Results for “scrna-seq”
17 skills
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
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
More results
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 bulk-rnaseq
Orchestrates a complete bulk RNA-seq differential-expression study from raw FASTQ reads through QC, alignment, quantification, differential expression, pathway enrichment, and publication figures.
30.2k · bundle
gabrielmoreira rnaseq-de
Performs differential expression analysis on bulk RNA-seq or pseudo-bulk count matrices with QC, PCA, and contrast testing.
17 · bundle
jorcan scanpy
Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, dimensionality reduction, clustering, marker gene identification, and visualization.
0 · 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
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
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 scikit-bio
Analyze biological sequences, alignments, phylogenetic trees, and diversity metrics (alpha/beta, UniFrac) with ordination (PCoA) and PERMANOVA for microbiome and community ecology data.
30.2k · bundle
k-dense-ai pydeseq2
Perform differential gene expression analysis for bulk RNA-seq data using PyDESeq2, supporting formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
30.2k · 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
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
mariadb-corporation mariadb-create-sequence
Generates and reviews MariaDB CREATE SEQUENCE statements with version-specific syntax, defaults, and gotchas.
0
24601 surrealkit
Manages SurrealDB schemas as desired-state .surql files, with sync for disposable databases and reviewed rollouts for shared or production environments, plus seeding and declarative testing.
34
lingxling lamindb
Manages biological datasets and models with LaminDB, covering setup, artifact registration, querying, lineage tracking, validation, ontology annotation, collections, branches, storage, and workflow integrations.
253 · bundle