Single-cell VIPER activity matrix (internal signature)

Single-cell protein activity was computed on the SCTransform-scaled and Anchor-Integrated gene expression signatures across metaCells by the metaVIPER function in the VIPER package (Bioconductor). Briefly, metaVIPER was developed as an adaptation of VIPER to single-cell data. Protein activity is inferred for a given gene expression signature using multiple networks which are integrated on a protein-by-protein basis using the square of the NES generated by each individual network. Since a non-relevant network would generate a protein activity score close to zero under the null model, networks that generate more extreme NES can be interpreted to more accurately match the given biological context and are thus weighted more heavily for each protein. VIPER-inferred protein activity was computed on the gene expression signatures of all the single cells using the gene expression cluster-based single-cell ARACNe networks, and on the gene expression signatures of the tumor compartment single cells using the six patient-specific tumor single-cell ARACNe networks. The VIPER matrix includes all significant Master Regulators (MR).本研究借助VIPER包（Bioconductor）中的metaVIPER函数，针对跨元细胞（metaCells）层面经SCTransform标准化缩放且经Anchor整合的基因表达特征，计算单细胞蛋白活性。简要而言，metaVIPER是适配单细胞数据分析场景的VIPER算法改进版本。对于给定的基因表达特征，蛋白活性可通过多组网络进行推断；各组网络以单个蛋白为单位，通过各独立网络生成的归一化富集得分（Normalized Enrichment Score，NES）的平方完成整合。在零假设模型下，非相关网络生成的蛋白活性得分会趋近于0，因此能够生成更极端NES的网络，可被认定为更匹配当前生物学背景，故而会为对应蛋白赋予更高的权重。本研究使用两类ARACNe网络计算VIPER推断的蛋白活性：其一为基于基因表达聚类的单细胞ARACNe网络，用于处理所有单细胞的基因表达特征；其二为6种患者特异性肿瘤单细胞ARACNe网络，仅针对肿瘤区室单细胞的基因表达特征进行计算。最终生成的VIPER矩阵包含所有具有统计学显著性的主调控因子（Master Regulators，MR）。