Evaluation of classification in single cell atac-seq data with machine learning methods

The technologies advances of single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) allowed to generate thousands of single cells in a relatively easy and economic manner and it is rapidly advancing the understanding of the cellular composition of complex organisms and tissues. The data structure and feature in scRNA-seq is similar to that in scATAC-seq, therefore, it's encouraged to identify and classify the cell types in scATAC-seq through traditional supervised machine learning methods, which are proved reliable in scRNA-seq datasets.

In this study, we evaluated the classification performance of 6 well-known machine learning methods on scATAC-seq. A total of 4 public scATAC-seq datasets vary in tissues, sizes and technologies were applied to the evaluation of the performance of the methods. We assessed these methods using a 5-folds cross validation experiment, called intra-dataset experiment, based on recall, precision and the percentage of correctly predicted cells. The results show that these methods performed well in some specific types of the cell in a specific scATAC-seq dataset, while the overall performance is not as well as that in scRNA-seq analysis. In addition, we evaluated the classification performance of these methods by training and predicting in different datasets generated from same sample, called inter-datasets experiments, which may help us to assess the performance of these methods in more realistic scenarios.

Both in intra-dataset and in inter-dataset experiment, SVM and NMC are overall outperformed others across all 4 datasets. Thus, we recommend researchers to use SVM and NMC as the underlying classifier when developing an automatic cell-type classification method for scATAC-seq.