# PeacoQC-CLI
[](https://www.rust-lang.org/)
[](https://opensource.org/licenses/MIT)
Command-line tool for PeacoQC (Peak-based Quality Control) for flow cytometry FCS files built on top of the [`peacoqc-rs` crate](https://crates.io/crates/peacoqc-rs), which implements the PeacoQC algorithm. The CLI provides a simple interface to run quality control on one or more FCS files with parallel processing support.
## Installation
### From Source
```bash
git clone <repository-url>
cd peacoqc-cli
cargo build --release
```
The binary will be at `target/release/peacoqc`.
### Using Cargo
Install the binary as a cargo tool by pointing to its location (e.g. using the path of the repo cloned above):
```bash
cargo install --path peacoqc-cli
```
## Usage
### Basic Usage (Single File)
Process a single FCS file:
```bash
peacoqc input.fcs
```
With output file:
```bash
peacoqc input.fcs -o output.fcs
```
### Multiple Files
Process multiple files in parallel:
```bash
peacoqc file1.fcs file2.fcs file3.fcs
```
With output directory:
```bash
peacoqc file1.fcs file2.fcs file3.fcs -o /path/to/output/
```
### Directory Processing
Process all FCS files in a directory (recursively):
```bash
peacoqc /path/to/data/
```
The tool will:
- Recursively find all `.fcs` files in the directory
- Process them in parallel
- Save outputs to the specified output directory (or alongside input files with `_cleaned` suffix)
### Options
```bash
peacoqc [OPTIONS] <INPUT_FILES>...
Arguments:
<INPUT_FILES>... Path(s) to input FCS file(s) or directory containing FCS files
Options:
-o, --output <OUTPUT_DIR> Output directory for cleaned FCS files
-c, --channels <CHANNELS> Channels to analyze (comma-separated, e.g., "FSC-A,SSC-A,FL1-A")
-m, --qc-mode <QC_MODE> Quality control mode [default: all] [possible values: all, it, mad, none]
--mad <MAD> MAD threshold (default: 6.0) - Higher = less strict
--it-limit <IT_LIMIT> Isolation Tree limit (default: 0.6) - Higher = less strict
--consecutive-bins <BINS> Consecutive bins threshold (default: 5)
--remove-zeros Remove zeros before peak detection
--keep-margins Keep margin events (default: margins are removed)
--keep-doublets Keep doublet events (default: doublets are removed)
--doublet-nmad <NMAD> Doublet nmad threshold (default: 4.0)
--report <REPORT_PATH> Save QC report as JSON (file for single input, directory for multiple)
--export-csv <CSV_PATH> Export QC results as boolean CSV (0/1 values)
--export-csv-numeric <PATH> Export QC results as numeric CSV (2000/6000 values, R-compatible)
--export-json <JSON_PATH> Export QC metadata as JSON
--csv-column-name <NAME> Column name for CSV exports (default: "PeacoQC")
-v, --verbose Verbose output
-h, --help Print help
-V, --version Print version
```
## Examples
### Process Single File
```bash
# Basic processing
peacoqc sample.fcs
# With custom channels
peacoqc sample.fcs -c FL1-A,FL2-A,FL3-A
# Save report
peacoqc sample.fcs --report report.json
# Export QC results as CSV
peacoqc sample.fcs --export-csv qc_results.csv
# Export numeric CSV (R-compatible)
peacoqc sample.fcs --export-csv-numeric qc_results_r.csv
# Export JSON metadata
peacoqc sample.fcs --export-json qc_metadata.json
# Export to directory (auto-named files)
peacoqc sample.fcs --export-csv ./exports/ --export-json ./exports/
# Verbose output
peacoqc sample.fcs -v
```
### Process Multiple Files
```bash
# Process all files in directory
peacoqc /path/to/data/ -o /path/to/output/
# Process specific files
peacoqc file1.fcs file2.fcs file3.fcs -o ./cleaned/
# Save individual reports
peacoqc /path/to/data/ --report /path/to/reports/
```
### Custom QC Settings
```bash
# Use only Isolation Tree method
peacoqc sample.fcs -m it
# Adjust MAD threshold (higher = less strict)
peacoqc sample.fcs --mad 8.0
# Adjust Isolation Tree limit
peacoqc sample.fcs --it-limit 0.7
# Keep margins and doublets (disable removal)
peacoqc sample.fcs --keep-margins --keep-doublets
# Note: Keeping doublets may cause more bins to be flagged as outliers
# on some datasets, as doublets can interfere with peak detection and MAD calculations.
# Use --keep-doublets only if needed to match specific published results
# or if doublet removal is too aggressive for your dataset.
```
## Performance
The CLI automatically processes files in parallel:
- **Multiple files**: Processed simultaneously using all available CPU cores
- **Multiple channels**: Processed in parallel within each file
- **Multiple bins**: Processed in parallel within each channel
Expected speedup:
- **Single file with many channels**: 2-4x speedup on typical multi-core systems
- **Multiple files**: ~N cores speedup (e.g., 8 files on 8 cores → ~8x speedup)
## Output
### Console Output
The tool prints progress and summary information:
```txt
🧬 PeacoQC - Flow Cytometry Quality Control
============================================
📂 Found 5 file(s) to process
✅ Processing Complete!
