我有一个名为NE的表，其中包含剪接的RNA连接：
#Chr start end ID . + GTEX-Q2AG-0126-SM-2HMLB
1: 13 20244503 20244980 13:20244503:20244980:clu_1587_NA . + 0.01122552
2: 13 20244503 20245346 13:20244503:20245346:clu_1587_NA . + 0.09151192
3: 13 20249124 20251864 13:20249124:20251864:clu_1588_NA . + 0.94156107
4: 13 20249793 20251864 13:20249793:20251864:clu_1588_NA . + -1.00545250
5: 13 20251963 20304379 13:20251963:20304379:clu_1589_NA . + -0.17101732
6: 13 20283739 20304379 13:20283739:20304379:clu_1589_NA . + 0.26907797
GTEX-N7MT-0011-R5a-SM-2I3G6 GTEX-PW2O-0526-SM-2I3DX GTEX-OHPK-0526-SM-2HMJB
1: -0.73425796 0.32721133 -0.05645774
2: 0.83044440 -0.08213476 0.23888779
3: -0.02207567 -1.68168241 1.69042151
4: 0.16780741 1.55309040 -1.83313242
5: -0.96313998 0.96385901 0.40292406
6: 1.00445387 -0.89044547 -0.24664686
我有另一个名为tissue_table的表，其中包含以下信息：
Run Sample_Name body_site
1: SRR598484 GTEX-PW2O-0526-SM-2I3DX Lung
2: SRR598124 GTEX-NPJ8-0011-R4a-SM-2HML3 Brain - Amygdala
3: SRR599192 GTEX-N7MT-0011-R5a-SM-2I3G6 Brain - Caudate (basal ganglia)
4: SRR601925 GTEX-OHPK-0526-SM-2HMJB Lung
5: SRR601068 GTEX-Q2AG-0126-SM-2HMLB Skin - Sun Exposed (Lower leg)
6: SRR602598 GTEX-Q2AG-0011-R9A-SM-2HMJ6 Brain - Spinal cord (cervical c-1)
我想做的是根据tissue_table$body_site从NE生成新表;这意味着，我希望与每种组织类型匹配的每一列的所有行都输出为文件。例如，如果GTEX-PW2O-0526-SM-2I3DX和GTEX-OHPK-0526-SM-2HMJB都与tissue_table$body_site中的"Lung"匹配，我想创建一个名为 likeLungPhenotypes.txt的新表，看起来像NE(因为它有列#Chr、start、end和ID)，但只包含根据tissue_table从肺采样的信息。
这是我已有的代码：
require("data.table")
require("R.utils")
args = commandArgs(trailingOnly=TRUE)
# args[1] is the leafcutter-generated phenotypes, args[2] is the tissue table
NE <- fread("NE_sQTL_perind.counts.gz.qqnorm_chr13")
tistab <- fread("tissue_table.txt")
# below takes the SRR IDs found in NE column headers, matches them to those found in the
# tissue table, and then changes them the GTEX sample ID
ind <- match(names(NE), tistab$Run)
names(NE) <- tistab$Sample_Name[ind]
# I guess now what I want to do is find the tissue that corresponds to each sample, and write
# to file the phenotypes or whatever
这就是我所得到的：我能够将列标题从其原始标题(在tissue_table$Run中找到)更改为在tissue_table$Sample_Name中找到的标题。否则，我什至不知道我将如何处理这个问题。我相信这很容易，我只是对 R 不够熟悉而无法弄清楚。如果我能澄清我的问题，请告诉我。
谢谢。
编辑：根据要求，示例数据：
> dput(head(NE, 10))
structure(list(`#Chr` = c(13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L), start = c(20244503L, 20244503L, 20249124L, 20249793L,
20251963L, 20283739L, 20803888L, 20803888L, 20803888L, 20804946L
), end = c(20244980L, 20245346L, 20251864L, 20251864L, 20304379L,
20304379L, 20804837L, 20805005L, 20805521L, 20805521L), ID = c("13:20244503:20244980:clu_1587_NA",
"13:20244503:20245346:clu_1587_NA", "13:20249124:20251864:clu_1588_NA",
"13:20249793:20251864:clu_1588_NA", "13:20251963:20304379:clu_1589_NA",
"13:20283739:20304379:clu_1589_NA", "13:20803888:20804837:clu_1590_NA",
"13:20803888:20805005:clu_1590_NA", "13:20803888:20805521:clu_1590_NA",
"13:20804946:20805521:clu_1590_NA"), . = c(".", ".", ".", ".",
".", ".", ".", ".", ".", "."), `+` = c("+", "+", "+", "+", "+",
"+", "+", "+", "+", "+"), `GTEX-Q2AG-0126-SM-2HMLB` = c(0.011225518662542,
0.0915119165352026, 0.941561071760354, -1.00545250297076, -0.171017320204747,
0.269077973405877, 2.26711789363315, -2.79253861638934, -0.732483471764967,
1.14279009708336), `GTEX-N7MT-0011-R5a-SM-2I3G6` = c(-0.734257957140664,
0.830444403564401, -0.0220756713815287, 0.167807411034554, -0.963139984748595,
1.00445387270491, -1.10454772191492, 0.872446843686367, -1.47490517820283,
-1.69929164403211), `GTEX-PW2O-0526-SM-2I3DX` = c(0.327211326349541,
