scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding

Abstract

Single-cell Hi-C data are a common data source for studying the differences in the three-dimensional structure of cell chromosomes. The development of single-cell Hi-C technology makes it possible to obtain batches of single-cell Hi-C data. How to quickly and effectively discriminate cell types has become one hot research field. However, the existing computational methods to predict cell types based on Hi-C data are found to be low in accuracy. Therefore, we propose a high accuracy cell classification algorithm, called scHiCStackL, based on single-cell Hi-C data. In our work, we first improve the existing data preprocessing method for single-cell Hi-C data, which allows the generated cell embedding better to represent cells. Then, we construct a two-layer stacking ensemble model for classifying cells. Experimental results show that the cell embedding generated by our data preprocessing method increases by 0.23, 1.22, 1.46 and 1.61$\%$ comparing with the cell embedding generated by the previously published method scHiCluster, in terms of the Acc, MCC, F1 and Precision confidence intervals, respectively, on the task of classifying human cells in the ML1 and ML3 datasets. When using the two-layer stacking ensemble framework with the cell embedding, scHiCStackL improves by 13.33, 19, 19.27 and 14.5 over the scHiCluster, in terms of the Acc, ARI, NMI and F1 confidence intervals, respectively. In summary, scHiCStackL achieves superior performance in predicting cell types using the single-cell Hi-C data. The webserver and source code of scHiCStackL are freely available at http://hww.sdu.edu.cn:8002/scHiCStackL/ and https://github.com/HaoWuLab-Bioinformatics/scHiCStackL, respectively.

Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning

“…Stacking architecture has been demonstrated as a reliable technique for enhancing the predictive performance of bioinformatics tools, such as the identification of noncoding RNAs, prediction of IL-6 inducing peptides, and single-cell Hi-C classification . It consists of two stages of learning.…”

“…Stacking architecture has been demonstrated as a reliable technique for enhancing the predictive performance of bioinformatics tools, such as the identification of noncoding RNAs, 51 prediction of IL-6 inducing peptides, 52 and single-cell Hi-C classification. 53 It consists of two stages of learning. In the first stage, a series of heterogeneous learning algorithms, commonly termed base learners, directly take input from the chosen optimal features.…”

Mapping nucleosome and chromatin architectures: A survey of computational methods

“…Quality control of sequencing data is crucial to avoid technical artifacts. Despite of these challenges, new sets of computational methods have been developed for processing scHi-C data to reconstruct single-cell 3D chromatin structures [107] , [108] , [109] , to impute the chromosome contact matrices [110] , [111] , [112] , to identify TAD-like domains [113] , to classify single cells [114] , to identify chromatin loops [115] , and to provide toolbox of scHi-C [116] .…”

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

“…The emergence of single-cell Hi-C technology has revolutionized the study of chromatin’s three-dimensional structure 20 . The ability to determine cell cycle phases from single-cell Hi-C data is critical for analyzing and comprehending changes in chromatin’s spatial structure during various cell cycle phases.…”