Processed: 5 file(s)
Successful: 5
⏱️ Total time: 12.34s
```
With `--verbose` flag, additional details are shown for each file.
### Reports
Reports are saved as JSON files with the following structure:
```json
{
"filename": "sample.fcs",
"n_events_before": 50000,
"n_events_after": 47500,
"percentage_removed": 5.0,
"it_percentage": 3.2,
"mad_percentage": 1.8,
"consecutive_percentage": 0.5,
"processing_time_ms": 1234
}
```
For multiple files:
- If `--report` points to a directory: Individual JSON files are created for each input file
- If `--report` points to a file: A combined report with all results is created
### Export Formats
The CLI supports exporting QC results in multiple formats:
#### Boolean CSV (Recommended)
```bash
peacoqc sample.fcs --export-csv qc_results.csv
```
Exports a CSV file with 0/1 values:
- `1` = good event (keep)
- `0` = bad event (remove)
Best for: pandas, R, SQL, general data analysis
#### Numeric CSV (R-Compatible)
```bash
peacoqc sample.fcs --export-csv-numeric qc_results_r.csv
```
Exports a CSV file with numeric codes matching R PeacoQC:
- `2000` = good event (keep)
- `6000` = bad event (remove)
Best for: R compatibility, FlowJo CSV import, legacy pipelines
#### JSON Metadata
```bash
peacoqc sample.fcs --export-json qc_metadata.json
```
Exports comprehensive QC metrics including:
- Event counts (before/after/removed)
- Percentage removed by method (IT, MAD, consecutive)
- Configuration used
- Channels analyzed
Best for: Programmatic access, reporting, provenance tracking
#### Export to Directory
When exporting to a directory, files are automatically named:
```bash
peacoqc sample.fcs --export-csv ./exports/ --export-json ./exports/
# Creates: ./exports/sample.PeacoQC.csv and ./exports/sample.PeacoQC.json
```
### Output Files (FCS)
When an output path is specified (using `-o` or `--output`), the CLI will write cleaned FCS files containing only the events that passed quality control.
- **Single file**: Output file will be saved with `_cleaned` suffix (e.g., `sample.fcs` → `sample_cleaned.fcs`)
- **Multiple files**: If output directory is specified, files will maintain their original names with `_cleaned` suffix
- **Filtered data**: Output FCS files contain only events that passed all QC steps (IT, MAD, consecutive filtering)
**Example**:
```bash
# Single file - saves to sample_cleaned.fcs in same directory
peacoqc sample.fcs -o sample_cleaned.fcs
# Multiple files - saves to output directory with _cleaned suffix
peacoqc file1.fcs file2.fcs -o ./clean_dir/_
# Creates: ./clean_dir/file1_cleaned.fcs, ./clean_dir/file2_cleaned.fcs
```
This provides feature parity with the R PeacoQC package's `save_fcs=TRUE` option.
## Error Handling
The CLI continues processing even if individual files fail:
- **Successful files**: Processed and included in results
- **Failed files**: Error messages are printed, processing continues
- **Exit code**: Returns non-zero exit code if any files failed
## Integration with peacoqc-rs
This CLI is built on top of the [`peacoqc-rs` library](https://crates.io/crates/peacoqc-rs), which provides:
- Trait-based design for maximum flexibility
- Efficient parallel processing
- Comprehensive quality control algorithms
- Integration with [`flow-fcs`](https://crates.io/crates/flow-fcs) for FCS file support
See the [`peacoqc-rs` documentation](https://crates.io/crates/peacoqc-rs) for library usage.
## License
MIT License - see LICENSE file for details
## Attribution
We gratefully acknowledge the original PeacoQC algorithm authors:
**Original Paper:**
- [Emmaneel, A., Quintelier, K., Sichien, D., Rybakowska, P., Marañón, C., Alarcón-Riquelme, M. E., Van Isterdael, G., Van Gassen, S., & Saeys, Y. (2022). PeacoQC: Peak-based selection of high quality cytometry data. *Cytometry A*, 101(4), 325-338. `https://doi.org/10.1002/cyto.a.24501`](https://doi.org/10.1002/cyto.a.24501)
**Original R Implementation:**
- [GitHub: `https://github.com/saeyslab/PeacoQC`](https://github.com/saeyslab/PeacoQC)
- Authors: Annelies Emmaneel, Katrien Quintelier, and the Saeys Lab
## Contributing
Contributions are welcome! Please feel free to open issues or submit a Pull Request on [Github](https://github.com/jrmoynihan/flow).