Concept

Cisformer is a Transformer-based framework for cross-modality generation and regulatory inference in single-cell RNA-seq and single-cell ATAC-seq data.

What Cisformer Does

Cisformer supports three related tasks:

RNA-to-ATAC generation: predict chromatin accessibility from scRNA-seq.
ATAC-to-RNA generation: predict gene expression from scATAC-seq.
cCRE-gene link inference: use ATAC-to-RNA cross-attention to infer cell-type-specific regulatory associations between cis-regulatory elements and genes.

Model Rationale

Single-cell multiome assays can jointly measure gene expression and chromatin accessibility, but paired profiling is more expensive and experimentally constrained than single-modality assays. Cisformer addresses this by learning the translation between transcriptome and chromatin accessibility profiles.

The method uses a decoder-only Transformer design with cross-attention. This architecture avoids compressing genes or peaks into a low-dimensional latent space before translation, which helps preserve feature-level interpretability. For ATAC features, Cisformer uses an index encoding strategy to handle very long peak vocabularies efficiently.

Interpreting ATAC-to-RNA Links

The ATAC-to-RNA model predicts gene expression from accessible cCREs. During link generation, Cisformer extracts cross-attention-derived scores for cCRE-gene pairs within the configured genomic distance window and reports cell-type-specific matrices.

The output values from atac2rna_link are rank-normalized link scores. They are derived by ranking valid attention scores and writing the ranked values to a sparse gene-by-cCRE matrix. They should be interpreted as relative association strengths within the generated matrix, not as raw attention probabilities or Pearson correlation coefficients.

Species Support

Version 1.1.0 supports:

human: 38,244 genes and 1,033,239 cCREs in the bundled reference.
mouse: 23,234 genes and 262,853 cCREs in the bundled reference.

Use the same --species value when generating configs, preprocessing data, running prediction, and generating links. Model checkpoints and configs are not interchangeable across species unless they were trained with matching reference vocabularies.