, scMulan / MetaTiME / TOSICA in single.Annotation, CellVote consensus.

---

ov.es — vendored decoupler kernels with GPU acceleration (PR #722)

A new top-level enrichment-scoring namespace, vendoring the decoupler 1.x algorithms behind a clean omicverse API and rewriting every method with an optional torch/GPU kernel.

• ov.es.decoupler unified dispatcher registered with @register_function — single entry point that picks the right backend by method name.

• GPU kernels for 10 methods: aucell, ulm, zscore, mlm, waggr (first wave), then gsea, ora, gsva, viper, mdt/udt. Speedups vs the CPU decoupler reference, on the same matrix:

  • aucell: 0.2× → 7.5× after kernel redesign (bit-exact vs CPU).

  • gsea: 13–16× via batched cumsum ES.

  • ora: 48× via batched hypergeom on torch.

  • gsva: 23–28× across the three pipeline stages.

  • mdt / udt: 83× / 158× via pure-torch GBDT, dropping the xgboost CPU path.

  • viper: 20× on aREA, 4× full pipeline.

• GPU-side sparse densify (2–3× across the board), math approximations + loop vectorisation, and GPU memory pattern aligned with ov.pp._pca (drops per-call cleanup overhead).

• waggr GPU dispatcher densifies before CPU fallback to avoid hidden round-trips.

• Method dispatch lifted into each method (no MethodMeta facade); decoupler’s _docs / _log infrastructure stripped.

• Legacy ov.single.aucell helpers still work but print a migration notice to ov.es.aucell.

• pytest smoke coverage for all 11 scoring methods.

• Tutorial: t_es_compare — side-by-side comparison of all 11 enrichment methods on the same matrix.

Single-cell analysis

• ov.single.NMF — Rust-backed fast NMF (PR #717) via nmf-rs. Auto-K selection via stability-drop heuristic and Brunet K-selection with a consensus heatmap; consensus switched from cell-level to spectra-level for stability. Adds per-factor obs columns and drops empty categories cleanly. Tutorial: uses the cNMF workflow (t_cnmf) as its reference pipeline.

• ov.single.CNV + ov.pl.cnv_* (PR #723) — single-cell CNV inference covering both copykat and infercnv backends. Plotting uses a marsilea backend for cnv_heatmap with multi-strip annotations; split between groupby (ordering) and annotations (overlay) for layering. Tutorials: t_copykat, t_infercnv.

• ov.single.Annotation — scMulan / MetaTiME / TOSICA backends (PRs #719, #720):

  • scMulan added to Annotation.annotate (modern transformers compat) — tutorial: t_scmulan.

  • MetaTiME and TOSICA wired into the same dispatcher — tutorials: t_metatime, t_tosica.

  • CellVote consensus score — confidence-aware multi-annotator consensus (PR #719). Tutorial: t_cellvote.

• ov.single.batch_correction — Seurat-style CCA (PR #670, #669) — drop-in CCA backend alongside the existing methods; 3 review bug-fixes + UX improvements landed in the same PR (PR #670 review feedback). Tutorial: t_single_batch.

• ov.single.auto_resolution — null-adjusted (PR #662), per Lange et al. 2004. Renamed from autoResolution, with a selection-curve plot helper and a new method='champ' backend. Tutorial: covered in t_cluster.

• ov.single.cal_grn fix (#681) — passes gene_names to grnboost2 / genie3 so the resulting GRN keeps the real gene labels. Tutorial: t_scenic.

• SCLLMManager is now the canonical foundation-model entry (PR #704) — ov.fm namespace dropped entirely; SCLLM is the single FM surface. Defensive HVG warning + actionable RegDiffusion OOM error in single/SCENIC. Tutorials: t_scgpt, t_scfoundation, t_cellplm, t_uce.

Geneformer in-silico perturbation (PRs #725, #726)

• ov.llm Geneformer perturbation — in-silico knockout / knock-in via embedding shifts in the Geneformer foundation model.

• TF-perturbation per-gene downstream-shift analysis (PR #726) — quantifies how each downstream gene shifts in embedding space after a TF perturbation.

• New ov.pl helpers for the perturbation result tables.

• Tutorial: t_geneformer — shipped end-to-end with executed outputs.

Preprocessing & GPU performance

• Pure-torch GPU Leiden (PR #657) — ov.pp.leiden(method='torch') is 74× faster than the CPU reference and has no torch_sparse / torch_scatter dependency (one of the most fragile installs in the stack). Pure tensor ops only.

