Migrating to SLAF¶

Quick Start¶

Convert your single-cell data to SLAF format with just one command:

# Convert any supported format (auto-detection)
slaf convert data.h5ad output.slaf
slaf convert filtered_feature_bc_matrix/ output.slaf
slaf convert data.h5 output.slaf
slaf convert data.tiledb output.slaf

That's it! SLAF automatically detects your file format and converts it with optimized settings.

Multi-File Conversion¶

Convert multiple files to a single SLAF dataset:

# Convert multiple files from a directory
slaf convert data_folder/ output.slaf

# Convert specific files
slaf convert file1.h5ad file2.h5ad file3.h5ad output.slaf

# Auto-detection works for all formats
slaf convert 10x_data_folder/ output.slaf

SLAF automatically:

✅ Validates all files are compatible
✅ Assigns unique cell IDs across all files
✅ Tracks which file each cell came from
✅ Combines metadata intelligently

Appending to Existing Datasets¶

Add new data to an existing SLAF dataset:

# Append a single file
slaf append new_data.h5ad existing.slaf

# Append multiple files from a directory
slaf append new_data_folder/ existing.slaf

# Skip validation if already validated (faster)
slaf append new_data.h5ad existing.slaf --skip-validation

Perfect for:

Incremental data collection - Add new batches as they arrive
Data updates - Append new samples to existing datasets
Combining datasets - Merge related datasets over time

Supported Formats¶

SLAF supports conversion from these common single-cell formats:

AnnData (.h5ad files) - The standard single-cell format
10x MTX (filtered_feature_bc_matrix directories) - Cell Ranger output
10x H5 (.h5 files) - Cell Ranger H5 output format
TileDB SOMA (.tiledb directories) - High-performance single-cell format

Python API¶

from slaf.data import SLAFConverter

# Basic conversion (auto-detection)
converter = SLAFConverter()
converter.convert("data.h5ad", "output.slaf")
converter.convert("filtered_feature_bc_matrix/", "output.slaf")
converter.convert("data.h5", "output.slaf")
converter.convert("data.tiledb", "output.slaf")

# Multi-file conversion
converter.convert("data_folder/", "output.slaf")  # Directory of files
converter.convert(["file1.h5ad", "file2.h5ad"], "output.slaf")  # List of files

# Append to existing dataset
converter.append("new_data.h5ad", "existing.slaf")
converter.append("new_data_folder/", "existing.slaf")

# Convert existing AnnData object
import scanpy as sc
adata = sc.read_h5ad("data.h5ad")
converter.convert_anndata(adata, "output.slaf")

Large Datasets¶

For large datasets (>100k cells), you can optimize performance:

# Use larger chunks for speed (if you have enough RAM)
slaf convert large_data.h5ad output.slaf --chunk-size 100000

# Create indices for faster queries
slaf convert large_data.h5ad output.slaf --create-indices

# Python API for large datasets
converter = SLAFConverter(chunk_size=100000, create_indices=True)
converter.convert("large_data.h5ad", "output.slaf")

Validation and Quality Control¶

The slaf validate-input-files command helps you catch compatibility issues before conversion:

Basic Validation¶

# Validate a single file
slaf validate-input-files data.h5ad

# Validate multiple files from a directory
slaf validate-input-files data_folder/

# Validate specific files
slaf validate-input-files file1.h5ad file2.h5ad file3.h5ad

Verbose Output¶

# Get detailed information about files being validated
slaf validate-input-files data_folder/ --verbose

# Output shows:
# 📁 Found 3 h5ad files
#   1. batch_001.h5ad
#   2. batch_002.h5ad
#   3. batch_003.h5ad
# ✅ All files are compatible for conversion

Format-Specific Validation¶

# Validate 10x MTX directories
slaf validate-input-files filtered_feature_bc_matrix/ --format 10x_mtx

# Validate 10x H5 files
slaf validate-input-files data.h5 --format 10x_h5

# Validate TileDB SOMA files
slaf validate-input-files experiment.tiledb --format tiledb

What Validation Checks¶

The validation command performs comprehensive compatibility checks:

✅ File Integrity: Files exist, are readable, and not empty
✅ Format Consistency: All files use the same format (h5ad, 10x_mtx, etc.)
✅ Gene Compatibility: All files have identical gene sets
✅ Metadata Schema: Cell metadata columns are compatible across files
✅ Value Types: Expression data types are consistent (uint16, float32, etc.)
✅ File Sizes: Ensures files contain actual data (not empty)

Common Validation Scenarios¶

# Validate before multi-file conversion
slaf validate-input-files batch1/ batch2/ batch3/
slaf convert batch1/ output.slaf  # Safe to proceed

# Validate before appending
slaf validate-input-files new_batch/
slaf append existing.slaf new_batch/  # Safe to proceed

# Check specific format compatibility
slaf validate-input-files 10x_data/ --format 10x_mtx

Error Examples¶

When validation fails, you get clear error messages:

