# Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation

## TL;DR

This paper introduces a new method to detect unknown cell types in single-cell chromatin data using advanced machine learning techniques.

## Contribution

OVAAnno is a novel method that detects unknown cell types in single-cell chromatin accessibility data using open-set domain adaptation.

## Key findings

- OVAAnno successfully identifies both known and unknown cell types in scATAC-seq data.
- The method also performs well on scRNA-seq data in additional experiments.

## Abstract

Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https://github.com/lisaber/OVAAnno/tree/master.