flow-fcs

A high-performance Rust library for reading, parsing, and manipulating Flow Cytometry Standard (FCS) files.

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⚠️ Under Construction: This library is actively under development. APIs may change, and some features may be incomplete. Use with caution in production environments.

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Overview

flow-fcs provides a comprehensive, type-safe API for working with FCS files used in flow cytometry analysis. Built on top of Polars for efficient columnar data operations, the library offers zero-copy data access, SIMD-accelerated operations, and support for common flow cytometry data transformations.

Features

• Full FCS Standard Support: Supports FCS versions 1.0 through 4.0

• High Performance:

  • Memory-mapped file I/O for efficient large file handling
  • Zero-copy column access via Polars
  • SIMD-accelerated operations
  • Parallel processing with Rayon

• Data Transformations:

  • Arcsinh transformation (with configurable cofactors)
  • Compensation (spillover matrix)
  • Spectral unmixing

• Polars Integration:

  • Lazy evaluation for complex queries
  • Streaming execution for large datasets
  • Apache Arrow interop

• Comprehensive Metadata Access:

  • Type-safe keyword access
  • Parameter metadata (names, labels, transforms)
  • GUID and file information

• Type Safety: Strong typing throughout with clear error messages

Installation

Add this to your Cargo.toml:

[dependencies]
flow-fcs = "0.1.0"

Optional Features

• typescript: Generate TypeScript bindings for Rust types (requires ts-rs)

[dependencies]
flow-fcs = { version = "0.1.0", features = ["typescript"] }

Quick Start

Opening an FCS File

use flow_fcs::Fcs;

// Open an FCS file
let fcs = Fcs::open("path/to/file.fcs")?;

// Get basic information
let num_events = fcs.get_number_of_events()?;
let num_parameters = fcs.get_number_of_parameters()?;
let guid = fcs.get_guid()?;

println!("File: {} events, {} parameters", num_events, num_parameters);
println!("GUID: {}", guid);

Accessing Parameter Data

// Get events for a specific parameter (zero-copy slice)
let fsc_data = fcs.get_parameter_events_slice("FSC-A")?;

// Get (x, y) pairs for plotting
let xy_pairs = fcs.get_xy_pairs("FSC-A", "SSC-A")?;

// Get parameter statistics using streaming (memory-efficient)
let (min, max, mean, std) = fcs.get_parameter_statistics("FL1-A")?;
println!("FL1-A: min={}, max={}, mean={:.2}, std={:.2}", min, max, mean, std);

Data Transformations

// Apply arcsinh transformation to a parameter
let transformed = fcs.apply_arcsinh_transform("FL1-A", 200.0)?;

// Apply arcsinh to all fluorescence parameters with default cofactor
let transformed = fcs.apply_default_arcsinh_transform()?;

// Apply compensation from file's $SPILLOVER keyword
let compensated = fcs.apply_file_compensation()?;

// Apply custom compensation matrix
use ndarray::Array2;
let comp_matrix = Array2::from_shape_vec((2, 2), vec![
    1.0, 0.1,
    0.05, 1.0,
])?;
let channels = vec!["FL1-A", "FL2-A"];
let compensated = fcs.apply_compensation(&comp_matrix, &channels)?;

Working with Metadata

// Get keyword values
let filename = fcs.get_fil_keyword()?;
let cytometer = fcs.get_keyword_string_value("$CYT")?;

// Access parameter information
let param = fcs.find_parameter("FL1-A")?;
println!("Channel: {}, Label: {}", param.channel_name, param.label_name);

API Overview

Core Types

• Fcs: Main struct representing an FCS file
• Header: FCS file header information
• Metadata: Text segment metadata and keywords
• Parameter: Parameter/channel information
• EventDataFrame: Polars DataFrame containing event data

Key Methods

File Operations

• Fcs::open(path): Open and parse an FCS file
• Fcs::new(): Create an empty FCS struct

Data Access

• get_parameter_events_slice(channel_name): Get zero-copy slice of parameter data
• get_xy_pairs(x_param, y_param): Get (x, y) coordinate pairs for plotting
• get_parameter_statistics(channel_name): Calculate min, max, mean, std (streaming)
• get_event_count_from_dataframe(): Get number of events
• get_parameter_count_from_dataframe(): Get number of parameters

Transformations

• apply_arcsinh_transform(parameter, cofactor): Apply arcsinh transformation
• apply_arcsinh_transforms(params): Apply to multiple parameters
• apply_default_arcsinh_transform(): Transform all fluorescence parameters
• apply_compensation(matrix, channels): Apply compensation matrix
• apply_file_compensation(): Apply compensation from $SPILLOVER keyword
• apply_spectral_unmixing(matrix, channels, cofactor): Apply spectral unmixing

Metadata

• get_guid(): Get file GUID
• get_fil_keyword(): Get filename
• get_keyword_string_value(keyword): Get any keyword as string
• get_number_of_events(): Get total event count
• get_number_of_parameters(): Get parameter count
• find_parameter(channel_name): Find parameter by name

Performance

The library is optimized for performance:

• Memory-mapped I/O: Large files are memory-mapped for efficient access
• Zero-copy operations: Polars enables zero-copy column access
• SIMD acceleration: Built-in SIMD operations via Polars
• Streaming execution: Statistics and aggregations use streaming mode for large datasets
• Parallel processing: Rayon enables parallel operations where applicable

FCS Standard Support

The library supports FCS versions:

• FCS 1.0
• FCS 2.0
• FCS 3.0
• FCS 3.1 (default)
• FCS 3.2
• FCS 4.0

Error Handling

The library uses anyhow::Result for error handling, providing detailed error messages for common issues:

• File I/O errors
• Invalid FCS format
• Missing required keywords
• Type conversion errors
• Data validation failures

Examples

See the tests/ directory for more comprehensive examples of library usage.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with Polars for high-performance data operations
• Uses ndarray for matrix operations
• Inspired by the need for fast, type-safe FCS file handling in Rust

Related Projects

• Polars: Fast DataFrame library
• ndarray: N-dimensional array library