RNA 20. SCI 文章中单样本免疫浸润分析 （ssGSEA
这期讲讲单样本基因富集分析，这个也蛮有意思的之前我已经介绍过基因集富集分析（GSEA），但是当时是用差异基因来分析，这期我们就通过单基因免疫浸润的方法来介绍一下。
ssgsea<-gsva(as.matrix(expMatrix), geneSets, method = "ssgsea",min.sz = 10)ssgsea[1:3, 1:2]
## TCGA-3L-AA1B-01A-11R-A37K-07 TCGA-4N-A93T-01A-11R-A37K-07
## Activated B cell 0.4251134 0.3742325
## Activated CD4 T cell 0.5324122 0.5193579
## Activated CD8 T cell 0.6129071 0.5807931
4. 结果展示
我们可以对数据进一步的通过图形展示，包括火山图和热图，清晰的看到免疫细胞的分组。
免疫浸润火山图
对免疫细胞做差异分析，由于我们获得的矩阵是非整数型的，所以我们选择limma计算差异，如下：
DEA.ssgsea <- TCGAanalyze_DEA(mat1 = ssgsea[, group[group$Group == "NT", ]$Sample],
mat2 = ssgsea[, group[group$Group == "TP", ]$Sample], metadata = FALSE, pipeline = "limma",
Cond1type = "NT", Cond2type = "TP", fdr.cut = 0.05, logFC.cut = 2, )
免疫浸润热图
读取临床数据，并选择有效的临床信息，如下：
rowclin <- read.table("clinical.tsv", header = 1, check.names = F, sep = "\t")
clin <- data.frame(rowclin[, c(2, 4, 12, 16, 28)])
clin <- clin[!duplicated(clin$case_submitter_id), ]
head(clin)
## case_submitter_id age_at_index gender vital_status ajcc_pathologic_stage
## 1 TCGA-G4-6307 37 female Alive Stage IIIB
## 3 TCGA-AA-3522 67 male Alive Stage IIA
## 5 TCGA-AA-3530 80 male Alive Stage I
## 7 TCGA-AA-3511 64 male Alive Stage II
## 9 TCGA-AA-3842 51 male Alive Stage IIIA
## 11 TCGA-AA-3561 72 male Alive Stage IIA
colnames(ssgsea) = substr(colnames(ssgsea), 1, 12)
group$Sample = substr(group$Sample, 1, 12)
head(group)
## Sample Group
## 1 TCGA-D5-6530 TP
## 2 TCGA-G4-6320 TP
## 3 TCGA-AD-6888 TP
## 4 TCGA-CK-6747 TP
## 5 TCGA-AA-3975 TP
## 6 TCGA-A6-6780 TP
clin_group = merge(group, clin, by.x = "Sample", by.y = "case_submitter_id")
head(clin_group)
## Sample Group age_at_index gender vital_status ajcc_pathologic_stage
## 1 TCGA-3L-AA1B TP 61 female Alive Stage I
## 2 TCGA-4N-A93T TP 67 male Alive Stage IIIB
## 3 TCGA-4T-AA8H TP 42 female Alive Stage IIA
## 4 TCGA-5M-AAT4 TP 74 male Dead Stage IV
## 5 TCGA-5M-AAT6 TP 40 female Dead Stage IV
## 6 TCGA-5M-AATE TP 76 male Alive Stage IIA
table(clin_group$Group)
##
## NT TP
## 41 469
clin_group <- clin_group[!duplicated(clin_group$Sample), ]
ann_col <- clin_group[, 2:6]
rownames(ann_col) <- clin_group$Sample
# names(ann_col)=c('TNM Stage','Cluster')
head(ann_col)
## Group age_at_index gender vital_status ajcc_pathologic_stage
## TCGA-3L-AA1B TP 61 female Alive Stage I
## TCGA-4N-A93T TP 67 male Alive Stage IIIB
## TCGA-4T-AA8H TP 42 female Alive Stage IIA
## TCGA-5M-AAT4 TP 74 male Dead Stage IV
## TCGA-5M-AAT6 TP 40 female Dead Stage IV
## TCGA-5M-AATE TP 76 male Alive Stage IIA绘制火山图，并添加临床信息，如下：
pheatmap(
ssgsea[,clin_group$Sample],
show_colnames = F,
# 不展示行名
cluster_rows = T,
# 不对行聚类
cluster_cols = T,
# 不对列聚类
annotation_col = ann_col,
# 加注释
# cellwidth = 5,
#cellheight = 5,
# 设置单元格的宽度和高度
fontsize = 8
)
结果解读
从差异分析的结果图上我们能看到28种免疫细胞的差异，而热图中可将28种细胞分为三大类。都是SCI级别分析方法，今天刚好整理了一下：
RNA 2. SCI文章中基于GEO的差异表达基因之 limma
RNA 3. SCI 文章中基于T CGA 差异表达基因之 DESeq
RNA 4. SCI 文章中基于TCGA 差异表达之 edgeR
RNA 6. 差异基因表达之-- 火山图 (volcano)
RNA 7. SCI 文章中的基因表达——主成分分析 (PCA)
RNA 8. SCI文章中差异基因表达--热图 (heatmap）
RNA 12. SCI 文章中肿瘤免疫浸润计算方法之 CIBERSORT
RNA 14. SCI 文章中差异表达基因之 蛋白互作网络 (PPI)
RNA 15. SCI 文章中的融合基因之 FusionGDB2
RNA 17. SCI 文章中的筛选 Hub 基因 (Hub genes)
RNA 19. SCI 文章中无监督聚类法 （ConsensusClusterPlus）
RNA 20. SCI 文章中单样本免疫浸润分析 （ssGSEA）
References:
Bindea C, Mlecnik B,Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. lmmunity. 2013;39(4):782-95.
Charoentong P, Finotello F, Angelova M, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017;18(1):248-262. doi:10.1016/j.celrep.2016.12.019
Barbie DA, Tamayo P, Boehm JS, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108-112. doi:10.1038/nature08460
Xiao B, Liu L, Li A, et al. Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma. Front Oncol. 2020;10:607622. Published 2020 Dec 15. doi:10.3389/fonc.2020.607622
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