R以特定方式规范化数据集

时间：2015-09-11 14:31:38

标签： r normalization sliding-window

我有一个包含＆＃39; hits＆＃39;在基因组的每个位置。我想以一种非常具体的方式将其标准化：

  1. 当列df$HC包含值＆＃39; HC＆＃39;，
    2. 时
  2. 从df$pos获取包含bp中位置的值
  3. 总结df $ Hits +/- 1000bp远离有问题的那个，例如如果df$pos = 3000，则在df$pos＆gt; = 2000且＆lt; = 4000，
    4. 的地方添加匹配
  4. 将这些2000个职位的每个df$Hits值除以步骤3中计算出的总数。

    5. 因此，每个2000bp的补丁都围绕着HC＆＃39; HC＆＃39; （HC列中的大多数值都是NA，并且不需要进行标准化），每次命中除以该补丁中的总命中数。

    我想我可以通过在每个HC＆＃39;周围分配2000bp的每个块来实现这一点。并单独处理它们，但有~3000＆＃39; HC＆＃39;位置。

    编辑：由于地区的HC +/- 1000bp＆＃39;区域重叠，我认为现在我需要单独提取和处理每个区域，因此在每个子集中将重复重叠区域。

    感谢您对此提供任何帮助，令我感到困惑，我很头疼！

    dput示例数据帧（由于字符限制，它只包含1000行，所以尝试小于2000bp的窗口）：

    structure(list(chr = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L), .Label = "chr1", class = "factor"), pos = 1:1000, Hits = c(0L, 
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), HC = structure(c(NA, 
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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, 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, 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, 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, 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, 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, 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, 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, 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, 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, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA), .Label = "HC", class = "factor")), .Names = c("chr", 
"pos", "Hits", "HC"), class = "data.frame", row.names = c(NA, 
-1000L))

    较小的样本数据集和预期输出：

    pos <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
Hits <- c(0, 1, 1, 2, 2, 3, 2, 2, 1, 1)
HC <- c(NA, NA, NA, NA, NA, 'HC', NA, NA, NA, NA)
df <- data.frame(pos, Hits, HC)

#total hits in a +/-3bp window around HC = 13
#divide each read in the window by 13:

Hits <- c(0, 1, 0.077, 0.154, 0.154, 0.231, 0.154, 0.154, 0.077, 1)

1 个答案:

答案 0 :(得分：1)

好的，这至少应该涵盖简化的问题：

n   <- 3
len <- length(df[['Hits']])
for(i in which(df[['HC']] %in% 'HC')){
    ran <- max(i-n,1):min(i+n,len)
    reg <- df[['Hits']][ran]
    s <- sum(reg)
    reg <- reg / s
    df[['Hits']] <- replace(df[['Hits']],ran,reg)
}

fiddle

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