multimodal-omics-integration
De la autor
Multimodal and multi-section integration for spatial and single-cell omics: totalVI joint RNA + protein (Visium CytAssist Gene and Protein, CITE-seq), MultiVI joint RNA + ATAC (10x Multiome, spatial ATAC-RNA), BBKNN integration of several tissue sections, scanpy ingest label transfer between sections, and gene signature scores mapped on tissue. Outputs joint latent spaces, denoised protein maps, integrated clusters, transferred labels. For dissociated CITE-seq and scVI/scANVI use scvi-tools-single-cell; for MOFA+ factor analysis use mofaplus-multi-omics or muon-multiomics-singlecell; for Harmony use harmony-batch-correction.
Instalare
npx skills add jaechang-hits/SciAgent-Skills --skill multimodal-omics-integration -a claude-code
Se instalează în .claude/skills/multimodal-omics-integration/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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Skill-uri similare
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- muon-multiomics-singlecelljaechang-hits/SciAgent-SkillsMulti-modal single-cell analysis with muon/MuData. Joint RNA+ATAC (10x Multiome), CITE-seq (RNA+protein), other multi-omics. MuData holds per-modality AnnData with shared obs. WNN joint embedding, per-modality preprocessing, MOFA factor analysis. Use scanpy-scrna-seq for single-modality RNA; use muon when combining 2+ omics from the same cells.Știință★ 382
- scvi-toolsanthropics/knowledge-work-pluginsDeep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.OficialAltele★ 31.9K
- scvi-tools-single-celljaechang-hits/SciAgent-SkillsDeep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without deep learning; muon for multi-modal MuData workflows.Știință★ 382
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- spatial-transcriptomics-cell-type-annotationjaechang-hits/SciAgent-SkillsSpatial transcriptomics cell type annotation for single-cell-resolution platforms (Xenium, MERFISH, CosMx, SeqFISH): CELLxGENE reference download, Harmony label transfer, per-cluster marker evidence naming lineages then subtypes, spatial niches. Visium/Slide-seq spots: GPU -> spatial-transcriptomics-deconvolution-cell2location; CPU -> spatial-mapping-tangram (cell types: per-spot proportions); no reference -> spatial-domain-detection (regions: domains from markers). Beyond labels (microenvironment, arrangement, spatial patterns): read spatial-transcriptomics-analysis-squidpy before asking the user.Documente★ 382
- bedtools-genomic-intervalsjaechang-hits/SciAgent-SkillsGenomic interval ops on BED/BAM/GFF/VCF. Find overlaps, merge intervals, compute coverage, extract FASTA, find nearest features. Core for ChIP-seq peak annotation, region filtering, genome arithmetic. Use tabix for indexed single-region queries; use deeptools for normalized bigWig coverage.Știință★ 382
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