• Full-GPU parametric UMAP (PR #652) — pumap path is 21× faster with bounded VRAM; batch size bumped to 16384 across the board.

• GPU KNN by default for method='torch' (PR #654) in ov.pp.neighbors, with the correct fuzzy simplicial graph (matches the UMAP reference, not an approximation).

• Native torch t-SNE + louvain auto-redirect (PR #658) — mixed-mode polish so the GPU path is the default when torch is available.

• ov.pp.tsne routes directly to sklearn (drops sc.tl.tsne) — fixes a stale n_components forwarding bug (#683).

• ov.pp.qc(doublets=…) default is now scdblfinder for CPU and mixed modes (PR #655).

• ov.pp.champ — Convex Hull of Admissible Modularity Partitions (PR #666) — new resolution-stability backend with n_seeds, modularity='cpm', adaptive refinement, three width_metric modes (log/linear/relative), and NaN-safe gamma clamping. Wired into auto_resolution as method='champ' with a landscape plot. Tutorial: t_cluster.

• GPU preprocessing tutorials: t_preprocess_gpu (Leiden / UMAP / KNN GPU path), t_preprocess_cpu (CPU baseline with the new defaults).

Spatial omics

• ov.space.RCTD deconvolution backend (PR #710, issue #682) — RCTD added alongside the existing spatial deconvolution backends. Full-slide tutorial with executed outputs; reference dataset shipped as ov.datasets.visium_lymph_node. Tutorial: t_decov_rctd.

• ov.space.nmf_tissue_zones (PR #673) — NMF colocation over spot abundance matrices, with normalize='rows' option and DataFrame-column inference. Prefers uns factor names and strips shared prefixes for cleaner zone labels. Tutorial: t_decov Step 6.

• ov.io.read_atera (PR #700) — full reader for the 10x Atera (WTA Preview) bundle (Xenium-format core + nucleus boundaries + multi-stain morphology + optional H&E + vendor cell_groups.csv). See the dedicated section under “I/O” below. Tutorial: t_atera_preprocess.

• ov.io.read_xenium V2 / Prime fix (PR #716, issue #708) — Xenium V2 / Prime data ships morphology_focus/morphology_focus_NNNN.ome.tif per stain channel; the OME-XML cross-references between siblings made tifffile’s default _multifile=True silently merge them and break the pyramid walk, producing [Xenium] No morphology image loaded. The fix walks each per-channel pyramid standalone with _multifile=False and prioritises the user-requested channel. Verified on Xenium Prime FFPE Human Prostate (5,006 genes / 193K cells) — all four channels now load. Tutorial: t_xenium_preprocess.

• New helpers extracted from the Atera tutorial — ov.pl.to_rgb_grayscale, ov.pl.sync_categorical_palette, ov.space.subset_window.

• ov.pl.spatial — frameon=False now truly hides the frame (matches scanpy).

Microbiome & Metabolomics

ov.micro — paired microbe ↔ metabolite + meta-analysis

• Paired microbe↔metabolite integration (PR #664) — new simulate_paired, paired_spearman, paired_cca, and MMvec for unpaired co-occurrence inference, with companion plotting helpers. Tutorial: t_micro_metabol_paired.

• Cross-cohort 16S meta-analysis (PR #663) — combine_studies + meta_da for proper between-study differential abundance, handling per-study confounders. Tutorials: t_16s_meta_analysis, t_16s_da_comparison.

• fetch_franzosa_ibd_2019 (PR #664) — first built-in real paired multi-omics dataset; the paired tutorial was rewritten end-to-end against it.

ov.metabol — MTBLS1 case study (PRs #665)

• MTBLS1 case-study helpers + 4 plot helpers for the MetaboLights MTBLS1 benchmark.

• v0.5 perf/usability fixes discovered during the MTBLS1 smoke-test rollout.

• Tutorial: t_metabol_11_real_data_mtbls1.

ov.report — one-call HTML pipeline report from AnnData provenance (PRs #659, #660)

• ov.report — one-call HTML pipeline report generated from AnnData provenance.

• @tracked decorator consolidates timing / nesting / record-call concerns into a single annotation.

• Extends to ov.single.batch_correction, ov.single.Annotation, ov.single.AnnotationRef (PR #660) — class-method tracking, not just function tracking.

• Developer guide: see the “Making a dispatcher appear in ov.report.from_anndata” section of Developer_guild for how to wire your own dispatcher into the report.

