Predicting Heart Cell Types by Using Transcriptome Profiles and a Machine Learning Method.

Shijian Ding,Lei Chen,Tao Huang,Xianchao Zhou,Xianling Xu,Kaiyan Feng,Deling Wang,Yudong Cai,Zhandong Li

doi:10.3390/life12020228

Abstract

The heart is an essential organ in the human body. It contains various types of cells, such as cardiomyocytes, mesothelial cells, endothelial cells, and fibroblasts. The interactions between these cells determine the vital functions of the heart. Therefore, identifying the different cell types and revealing the expression rules in these cell types are crucial. In this study, multiple machine learning methods were used to analyze the heart single-cell profiles with 11 different heart cell types. The single-cell profiles were first analyzed via light gradient boosting machine method to evaluate the importance of gene features on the profiling dataset, and a ranking feature list was produced. This feature list was then brought into the incremental feature selection method to identify the best features and build the optimal classifiers. The results suggested that the best decision tree (DT) and random forest classification models achieved the highest weighted F1 scores of 0.957 and 0.981, respectively. The selected features, such as NPPA, LAMA2, DLC1, and the classification rules extracted from the optimal DT classifier played a crucial role in cardiac structure and function in recent research and enrichment analysis. In particular, some lncRNAs (LINC02019, NEAT1) were found to be quite important for the recognition of different cardiac cell types. In summary, these findings provide a solid academic foundation for the development of molecular diagnostics and biomarker discovery for cardiac diseases.

Highlights

Introduction distributed under the terms andThe heart is a complex organ containing various cardiac cell types, and the interaction between different heart cell types could realize the important functions of the heart.Previous pioneering studies have shown that the heart is composed of approximately70% non-cardiomyocytes and 30% cardiomyocytes [1]
Single cell expression profiles of 451,513 cell samples and 11 cell types for heart disease were obtained, and each sample of heart cell type was represented by the expression of 33,537 genes
An optimum decision tree (DT) classifier can be built with these features

Summary

Introduction

Introduction distributed under the terms andThe heart is a complex organ containing various cardiac cell types, and the interaction between different heart cell types could realize the important functions of the heart.Previous pioneering studies have shown that the heart is composed of approximately70% non-cardiomyocytes and 30% cardiomyocytes [1]. Into atrial myocytes and ventricular myocytes, while non-cardiomyocytes mainly include fibroblasts, smooth muscle cells, pericytes, and endothelial cells. These cells form four chambers with different morphologies and functions, and they complete the systemic blood circulation [2]. Fibroblasts account for more than 40% of the total cells in the ventricle Their core function is to maintain the cardiac extracellular matrix homeostasis and provide structural and mechanical support for the cardiomyocytes [3]. The mural cells of the vessel wall are mainly composed of smooth muscle cells and pericytes, and these two cell types are important for vascular integrity and heart function [4]. Immune cells and neurons are very important for the functional homeostasis [10,11]

Methods

Results

Discussion

Conclusion