# CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal

## TL;DR

CrossMP is a web-based tool that translates between single-cell RNA and chromatin data, enabling researchers to predict one modality from the other.

## Contribution

The novel contribution is a deep learning model and web portal for cross-modality translation between scRNA-seq and scATAC-seq data.

## Key findings

- The model reliably translates between scRNA-seq and scATAC-seq data across multiple human datasets.
- CrossMP provides an interactive web interface for uploading and predicting single-cell modality data.
- High-performance computing resources are used to support the translation process.

## Abstract

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.