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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

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

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

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")

# 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")

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

# 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

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)
)

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

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 (cell types, gene info, etc.)

Next Steps

After converting your data:

  1. Explore: slaf info output.slaf
  2. Query: slaf query output.slaf "SELECT * FROM expression LIMIT 10"
  3. Use in Python: import slaf; data = slaf.SLAFArray("output.slaf")

See the Getting Started guide for more examples.