# # # R代码从装饰图案的装饰图案来源。Rnw“# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块1号:开始# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #库(REBET) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块2号:基因族群文件# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # genofile < -系统。文件(“sampleData”、“geno_impute.txt。广州”,包= " REBET”)子文件< -系统。文件(“sampleData”、“subjects.txt。广州”,包= " REBET”) phenofile < -系统。文件(“sampleData”、“pheno.txt。广州”,包= " REBET”) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块3号:把数据# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # < -读取的数据。表(phenofile头= 1,9 = \ t)数据[1:5],# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块数量4:添加虚拟var # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #数据(,“男性”)< -。数字(数据(,“性别”)% %“男性”)# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块5号:次区域# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #条件< - rbind (c (87654800, 87661050), c (87661051, 87668870), c (87668871, 87671945), c (87671946, 87673200)) rownames(条件)< - - - - - -粘贴(“SR”, 1:4, 9 = " ")条件# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块6号:min马克斯pos # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # min.loc < - min(条件)马克斯。loc < - max(条件)# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块7号:主题文件# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #基因族群。潜艇< -扫描(子文件,=“性格”)# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块8号:匹配id # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # tmp < -数据(,“主题”)% %基因族群。潜艇< -数据(tmp)秩序< -匹配(数据(“主题”)、geno.subs) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块9号:初始化# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #上。n < - 100 G < -矩阵(data = NA nrow = nrow(数据),ncol = upper.n)单核苷酸多态性< -代表(“upper.n) loc <——代表(NA upper.n) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块10号:概率向量# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # id1 < - seq(从= 1 = 3 *长度(geno.subs) = 3) id2 < - id1 + 1 id3 < - id1 + 2 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块11号:读取整个文件# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #指数< - 0 fid < - gzfile (genofile,“r”),而(1){vec < -扫描(fid,什么=“字符”,9 = ",安静= TRUE,在线= 1)如果(!长度(vec))打破snp vec [2] < - loc < - as.numeric (vec[3])如果((loc > = min.loc) & (loc < = max.loc)){基因族群。聚合氯化铝< - as.numeric vec (- (1:5))) (probs1 < -基因族群。聚合氯化铝(id1) probs2 < -基因族群。聚合氯化铝(id2) probs3 < -基因族群。probs[id3] dosage <- probs2 + 2*probs3 # Check for missing genotypes tmp <- (probs1 == 0) & (probs2 == 0) & (probs3 == 0) tmp[is.na(tmp)] <- TRUE if (any(tmp)) dosage[tmp] <- NA index <- index + 1 G[, index] <- dosage[order] snps[index] <- snp locs[index] <- loc } } close(fid) ################################################### ### code chunk number 12: subset ################################################### G <- G[, 1:index, drop=FALSE] snps <- snps[1:index] locs <- locs[1:index] colnames(G) <- snps ################################################### ### code chunk number 13: Y and X ################################################### Y <- as.numeric(data[, "Response"]) X <- as.matrix(data[, c("Age", "MALE")]) ################################################### ### code chunk number 14: E ################################################### E <- rep("", index) for (i in 1:nrow(subRegions)) { tmp <- (locs >= subRegions[i, 1]) & (locs <= subRegions[i, 2]) tmp[is.na(tmp)] <- FALSE if (any(tmp)) E[tmp] <- rownames(subRegions)[i] } ################################################### ### code chunk number 15: call rebet ################################################### ret <- rebet(Y, G, E, covariates=X) ################################################### ### code chunk number 16: rebet summary ################################################### print(h.summary(ret)) ################################################### ### code chunk number 17: sessionInfo ################################################### sessionInfo()