R黄土回归答案

【问题标题】：R loess regressionR黄土回归
【发布时间】：2022-01-23 18:50:55
【问题描述】：

我想我在使用loess 函数时遗漏了一些东西，我不明白我做错了什么。我有一个数据框，其中存储了 3 个不同软件的输出 (count)，用于不同患者基因组上的 26 个不同基因。这 3 款软件分别用于相同的基因组，但下采样率不同。

我通过genes 汇总了所有患者的结果。最后，我有一个包含 4 列的数据框：samplexxx（下采样率）、software（我使用的软件名称）、gene（基因名称）和count（给出的计数结果由软件）。

我的目标是估计由software 给出的count 的下采样效果（samplexxx），我想做一些回归以便能够将它们相互比较。

rate <- c(5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 
          95, 100)

我的尝试：

datalist <- list()   

for (i in 1:22) {
  name <- genes[i]
  print(name)
  mod <- paste("mod_", name)
  xfit <- paste("xfit_", name)
  df <- paste("df_", name)
  mod <- loess(data2[data2$gene == name,]$count ~ 
                 data2[data2$gene == name,]$samplexxx)
  xfit <- predict(mod, newdata=data2[data2$gene == name,]$samplexxx)
  df <- setNames(data.frame(matrix(ncol=4, nrow=60)), 
                 c("down", "software", "gene", "loess"))
  df$down <- data2[data2$gene == name,]$samplexxx
  df$software <- data2[data2$gene == name,]$software 
  df$gene <- data2[data2$gene == name,]$gene    
  df$loess <- xfit
  print(xfit)                        
  datalist[[i]] <- df                      
}

data_loess <- do.call(rbind, datalist)

ggplot(data_loess, aes(x=gene, y=loess, fill=software)) + 
  geom_boxplot()

和：

mod <- loess(data2$count ~ data$samplexxx)
xfit <- predict(mod, newdata=data2$samplexxx)

                 
for (i in 1:20) {
  down <- rate[i]
  print(name)
  title <- paste("loess_downsampling", down)
  out <- paste("loess_downsampling", down, ".pdf", sep="")
  pdf(out, width=10)
  print(ggplot(data2, aes(x=down, y=loess, fill=software))) + 
    geom_boxplot() + ggtitle(title))
dev.off()
}

样本数据：

> dput(data2)
structure(list(samplexxx = c(5L, 10L, 15L, 20L, 25L, 30L, 35L, 
40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 
80L, 85L, 90L, 95L, 100L), software = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L), .Label = c("EH", "GangSTR", "Tred"), class = "factor"), 
    gene = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
    13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
    14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
    14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
    16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
    17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
    17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 
    18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
    18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
    19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 
    21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
    21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
    22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
    13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 
    14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
    14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
    16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
    16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 
    17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
    17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
    18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
    19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
    19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
    21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
    22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
    22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 
    13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
    13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
    14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
    15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 
    16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
    16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
    17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
    18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
    18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 
    19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
    19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
    21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
    21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 
    22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
    22L, 22L, 22L, 22L), .Label = c("AFF2", "AR", "ATN1", "ATXN1", 
    "ATXN10", "ATXN2", "ATXN3", "ATXN7", "C9ORF72", "CACNA1A", 
    "CBL", "CNBP", "CSTB", "DIP2B", "DMPK", "FMR1", "FXN", "HTT", 
    "JPH3", "NOP56", "PPP2R2B", "TBP"), class = "factor"), count = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, 24L, 24L, 24L, 24L, 24L, 
    24L, 24L, 24L, 24L, 24L, 24L, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
    21L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 17L, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15L, 15L, 
    16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
    16L, NA, NA, NA, NA, 20L, 34L, 31L, 33L, 34L, 34L, 34L, 34L, 
    34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, NA, NA, NA, NA, NA, 
    22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
    22L, 22L, 22L, NA, NA, NA, NA, NA, 22L, 24L, 24L, 24L, 24L, 
    24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, NA, NA, 
    NA, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, NA, NA, NA, NA, 6L, 8L, 8L, 
    8L, 8L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, NA, NA, 
    NA, NA, 11L, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, NA, NA, NA, 12L, 5L, NA, 12L, 
    12L, 5L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, NA, NA, NA, NA, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, 20L, 20L, 18L, 20L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L, NA, NA, NA, NA, 27L, 24L, 
    21L, 14L, 27L, 14L, 21L, 27L, 27L, 14L, 27L, 27L, 27L, 27L, 
    27L, 27L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68L, 73L, 
    78L, 54L, 79L, 76L, 87L, 72L, 62L, 63L, NA, NA, NA, NA, NA, 
    27L, 27L, 27L, 28L, 27L, 27L, 64L, 27L, 64L, 64L, 27L, 27L, 
    27L, 27L, 27L, NA, NA, NA, NA, NA, 18L, 20L, 18L, 20L, 20L, 
    18L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, NA, NA, 
    NA, NA, NA, 15L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, 9L, 7L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, NA, NA, NA, NA, NA, 14L, 
    14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
    14L, 14L, NA, NA, NA, NA, NA, 35L, 29L, 35L, 35L, 30L, 35L, 
    32L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 11L, 19L, 19L, 
    19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
    19L, 19L, 19L, 19L, 19L, 20L, 11L, 20L, 20L, 20L, 20L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
    20L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, 16L, 16L, 16L, 16L, 16L, 16L, 
    16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
    16L, 16L, 33L, 33L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 
    33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, NA, 21L, 
    22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
    22L, 22L, 22L, 22L, 22L, 22L, 19L, 21L, 21L, 21L, 21L, 21L, 
    21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
    21L, 19L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 8L, 8L, 
    7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 11L, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 7L, 15L, 15L, 13L, 15L, 15L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, 27L, 19L, 27L, 27L, 27L, 
    27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 
    27L, 27L, NA, 76L, 23L, 23L, 23L, 32L, 65L, 32L, 28L, 32L, 
    28L, 32L, 32L, 23L, 28L, 32L, 28L, 28L, 32L, 84L, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, 14L, 18L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
    17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 15L, 
    NA, NA, 15L, NA, 15L, NA, NA, 15L, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, 9L, NA, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 14L, 14L, 14L, 14L, 
    14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
    14L, 14L, 14L, 14L, NA, 28L, 36L, 36L, NA, 36L, 36L, 36L, 
    36L, NA, 36L, NA, 36L, 36L, 36L, 36L, 36L, NA, 36L, 36L, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
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    35L, 33L, 35L, 35L, 35L, 35L, 35L)), class = "data.frame", row.names = c(NA, 
-1320L))

