将函数应用于数据框

时间：2016-03-02 20:56:32

标签： r dplyr apply

以下是我正在使用的数据的示例数据框。对于熟悉遗传数据格式的人来说，它基本上是一个修改过的VCF文件。如果不是，基本上每行包含基因组中可能存在变体的位置的信息。 

samp <- structure(list(Chrom = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L), .Label = "chr12", class = "factor"), 
    Pos = c(8613204L, 8613412L, 8614238L, 8614506L, 8614652L, 
    8614669L, 8614768L, 8614951L, 8614986L, 8615225L, 8615809L, 
    8616149L, 8616392L), Ref = structure(c(1L, 1L, 4L, 3L, 3L, 
    3L, 2L, 3L, 2L, 4L, 2L, 4L, 3L), .Label = c("A", "C", "G", 
    "T"), class = "factor"), Alt = structure(c(3L, 2L, 2L, 1L, 
    1L, 1L, 3L, 1L, 1L, 3L, 4L, 2L, 4L), .Label = c("A", "C", 
    "G", "T"), class = "factor"), Info = c("AC=3913;AF=0.78135;AN=5008;NS=2504;DP=-128;EAS_AF=0.9921;AMR_AF=0.8357;AFR_AF=0.5779;EUR_AF=0.7366;SAS_AF=0.8466;AA=G|||;CSQ=G|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.1881", 
    "AC=4051;AF=0.808906;AN=5008;NS=2504;DP=-128;EAS_AF=0.9921;AMR_AF=0.8444;AFR_AF=0.6725;EUR_AF=0.7366;SAS_AF=0.8538;AA=C|||;CSQ=C|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.1881", 
    "AC=4021;AF=0.802915;AN=5008;NS=2504;DP=-128;EAS_AF=0.9921;AMR_AF=0.8415;AFR_AF=0.6558;EUR_AF=0.7376;SAS_AF=0.8466;AA=T|||;CSQ=C|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.7997", 
    "AC=3990;AF=0.796725;AN=5008;NS=2504;DP=-128;EAS_AF=0.9921;AMR_AF=0.8386;AFR_AF=0.6339;EUR_AF=0.7376;SAS_AF=0.8466;AA=A|||;CSQ=A|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.1881", 
    "AC=4069;AF=0.8125;AN=5008;NS=2504;DP=17188;EAS_AF=0.9921;AMR_AF=0.8487;AFR_AF=0.6528;EUR_AF=0.7714;SAS_AF=0.8599;AA=A|||;CSQ=A|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.0029", 
    "AC=4044;AF=0.807508;AN=5008;NS=2504;DP=-128;EAS_AF=0.9911;AMR_AF=0.8458;AFR_AF=0.6362;EUR_AF=0.7714;SAS_AF=0.8599;AA=G|||;CSQ=A|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.0029", 
    NA, NA, "AC=3795;AF=0.757788;AN=5008;NS=2504;DP=-128;EAS_AF=0.9653;AMR_AF=0.7954;AFR_AF=0.5651;EUR_AF=0.7167;SAS_AF=0.82;AA=c|||;CSQ=A|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.0029", 
    NA, "AC=4053;AF=0.809305;AN=5008;NS=2504;DP=-128;EAS_AF=0.9921;AMR_AF=0.8458;AFR_AF=0.6362;EUR_AF=0.7724;SAS_AF=0.8671;AA=C|||;CSQ=T|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.0029", 
    "AC=4076;AF=0.813898;AN=5008;NS=2504;DP=-128;EAS_AF=0.9921;AMR_AF=0.8473;AFR_AF=0.6528;EUR_AF=0.7724;SAS_AF=0.8671;AA=C|||;CSQ=C|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.0029", 
    "AC=4052;AF=0.809105;AN=5008;NS=2504;DP=-128;EAS_AF=0.9921;AMR_AF=0.8473;AFR_AF=0.6346;EUR_AF=0.7724;SAS_AF=0.8671;AA=T|||;CSQ=T|ENSG00000205846|ENST00000382073|Transcript|intron_variant||||||||1||||||;GENCODE=ENST00000382073;FUNSEQ=0.0029"
    ), TG_rs = c("rs10770739", "rs10770740", "rs4883148", "rs4883149", 
    "rs4883150", "rs4883151", NA, NA, "rs7303948", NA, "rs4242889", 
    "rs4883154", "rs4242890")), row.names = c(NA, -13L), .Names = c("Chrom", 
"Pos", "Ref", "Alt", "Info", "TG_rs"), class = "data.frame")