# Gene mismatch error
❌ Validation failed: File batch_002.h5ad is incompatible:
  Missing genes: GENE_001, GENE_002, GENE_003
  Extra genes: GENE_999, GENE_1000

# Schema mismatch error
❌ Validation failed: File batch_003.h5ad has incompatible cell metadata schema:
  Missing columns: ['cell_type', 'batch']
  Extra columns: ['cluster_id']

# Format mismatch error
❌ Validation failed: Multiple formats detected in directory
  Found: h5ad, 10x_mtx
  All files must use the same format

Integration with Conversion¶

Validation runs automatically during conversion, but you can skip it for performance:

# Automatic validation (default)
slaf convert data_folder/ output.slaf

# Skip validation (faster, but less safe)
slaf convert data_folder/ output.slaf --skip-validation
slaf append new_data.h5ad existing.slaf --skip-validation

Advanced Options¶

Most users won't need these, but they're available if needed:

CLI Options¶

# Specify format explicitly (if auto-detection fails)
slaf convert data.h5 output.slaf --format 10x_h5
slaf convert data.tiledb output.slaf --format tiledb

# Use non-chunked processing (not recommended for large datasets)
slaf convert small_data.h5ad output.slaf --no-chunked

# Disable storage optimization (larger files but includes string IDs)
slaf convert data.h5ad output.slaf --no-optimize-storage

# Verbose output
slaf convert data.h5ad output.slaf --verbose

# Skip validation (if already validated)
slaf convert data_folder/ output.slaf --skip-validation
slaf append new_data.h5ad existing.slaf --skip-validation

# TileDB-specific options
slaf convert data.tiledb output.slaf --tiledb-collection RNA

Python API Options¶

# Custom settings
converter = SLAFConverter(
    chunk_size=50000,           # Cells per chunk
    create_indices=True,        # Faster queries
    optimize_storage=True,      # Smaller files (default)
    use_optimized_dtypes=True,  # Better compression (default)
    tiledb_collection_name="RNA",  # TileDB collection name (default: "RNA")
)

converter.convert("data.h5ad", "output.slaf")
converter.convert("data.tiledb", "output.slaf")

TileDB SOMA Conversion¶

SLAF provides excellent support for TileDB SOMA format, which is increasingly popular for large-scale single-cell datasets:

Basic TileDB Conversion¶

# Auto-detect TileDB format
slaf convert experiment.tiledb output.slaf

# Specify collection name (default: "RNA")
slaf convert experiment.tiledb output.slaf --tiledb-collection RNA

Python API for TileDB¶

from slaf.data import SLAFConverter

# Basic TileDB conversion
converter = SLAFConverter()
converter.convert("experiment.tiledb", "output.slaf")

# With custom collection name
converter = SLAFConverter(tiledb_collection_name="RNA")
converter.convert("experiment.tiledb", "output.slaf")

TileDB Benefits¶

Memory Efficient: TileDB's chunked storage works seamlessly with SLAF's chunked processing
Large Datasets: Optimized for datasets with millions of cells
Data Preservation: Maintains exact floating-point precision from TileDB
Fast Conversion: Leverages TileDB's efficient data access patterns

What SLAF Does¶

SLAF converts your data to an optimized format that:

Enables fast SQL queries on your data
Works with any size dataset (memory-efficient processing)
Preserves all metadata including:
Cell and gene annotations (obs and var columns)
Alternative expression matrices (layers like spliced, unspliced, counts)
Multi-dimensional arrays (obsm like UMAP coordinates, PCA embeddings)
Gene-level embeddings (varm like PCA loadings)
Unstructured metadata (uns like analysis parameters)

Workflow Examples¶

Multi-File Workflow¶

# 1. Validate files first (recommended)
slaf validate-input-files batch1/ batch2/ batch3/

# 2. Convert all batches to single SLAF
slaf convert batch1/ initial.slaf

# 3. Append additional batches
slaf append batch2/ initial.slaf
slaf append batch3/ initial.slaf

# 4. Explore the combined dataset
slaf info initial.slaf

Incremental Data Collection¶

# Start with first batch
slaf convert batch_001/ dataset.slaf

# Add new batches as they arrive
slaf append batch_002/ dataset.slaf
slaf append batch_003/ dataset.slaf
slaf append batch_004/ dataset.slaf

# Each append maintains data integrity and source tracking

Quality Control Workflow¶

# 1. Validate all files before conversion
slaf validate-input-files all_batches/

# 2. Convert with validation (automatic)
slaf convert all_batches/ combined.slaf

# 3. Check source file tracking
slaf query combined.slaf "SELECT source_file, COUNT(*) FROM cells GROUP BY source_file"

Next Steps¶

After converting your data:

Explore: slaf info output.slaf
Query: slaf query output.slaf "SELECT * FROM expression LIMIT 10"
Use in Python: import slaf; data = slaf.SLAFArray("output.slaf")
Check Source Files: slaf query output.slaf "SELECT DISTINCT source_file FROM cells"

See the Getting Started guide for more examples.