Plotting

• ov.pl.plot1cell — circular UMAP with concentric metadata tracks (PRs #674, #675, #676, #679): new circular embedding plot with bending.inside labels, axis ticks, per-track palettes, and horizontal outer-ring labels with wrap + repel. Respects plot_set background (no hardcoded figure background). Registered in Sphinx toctrees with a 4-scale tutorial slimmed to 2 real datasets. Tutorial: t_plot1cell.

• ov.pl.embedding flow layout (PR #719) — flow layout is now the default for multi-panel embedding plots (replaces the grid layout). Multiple polish fixes:

  • Flow layout accounts for default colorbar + title in panel footprint.

  • Colorbar width halved (fraction 0.08 → 0.04); slim inset colorbar default that reclaims unused figure width.

  • _flow_layout_panels measures axis & colorbar tightbboxes so layout is colorbar-aware.

  • inset_axes colorbar registered so flow layout measures its tightbbox correctly.

• ov.pl.ccc_* — unified communication adapters for LIANA and CellPhoneDB (PR #666 area) — single adapter layer so ccc_circle, ccc_heatmap, ccc_network_plot, ccc_stat_plot work against either source. Empty-interaction palettes guarded. Volcano fix (#712) for non-standard significance column values. Tutorials: t_ccc_cellphonedb, t_ccc_liana.

• ov.pl.trajectory (PR #707) — generic trajectory plotting backend with a robust label assertion in tests.

• ov.pl.cnv_* — see ov.single.CNV above for the marsilea backend.

• Dynamic heatmap fixes — preserve explicit feature_labels; preserve dynamic annotations through layout.

• WGCNA color assignment refactor (PR #684) — community contribution from @libmelo.

I/O

• ov.io.read_atera (PR #700) — full reader for 10x Atera (WTA Preview) outs/. Atera ships a Xenium-format core (cell_feature_matrix.h5, cells.parquet, cell_boundaries.parquet, experiment.xenium) plus four Atera-only additions, all handled by the reader:

  • nucleus_boundaries.parquet → obs['nucleus_geometry'] (WKT POLYGON).

  • morphology_focus/ch####_<tag>.ome.tif — content-named multi-stain pyramid TIFFs. Channel selector accepts a semantic tag ('dapi' / 'boundary' / 'rna' / 'stroma'), a filename substring ('cd45', '18s'), or an integer-as-string index. tifffile’s OME multi-file series is bypassed (is_ome=False) so each channel’s pyramid IFDs are walked standalone.

  • Optional vendor cell_groups.csv merge → obs['cell_group'] and obs['cell_group_color'], NaN-preserving for cells absent from the CSV.

  • Optional H&E OME-TIFF + 3×3 affine CSV → uns['spatial'][lib]['images']['he'] and scalefactors['he_affine'] / 'he_downsample'.

  • Verified on the public WTA_Preview_FFPE_Breast_Cancer sample (170,057 cells × 18,028 genes after dropping 9,076 control probes / codewords).

  • Tutorial: t_atera_preprocess.

• ov.io.read_csv (latest) — now flags pandas’s silent duplicate-column rename (col, col.1, col.2, …) instead of letting it pass through unnoticed.

• ov.io.read_xenium V2 / Prime fix — see Spatial.

Datasets & utilities

• ov.datasets.visium_lymph_node loader — first-class reference dataset for the RCTD tutorial.

• ov.utils.preflight_alignment (latest) — sample-metadata alignment pre-flight helper; checks that the obs table you’re about to merge will actually align with the AnnData index before you do it. Registered with the function registry.

• ov.bulk — pyWGCNA / readWGCNA registry-discoverable shim (PR #700) so the WGCNA path is discoverable from ov.Agent without importing the heavy backend at top level.

OVAgent / Jarvis / registry

• registry_lookup improvements (PR #691):

  • Shows docstrings + visual dividers in registry_lookup output (was a bare table before).

  • _registry includes keyword-only args in AST-derived signatures (was previously dropping them).

  • Caches RegistryScanner + skill registry across calls (perf win on long agent sessions).

• @register_function backfill on docstring-backfilled APIs (PR #695) — every metabol / micro / single API whose docstring was filled in from omicverse-skills 0.3.0 grounding is now registry-discoverable.

• OVAgent resilience + live LLM trace + auto-download (PR #688) — better recovery on transient backend failures, live trace of the LLM call as it streams, and auto-download of small dependencies on first use.

• Hardcoded user-specific paths removed (PR #689) — possible_paths fallback lists deleted in favour of explicit configuration.

• omicverse-skills bumped to >=0.3.0 (PR #696) to pick up the v0.3 docstring corpus.

