Scvi Tools Deep Learning Skill
技能 活跃Deep 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.
To enable users to perform advanced deep learning-based analyses on single-cell omics data using the scvi-tools library.
功能
- Data integration and batch correction
- ATAC-seq analysis with PeakVI
- CITE-seq multi-modal analysis with totalVI
- Multiome (RNA+ATAC) analysis with MultiVI
- RNA velocity analysis with veloVI
使用场景
- When scvi-tools, scVI, scANVI, or related models are mentioned
- When deep learning-based batch correction or integration is needed
- When working with multi-modal data (CITE-seq, multiome)
- When reference mapping or label transfer is required
非目标
- Performing wet-lab experiments or generating raw data
- Replacing specialized bioinformatics software for tasks outside scvi-tools
- Providing generic Python utilities not related to scvi-tools
Trust
- warning:Issues AttentionThere are 29 open issues and 4 closed issues in the last 90 days, indicating a low closure rate (approx. 12%) and potentially slow maintainer response.
安装
请先添加 Marketplace
/plugin marketplace add anthropics/knowledge-work-plugins/plugin install bio-research@knowledge-work-plugins质量评分
类似扩展
PyDESeq2
100Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scanpy
99Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Gtars
99High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
AnnData
99Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Pysam
99Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.
Polars Bio
99High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.