dplyr::select(c("Chromosome","Start_Position","End_Position","Variant_Classification","Hugo_Symbol")) %>% distinct_all() %>% { #将 typeNames 作为全局变量,后面还要使用 typeNames <<- unique(.$Variant_Classification) type = structure(1:length(typeNames), names = typeNames) mutate(., typ...
Strand: 染色体链的方向 Variant_Classification: 变异的分类,如突变、插入、删除等 Variant_Type: 变异的类型,如单核苷酸多态性(SNP)、插入或缺失等 Reference_Allele: 变异前的等位基因 Tumor_Seq_Allele1: 肿瘤细胞中检测到的等位基因1 Tumor_Seq_Allele2: 肿瘤细胞中检测到的等位基因2 HGVSc: HGVS基因组级别的...
Variant_Classification: 变异的分类,如突变、插入、删除等Variant_Type: 变异的类型,如单核苷酸多态性(SNP)、插入或缺失等Reference_Allele: 变异前的等位基因Tumor_Seq_Allele1: 肿瘤细胞中检测到的等位基因1Tumor_Seq_Allele2: 肿瘤细胞中检测到的等位基因2HGVSc: HGVS基因组级别的转录本描述HGVSp: HGVS蛋白质...
AI代码解释 source("https://bioconductor.org/biocLite.R")chooseBioCmirror()#选择一个就近的镜像biocLite("maftools")#安装(maftools:http://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html)library(maftools)Mandatoryfields(必须字段):Hugo_Symbol,Chromosome,Start_Position,End_Po...
Mandatory fields(必须字段): Hugo_Symbol, Chromosome, Start_Position, End_Position, Reference_Allele, Tumor_Seq_Allele2, Variant_Classification, Variant_Type and Tumor_Sample_Barcode(样本名,此字段沟通样本的maf文件和临床信息的关键,前者).laml = read.maf(maf="STAD.mutectAdjustBarcode.maf.txt",...
##41PRKCZ5590BCMGRCh38 chr12150972##51WRAP7349856BCMGRCh38 chr13635995##61CHD526038BCMGRCh38 chr16142440##71CAMTA123261BCMGRCh38 chr17663513##81ERRFI154206BCMGRCh38 chr18014193##91SLC2A7155184BCMGRCh38 chr19022922##101PGD5226BCMGRCh38 chr110411462## End_Position Strand Variant_Classification...
在Variant_Classification这一列中,有一些3'UTR / 5'UTR这样的类型,但是在使用read.table()读取的时候竟然识别不出来! 小丑竟是我自己! 3'UTR识别出错 这才是正确的 所以直接导致遇到这个之后的所有行都是错位的,而且少了非常多行。 生气啊! 但是找到问题之后解决就非常简单,换个函数就行了,我们直接用data.tab...
10 Variant_Classification 11 Variant_Type 12 Reference_Allele 13 Tumor_Seq_Allele1 14 Tumor_Seq_Allele2 15 dbSNP_RS 16 dbSNP_Val_Status 17 Tumor_Sample_Barcode 18 Matched_Norm_Sample_Barcode 19 Match_Norm_Seq_Allele1 20 Match_Norm_Seq_Allele2 ...
ifelse(!(Variant_Classification), "Mut","WILDorNOINFO")) -> BRCA_OV.clinical_mutations BRCA_OV.clinical_mutations %>% select(times, patient.vital_status, disease, TP53) -> BRCA_OV.2plot kmTCGA( BRCA_OV.2plot, explanatory.names = c("TP53", "disease"), ...
ifelse(!(Variant_Classification), "Mut","WILDorNOINFO")) -> BRCA_OV.clinical_mutations BRCA_OV.clinical_mutations %>% select(times, patient.vital_status, disease, TP53) -> BRCA_OV.2plot kmTCGA( BRCA_OV.2plot, explanatory.names = c("TP53", "disease"), ...