Single-cell dataset of renal cell carcinoma
Description
We collected the single-cell transcriptome data of patients receiving ICB treatments, including ICB treatment-naïve tumor samples, ICB-resistant samples (post treatments) and ICB-sensitive samples for the investigation of ICB-resistant mechanisms. Besides, we also included adjacent normal tissues and PBMC samples for comparison. Taken together, we obtained a total of 6 adjacent normal, 9 ICB treatment-naïve tumor, 5 ICB-sensitive tumor, 4 ICB-resistant tumor and 2 PBMC samples. We used the Cell Ranger software (Version 7.0.0) with default settings to align and quantify single-cell transcriptome data in FASTQ format published by 10x Genomics against the GRCh38 human reference genome[67]. The Cell Ranger software's quantified count matrix was loaded into the Seurat tool (Version 4.1.1) for further analyses.The cells were then subjected to quality control. Basically, cells with fewer than 500 identified genes and those with greater than 10% mitochondrial content were eliminated. To further exclude probable doublets, cells containing more than 8000 identified genes were discarded. Possible doublets predicted by the DoubletFinder software were eliminated so as not to impede the analyses. After filtering, samples containing fewer than 500 cells were deemed of poor quality and eliminated. More than 1000 thousand cells of good quality were preserved for further analysis. All individual high-quality single-cell samples were then curated into a single object and to eliminate batch effects. The dimensionality of this dataset was reduced through principal component analysis (PCA) with highly variable features, and the first 15 PCs were selected for investigation. Then, unsupervised clustering was approximated using the shared nearest-neighbor network produced by the Louvain algorithm and the edge weights between any two cells. Using the t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) methods, the identified clusters were displayed. We identified the differentially expressed markers of the resultant clusters and used the default nonparametric test, the Wilcoxon rank sum test with Bonferroni correction, to label the cell clusters.