Bug fixes

• #706 — ov.pp.preprocess(mode='shiftlog|seurat') no longer raises UnboundLocalError (PR #709).

• #683 — ov.pp.tsne no longer forwards n_components to the scanpy backend (PR #698).

• #685 — bulk/single enrichment logp now matches the −log10 colour-bar label (PR #697).

• #681 — SCENIC cal_grn passes gene_names to grnboost2 / genie3 (PR #699).

• #708 — Xenium V2 / Prime morphology_focus_NNNN.ome.tif channels load correctly (PR #716).

• #612 — bulk2single/bayesprism: ThetaPost columns are cell types, not genes (PR #672).

• #712 — volcano misclassifies significant genes when sig column uses non-standard values (community PR).

• bulk2single TAPE (PR #724) — high-res mode now exposes signature matrices via self.signature_matrix.

• CCC circle (latest) — circle aggregation now aligns with significant interactions (community PR #715).

• MOFA — raw regex for factor parsing.

• GPU connectivity device — lazily resolved (no spurious CUDA imports).

Removed / deprecated

• ov.fm namespace dropped — use SCLLMManager as the canonical foundation-model entry (PR #704). The tests/fm/ directory was removed; architecture tests were updated to pin the bulk lazy route through _wgcna instead of _Gene_module.

• xgboost CPU path for ov.es.mdt / ov.es.udt — replaced by the pure-torch GBDT (83× / 158× faster).

• possible_paths hardcoded fallback lists — removed in favour of explicit configuration.

v 2.2.1

A large feature release: eight brand-new top-level modules (statistical genetics, bulk/single proteomics, molecular structures, epigenomics, immune repertoire, single-cell metabolism, extracellular-vesicle proteomics, H&E → spatial prediction), a rebuilt trajectory/pseudotime stack, out-of-core preprocessing for datasets that don’t fit in RAM, and an Apple-Silicon GPU UMAP backend. ~190 commits since v2.2.0.

New modules

• ov.genetics — statistical genetics / GWAS (PRs #743, #745) — new statistical-genetics module with a systematic GWAS best-practice pipeline, simulate_gwas_study, and cross_trait_coloc for shared genetic factors across traits.

• ov.protein — bulk proteomics (PR #730) — bulk-proteomics module with real datasets in ov.datasets, pipeline plots, and the protein_pxd000279 DEqMS-vignette MaxQuant benchmark.

• ov.mol — molecular structure & drug-binding (PR #765) — molecular structure and drug-binding module.

• ov.epi — epigenomics (PR #800) — epigenomics module wrapping epione.

• ov.airr — immune repertoire / AIRR-seq (PRs #751, #754, #795) — immune-repertoire (AIRR-seq) module: TCR-specificity + CoNGA layers (25 extracted analysis functions), extract_heavy_chains, ov.datasets.airr_singlecell_bcr, real AIRR datasets and a BCR tutorial. tcr_clumping now uses a Poisson-tail p-value.

• ov.single.Metabolism + MetaboliteCCC — single-cell metabolism (PR #764) — single-cell metabolism and metabolite-mediated cell-cell communication.

• ov.single.ev — single extracellular-vesicle proteomics (PR #759).

• ov.space.histo — H&E → spatial transcriptomics (PR #789) — histology-to-expression prediction; load_breast(section=2) for held-out evaluation. (omicverse[histo] deps gated to python>=3.10.)

Preprocessing — out-of-core (AnnDataOOM)

• Out-of-core preprocessing for AnnData that doesn’t fit in RAM (PRs #806, #804, #802) — chunked, out-of-core filter_cells / filter_genes, highly_variable_genes (flavor-aware dispatch), and normalize_total / log1p / Pearson-HVF / regress / scrublet. Implicit centering via the CenteredSparseArray (use_implicit_centering).

• ov.pp.ambient — ambient / contamination-RNA removal (PR #762).

• ov.pp.regress custom keys (PRs #798, #799) — community contribution from @mengxu98; regress out user-specified obs keys.

• perf(preprocess) — fused normalize_total + Pearson-HVG pass-1 (PR #805) for the in-memory path.

Trajectory & pseudotime

• PseudotimeFate — unified terminal-state + fate downstream (PRs #787, #790) — single entry for terminal-state identification and fate analysis, with fit_lineage_trends (CellRank-style fate-weighted GAMs) and MIRA + CellRank downstream methods on the estimator.

• compute_pseudotime_velocity for streamplot rendering (PR #792).

• GRN-aware trajectory — RegVelo GRN workflow, StaVIA trajectory wrapper, a GRN-inference entry point, and SCENIC resource setup.

