omicverse
Official@omicverse
Building interoperable open-source tools for next-generation omics data analysis.
Agent Skills by omicverse
Showing 72 vetted skills indexed across 3 GitHub repositories.
omicverseomicverse-reference-label-transfer
Transfer cell-type labels from reference to query AnnData objects.
omicverseomicverse-bulk-celltype-deconvolution
Infer cell-type fractions from bulk RNA-seq using a paired single-cell reference.
omicverseomicverse-single-cell-cellrank-fate
Maps RNA velocity to CellRank terminal-state probabilities for AnnData workflows.
omicverseomicverse-single-cell-cellvote-consensus
Build consensus cell-type labels for AnnData objects with conflicting single-cell annotations.
omicverseomicverse-single-cell-cellphonedb-communication
Analyze single-cell ligand-receptor communication from annotated AnnData objects.
omicverseomicverse-microbiome-da-comparison
Compare microbiome differential-abundance results across Wilcoxon, pyDESeq2, and ANCOM-BC on AnnData cohorts.
omicverseomicverse-single-cell-monocle2-trajectory
Fit Monocle2-style trajectories on AnnData to derive pseudotime and branch-dependent gene programs.
omicverseomicverse-single-cell-liana-communication
Infer ligand-receptor communication from annotated single-cell AnnData objects.
omicverseomicverse-single-cell-scenic
Convert single-cell SCENIC analysis into a reusable regulon workflow for AnnData datasets.
omicverseomicverse-single-cell-cnmf-program-discovery
Discover gene programs in single-cell AnnData datasets with OmicVerse cNMF.
omicverseomicverse-single-cell-cellmatch-ontology
Map free-text cell-type labels to canonical Cell Ontology terms.
omicverseomicverse-cross-modal-celltype-transfer
Transfer cell-type labels between AnnData objects using weighted KNN mapping.
omicverseomicverse-single-cell-cytotrace2
Predict single-cell developmental potency from AnnData with OmicVerse CytoTRACE2.
omicverseomicverse-bulk-metabol-untargeted-lipidomics
Analyze untargeted LC-MS metabolomics and lipidomics data in AnnData.
omicverseomicverse-single-cell-preprocessing
Convert OmicVerse single-cell AnnData objects into cluster-ready preprocessing and marker-discovery workflows.
omicverseomicverse-microbiome-16s-amplicon-dada2
Builds 16S amplicon analysis from FASTQs to AnnData with taxonomy and diversity.
omicverseomicverse-microbiome-phylogeny
Build phylogenetic trees and compute tree-aware diversity metrics from 16S amplicon data.
omicverseomicverse-micro-metabol-paired
Detect microbe-metabolite associations across matched samples using Spearman correlation, CCA, and MMvec workflows.
omicverseomicverse-single-cell-annotation
Annotate clustered AnnData objects with OmicVerse cell-type labels.
omicverseomicverse-single-cell-batch-integration
Run OmicVerse single-cell batch integration on preprocessed AnnData objects.
omicverseomicverse-single-cell-metatime-annotation
Annotate tumor single-cell AnnData objects with MetaTiME cell-state labels.
omicverseomicverse-bulk-metabol-preprocessing
Preprocess metabolomics peak tables into analysis-ready AnnData objects.
omicverseomicverse-bulk-metabol-multivariate
Discriminate two metabolomics groups and select biomarkers from preprocessed AnnData.
omicverseomicverse-single-cell-differential-expression
Run single-cell differential expression analysis on AnnData datasets with Wilcoxon, t-test, or memento-de workflows.
Frequently Asked Questions About omicverse
FAQPage SchemaWhat specific biological data analysis tasks does Omicverse support?▼
Omicverse enables end-to-end processing of single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and microbiome data. It supports tasks including cell-type annotation, trajectory inference, ligand-receptor communication mapping, differential expression analysis, and multi-omics data integration using standardized AnnData objects.
Who is the target user for these computational analysis methods?▼
The target users are bioinformaticians, computational biologists, and genomics researchers who require standardized, interoperable methods for processing high-dimensional biological datasets. It is designed for researchers performing secondary and tertiary analysis on sequencing data generated from platforms like 10x Genomics or LC-MS.
What are the primary dependencies for running these analysis methods?▼
Omicverse relies on the AnnData ecosystem and integrates with standard libraries such as Scanpy, SciPy, and StatsModels. Users must have a local environment capable of handling large-scale matrix operations and should be familiar with standard bioinformatics file formats like h5ad, gmt, and fastq.