我想要做的是从＆＃34;信息＆＃34;中提取值。柱。但是，此列中包含的信息对于每一行都不相同，并且不总是以相同的顺序出现。因此，我想使用模式匹配来获取我感兴趣的值。

我写了一个小函数来提取＆＃34;等位基因频率＆＃34; （AF）适用于各种超级人群＆＃34; （例如，AMR，AFR，EUR，SAS，EAS）包含在“信息”列中。 

extractAF <- function(pop, vec) {
  info <- unlist((strsplit(vec, ";", fixed=TRUE)))
  AF <- as.numeric(unlist(strsplit((info[grep(pop, (unlist((strsplit(vec, ";", fixed=TRUE)))))]), "=", fixed=TRUE))[2])
  return(AF)
}

这个功能需要两个参数：＆＃39; pop＆＃39;这是一个字符串，指定要提取的超级人口，以及＆＃39; vec＆＃39;它旨在获取我的数据框的Info列。

当通过单个向量时，该函数按预期工作：

extractAF("AFR_AF", samp[1,'Info'])
#[1] 0.5779

extractAF("AFR_AF", samp[5,'Info'])
#[1] 0.6528

但是，我希望对数据帧的每一行执行此操作，并创建一个包含数据的新列。当我使用dplyr的mutate函数时，我会得到一个具有相同值的列：

library("dplyr")
mutate(samp, AFR_AF = extractAF("AFR_AF", Info))

我读了一篇文章（我现在似乎无法找到，否则我会引用它），即mutate一次传递所有行，而不是我需要的逐行传递。

所以我根据这个post尝试了以下几种方式：

apply(samp[,'Info'], 1, function(x) extractAF("AFR_AF", x))

应用中出错（samp [，＆＃34; Info＆＃34;]，1，function（x）extractAF（＆＃34; AMR_AF＆＃34;，x））：   dim（X）必须具有正长度

samp[, extractAF("AMR_AF", Info), by = .I]

[.data.frame中的错误（samp，，extractAF（＆＃34; AMR_AF＆＃34;，Info），by = .I）：   未使用的参数（by = .I）

samp[, extractAF("AMR_AF", Info), by = 1:nrow(samp)]

Error in `[.data.frame`(samp, , extractAF("AMR_AF", Info), by = 1:nrow(samp)) : 
  unused argument (by = 1:nrow(samp))
＃

更新

在下面的INFO列中包含NA和AF = 0的其他样本数据集：

  