• TrajInfer dynbenchmark zoo + omicverse[trajectory] extras (PR #786).

• VIA edge-case fixes (PRs #766, #774) — community fixes from @mengxu98; restored VIA-native boundary and several lineage-handling bugs flagged from the trajectory tutorial.

Single-cell

• Unified MetaCell — 7 backends + mcRigor validator (PRs #703, #729) — one MetaCell interface across seven backends with the mcRigor validator and an I/O contract on the @register_function decorators. Fixes to soft metacell aggregation and SEACells ordering.

• ov.single.pseudobulk (PR #735) — pseudobulk aggregation.

• scVI-family backends — scANVI, totalVI, scPoli; scvi kwargs now routed to SCVI.__init__ vs SCVI.train by signature (PR #797).

• Batch-correction benchmarking — scIB-style batch-correction zoo tutorial (scIB output icons now render in the docs).

Bulk & DEG

• edgeR + limma-voom backends for pyDEG.deg_analysis (PR #734) — eBayes moderated-t fixes for correct variance handling.

• pyDEG.continuous_deg — DE against a continuous trait (gains a data_type argument: counts / continuous).

• pyDEG.timecourse_deg + temporal_clusters (PR #746) — time-course differential expression.

• Memory-bounded blockwise PyWGCNA (PR #738) — blockwise modules for large gene sets; signed scale-free fit index + sample-size fallback in pickSoftThreshold; cutreeHybrid no longer crashes when every gene is unassigned.

• Lipidomics — Bioconductor lipidr workflow (PR #741).

Perturbation

• ov.single.perturb — unified KO/OE + downstream GRN (PR #739) — single knock-out / over-expression interface with downstream GRN analysis.

• In-silico perturbation (PRs #781, #784) — in-silico perturbation workflow on the real scTenifold API (sctenifoldknk now uses the genuine scTenifold backend). Tutorial: t_perturb_in_silico.

Spatial

• gsMap — spatially-resolved GWAS → cell-type mapping — integration of the gsMap workflow (latent_to_gene and friends), wired into the localized READMEs with a dedicated tutorial.

• Spatial-LDSC — heritability-partitioning persistence: p-values are now saved, with plotting polish and a single overall tqdm progress bar (restored after a regression) across the LDSC sweep.

• ov.space.histo — see New modules (H&E → spatial transcriptomics).

GPU & performance

• GPU non-parametric UMAP backend — ov.utils.gpuex.umap (PR #812) — a GPU/Apple-Silicon (MLX/metal) twin of umap-learn’s non-parametric simplicial_set_embedding, structurally equivalent to the CPU path (backend='auto'|'torch'|'mlx'). Fixes the previous parametric-UMAP inconsistency on GPU.

• ov.es.ucell — Mann-Whitney U signature scoring (CPU + GPU).

Alignment / I/O

• simpleaf / salmon / alevin-fry scRNA-seq backend (PRs #750, #758).

• align_samples reads non-h5ad HDF5 matrices (PR #753).

Enrichment

• geneset_prepare auto-downloads any Enrichr library on demand; enrichment entry points accept geneset names or paths (PR #755).

Plotting

• ov.pl.funky_heatmap (PR #776) — pyfunkyheatmap wrapper, registered with @register_function, with tutorial and Sphinx toctree wiring.

• Dynamic lineage trends — preserve all lineage categories, legend-only-lines, group-palette line colours; small lineage-edge guards.

• Trajectory animation / GIF helpers — auto-compress saved GIFs and trim the stationary tail; frameless GIFs with locked data limits (used by the MIRA plot_stream trajectory tutorials).

Datasets

• Real proteomics datasets in ov.datasets (incl. the protein_pxd000279 / PXD000279 MaxQuant benchmark) backing the new ov.protein module.

• ov.datasets.airr_singlecell_bcr — single-cell BCR dataset for the ov.airr tutorials.

• load_breast(section=2) — held-out section for evaluating the ov.space.histo H&E → expression models.

Registry / Agent

• Register all public functions in ov._registry — broad backfill so the new modules (genetics, protein, mol, epi, airr, metabolism, ev, histo) are discoverable from ov.Agent.

• I/O contract on metacell @register_function decorators — metacell backends declare their read/write shape to the registry.

• ov._monitor — FAILED summary when the wrapped function raises (PR #813) instead of a success-styled 0.0s no-op box.

