Release Notes

Contents

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.

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