# #——global_options,包括= FALSE -------------------------------------------- knitr: opts_chunk美元(警告= FALSE,消息= FALSE ) ## ---- load_package ------------------------------------------------------------ 库(scoreInvHap ) ## ---- eval = FALSE -------------------------------------------------------------- # 库(snpStats)从床# # # # # snp < - read.plink(“example.bed ") # # ## 从pedfile # snp < - read.pedfile(“的例子。ped”,snp = " example.map ") ## ---- 负载snp,消息= FALSE ------------------------------------------------ 库(VariantAnnotation) vcf_file < -系统。文件(“extdata”、“例子。vcf”,包= " scoreInvHap”)vcf < - readVcf vcf (vcf_file,“hg19”) ## ----------------------------------------------------------------------------- 检查< - checkSNPs (vcf)检查vcf < -检查基因族群# #美元——分类 ---------------------------------------------------------------- res < scoreInvHap (SNPlist = vcf发票=“inv7_005”)res # #——classify_par eval = FALSE ------------------------------------------------ # res < scoreInvHap (SNPlist = vcf发票=“inv7_005”,# BPPARAM = BiocParallel:: MulticoreParam (8 )) ## ---- scoreInvHap结果 ----------------------------------------------------- # 得到分类(分类(res)) #得到成绩(分数(res )) ## ---- scoreInvHap分数 ------------------------------------------------------ # 马克斯得分头(maxscores (res)) #得到差的分数头(diffscores (res )) ## ----------------------------------------------------------------------------- plotScores (res pch = 16,基于分数主要= " QC ") ## ----------------------------------------------------------------------------- # 得分多的用头(numSNPs (res)) #调用速度头(propSNPs (res )) ## ----------------------------------------------------------------------------- plotCallRate QC (res,主要= "调用率 ") ## ----------------------------------------------------------------------------- ## 没有过滤的长度(分类(res)) # # QC滤波长度(分类(res minDiff = 0.1,callRate = 0.9 )) ## ----------------------------------------------------------------------------- ## 反演分类表(分类(res)) # #单体型分类表(分类(res,反演= FALSE )) ## ---- 分类估算 -------------------------------------------------------- res_imp < scoreInvHap (SNPlist = vcf发票=“inv7_005”,聚合氯化铝= TRUE) res_imp # #——比较分类 ------------------------------------------------- 表(PostProbs =分类(res_imp) BestGuess =分类(res )) ## ----------------------------------------------------------------------------- 数据(inversionGR) inversionGR ## ----------------------------------------------------------------------------- sessionInfo ()