单样本基因集富集分析（ssGSEA）

实例

Immune infiltration analysis based on single-sample geneset enrichment analysis (ssGSEA) scores

To investigate the immune infiltration landscape of bladder cancer, ssGSEA was performed to assess the level of immune infiltration (recorded as ssGSEA score) in a sample according to the expression levels of immune cell-specific marker genes. Marker genes for most immune cell types were obtained from the article published by Bindea et al (16). Marker genes for M1 macrophages, M2 macrophages, myeloid-derived suppressor cells (MDSCs) and Tregs were obtained from published studies (10,14,17–25). The ssGSEA analysis was performed based on GenePattern environment (26). To run ssGSEA online analysis (https://cloud.genepattern.org), gene expression dataset file (GCT file), immune marker gene set file (GMT file), and other parameters were uploaded as a set. Finally, the ssGSEA scores, representing infiltration levels of immune cells for individual samples, were presented in the output file.

Immune infiltration analysis based on cell type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) method

The CIBERSORT analytical tool was developed to analyze the 22 distinct leukocyte subsets in the tumors based on bulk transcriptome data (27). CIBERSORT (https://cibersort.stanford.edu/) was employed to analyze the immune landscape of bladder cancer microenvironment based on the TCGA RNA-seq dataset. The TCGA RNA-seq dataset was used as the gene expression input and LM22 (22 immune cell types) was set as the signature gene file. The analysis was conducted with 1,000 permutations. The CIBERSORT values generated were defined as immune cell infiltration fraction per sample.

基于单样本基因集富集分析（ssGSEA）分数的免疫浸润分析 为了研究膀胱癌的免疫浸润情况，根据免疫细胞特异性标记基因的表达水平，对ssGSEA进行了评估，以评估样品中的免疫浸润水平（记录为ssGSEA评分）。 ssGSEA分析是基于GenePattern环境进行的（26）。为了运行ssGSEA在线分析（https://cloud.genepattern.org），将基因表达数据集文件（GCT文件），免疫标记基因集文件（GMT文件）和其他参数作为一组上传。最后，代表单个样品的免疫细胞浸润水平的ssGSEA分数显示在输出文件中。

基于细胞类型识别的免疫浸润分析，方法是估计已知RNA转录本的相对子集（CIBERSORT）方法 开发了CIBERSORT分析工具，可基于大量转录组数据分析肿瘤中的22个不同的白细胞亚群。基于TCGA RNA-seq数据集，使用CIBERSORT（https://cibersort.stanford.edu/）来分析膀胱癌微环境的免疫状况。 TCGA RNA-seq数据集用作基因表达输入，并将LM22（22种免疫细胞类型）设置为签名基因文件。以1,000个排列进行分析。产生的CIBERSORT值定义为每个样品的免疫细胞浸润分数