transcript counts_GBM T cells_scRNAseq.xlsx

Raw transcript counts of GBM T cells from 3 patient GBM tissue samples (BT20, BT23 and BT26).<br>Using R version 3.6.3 and Seurat version 3.1.5, cells with (i) fewer than 200 genes, (ii) gene numbers outside ±2 standard deviations from the mean, or (iii) a mitochondrial gene fraction greater than 10% were excluded. Remaining cells were log normalised by total expression and scaled to 10,000 transcripts/cell with the NormalizeData function in Seurat. Using the FindIntegrationAnchors (dims = 1:30, k.filter = 200) and IntegrateData (dims = 1:30) functions, cells from different libraries were combined by assessing the pairwise correspondence between a set of representative genes (anchors). The FindVariableGenes function was used to identify variable genes returning 2000 features using vst as the selection method. The data were then scaled and principal component analysis applied using the ScaleData and RunPCA functions respectively. Cells were clustered based on gene expression profiles using the FindNeighbors and FindClusters functions with resolution set to 0.5. <br>

来自3例患者胶质母细胞瘤（Glioblastoma, GBM）组织样本（BT20、BT23与BT26）的T细胞原始转录本计数。
使用R 3.6.3版本与Seurat 3.1.5版本开展数据分析，过滤掉满足以下任一条件的细胞：(i) 检测到的基因数少于200个；(ii) 基因表达量偏离平均值±2倍标准差范围；(iii) 线粒体基因占比超过10%。对剩余细胞基于总表达量进行对数归一化，并通过Seurat的NormalizeData函数将每个细胞的转录本数标准化至10000条。
调用FindIntegrationAnchors（参数设置为dims=1:30、k.filter=200）与IntegrateData（参数设置为dims=1:30）函数，通过评估一组代表性基因（锚点）间的成对对应关系，整合不同测序文库的细胞数据。使用FindVariableGenes函数，以vst作为筛选方法，识别得到2000个可变特征基因。随后通过ScaleData函数对数据进行标准化处理，并借助RunPCA函数执行主成分分析。基于基因表达谱，使用FindNeighbors与FindClusters函数对细胞进行聚类，聚类分辨率设置为0.5。