AtacAnnoR: a reference-based annotation tool for single cell ATAC-seq data

annATAC: automatic cell type annotation for scATAC-seq data based on language model

BACKGROUND

Cell type annotation serves as the cornerstone for downstream analysis of single cell data. Nevertheless, scATAC-seq data is characterized by high sparsity and dimensionality, presenting significant challenges to its annotation process.

RESULTS

We introduce a novel method based on language model, named annATAC, which is designed for the automatic annotation of cell types in scATAC-seq data. This method primarily consists of three stages. During the pre-training stage, by training on a vast amount of unlabeled data, the model can learn the interaction relationships between peaks, thus building a preliminary understanding of the data features. Subsequently, in the fine-tuning stage, a small quantity of labeled data is utilized to conduct secondary training on the model, which enables the model to identify cell types accurately. Finally, in the prediction stage, the trained model is applied to annotate scATAC-seq data.

CONCLUSIONS

Compared with other automatic annotation methods across multiple datasets, annATAC demonstrates superiority on the annotation performance. Further experiments have validated that annATAC holds great potential in identifying marker peaks and marker motifs. It is expected that annATAC will provide more profound and precise analysis outcomes for scATAC-seq research. As a result, it will effectively promote the progress of relevant biomedical research.

HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data

Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.