iCpG-Pos: an accurate computational approach for identification of CpG sites using positional features on single-cell whole genome sequence data

Abstract

Motivation: The investigation of DNA methylation can shed light on the processes underlying human well-being and help determine overall human health. However, insufficient coverage makes it challenging to implement single-stranded DNA methylation sequencing technologies, highlighting the need for an efficient prediction model. Models are required to create an understanding of the underlying biological systems and to project single-cell (methylated) data accurately.

Results: In this study, we developed positional features for predicting CpG sites. Positional characteristics of the sequence are derived using data from CpG regions and the separation between nearby CpG sites. Multiple optimized classifiers and different ensemble learning approaches are evaluated. The OPTUNA framework is used to optimize the algorithms. The CatBoost algorithm followed by the stacking algorithm outperformed existing DNA methylation identifiers.

Availability and implementation: The data and methodologies used in this study are openly accessible to the research community. Researchers can access the positional features and algorithms used for predicting CpG site methylation patterns. To achieve superior performance, we employed the CatBoost algorithm followed by the stacking algorithm, which outperformed existing DNA methylation identifiers. The proposed iCpG-Pos approach utilizes only positional features, resulting in a substantial reduction in computational complexity compared to other known approaches for detecting CpG site methylation patterns. In conclusion, our study introduces a novel approach, iCpG-Pos, for predicting CpG site methylation patterns. By focusing on positional features, our model offers both accuracy and efficiency, making it a promising tool for advancing DNA methylation research and its applications in human health and well-being.

Figure 1.

Cell-wise methylated and non-methylated, training, and testing data size visualization of HCCs and HepG2 datasets.

Figure 2.

iCpG-Pos development flowchart in schematic form. It comprises of dataset creation, feature extraction, baseline model building, and stacking-predictor development.

Figure 3.

Visual illustration for extraction of positional features.

Figure 4.

Evaluation of different feature extraction and classification techniques on two adopted benchmark datasets HCCs (a) and HepG2 (b). The box plot shows the average values and the variation over of all cells in the dataset. Acronyms are; Random Forest (RF), AdaBoost(AB); eXtreme Gradient Boosting (XGB), Sensitivity (Sn), Specificity(Sp), Accuracy (ACC), Matthews correlation coefficient (MCC), and Area Under the Receiver Operating Characteristic Curve (AUC).

Figure 5.

t-distributed stochastic neighbor embedding (t-SNE) positional feature distribution of CpG and Non-CpG sites.

Figure 6.

Using the HCCs (a) and HepG2 (b) dataset to illustrate the distribution of evaluation metrics. “L” stands for our LightCpG, “Z” stands for Zhang’s method, “PC” represents proposed algorithm with CatBoost, and “PS” represents proposed algorithm with Stacking framework.