School of Life Sciences and Chemistry, Hunan University of Technology, Hunan, China.
School of Computer Science, Hunan Institute of Technology, Hengyang, China.
J Cell Mol Med. 2024 May;28(9):e18372. doi: 10.1111/jcmm.18372.
Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.
多细胞生物与细胞活动的协调具有密切的亲和力，而细胞活动的协调严重依赖于不同细胞类型之间的通讯。细胞间通讯（CCC）通常通过配体-受体相互作用（LRIs）介导。现有的 CCC 推断方法仅限于已知的 LRIs。为了解决这个问题，我们通过整合单细胞 RNA 测序和蛋白质组数据开发了一种全面的 CCC 分析工具 SEnSCA。SEnSCA 主要包含潜在 LRI 采集和 CCC 强度评估。为了获取潜在的 LRIs，它首先提取 LRI 特征并降低特征维度，然后通过 K-means 聚类构建负 LRI 样本，最后基于包含支持向量机、1D 卷积神经网络和多头注意力机制的堆叠集成来获取潜在的 LRIs。在 CCC 强度评估过程中，SEnSCA 进行 LRI 过滤，然后结合三点估计方法和单细胞 RNA 测序数据推断 CCC。SEnSCA 在预测可能的 LRIs 时，在大多数情况下计算出更好的精度、召回率、准确性、F1 分数、AUC 和 AUPR。为了更好地说明推断的 CCC 网络，SEnSCA 提供了三种可视化选项：热图、气泡图和网络图。它在人类黑色素瘤组织中的应用证明了其在 CCC 检测中的可靠性。总之，SEnSCA 提供了一种有用的 CCC 推断工具，可在 https://github.com/plhhnu/SEnSCA 上免费获取。
