cellitac 1.0.1

Cell type identification using Transcription factor Analysis and Chromatin accessibility

cellitac

Single-Cell ATAC + RNA Multiome Processing & ML Classification Pipeline

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What It Does

Stage	Steps	Tools
Preprocessing	RNA QC → normalization → cell-type annotation	Seurat + SingleR (R via rpy2)
Preprocessing	ATAC QC → TF-IDF → LSI	Signac (R via rpy2)
Preprocessing	RNA + ATAC integration → ML-ready CSVs	Pure Python
ML	Imbalance analysis → SMOTE → feature selection	scikit-learn, imbalanced-learn
ML	RF + XGBoost + SVM training & evaluation	scikit-learn, xgboost
ML	19 plots + JSON report + XLSX	matplotlib, seaborn, networkx

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Install R packages (run once inside R)

Rscript -e " install.packages('BiocManager') BiocManager::install(c( 'Seurat', 'Signac', 'SingleR', 'celldex', 'SingleCellExperiment', 'GenomicRanges', 'EnsDb.Hsapiens.v75', 'biovizBase', 'hdf5r' )) "

Install Python package

pip install -e ".[dev]"

### Option B – PyPI

```bash
pip install cellitac
# R must be installed separately

Option C – Docker (recommended for full reproducibility)

docker build -t cellitac:1.0.0 -f docker/Dockerfile .

docker run --rm \
  -v /your/data:/data \
  -v $(pwd)/results:/results \
  cellitac:1.0.0 \
  --input /data --output /results

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Data Download

https://www.10xgenomics.com/datasets/pbmc-from-a-healthy-donor-no-cell-sorting-10-k-1-standard-1-0-0

Required files (place in your --input directory):

pbmc_unsorted_10k_filtered_feature_bc_matrix.h5
pbmc_unsorted_10k_per_barcode_metrics.csv
pbmc_unsorted_10k_atac_fragments.tsv.gz
pbmc_unsorted_10k_atac_fragments.tsv.gz.tbi
pbmc_unsorted_10k_atac_peaks.bed

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Usage

Command Line

# Full pipeline (preprocessing + ML)
cellitac --input ~/singlecell/ATAC --output my_results

# Preprocessing only (generates python_ready_data/)
cellitac-preprocess --input ~/singlecell/ATAC --output my_results

# ML only (if you already have python_ready_data/)
cellitac-model --data my_results/python_ready_data --output my_results/ml

Python API

from cellitac import run_full_pipeline, run_preprocessing, run_model

# Full pipeline
run_full_pipeline(input_dir="~/singlecell/ATAC", output_dir="my_results")

# Preprocessing only
run_preprocessing(input_dir="~/singlecell/ATAC", output_dir_python="python_ready_data")

# ML only
run_model(data_dir="python_ready_data", output_dir="ml_results")

# Use the ML class directly for more control
from cellitac.mainModel import scATACMLPipeline
pipeline = scATACMLPipeline(data_dir="python_ready_data", output_dir="ml_results")
pipeline.run_complete_pipeline()

Environment Variables

export SCATAC_INPUT_DIR=~/singlecell/ATAC
export SCATAC_OUT_ML=ml_results
cellitac

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Output Files

ml_results/

File	Description
ml_pipeline_report.json	Full JSON report
model_performance_summary.csv	Accuracy/F1/AUC per model
detailed_model_results.xlsx	Per-class metrics, CV results
model_performance_comparison.png	Bar chart comparison
confusion_matrices.png	Confusion matrices
class_distribution_analysis.png	Cell type distribution
class_balancing_comparison.png	Before/after SMOTE
feature_importance.png	RF + XGBoost top 20 features
simple_feature_heatmap.png	Feature importance heatmap
overfitting_analysis.png	CV train vs validation
learning_curves.png	Learning curves per model
performance_radar.png	Radar chart
feature_distributions.png	Violin plots
class_separation_pca.png	PCA scatter
basic_tf_network.png	Feature–cell-type network

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Package Structure

cellitac/
├── src/cellitac/
│   ├── __init__.py          # Public API
│   ├── _version.py
│   ├── config.py            # All parameters (paths, QC thresholds, ML hyperparams)
│   ├── pipeline.py          # run_preprocessing, run_model, run_full_pipeline
│   ├── preprocessing.py     # R preprocessing via rpy2
│   ├── mainModel.py         # scATACMLPipeline class (19-step ML pipeline)
│   ├── cli.py               # cellitac / cellitac-preprocess / cellitac-model
│   └── rscripts/
│       ├── team1_rna.R      # Exact Seurat + SingleR code
│       └── team2_atac.R     # Exact Signac code
├── tests/
│   └── test_model.py
├── pyproject.toml
└── README.md

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Tests

pip install -e ".[dev]"
pytest tests/ -v

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License

MIT