如何将数据与预置字符串匹配?

I have the following data frame from which I would like to extract rows based on matching strings.

我有以下的数据框架，我想从其中提取基于匹配字符串的行。

> GEMA_EO5
gene_symbol  fold_EO  p_value                           RefSeq_ID      BH_p_value
       KNG1 3.433049 8.56e-28              NM_000893,NM_001102416    1.234245e-24
      REXO4 3.245317 1.78e-27                           NM_020385    2.281367e-24
      VPS29 3.827665 2.22e-25                 NM_057180,NM_016226    2.560770e-22
    CYP51A1 3.363149 5.95e-25              NM_000786,NM_001146152    6.239386e-22
      TNPO2 4.707600 1.60e-23 NM_001136195,NM_001136196,NM_013433    1.538000e-20
      NSDHL 2.703922 6.74e-23              NM_001129765,NM_015922    5.980454e-20
     DPYSL2 5.097382 1.29e-22                           NM_001386    1.062868e-19

So I would like to extract e.g. two rows based on matching strings in $RefSeq_ID, that works fine with the following:

因此，我想提取，例如，基于$RefSeq_ID中的匹配字符串的两行，它可以很好地处理以下内容:

> list<-c("NM_001386", "NM_020385")
> GEMA_EO6<-subset(GEMA_EO5, GEMA_EO5$RefSeq_ID %in% list, drop = TRUE)

> GEMA_EO6

gene_symbol  fold_EO  p_value RefSeq_ID    BH_p_value
      REXO4 3.245317 1.78e-27 NM_020385  2.281367e-24
     DPYSL2 5.097382 1.29e-22 NM_001386  1.062868e-19

But some of the rows have several RefSeq_IDs separated with commas, so I am looking for a general way of telling if $RefSeq_ID contains a certain string pattern and then subset that row.

但是有些行中有几个RefSeq_ID，它们之间用逗号分隔，因此我正在寻找一种通用的方法来判断$RefSeq_ID是否包含某个字符串模式，然后再将该行划分为子集。

2 个解决方案

#1

To do partial matching you'll need to use regular expressions (see ?grepl). Here's a solution to your particular problem:

要进行部分匹配，需要使用正则表达式(参见grepl)。这里有一个解决你的特殊问题的方法:

##Notice that the first element appears in 
##a row containing commas
l = c( "NM_013433", "NM_001386", "NM_020385")

To test one sequence at a time, we just select a particular seq id:

要一次测试一个序列，我们只需选择一个特定的seq id:

R> subset(GEMA_EO5, grepl(l[1], GEMA_EO5$RefSeq_ID))
  gene_symbol fold_EO p_value                           RefSeq_ID BH_p_value
5       TNPO2   4.708 1.6e-23 NM_001136195,NM_001136196,NM_013433  1.538e-20

To test for multiple genes, we use the | operator:

为了检测多个基因，我们使用|算子:

R> paste(l, collapse="|")
[1] "NM_013433|NM_001386|NM_020385"
R> grepl(paste(l, collapse="|"),GEMA_EO5$RefSeq_ID)
[1] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE

所以

subset(GEMA_EO5, grepl(paste(l, collapse="|"),GEMA_EO5$RefSeq_ID))

should give you what you want.

应该给你你想要的。

#2

A different approach is to recognize the duplicate entries in RefSeq_ID as an attempt to represent two data base tables in a single data frame. So if the original table is csv, then normalize the data into two tables

另一种方法是识别RefSeq_ID中的重复项，以尝试在单个数据框架中表示两个数据基表。因此，如果原始表是csv，那么将数据规范化为两个表。

Anno <- cbind(key = seq_len(nrow(csv)), csv[,names(csv) != "RefSeq_ID"])
key0 <- strsplit(csv$RefSeq_ID, ",")
RefSeq <- data.frame(key = rep(seq_along(key0), sapply(key0, length)),
                     ID = unlist(key0))

and recognize that the query is a subset (select) on the RefSeq table, followed by a merge (join) with Anno

并认识到查询是RefSeq表上的一个子集(select)，然后是带有Anno的merge (join)

l <- c( "NM_013433", "NM_001386", "NM_020385")
merge(Anno, subset(RefSeq, ID %in% l))[, -1]

leading to

导致

> merge(Anno, subset(RefSeq, ID %in% l))[, -1]
  gene_symbol  fold_EO  p_value   BH_p_value        ID
1       REXO4 3.245317 1.78e-27 2.281367e-24 NM_020385
2       TNPO2 4.707600 1.60e-23 1.538000e-20 NM_013433
3      DPYSL2 5.097382 1.29e-22 1.062868e-19 NM_001386

Perhaps the goal is to merge with a `Master' table, then

也许我们的目标是合并一个“Master”表

Master <- cbind(key = seq_len(nrow(csv)), csv)
merge(Master, subset(RefSeq, ID %in% l))[,-1]

or similar.

或类似的。

#1