bio-single-cell-multimodal-integration
Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.
npx skills add majiayu000/claude-skill-registry --skill multimodal-integration-gptomics-bioskills-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Multimodal Integration Analyze multi-modal single-cell data where multiple measurements are made per cell. ## Common Modalities | Technology | Modalities | Package | |------------|------------|---------| | CITE-seq | RNA + surface proteins (ADT) | Seurat | | 10X Multiome | RNA + ATAC | Seurat, Signac, ArchR | | SHARE-seq | RNA + ATAC | Seurat, Signac | | Spatial (Visium) | RNA + spatial coordinates | Seurat, Squidpy | ## CITE-seq Analysis (Seurat) ### Load Data ```r library(Seurat) # Read 10X data with antibody capture data <- Read10X('filtered_feature_bc_matrix/') # Separate RNA and ADT rna_counts <- data$`Gene Expression` adt_counts <- data$`Antibody Capture` # Create Seurat object with both assays obj <- CreateSeuratObject(counts = rna_counts, assay = 'RNA') obj[['ADT']] <- CreateAssayObject(counts = adt_counts) ``` ### QC and Normalization ```r # RNA QC (standard) obj <- PercentageFeatureSet(obj, pattern = '^MT-', col.name = 'percent.mt') obj <- subset(obj, nFeature_RNA > 200 & percent.mt < 20) # Normalize RNA obj <- NormalizeData(obj, assay = 'RNA') obj <- FindVariableFeatures(obj, assay = 'RNA') obj <- ScaleData(obj, assay = 'RNA') # Normalize ADT (CLR normalization) obj <-
- Common Modalities
- CITE-seq Analysis (Seurat)
- Load Data
- QC and Normalization
- Weighted Nearest Neighbor (WNN) Clustering
- Visualize
- 10X Multiome (RNA + ATAC)
- Process ATAC
- Joint Analysis
- Scanpy/MuData (Python)
- CITE-seq with MuData
- Integration Metrics
- Modality Weights
- Correlation Between Modalities
What does the bio-single-cell-multimodal-integration skill do?
Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill multimodal-integration-gptomics-bioskills-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.