8#9 - 多校6

本文汇总了多校联合赛第六场的部分题目解析及通过率,包括CutPieces、Evaluation等题目的解题思路与分类,特别关注数学、博弈论、图论等领域的问题解决方法。
8#9 - 多校6
 
[已经解决的题目 红色标记,hard题目是AC队伍<10]
[题目后面是分类,括号里可以写些需要的知识点和简要做法]
 
01 Cut Pieces - 数学(寻找数学递推式,然后实现,注意取余。)
04 Integer Partition - 数学 (整数拆分,五边形数)
07 Message Passing
08 MU Puzzle
10 Triangulation(经典博弈,构造SG函数,打表找规律,确定循环节以后异或出结果即可)
11 Unshuffle - 2SAT/DFS(官方题解的做法是2SAT,用DFS也能过。DFS每层搜索出2个相同的字母,理论上时间复杂度会很高,但是测试数据不强,没有卡搜索)
 
Hard:
02 Evaluation - 数学(FFT)
03 Find Permutation -
05 Liveness Analysis
06 Mathematical Olympiad
09 Plane Partition

传送门:
帮我解释下面的代码,每个步骤的功能要说清楚 # 靶基因实验代码 # 安装R包 #假设验证A靶点通过biomaker B(暴露)影响疾病C(结局) library(TwoSampleMR) library(gwasglue) library(VariantAnnotation) library(data.table) library(vroom) #暴露变量 BMI:ieu-b-40 #结局变量 T2D finngen_R10_T2D #靶点:FTO rm(list = ls()) gc() setwd("D:/Desktop/mr/药靶") # 读取暴露 CPR_gwas = VariantAnnotation::readVcf("ieu-b-40.vcf.gz") CPR_gwas = gwasvcf_to_TwoSampleMR(vcf = CPR_gwas, type = "exposure") head(CPR_gwas) # 筛选P值 CPR_gwas_p = subset(CPR_gwas, pval.exposure < 5e-08) # 计算maf CPR_gwas_p$maf = ifelse(CPR_gwas_p$eaf.exposure > 0.5, 1-CPR_gwas_p$eaf.exposure, CPR_gwas_p$eaf.exposure) #去除连锁不平衡 CPR_gwas_clump = ieugwasr::ld_clump(dplyr::tibble(rsid = CPR_gwas_p$SNP, pval = CPR_gwas_p$pval.exposure), clump_kb = 100, clump_r2 = 0.3, clump_p = 1, bfile = "../1kg.v3/EUR", plink_bin = plinkbinr::get_plink_exe(), pop = "EUR") CPR<-CPR_gwas_p[CPR_gwas_p$SNP %in% CPR_gwas_clump$rsid,] # eQTL选取基因座的位置±100kb FTO_gwas = subset(CPR, chr.exposure==16 & pos.exposure>53737875-100000 & pos.exposure<54155853+100000) # 选取maf>0.01的eQTL FTO_gwas = subset(FTO_gwas, maf>0.01) #读取结局数据 T2D_gwas = vroom("finngen_R10_T2D.gz") # 从暴露数据中挑选与结局相关的SNP T2D_gwas = subset(T2D_gwas, T2D_gwas$rsids %in% FTO_gwas$SNP) T2D_gwas=format_data(dat = T2D_gwas, type = "outcome", snp_col = "rsids", beta_col = "beta", pval_col = "pval", se_col = "sebeta", eaf_col = "af_alt", effect_allele_col = "alt", other_allele_col = "ref") # 删除与结局强相关的SNP T2D_gwas = subset(T2D_gwas, pval.outcome>5e-08) # 修改暴露和结局的名称 FTO_gwas$id.exposure = "FTO" T2D_gwas$id.outcome = "T2D" # 数据整合 dat = harmonise_data(FTO_gwas, T2D_gwas, action = 2) # MR分析 res = mr(dat) res = generate_odds_ratios(res) # 敏感性分析 het = mr_heterogeneity(dat) pleio = mr_pleiotropy_test(dat) # leave-one-out analysis(留一法分析) pdf(file = "graph/leave-one-out.pdf", width = 8, heigh = 6) mr_leaveoneout_plot(leaveoneout_results = mr_leaveoneout(dat)) dev.off()
11-02
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