Plugins
2 pluginscurated
Analyze Single-Cell RNA-Seq
Analyze single-cell RNA-seq data using Scanpy, including quality control, normalization, clustering, marker gene identification, and visualization.
9 skills · plugin
@brycewang-stanford
Cell Skills
Twelve-skill bundle covering the Cell manuscript lifecycle: workflow router, scope/significance fit, single-narrative framing, the Highlights + eTOC + Graphical Abstract trio, the ≤150-word Summary, main-text writing, display items, STAR Methods + Key Resources Table, data/code availability, Cell Press author–date references, submission preflight + cover letter, and reviewer rebuttal.
9 skills · plugin
Results for “single-cell”
21 skills
majiayu000 Universal Single Cell Annotator
Annotates single-cell RNA-seq data by scoring marker genes, transferring labels with CellTypist, or reasoning over cluster markers with an LLM.
567 · bundle
majiayu000 Rna
Annotates single-cell RNA-seq data by scoring marker genes, transferring labels with CellTypist, or reasoning over marker lists with an LLM.
567 · bundle
k-dense-ai Cellxgene Census
Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data, enabling efficient access to cell metadata, gene expression slices, summary counts, and embeddings without downloading whole datasets.
30.2k · bundle
lingxling Scanpy
Runs standard single-cell RNA-seq analysis with Scanpy, covering QC, normalization, dimensionality reduction, clustering, marker identification, visualization, and conversion of R single-cell formats to h5ad.
253 · bundle
k-dense-ai Scvelo
Estimate cell state transitions from unspliced/spliced mRNA dynamics using scVelo, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data.
30.2k · bundle
antigravity Scanpy
Analyze single-cell RNA-seq data using Scanpy, including quality control, normalization, clustering, marker gene identification, and visualization.
42.4k
More results
jorcan Scanpy
Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, dimensionality reduction, clustering, marker gene identification, and visualization.
0 · bundle
phoroth Scanpy
Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.
3
lingxling Scvelo
Analyze RNA velocity in single-cell RNA-seq data with scVelo, estimating cell state transitions from unspliced/spliced mRNA dynamics, inferring trajectory directions, computing latent time, and identifying driver genes.
253 · bundle
k-dense-ai Scanpy
Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.
30.2k · bundle
lingxling Anndata
Manages annotated data matrices for single-cell genomics, covering creation, I/O, concatenation, and manipulation of AnnData objects in h5ad and zarr formats.
253 · bundle
nimoqup046-collab Scanpy
Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.
2
lucaspmarie-a11y Scanpy
Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.
5
k-dense-ai Scvi Tools
Provides deep generative models for single-cell omics analysis, including probabilistic batch correction, transfer learning, differential expression, and multi-modal integration.
30.2k · bundle
k-dense-ai Anndata
Create, read, manipulate, and store annotated data matrices using the AnnData Python package, designed for single-cell genomics and general-purpose annotated data workflows.
30.2k · bundle
levalencia Scvelo
RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference.
3 · bundle
k-dense-ai Arboreto
Infer gene regulatory networks from gene expression data using scalable algorithms (GRNBoost2, GENIE3) with support for distributed computation.
30.2k · bundle
lingxling Geniml
Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
253 · bundle
k-dense-ai Geniml
Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
30.2k · bundle
k-dense-ai Gtars
High-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.
30.2k · bundle
alterlab-ieu Alterlab Borzoi
Predict genome-wide functional genomics tracks from DNA sequence with Borzoi (Linder 2025) — a sequence-to-function model outputting RNA-seq, CAGE, ATAC, and ChIP coverage across long context, used to score non-coding and regulatory variant effects. Use when predicting functional tracks from a DNA sequence, scoring a non-coding/regulatory variant's effect on expression or chromatin, or doing in-silico mutagenesis of a locus. To LOOK UP a variant's population frequency prefer alterlab-gnomad; for its clinical significance prefer alterlab-clinvar; for protein-structure effects prefer alterlab-alphafold; for single-cell foundation models prefer alterlab-scgpt. Part of the AlterLab Academic Skills suite.
60 · bundle