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    单样本基因集富集分析(ssGSEA)

    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.

    03

    你不会以为它们的免疫评分都是自己算的吧

    但是接下来大家又想问,同样的想比较两个分组的免疫评分的差异,但是免疫评分的工具太多了,比如有一个2019的综述文章:《Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology》比较了常见的免疫细胞比例推断工具的表现,另外一个2018的综述《Quantifying tumor-infiltrating immune cells from transcriptomics data》提到工具更多,起码十几款了。大家也不可能一一研读,下载,测试,使用它。但是又确实看到了大量数据挖掘文章都使用了这些免疫评分信息啊,比如:新鲜出炉(2021年6月)的文章:《Identification of a Ferroptosis- Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma》 ,就对比了 ESTIMATE, TIMER, MCP counter, CIBERSORTx,和ssGSEA ,如下所示:

    02

    RNAseq|WGCNA-组学数据黏合剂,代码实战-一(尽)文(力)解决文献中常见的可视化图

    WGCNA (weighted gene co-expression network analysis)权重基因共表达网络分析(流程模块见下图),可将表达模式相似的基因进行聚类,并分析模块与特定性状或表型之间的关联,常用于筛选关键表型的hub基因 ,是RNAseq分析中的一块很重要的拼图。而之所以叫组学数据黏合剂是因为表型可以是患者的临床信息(生存信息,分期信息,基线信息等),可以是重测序信息肿瘤(驱动基因的变异与否,signature ,CNV信息等),可以是转录组结果(免疫浸润,risk score ,GSVA ,分子分型结果),可以是单细胞数据(celltype ,AUCell 打分)等等 。注:这些在公众号之前的文章中大多都有涉及,文末有部分链接。

    02

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