Hijjawi Faculty for Engineering Technology, Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid, 21163, Jordan.
Hijjawi Faculty for Engineering Technology, Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid, 21163, Jordan.
Comput Biol Med. 2024 Oct;181:109066. doi: 10.1016/j.compbiomed.2024.109066. Epub 2024 Aug 24.
The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic research by enabling the exploration of gene expression at an individual cell level. This advancement sheds light on how cells differentiate and evolve over time. Effectively classification cell types within scRNA-seq datasets are essential for understanding the intricate cell compositions within tissues and elucidating the origins of various diseases. Challenges persist in the field, emphasizing the need for precise categorization across diverse datasets, addressing aggregated cell data, and managing the complexity of high-dimensional data spaces.
XgCPred is a novel approach combining XGBoost with convolutional neural networks (CNNs) to provide cell type classification with better accuracy in single-cell RNA-seq data. This combo reveals how well CNNs can detect spatial hierarchy in gene expression images and how XGBoost performs with large volumes of data. XgCPred utilizes an imaging representation of gene expression that is based on the hierarchical organization of genes found in the KEGG BRITE database.
Rigorous testing of XgCPred across multiple scRNA-seq datasets, each presenting unique challenges such as varying cell counts, gene expression diversity, and cellular heterogeneity, has demonstrated its superiority compared to earlier methods. The algorithm shows remarkable accuracy and precision in cell type annotation, achieving near-perfect classification scores in some cases. These results underscore its capability to effectively manage data variability.
XgCPred distinguishes itself through its dependable and accurate cell type classification across a range of scRNA-seq datasets. Its effectiveness stems from sophisticated data handling and its ability to adapt to the complexities inherent in scRNA-seq data. XgCPred delivers reliable cell annotations essential for further biological analysis and research, marking a significant advancement in genomic studies. With scRNA-seq datasets growing in size and complexity, XgCPred offers a scalable and potent solution for cell type identification, potentially enhancing our understanding of cellular biology and aiding in the precise detection of diseases. XgCPred is a useful tool in genomic research and tailored therapy because it solves current constraints on computing efficiency and generalizability.
单细胞 RNA 测序（scRNA-seq）的出现彻底改变了转录组学研究，使人们能够在单个细胞水平上探索基因表达。这一进展揭示了细胞是如何随着时间的推移而分化和进化的。在 scRNA-seq 数据集中有效地对细胞类型进行分类对于理解组织内复杂的细胞组成以及阐明各种疾病的起源至关重要。该领域仍然存在挑战，强调需要在不同数据集之间进行精确分类，处理聚合细胞数据，并管理高维数据空间的复杂性。
XgCPred 是一种将 XGBoost 与卷积神经网络（CNNs）相结合的新方法，用于提供单细胞 RNA-seq 数据中具有更高准确性的细胞类型分类。该组合揭示了 CNN 如何检测基因表达图像中的空间层次结构，以及 XGBoost 如何处理大量数据。XgCPred 利用基于 KEGG BRITE 数据库中发现的基因层次结构的基因表达图像表示。
在多个 scRNA-seq 数据集上对 XgCPred 进行了严格的测试，每个数据集都提出了独特的挑战，如不同的细胞计数、基因表达多样性和细胞异质性，结果表明它优于早期的方法。该算法在细胞类型注释方面表现出了出色的准确性和精度，在某些情况下达到了近乎完美的分类分数。这些结果强调了它有效管理数据变异性的能力。
XgCPred 通过在一系列 scRNA-seq 数据集中进行可靠和准确的细胞类型分类而脱颖而出。其有效性源于复杂的数据处理和对 scRNA-seq 数据固有复杂性的适应能力。XgCPred 提供了可靠的细胞注释，这对于进一步的生物学分析和研究至关重要，标志着基因组研究的重大进展。随着 scRNA-seq 数据集的规模和复杂性的增加，XgCPred 为细胞类型识别提供了一个可扩展且强大的解决方案，可能增强我们对细胞生物学的理解，并有助于精确检测疾病。XgCPred 是基因组研究和个体化治疗的有用工具，因为它解决了计算效率和通用性方面的当前限制。
