Enforce NCBI rate limit of 3 requests/second.

Add an error (makes validation fail).

Add a warning (doesn't fail validation).

Format file size in human-readable format.

Add a recommendation for improvement.

Detect technique from ASM structure.

Fetch SRA run information for all samples in a GEO study. Args: geo_id: GEO accession (e.g., 'GSE110004') bioproject: Option

Extract sample metadata from filepath. Args: filepath: Path to sequencing file Returns: Dict with: sample, patient, lan

Count measurement documents in ASM.

Detect outliers using Median Absolute Deviation (MAD). Parameters ---------- adata : AnnData Annotated data matrix with QC m

Fetch detailed SRA run information using efetch CSV format. This provides richer metadata than esummary, including sample names. Args:

Flatten ASM JSON to list of row dictionaries. Each measurement becomes one row with metadata repeated.

Format sample groups as a readable table.

Get genome cache directory.

Get local path to genome if installed.

Group SRA runs by library type and layout. Returns dict with group names as keys and info dicts as values: { 'RNA-Seq:PAIRED': {

Check if genome is installed locally.

Resolve genome ID from name or alias.

Get highest matching pattern score.

Generate a key for grouping related files.

Recursively discover files of specified type. Args: directory: Root directory to search file_type: One of 'fastq', 'bam', 'c

Download a file with progress indication. Args: url: URL to download output_path: Path to save file timeout: Downloa

Fetch BioProject accession linked to a GEO study. Args: geo_id: GEO accession (e.g., 'GSE110004') Returns: BioProject a

Fetch GEO study metadata using NCBI Entrez E-utilities. Args: geo_id: GEO accession (e.g., 'GSE110004') Returns: Dict w

Flatten ASM and write to CSV file. Args: asm: Parsed ASM JSON dictionary output_path: Path for output CSV

Identify mitochondrial genes for both human and mouse data. Handles common prefixes: - Human: MT- (e.g., MT-CO1, MT-ND1) - Mouse: mt

Load all pipeline configurations.

Load pipeline configuration from YAML.

Print summary statistics for QC metrics. Parameters ---------- adata : AnnData Annotated data matrix with QC metrics lab

Scan directory and collect file information.

Suggest a genome based on organism name.

Suggest nf-core pipeline based on library strategy.

Validate ASM JSON file. Args: filepath: Path to ASM JSON file reference_path: Optional path to reference ASM for comparison

Validate samplesheet rows against pipeline requirements. Args: rows: List of row dictionaries pipeline: Pipeline name (e.g.,

Format list of GSM IDs as a range if consecutive.

Parse a CSV line handling quoted fields.

Print preview of generated samplesheet.

Process BAM/CRAM files into samplesheet rows.

Process ATAC-seq samples: ensure replicate numbers.

Process FASTQ files into samplesheet rows.

Process sarek samples: infer and confirm tumor/normal status.

ATAC-seq specific validation for replicates.

Sarek-specific validation for tumor/normal pairing.

Write samplesheet to CSV file.

Add provenance metadata to ASM for reproducibility and audit trail. This metadata enables: - Reproducing conversions months later -

Apply a hard threshold filter. Parameters ---------- adata : AnnData Annotated data matrix metric : str Column n

Build ASM-like JSON structure from parsed DataFrame.

Calculate SHA256 hash of file for provenance tracking.

Calculate confidence scores for each pipeline.

Calculate QC metrics for single-cell RNA-seq data. Parameters ---------- adata : AnnData Annotated data matrix mt_patter

Check Docker availability, daemon status, and permissions.

Check Java version (requires >= 11).

Check network connectivity to Docker Hub and nf-core.

Check if NCBI/ENA servers are accessible. Returns: Tuple of (success, message)

Check Nextflow installation and version (requires >= 23.04).

Check system resources (CPU, memory, disk).

Cluster and compute UMAP embedding. Parameters ---------- adata : AnnData Data with latent representation in obsm use_re

Compare generated ASM to reference ASM.

Convert file using allotropy library. Returns: ASM dictionary or None if conversion fails

Count files by type in directory. Args: directory: Directory to scan Returns: Dict mapping file_type to count

Auto-detect predominant input file type in directory. Prioritizes: FASTQ > BAM > CRAM Args: directory: Directory to scan R

Auto-detect instrument type from file contents. Returns: Tuple of (vendor_name, confidence_score) confidence_score is 0-100

Detect the most appropriate pipeline for the data. Args: directory: Path to data directory Returns: Tuple of (recommend

Detect the ASM technique type from document structure.

Download genome reference files from iGenomes. Args: genome_id: Genome identifier (e.g., GRCh38) components: Specific compon

Estimate total download size from SRA run info. Args: runs: List of run info dicts with 'bases' field Returns: Estimate

Extract device/instrument information from ASM.

Extract replicate number from sample name. Args: sample_name: Sample identifier Returns: Replicate number if found, Non

Get FASTQ download URLs from ENA for an SRA study. ENA provides faster downloads than SRA with pre-split paired files. Args: st

Get the SRA study accession (SRPxxxxxx) for a GEO accession. Args: geo_id: GEO accession (e.g., 'GSE110004') Returns: S

Filter cells based on a boolean mask. Parameters ---------- adata : AnnData Annotated data matrix mask : np.ndarray or p

Filter genes based on detection thresholds. Parameters ---------- adata : AnnData Annotated data matrix min_cells : int

Recursively find fields that appear directly on measurement documents.

Find index file for a BAM or CRAM file. Args: alignment_file: Path to BAM or CRAM file Returns: Path to index file if f

Flatten ASM and return as dictionary with rows and columns. Useful for non-CSV outputs or further processing.

Flatten a single ASM value, handling value datum patterns. Returns dict of {column_name: value}

Flexible fallback parser when allotropy fails. Creates ASM-like structure from parsed data. **WARNING:** This parser creates simplified

Generate Jupyter notebook for given vendor.

Generate samplesheet for specified pipeline. Args: input_dir: Directory containing sequencing files pipeline: Pipeline name

Generate standalone Python script for given vendor.

Get deterministic timestamp for file. Uses file modification time for reproducibility. Returns: ISO format timestamp string

Get version of installed library.

Get Nextflow parameters for a genome. Returns dict with --fasta, --gtf if local, or just --genome if using iGenomes key.

Infer tumor (1) or normal (0) status from sample name. Args: sample_name: Sample identifier Returns: 1 for tumor, 0 for

Integrate multiple datasets. Parameters ---------- adatas : list of AnnData Datasets to integrate batch_names : list of

Interactively select a sample group.

List available genomes.

Load organism to genome mapping from config.

Load pipeline configuration from YAML file.