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):

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
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
np.product → np.prod (4 instances)
- omicverse/external/umap_pytorch/model.py: 2 instances
- omicverse/external/umap_pytorch/modules.py: 2 instances
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.