Command-line interface.

Main entry point.

Main entry point.

Match R1/R2 read pairs using scored pattern matching. Args: files: List of FileInfo objects (from file_discovery) Returns:

Package a skill folder into a .skill file. Args: skill_path: Path to the skill folder output_dir: Optional output directory

Visualize filtering thresholds overlaid on distributions. Parameters ---------- adata : AnnData Annotated data matrix with Q

Plot integration results.

Plot prediction results.

Create QC plots for filtered data (simplified version without outlier overlay). Parameters ---------- adata : AnnData Filter

Create comprehensive QC distribution plots. Parameters ---------- adata : AnnData Annotated data matrix with QC metrics

Generate and save visualization plots.

Create volcano plot of DE results.

Prepare AnnData for scvi-tools. Parameters ---------- adata : AnnData Raw count data batch_key : str, optional B

Print genome list.

Print human-readable report to stdout.

Print detection results.

Run all environment checks and return comprehensive report.

Run differential expression analysis. Parameters ---------- model : scvi model Trained model with differential_expression me

Suggest appropriate scvi-tools model based on data. Parameters ---------- adata : AnnData Data to analyze result : Valid

Generate human-readable summary.

Generate summary report.

Train MultiVI model for multiome (RNA + ATAC). Note: Expects MuData or AnnData with both RNA and ATAC data. For AnnData, ATAC peaks should be

Train PeakVI model for scATAC-seq.

Train scANVI model (scVI + labels).

Train scVI model.

Train totalVI model for CITE-seq.

Train veloVI model for RNA velocity. Note: Requires scvelo preprocessing. If Ms/Mu layers don't exist, will run preprocessing automatically.

Transfer labels from reference to query. Parameters ---------- reference_model : SCANVI model Pre-trained scANVI model a

Check calculated data has proper traceability.

Validate an existing samplesheet file.

Validate AnnData for scvi-tools compatibility. Parameters ---------- adata : AnnData Data to validate layer : str, optio

Specific validation for liquid handler ASM documents. Checks for: - Proper transfer pairing (aspirate + dispense = 1 measurement) -

Check for valid manifest.

Validate measurement documents.

Check for required metadata fields.

Check for proper space-separated naming (not hyphens).

Validate that fields are properly nested in their correct documents. This checks for common mistakes like: - Sample fields flattened dir

Check for valid sample roles.

Basic validation of skill structure.

Check for statistics documents where expected.

Validate technique selection.

Validate that entities have unique identifiers for traceability.

Check for valid units.

Suggest the best scvi-tools model based on available data. Parameters ---------- adata : AnnData Data to analyze Return

Download FASTQ files from ENA.

Display sample groups in a study for interactive selection.

Display study information.

List all samples and runs in a study.

Generate samplesheet for nf-core pipeline.

Compare multiple integration methods using standard metrics. Parameters ---------- adata : AnnData Data with integration emb

Download a single FASTQ file.

Evaluate integration quality using basic metrics. Parameters ---------- adata : AnnData Integrated data batch_key :

Fetch paper metadata from PubMed. Args: pmid: PubMed ID max_retries: Number of retries on failure Returns: Dict

Get marker genes using scVI differential expression. Parameters ---------- model : scvi model Trained scVI/scANVI model

Infer patient groupings from sample names. Groups samples that share a common prefix pattern. Args: sample_names: List of sampl

Plot model training history. Parameters ---------- model : scvi model Trained model save_path : str, optional

Prepare AnnData for scvi-tools models. Parameters ---------- adata : AnnData Raw count data batch_key : str, optiona

Quick clustering pipeline on latent representation. Parameters ---------- adata : AnnData Data with latent representation

Save model, processed data, and visualization. Parameters ---------- model : scvi model Trained model adata : AnnData

Train scVI or scANVI model. Parameters ---------- adata : AnnData Prepared data with counts layer batch_key : str, o

Check if path is absolute.

Check if file exists and is accessible.