Documentation & tutorials

• New tutorials for the release’s modules: 5 real-data proteomics notebooks, DE on the real PXD000279 benchmark, the HE-zoo (H&E → expression) and trajectory-fate zoos, batch-correction zoo, gsMap, bulk ChIP / footprint, epigenetics (ov.epi), ov.es.ucell, the GWAS best-practice pipeline, and t_perturb_in_silico.

• Tutorial slimming — t_traj_fate notebook 51 MB → 6 MB; downscaled / build-safe figures; inline MIRA-exact trace GIFs.

• ReadtheDocs build resilience (PRs #768–#771) — fixed malformed RST / docstrings (incl. omicverse.single.hematopoiesis) that were crashing sphinx_autodoc_typehints; parallel Sphinx build (-j auto); guide URL moved to the omicverse org.

Bug fixes

• #736 — dropped private scanpy-internal imports (PR #744).

• Lazy-import torch / trim unused optional deps; expose CEFCON helpers without torch.

• Ro/e — formula made internally consistent and statistically sound.

• CI — numpy 2 / jax compatibility (PR #811); metacell architecture tests; per-test event loops; ov.channel core event-loop fix.

• Dynamic-heatmap per-lineage fixes; suppress categorical embedding cmap warning.

v 2.2.2

⚠️ Breaking changes

• inferCNV now requires a platform. ov.single.CNV(method='infercnv').run() needs platform='10x' (gene-filter cutoff 0.1) or platform='smartseq2' (cutoff 1.0), or an explicit cutoff= — otherwise it raises a clear ValueError. The previous implicit default silently over-/under-filtered genes (PR #822).

• Vendored gseapy removed. GSEA and over-representation analysis are now native to omicverse; ov.bulk no longer imports gseapy or PyComplexHeatmap (PR #836).

Bulk & Enrichment

• From-scratch GSEA/ORA backend — gseapy removed (PR #836). ov.bulk.pyGSEA / geneset_enrichment_GSEA now use a single-process, vectorised NumPy pre-ranked GSEA (Subramanian 2005 — the clusterProfiler/fgsea algorithm), eliminating the joblib/loky process-pool dead-lock that hung GSEA in notebooks and agent kernels. Enrichment scores are bit-exact vs gseapy (ES Pearson r = 1.0), ~5× faster with an argsort-once permutation null and tqdm progress bars. Over-representation (geneset_enrichment) is now a local hypergeometric test (scipy.stats.hypergeom + Benjamini-Hochberg), resolved and cached offline — no Enrichr web API. geneset_prepare caches downloaded libraries under ./genesets/.

• geneset_plot_multi rendered with Marsilea instead of PyComplexHeatmap (sized dot-heatmap: dot size = gene count, colour = -log10 adjusted-p, rows split by group) (PR #836).

Single-cell

• ov.single.Augur — cell-type prioritization (PR #825). Integration of py-Augur: ranks cell types by how strongly their transcriptome responds to a perturbation (AUC for classification, Lin’s concordance correlation for regression / RNA-velocity mode).

• CNV heatmap & inferCNV (PR #822). Fixed ov.pl.cnv_heatmap chromosome tiling; optional p/q arm splitting; surfaced inferCNV phase 2/3 (HMM state calling + Bayesian filtering) outputs; platform/cutoff guard (see Breaking changes).

Spatial

• ov.space SPLIT purification workflow (PR #828). AnnData-native SPLIT (py-SPLIT) for purifying mixed spatial-transcriptomics signal.

Preprocessing & reproducibility

• ov.set_seed — global random seed (PRs #807, #820). One call propagates the seed through PCA / neighbors / UMAP / Leiden / t-SNE for reproducible runs.

• Unified QC workflow (PRs #808, #819). Consistent ov.pp QC-metric computation + ov.pl.qc plotting + filtering in one compute/plot/filter flow.

• Parametric UMAP returns a reusable model (PRs #809, #821). model = ov.pp.umap(adata, method='pumap') returns the trained model so new data can be projected into the same embedding; tutorial included.

Plotting

• UpSet plots — ov.pl (PR #823).

AIRR

• Custom clone-size bins in clonal_expansion (PRs #810, #818).

Bug fixes & compatibility

• matplotlib ≥3.9 — replaced the removed matplotlib.cm.get_cmap across VIA with plt.get_cmap (PR #836).

• NumPy 2 — pyscenic no longer uses the removed np.msort (PR #829).

• COSG dotplot — handle marker-list length mismatches (PR #830).

• flowsig — squidpy imports work without pkg_resources (PR #832).

• PyG neighbors — normalize CUDA device specs (PR #833).

• CI — avoid uv run dependency-resolution issues (PR #824).

