【00】

GEO数据库。与后面差不多。

【PCA，KEGG，GO，火山图等】

> getwd()
[1] "D:/R_code/follow_practice/xuetu_GEO_follow/week_practise/01_follow_practise/00_TNBC_GSE76275"
> dir()
 [1] "exprSet_by_group.Rdata"        "finalSet.Rdata"               
 [3] "GPL570.annot.gz"               "GSE76275_eSet.Rdata"          
 [5] "GSE76275_series_matrix.txt.gz" "heatmap_top100_logFC.png"     
 [7] "ID2gene.Rdata"                 "kegg_up_down.png"             
 [9] "nrDEG.out"                     "nrDEG_by_logFC.Rdata"         
[11] "pca_plot.png"                  "readme.txt"                   
[13] "Step01_getGEO.R"               "step02_getmarix.R"            
[15] "step03_gene_symbol.R"          "step04_rm_nogene.R"           
[17] "step05_PCA.R"                  "step06_DEG.R"                 
[19] "step07_pheatmap.R"             "Step08_Volcano_plot.R"        
[21] "step09_KEGG_GO.R"              "TNBC_breastcancer.Rproj"      
[23] "volcano.png"    

【1】.02_GSE108565【比较完整】

.GEO：【hclust富集分析，DEG（limma包），volcano，heatmap，KEGG，GO分析】

dir()
 [1] "02_GSE108565.Rproj"          
 [2] "dotplot_gene_diff_BP.png"    
 [3] "dotplot_gene_diff_CC.png"    
 [4] "dotplot_gene_diff_MF.png"    
 [5] "dotplot_gene_down_BP.png"    
 [6] "dotplot_gene_down_CC.png"    
 [7] "dotplot_gene_down_MF.png"    
 [8] "dotplot_gene_up_BP.png"      
 [9] "dotplot_gene_up_CC.png"      
[10] "dotplot_gene_up_MF.png"      
[11] "final_exprSet.Rdata"         
[12] "go_enrich_results.Rdata"     
[13] "gset.Rdata"                  
[14] "hclust.png"                  
[15] "heatmap.png"                 
[16] "kegg_up_down.png"            
[17] "nrDEG.out"                   
[18] "nrDEG.Rdata"                 
[19] "pca_plot.png"                
[20] "step01_download.R"           
[21] "step02_handle_data.R"        
[22] "step03_DEG_heatmap_volcano.R"
[23] "step04_KEGG_GO.R"            
[24] "volcano.png"   

【2】.UCSCXenaTools

【使用UCSCXenaTools下载TCGA数据并处理】

dir()
 [1] "01_UCSCXenaTools_download.R"       
 [2] "02_UCSCXenaTools_.R"               
 [3] "03_TCGA-BRCA.Rproj"                
 [4] "GDCdata"                           
 [5] "MANIFEST.txt"                      
 [6] "race_sample.Rdata"                 
 [7] "step01_download_handle.R"          
 [8] "TCGA-BRCA.GDC_phenotype_file.Rdata"
 [9] "TCGA-BRCA.GDC_phenotype_file.tsv"  
[10] "TCGA-BRCA.htseq_counts.Rdata"      
[11] "TCGA-BRCA.htseq_counts.tsv"        
[12] "必读.txt"    

 getwd()
[1] "D:/R_code/follow_practice/xuetu_GEO_follow/week_practise/01_follow_practise/03_TCGA-BRCA"

【3】下载合并TCGA文件

【合并文件（★），lncRNA,miRNA的提取，相关注释】

【未完成：①ncRNA的表达谱标准化一下，再自行下载microRNA的数据，就可以构建ceRNA的网络 ②按照pvalue和fc来排序，选择自己一定数量的基因，数量你来定，最终得到基因列表gene(我没有演示，需要自己做)】

【此处GO分析和KEGG分析似乎与TCGA不太一样，后面再看看】

dir()
 [1] "02_GTF_mRNA_ncRNA.R"                       
 [2] "03_DESeq.R"                                
 [3] "04_01_DIY.R"                               
 [4] "04_02_DIY.R"                               
 [5] "04_GO_KEGG.R"                              
 [6] "04_test.Rproj"                             
 [7] "BRCA_DEG.xls"                              
 [8] "dds_DEseq.Rda"                             
 [9] "expr_df.Rda"                               
[10] "expr_df_nopoint.Rda"                       
[11] "gdc_download_20210717_084045.768266"       
[12] "gdc_download_20210717_084045.768266.tar.gz"
[13] "gdc_manifest_20210717_083623.txt"          
[14] "gtf_df.Rda"                                
[15] "Homo_sapiens.GRCh38.104.chr.gtf"           
[16] "Homo_sapiens.GRCh38.104.chr.gtf.gz"        
[17] "Homo_sapiens.GRCh38.104.chr.gtf0000"       
[18] "LuminalABvsNormal_FC6.TSS.pdf"             
[19] "MANIFEST.txt"                              
[20] "metadata.cart.2021-07-17.json"             
[21] "metadata.Rda"                              
[22] "mRNA_exprSet.Rda"                          
[23] "mRNA_exprSet_vst.Rda"                      
[24] "readme.txt"                                
[25] "resSig.Rdata"                              
[26] "result.Rda"                                
[27] "TCGA-BRCA.htseq_counts.tsv"                
[28] "TCGA-BRCA.htseq_counts.tsv.gz"             
[29] "volcano.png"                               



 getwd()
[1] "D:/R_code/follow_practice/xuetu_GEO_follow/week_practise/01_follow_practise/04_test"

【4】这里是比较TP53的。跟【3】有类似之处。可以结合起来看。

【亮点：先将火山图和热图的代码用函数包装起来，然后，进行limma或edge进行差异分析，最后再画图】【KEGG和GO富集分析也是同样的】

【这里的函数包可以直接调用到别处】

【中间问题：】

  ggsave( volcano, filename = './fig/volcano.png' ))

###出现报错，运行【1】同样的代码，未报错，原因未明

报错图片

dir()
[1] "data"                 "fig"                 
[3] "nrDEG.Rdata"          "raw_data"            
[5] "step01_downpackage.R" "step02_download.R"   
[7] "step03_DEG.R"         "step04_KEGG_GO.R"    
[9] "TP53_BACR.Rproj"     
> dir()
[1] "data"                 "fig"                 
[3] "nrDEG.Rdata"          "raw_data"            
[5] "step01_downpackage.R" "step02_download.R"   
[7] "step03_DEG.R"         "step04_KEGG_GO.R"    
[9] "TP53_BACR.Rproj"     
> getwd()
[1] "D:/R_code/follow_practice/xuetu_GEO_follow/week_practise/01_follow_practise/01_TP53_BRCA"

【05】

【shell】cd/d D:

D:\R_code\follow_practice>cd/d xuetu_GEO_follow/week_practise/01_follow_practise/05_GBM_GSE4290/shell

【06】

GEO数据挖掘-第三期-口腔鳞状细胞癌（OSCC）

【绘制进化树 WGCNA】【构建共表达矩阵】【TOM图】

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2021.7.23 22:30

生存分析

以下内容重要

【生存分析】

存在问题：用包下载的方式和直接网上下载的存在差异。读取有异常。因此建议直接网上下载在分析。

R基于TCGA数据画生存曲线

##来源
"D:/R_code/follow_practice/xuetu_GEO_follow/week_practise/02_follow/02_TCGA_KM_KIRC"

后面分析内容