运行 meta-analysis R 中的多个数据帧

Question

我是 R 的初学者。我需要你的帮助来自动执行这些分析并获得包含结果的摘要输出。

我有 4 个像这样的不同数据框（见下文），具有相同的 headers 和阈值列中的相同值：

   Set   Threshold  R2           P          Coefficient  Standard.Error  Num_SNP
Base  0.0001     0.000233304  0.66047    0.0332613    0.0757204       47
Base  0.001      0.000387268  0.571772   -0.0438782   0.0775996       475
Base  0.05       0.00302399   0.114364   0.129474     0.082004        14164
Base  0.1        0.00252797   0.14897    0.117391     0.0813418       24616
Base  0.2        0.00481908   0.0465384  0.163571     0.0821767       41524
Base  0.3        0.00514761   0.0398082  0.170058     0.0827237       55307
Base  0.4        0.00699506   0.0166685  0.200571     0.083783        66943
Base  0.5        0.00634181   0.0226301  0.192314     0.0843623       76785

对于阈值列中的每个匹配值，我想使用 R 中的包 metafor 来 meta-analyse 相应的效果大小（在系数列中）和 4 个数据帧的标准误差。

使用 metafor 包：

rma.uni(yi=c(Coefficient_1,Coefficient_2,Coefficient_3,Coefficient_4),sei=c(Standard.Error_1,Standard.Error_2,Standard.Error_3,Standard.Error_4), measure="GEN", method='FE',intercept=T,weights=c(sample_size1,sample_size2,sample_size3,sample_size4))

我如何自动化分析并获得包含每个阈值结果的摘要数据框？

Answer 1

您好，这应该可以帮助您入门。本质上你可以遍历所有阈值将所有 4 个数据框中与每个阈值匹配的行提取到一个新数据框中，然后运行您的元分析

library(metafor)
# Make some fake data resembling your own
df1 = data.frame(Set=rep("Base",8), Threshold=c(0.0001,0.001,0.05,seq(0.1,0.5,0.1)),
                 R2=runif(8,0.001,0.005),P=runif(8,0.001,1),Coefficient=runif(8,-0.1,0.2),
                 Standard.Error=runif(8,0.07,0.08),Num_SNP=sample(1:1000,8))
df2 = data.frame(Set=rep("Base",8), Threshold=c(0.0001,0.001,0.05,seq(0.1,0.5,0.1)),
                 R2=runif(8,0.001,0.005),P=runif(8,0.001,1),Coefficient=runif(8,-0.1,0.2),
                 Standard.Error=runif(8,0.07,0.08),Num_SNP=sample(1:1000,8))
df3 = data.frame(Set=rep("Base",8), Threshold=c(0.0001,0.001,0.05,seq(0.1,0.5,0.1)),
                 R2=runif(8,0.001,0.005),P=runif(8,0.001,1),Coefficient=runif(8,-0.1,0.2),
                 Standard.Error=runif(8,0.07,0.08),Num_SNP=sample(1:1000,8))
df4 = data.frame(Set=rep("Base",8), Threshold=c(0.0001,0.001,0.05,seq(0.1,0.5,0.1)),
                 R2=runif(8,0.001,0.005),P=runif(8,0.001,1),Coefficient=runif(8,-0.1,0.2),
                 Standard.Error=runif(8,0.07,0.08),Num_SNP=sample(1:1000,8))

Thresholds = unique(df1$Threshold)

Results <- NULL
for(i in 1:length(Thresholds)){
  idf = rbind(df1[df1$Threshold==Thresholds[i],],
              df2[df2$Threshold==Thresholds[i],],
              df3[df3$Threshold==Thresholds[i],],
              df4[df4$Threshold==Thresholds[i],])
  i.meta <- rma.uni(yi=idf$Coefficient,sei=idf$Standard.Error, measure="GEN", method='FE',intercept=T,
                    weights=idf$Num_SNP)
  Results <- rbind(Results, c(Threshold=Thresholds[i],beta=i.meta$beta,se=i.meta$se,
                              zval=i.meta$zval,pval=i.meta$pval,ci.lb=i.meta$ci.lb,
                              ci.ub=i.meta$ci.ub,QEp=i.meta$QEp))
}
Results <- data.frame(Results)
Results

应该给你：

  Threshold         beta         se       zval       pval        ci.lb     ci.ub        QEp
1     1e-04 -0.012079013 0.04715546 -0.2561530 0.79783270 -0.104502022 0.0803440 0.08700919
2     1e-03  0.068932388 0.04006086  1.7206917 0.08530678 -0.009585452 0.1474502 0.22294419
3     5e-02  0.050069503 0.04094881  1.2227340 0.22143020 -0.030188694 0.1303277 0.07342661
4     1e-01  0.102598016 0.04188183  2.4497022 0.01429744  0.020511132 0.1846849 0.07380669
5     2e-01  0.069482160 0.04722693  1.4712401 0.14122619 -0.023080930 0.1620452 0.95494364
6     3e-01  0.009793206 0.05098346  0.1920859 0.84767489 -0.090132542 0.1097190 0.12191340
7     4e-01  0.030432884 0.03967771  0.7670021 0.44308028 -0.047333994 0.1081998 0.86270334
8     5e-01  0.073511575 0.03997485  1.8389458 0.06592316 -0.004837683 0.1518608 0.12333557

运行 meta-analysis R 中的多个数据帧

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