Documentation

• t_infercnv tutorial updated to pass platform='smartseq2' for the Smart-seq2 Maynard 2020 dataset (tutorials #81).

• New parametric-UMAP model tutorial; guide submodule pointer bumped.

v 2.2.3

Patch release on top of 2.2.2.

Bug fixes

• Registry hydration on first lookup (PR #839). A fresh import omicverse populated the function registry only with whatever modules happened to import eagerly, so ov.Agent / ov._registry.find(...) saw a near-empty registry. The first query now force-imports all OmicVerse submodules once (memoized via a _hydrated flag, set before hydrating to guard against re-entrancy; failures fall back to the partial registry rather than crashing the lookup). The runtime scanner gates on this hydration memo instead of on registry-emptiness.

Infrastructure

• Tag-triggered PyPI publishing — pushing a vX.Y.Z tag now builds and publishes to PyPI via GitHub Actions (.github/workflows/publish-pypi.yml, PyPI trusted publishing / OIDC), replacing the manual python -m build / twine upload flow.

v 2.2.4

Written after the fact: this version shipped to PyPI without notes.

New modules

• ov.synbio (PRs #887, #895) — a three-layer synthetic-biology module (metabolism / protein-enzyme / DNA), extended in the same cycle with mRNA design, de-novo binders, prime editing and dynamic FBA.

single module

• Cross-species integration (PRs #893, #894) — orthologs plus SAMap and SATURN, with CAMEX added as a further backend (method='camex').

• Seurat-style RPCA integration (PR #899) — methods='rpca'.

pp module

• GPU doublet detection (PRs #886, #888, #889) — chunked-GPU kNN for scDblFinder and DoubletFinder’s pANN, and GPU/torch post-processing for sccomposite, which had been running out of memory at 1e5 cells.

• Fixes — qc_gpu’s final filter counted the wrong thing (cells and genes, not counts, PR #890); qc_gpu doublet methods failed outright on a GPU matrix (PR #897); a stall on large datasets with pyscdblfinder below 0.2.0 (PR #892).

Other

• ov.pl.qc clips UMIs and genes for display only (PR #896); ov.space.svg guards mode='pearsonr' against all-zero spots (PR #898); FlowSig bootstrapping is process-safe (PR #878); cpu_gpu_mixed_init takes a devices argument (PR #891).

v 2.3.0

A minor release rather than a patch: eight modules are new since 2.2.4.

New modules

• ov.flow (PRs #906, #907) — flow cytometry. A general FCS reader (ov.io.read_fcs), display transforms, and the gating strategy tree. Two fixes in the same cycle: the Gating-ML being written was not valid Gating-ML (PR #910), and a quadrant’s sub-populations are populations in their own right and can be parents (PR #911).

• ov.mol molecular dynamics (PR #905) — GPU simulation, trajectory analysis and MM-GBSA.

• ov.alignment (PR #904) — homology search through DIAMOND or BLAST+, and Sanger .ab1 verification.

pl module

• Table-first statistical plots with an AnnData adapter (PR #920) — a DataFrame, bare arrays and an AnnData are interchangeable, and every plot that computes something is checked against scipy or statsmodels on the same data.

• Clinical statistics and publication figure assembly (PR #919).

space module

• SPATA2-style coordinate utilities (PR #847) — coordinate tables, variable joins, tissue outlines, spatial-outlier filtering, pixel/unit conversion.

• The arrangement layer (PR #924) — ov.space.nb for statistics over the spatial graph (neighbourhood enrichment, co-occurrence across distance, interaction matrix, group centrality, Ripley, sepal, masking, sliding windows, distance-from-anchor gradients); ov.space.niche for recurrent multicellular structure, completing squidpy’s three calculate_niche flavours and exposing nmf_tissue_zones as niche.molecular, the name the literature uses; ov.space.geom for serial-section alignment, 3-D stacking and interpolation. Semantics follow squidpy deliberately, so the numbers are checkable: measured against squidpy 1.6.5 on an identical graph, interaction_matrix, nhood_enrichment counts and centrality are exact, co_occurrence and all three Ripley modes give Pearson r = 1.0000, and var_by_distance agrees to 1.11e-16.

• Stereo-seq (PR #924) — ov.io.spatial.read_stereoseq reads a GEM and bins it; bin_stereoseq re-aggregates in memory and refuses a size the existing bins do not nest into.

• spatial_neighbors gains coord_type='grid' — plain k-NN cannot know an array platform has a lattice, so a spot on the rim of the tissue reaches across the gap to fill its quota and acquires neighbours it does not touch. The default stays 'generic' so existing results do not move.

synbio module

• The D-segment gaps closed (PR #916) — reconstruction, omics to GEM, manufacturability and biosecurity.