【问题讨论】：

有什么问题？感谢您以可重复的方式共享数据和一些代码，但“我的目标是估计下采样对软件给出的计数的影响，我想做一些回归以便能够比较自己” 不是很清楚。你的尝试有什么不满意的地方？是否有错误或警告信息？如果是这样，它们是什么？如果没有，你会得到你不喜欢的结果吗？他们怎么了？还是剧情有问题？
另外，您提供的样本数据太大，以至于您的问题与 Stack Overflow 上的问题大小限制相冲突。您是否需要所有数据来说明您的问题是什么，或者您能否找到一个更易于使用和理解的较小样本？
是的，当然你可以只使用一些基因来减少数据量，但需要的数据最少。我的问题是：每个下采样率都具有相同的值是否正常？我想做一些回归，但如果我对每个速率都有相同的结果，我担心出了点问题

标签： r regression

【解决方案1】：

我认为loess 应该在"software" 上进行拆分。

software <- unique(data2$software)

data_loess <- do.call(rbind, lapply(software, \(x) {
  X <- subset(data2, software == x)
  lo <- loess(count ~ samplexxx, X)
  count_pred <- predict(lo, newdata=X)
  return(cbind(X, count_pred))
}))

注意：R version 4.1.2 (2021-11-01)

给予：

head(data_loess[data_loess$samplexxx > 80, ], 10)
#    samplexxx software gene count count_pred
# 17        85       EH AFF2    24   22.69004
# 18        90       EH AFF2    24   22.31879
# 19        95       EH AFF2    24   21.83428
# 20       100       EH AFF2    24   21.25618
# 37        85       EH   AR    21   22.69004
# 38        90       EH   AR    21   22.31879
# 39        95       EH   AR    21   21.83428
# 40       100       EH   AR    21   21.25618
# 57        85       EH ATN1    NA   22.69004
# 58        90       EH ATN1    NA   22.31879

这里是plot 的"count" 预测"samplexxx"。

plot(count_pred ~ samplexxx, data_loess, col=as.numeric(software) + 1, 
     pch=20, xlab='Downsampling', ylab='Count (LOESS)')
legend('topleft', legend=software, pch=19, col=as.numeric(software) + 1,
       horiz=TRUE, cex=.7, title='Software')

看起来很有趣，但我不确定它是否绝对正确。

在我的回答中，您会看到与 for 循环不同的东西，这对您来说可能是新的，但它是 r-ish 方式，并且它的代码要短得多。这里的循环工作是lapply()。

无论如何，希望这会有所帮助。

【讨论】：

非常感谢您的回答！似乎这正是我想要的:) 我只是对你的代码有一些问题，不幸的是，我比 R 更多的是 python（正如你在我的 for 循环中注意到的那样）而且我不知道 do.call() 函数，那么这很难让我解决问题。使用您的代码我有一些错误： Erreur : 在“data_loess
@MarineBergot 与其说是do.call，不如说是\。问题很可能是你有一个旧的 R 版本，更新它！或者将\ 替换为function。干杯
感谢您的回答 :) 我尝试了我的 R 版本 4.1.0，它成功了！只是“软件”不起作用，我不得不用 data2$software 替换它，但也许你做了更聪明的事情？
@MarineBergot 差不多，是unique(data2$software)，忘记贴了，查看更新，感谢关注！
@MarineBergot 简短附件：there 是关于do.call 和lapply 的一些不错的答案！