cisformer 1.1.1

Cisformer command-line tools

Cisformer

Cisformer is a Transformer-based framework for cross-modality generation between single-cell RNA-seq and single-cell ATAC-seq. It supports RNA-to-ATAC generation, ATAC-to-RNA generation, and cell-type-specific cis-regulatory element (cCRE)-gene link inference from cross-attention.

The model uses a decoder-only cross-attention architecture designed for sparse, ultra-long single-cell genomic sequences. In practice, Cisformer can be used to predict a missing modality from an available assay and to extract interpretable regulatory links from trained ATAC-to-RNA models.

What's New in v1.1.0

• Added mouse support for preprocessing, config generation, prediction, and ATAC-to-RNA link analysis.
• Added species-aware default configs. Mouse configs use total_gene: 23234; human configs use total_gene: 38244.
• Added automatic log1p handling for ATAC-to-RNA preprocessing. Cisformer now checks the input scRNA-seq expression matrix and reports whether log1p is applied.
• Optimized preprocessing and ATAC-to-RNA link generation performance.
• Clarified that atac2rna_link outputs a rank-normalized link matrix. Values are ranking scores derived from valid attention scores, not raw correlation coefficients.

Documentation

Full documentation is available at cisformer.readthedocs.io.

Installation

Cisformer does not currently rely on PyPI to install all runtime dependencies automatically. Install the deep learning and genomics dependencies in a conda environment first, then install the Cisformer command-line package from PyPI:

conda create -n cisformer python=3.10
conda activate cisformer
bash ./requirement.sh
pip install cisformer

The PyPI package installs the cisformer command itself. Dependencies such as PyTorch/CUDA, flash-attn, Scanpy, pybedtools, torcheval, tensorboard, and the system bedtools backend should be installed according to your server environment. See the installation guide in the documentation for the full manual recipe.

Quick Start

Generate default configuration files:

# Human, default
cisformer generate_default_config --species human

# Mouse
cisformer generate_default_config --species mouse

This creates cisformer_config/ with:

• accelerate_config.yaml
• atac2rna_config.yaml
• rna2atac_config.yaml
• resource/<species> Gencode annotation .gtf.gz

Edit accelerate_config.yaml before distributed training, especially gpu_ids, num_processes, and main_process_port.

Large Gencode annotation files are downloaded during this step and are not bundled in the PyPI package. Human uses Gencode v49; mouse uses Gencode M39.

Input Requirements

Cisformer expects paired scRNA-seq and scATAC-seq inputs in Scanpy .h5ad format.

• RNA and ATAC cell barcodes must overlap.
• RNA features can be gene symbols or Ensembl IDs.
• ATAC features should be genomic peak coordinates in chr:start-end format.
• Set --species human or --species mouse consistently across preprocessing, config generation, prediction, and link inference.

RNA-to-ATAC Workflow

Preprocess paired data:

cisformer data_preprocess \
  -r test_data/rna.h5ad \
  -a test_data/atac.h5ad \
  -s preprocessed_dataset \
  --species human

Train:

cisformer rna2atac_train \
  -t preprocessed_dataset/cisformer_rna2atac_train_dataset \
  -v preprocessed_dataset/cisformer_rna2atac_val_dataset \
  -n rna2atac_test

Predict ATAC from RNA:

cisformer rna2atac_predict \
  -r preprocessed_dataset/test_rna.h5ad \
  -m save/2025-05-12_rna2atac_test/epoch34/pytorch_model.bin \
  --species human

The output is saved as output/cisformer_predicted_atac.h5ad by default.

ATAC-to-RNA Workflow

Preprocess paired data for ATAC-to-RNA:

cisformer data_preprocess \
  -r test_data/rna.h5ad \
  -a test_data/atac.h5ad \
  -s preprocessed_dataset \
  --atac2rna \
  --species human

During ATAC-to-RNA preprocessing, Cisformer inspects the loaded RNA matrix. If the maximum expression value is <= 10, it applies log1p; if the maximum is > 10, it assumes the matrix is already normalized and skips log1p. The decision is printed to the terminal.

Train:

cisformer atac2rna_train \
  -d preprocessed_dataset/cisformer_atac2rna_train_dataset \
  -n atac2rna_test

Predict RNA from ATAC:

cisformer atac2rna_predict \
  -d preprocessed_dataset/cisformer_atac2rna_test_dataset/atac2rna_0.pt \
  -m save/2025-05-12_atac2rna_test/epoch30/pytorch_model.bin \
  --species human

The output is saved as output/cisformer_predicted_rna.h5ad by default.

Link cCREs to Genes

After training an ATAC-to-RNA model, generate cell-type-specific link matrices:

cisformer atac2rna_link \
  -d preprocessed_dataset/cisformer_atac2rna_test_dataset/atac2rna_0.pt \
  -m save/2025-05-12_atac2rna_test/epoch30/pytorch_model.bin \
  -c test_data/celltype_info.tsv \
  --species human

celltype_info.tsv is a two-column, header-free TSV:

GTACCGGGTATACTGG-1	CD14 Mono
ACTGAATGTCACCAAA-1	cDC2
AACCTTGCAAACTGTT-1	CD14 Mono

The command writes one .h5ad matrix per cell type under output/cisformer_link/. Rows are genes and columns are cCREs. Non-zero matrix entries are rank-normalized link scores: Cisformer first ranks valid attention-derived cCRE-gene scores and then writes the ranked values. Treat these as relative regulatory association scores, not raw attention values or Pearson correlations.

Pretrained Models

Pretrained models associated with the Cisformer paper are available from Zenodo: Pretrained_Models.zip.

Citation

If you use Cisformer, please cite:

Ji L, Zou Q, Tang K, Wang C. Cisformer: a scalable cross-modality generation framework for decoding transcriptional regulation at single-cell resolution. Genome Biology, 2025.

Acknowledgements

• flash-attention
• Hugging Face Accelerate
• Scanpy