Skip to content

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.

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")

# 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
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

# 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 (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.