Department of Computer Science and Engineering, Tezpur University, Napaam, Tezpur 784028, Assam, India.
J Biosci. 2024;49.
Single-cell RNA sequencing (scRNA-Seq) technology provides the scope to gain insight into the interplay between intrinsic cellular processes as well as transcriptional and behavioral changes in gene-gene interactions across varying conditions. The high level of scarcity of scRNA-seq data, however, poses a significant challenge for analysis. We propose a complete differential co-expression (DCE) analysis framework for scRNA-Seq data to extract network modules and identify hub-genes. The performance of our method has been shown to be satisfactory after validation using an scRNA-Seq esophageal squamous cell carcinoma (ESCC) dataset. From comparison with four other existing hub-gene finding methods, it has been observed that our method performs better in the majority of cases and has the ability to identify unique potential biomarkers that were not detected by the other methods. The potential biomarker genes identified by our framework, differential co-expression analysis method for single-cell RNA sequencing data (scDiffCoAM), have been validated both statistically and biologically.
单细胞 RNA 测序 (scRNA-Seq) 技术提供了一种深入了解内在细胞过程以及基因-基因相互作用中转录和行为变化的机会，这些变化发生在不同的条件下。然而，scRNA-Seq 数据的高度稀缺性给分析带来了重大挑战。我们提出了一个完整的差异共表达 (DCE) 分析框架，用于 scRNA-Seq 数据，以提取网络模块和识别枢纽基因。使用 scRNA-Seq 食管鳞状细胞癌 (ESCC) 数据集进行验证后，表明我们的方法具有令人满意的性能。与其他四种现有的枢纽基因发现方法进行比较后，我们发现我们的方法在大多数情况下表现更好，并且能够识别其他方法未检测到的独特潜在生物标志物。我们的框架确定的潜在生物标志物基因，单细胞 RNA 测序数据的差异共表达分析方法 (scDiffCoAM)，已经在统计和生物学上得到了验证。