结构（列表（CHROM = c（＆＃34; chr1＆＃34;，＆＃34; chr1＆＃34;，＆＃34; chr1＆＃34;，＆＃34; chr1＆＃34;，＆＃34; ; CHR1＆＃34 ;,   ＆＃34; chr1＆＃34;），POS = c（16090898L，16091074L，16091583L，16092212L，   16093560L，16093639L），ID = c（＆＃34; rs6429774＆＃34;，＆＃34; rs6429776＆＃34;，NA，   ＆＃34; rs74528955＆＃34;，＆＃34; rs904912＆＃34;，NA），REF = c（＆＃34; G＆＃34;，＆＃34; A＆＃34;，＆＃34; T＆＃34 ;，＆＃34; C＆＃34;，＆＃34; T＆＃34;，＆＃34; C＆＃34;），   ALT = c（＆＃34; A＆＃34;，＆＃34; G＆＃34;，＆＃34; A＆＃34;，＆＃34; T＆＃34;，＆＃34; A＆＃34;，＆ ＃34; T＆＃34;），QUAL = c（NA，NA，NA，NA，NA，   NA），FILTER = c（NA，NA，NA，NA，NA，NA），INFO =   C（＆＃34; AC = 1606; AF = 0.320687; AN = 5008; NS = 2504; DP = 21565; EAS_AF = 0.1419; AMR_AF = 0.2983; AFR_AF = 0.525; EUR_AF = 0.3509; SAS_AF = 0.2137; AA = G | ||; CSQ = A | ENSG00000162458 | ENST00000441801 |文稿| upstream_gene_variant ||||||| 96 | 1 ||||||; ERB = A || proximal_1216 | Regulatory_Feature | proximal_enhancer; FUNSEQ = 0.3335＆＃34 ;, ＆＃34; AC = 1690; AF = 0.33746; AN = 5008; NS = 2504; DP = 20247; EAS_AF = 0.1498; AMR_AF = 0.3012; AFR_AF = 0.5681; EUR_AF = 0.3549; SAS_AF = 0.227; AA = G ||| ; CSQ = G | ENSG00000162458 | ENST00000441801 | Transcript | 5_prime_UTR_variant | 81 ||||||| 1 ||||||; ERB = G || proximal_1216 | Regulatory_Feature | proximal_enhancer; FUNSEQ = 0.3335＆＃34;，NA，   ＆＃34; AC = 8; AF = 0.00159744; AN = 5008; NS = 2504; DP = 19197; EAS_AF = 0.0079; AMR_AF = 0; AFR_AF = 0; EUR_AF = 0; SAS_AF = 0; AA = C ||| ; CSQ = T | ENSG00000162458 | ENST00000441801 |文稿| intron_variant |||||||| 1 ||||||; GENCODE = ENST00000441801; ERB = T || proximal_1216 | Regulatory_Feature | proximal_enhancer; FUNSEQ = 0.3335＆＃34; ，＆＃34; AC = 3282; AF = 0.655351; AN = 5008; NS = 2504; DP = 14721; EAS_AF = 0.8343; AMR_AF = 0.6916; AFR_AF = 0.4259; EUR_AF = 0.6531; SAS_AF = 0.7577; AA = A || |; CSQ = A | ENSG00000162458 | ENST00000441801 |文稿| intron_variant |||||||| 1 ||||||; GENCODE = ENST00000441801; FUNSEQ = 0.1483＆＃34 ;,   ＆＃34; AC = 5; AF = 0.000998403; AN = 5008; NS = 2504; DP = 14736; EAS_AF = 0.003; AMR_AF = 0; AFR_AF = 0; EUR_AF = 0; SAS_AF = 0.002; AA = C ||| ; CSQ = T | ENSG00000162458 | ENST00000441801 |文稿| intron_variant |||||||| 1 ||||||; GENCODE = ENST00000441801; FUNSEQ = 0.1483＆＃34;   ）），row.names = 14:19，class =＆＃34; data.frame＆＃34;，。Name = c（＆＃34; CHROM＆＃34;，   ＆＃34; POS＆＃34;，＆＃34; ID＆＃34;，＆＃34; REF＆＃34;，＆＃34; ALT＆＃34;，＆＃34; QUAL＆＃34;，＆＃34; FILTER＆ ＃34;，＆＃34; INFO＆＃34;））

1 个答案:

答案 0 :(得分：3)

您可能不需要这些公式，因为sub是矢量化的。首先创建所有可能代码的变量，如（AFR，AMR，EUR等）。使用该向量创建搜索模式以浏览Info列并返回包含所有匹配项的新数据框：

all_pop <- c("AMR_AF", "AFR_AF", "EUR_AF", "SAS_AF", "EAS_AF")
pat <- paste0(".*\\b", all_pop, "=(\\d+(\\.\\d+)?)\\b.*")

out <- sapply(pat, sub, "\\1", samp$Info)
newdf <- setNames(as.data.frame(out), all_pop)
#      AMR_AF AFR_AF EUR_AF SAS_AF EAS_AF
#   1  0.8357 0.5779 0.7366 0.8466 0.9921
#   2  0.8444 0.6725 0.7366 0.8538 0.9921
#   3  0.8415 0.6558 0.7376 0.8466 0.9921
#   4  0.8386 0.6339 0.7376 0.8466 0.9921
#   5  0.8487 0.6528 0.7714 0.8599 0.9921
#   6  0.8458 0.6362 0.7714 0.8599 0.9911
#   7    <NA>   <NA>   <NA>   <NA>   <NA>
#   8    <NA>   <NA>   <NA>   <NA>   <NA>
#   9  0.7954 0.5651 0.7167   0.82 0.9653
#   10   <NA>   <NA>   <NA>   <NA>   <NA>
#   11 0.8458 0.6362 0.7724 0.8671 0.9921
#   12 0.8473 0.6528 0.7724 0.8671 0.9921
#   13 0.8473 0.6346 0.7724 0.8671 0.9921
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