• Roughly forty defects corrected (PR #917) that produced plausible but wrong numbers — transformed Gibbs energies, tRNA counts and wobble conventions, replichore fractions, Echo transfer arithmetic, growth-model selection, and more. Each was found by checking a figure or a number against its source, not by a test failing.

• [synbio] was uninstallable because of the rs3 pin (PR #912); Rule Set 3 returns through a --no-deps runtime fetch (PR #913).

Bug fixes

• ov.pp.leiden through MCP (PR #925) — the MCP server advertised the tool while its extra could not install leidenalg.

• rpy2 mclust replaced (PR #918) — five spatial wrappers embedded separate rpy2 bridges; they now share the pure-Python pymclustR backend. Measured against R mclust 6.1.2 on identical coordinates, log-likelihood and BIC agree to six decimals and every cell lands in the same cluster.

• CopyKAT now says when its genome tables are missing (PR #922) instead of failing silently.

• AIRR multichain cells (PR #923) — resolved into their biologically distinct subtypes rather than discarded as artefacts.

• ov.tl / ov.es / ov.micro discoverable in a fresh session (PR #908).

Infrastructure

• The omicverse_guide submodule advances ten commits, to cover every tutorial merged since the ov.flow series. Documentation builds read the tutorials through this pointer, so leaving it behind kept that material off the docs site.

v 2.3.1

A patch release. Every entry came from checking 2.3.0’s documented API against its own source while building agent tooling — three of them break code that the documentation says should work, and the rest are places where a docstring described behaviour the code does not have. No numerical behaviour changed.

Fixes

• ov.synbio — 33 exported names were unreachable (PR #927). The facade resolves attributes through a lazy map, and 33 names across 20 submodules were in their module’s __all__ but missing from it, so ov.synbio.<name> raised AttributeError while the docs advertised them — ACQUISITIONS, METHODS, DECISIONS, DIFFUSION_LIMIT, ECHO_COLUMNS, MDFResult, RBSResult, Gene, OffTarget and the whole _tuning constant set among them. A test now walks every submodule’s __all__ and asserts each name resolves, in both directions, without needing the [synbio] extra.

• ov.pl.slopeplot(test='auto') raised. Every other table-first plot funnels test through the shared resolver where 'auto' becomes a rank test; slopeplot passed the raw string through. It now resolves to Wilcoxon signed-rank — the paired counterpart, since slopeplot requires subject= and is paired by construction — and the resolved name reaches stats['test'] and the on-plot label.

• ov.space.niche needed a scanpy keyword newer than our own pin. sc.tl.leiden(flavor='igraph') arrived in scanpy 1.10 while omicverse pins scanpy>=1.9, so niche.neighborhood and niche.utag raised TypeError on an otherwise valid environment. The keyword is now passed only when the installed signature accepts it.

Documentation corrected to match the code

• predict_localization advertised four compartments; the heuristic can only return three. periplasm strictly dominates secreted in both branches, which is right for E. coli — a Sec signal peptide delivers to the periplasm and real secretion needs a system this heuristic cannot see — so the scoring is unchanged and the claim is fixed instead. secreted stays in .scores so the margin is inspectable.

• mmgbsa(md_refine_ns=0) said it “scores the minimised pose only”; scoring needs a trajectory, so the floor is minimise plus 2 ps of NVT. Documented rather than changed: scoring a single frame would report std=0 and sem=0, a precision MM-GBSA does not have.

• test='auto' was described as automatic test selection. It is unconditionally Mann-Whitney U with no normality, variance or sample-size check. The omnibus row’s pvalue_corrected is a copy of the raw p value, and now says so.

• Plackett-Burman documented “n a multiple of 4” but the Sylvester construction only yields powers of two, so 12- and 20-run designs are unreachable. _refseq’s doctest asserted a cleavage site of 21 where the shipped tie resolves to 23. fetch_promoter_library asserted a 143-fold span the shipped table does not match, and now tells the reader to measure it. niche.molecular’s registered example called zones.plot_factor_loadings(), which does not exist — TissueZones is a dataclass with no methods.

Tutorials

• Tutorial 05 could not run against 2.3.0. reconstruct_gem('strainX.faa', ...) names a placeholder file the notebook never creates, and 2.3.0 closed exactly that hole. It now passes proteome=None, which is the documented way to say “mapping only, no proteome” — precisely what the cell demonstrates.