-0.0821347590368987, -1.68168241366976, 1.55309040177528, 0.963859014491135,
-0.890445468211905, 0.126678936291309, 0.135493519239826, 0.126527048968275,
0.14416639182154), `GTEX-OHPK-0526-SM-2HMJB` = c(-0.0564577430398568,
0.238887789085513, 1.69042150820732, -1.83313242253239, 0.402924064571882,
-0.246646862056526, 0.091360610412634, 0.0993070943580591, 0.0912093063816043,
0.107562947531429), `GTEX-OXRL-0526-SM-2I3EZ` = c(0.0674782081460005,
0.158645645285045, 1.78738099166716, -1.88842809909606, 0.508954892349862,
-0.315945651353048, 0.119695085673777, 0.126982719708521, 0.119543328884529,
0.134581189175627), `GTEX-NPJ8-0011-R4a-SM-2HML3` = c(-0.437321982565311,
0.507754687799161, -0.150716233289565, 0.259715866743248, -1.25996459356113,
1.18247794999203, -1.16864600399772, 0.315945651353048, -0.328801349819712,
-1.23212889898317), `GTEX-Q2AG-0011-R9A-SM-2HMJ6` = c(-1.09599155148678,
0.925486945143163, -0.404068240058415, 0.465014413370245, 0.0869736283894613,
-0.0496678054798097, -0.151935513719884, -0.144927678233019,
2.26125148845566, 0.321494053014199), `GTEX-OIZH-0005-SM-2HMJN` = c(2.00669264539791,
-2.53139742741042, -1.17975392273608, 0.834065972618895, 1.47222782753213,
-1.91886672909768, 0.530513153906467, 0.969147205230478, 0.37852951989621,
1.19315578476064), `GTEX-Q2AG-0011-R4A-SM-2HMKA` = c(-0.375779231335771,
0.446292587332684, -0.710978014218879, 0.662901557390466, -1.30771089265708,
1.19715667858446, -0.747740836500599, 0.160171661041644, -0.487460943331342,
-0.816486102660646), `GTEX-OXRK-0926-SM-2HMKP` = c(0.536071533897896,
-0.32498668145395, 0.286146956081191, -0.058419751422225, 0.245249146196741,
-0.0231306654644876, 0.134125066194346, 0.141045986021489, 0.133973031406055,
0.147669008162181)), .Names = c("#Chr", "start", "end", "ID",
".", "+", "GTEX-Q2AG-0126-SM-2HMLB", "GTEX-N7MT-0011-R5a-SM-2I3G6",
"GTEX-PW2O-0526-SM-2I3DX", "GTEX-OHPK-0526-SM-2HMJB", "GTEX-OXRL-0526-SM-2I3EZ",
"GTEX-NPJ8-0011-R4a-SM-2HML3", "GTEX-Q2AG-0011-R9A-SM-2HMJ6",
"GTEX-OIZH-0005-SM-2HMJN", "GTEX-Q2AG-0011-R4A-SM-2HMKA", "GTEX-OXRK-0926-SM-2HMKP"
), class = c("data.table", "data.frame"), row.names = c(NA, -10L
), .internal.selfref = <pointer: 0x1fcb378>)
> dput(head(tistab, 10))
structure(list(Run = c("SRR598484", "SRR598124", "SRR599192",
"SRR601925", "SRR601068", "SRR602598", "SRR607586", "SRR608288",
"SRR600445", "SRR608344"), Sample_Name = c("GTEX-PW2O-0526-SM-2I3DX",
"GTEX-NPJ8-0011-R4a-SM-2HML3", "GTEX-N7MT-0011-R5a-SM-2I3G6",
"GTEX-OHPK-0526-SM-2HMJB", "GTEX-Q2AG-0126-SM-2HMLB", "GTEX-Q2AG-0011-R9A-SM-2HMJ6",
"GTEX-OXRL-0526-SM-2I3EZ", "GTEX-OXRK-0926-SM-2HMKP", "GTEX-Q2AG-0011-R4A-SM-2HMKA",
"GTEX-OIZH-0005-SM-2HMJN"), body_site = c("Lung", "Brain - Amygdala",
"Brain - Caudate (basal ganglia)", "Lung", "Skin - Sun Exposed (Lower leg)",
"Brain - Spinal cord (cervical c-1)", "Lung", "Lung", "Brain - Amygdala",
"Whole Blood")), .Names = c("Run", "Sample_Name", "body_site"
), class = c("data.table", "data.frame"), row.names = c(NA, -10L
), .internal.selfref = <pointer: 0x1fcb378>)
如果你按body_sitesplitSample_Name，你会得到对应于每个body_site的Sample_Names 向量。然后，您只需要使用每个body_site的NE名称intersect，然后选择由该交集生成的列。结果是数据表的命名列表。名称是body_site值。
library(data.table) #not really necessary, just using it here since you're already using it
sites <- with(tistab, split(Sample_Name, body_site))
keep <- c('#Chr', 'start', 'end', 'ID')
lapply(sites, function(x)
NE[, .SD, .SDcols = c(keep, intersect(names(NE), x))])
lapply 代码使用lapply中定义的函数。这有时称为使用"匿名"函数。对于数据表，.SD是所有列的数据表，或者除分组列(如果使用分组)或.SDcols中指定的列(如果使用.SDcols参数)之外的所有列的数据表。所以我只是用它来选择特定的列。
使用常规数据框，您可以执行NE[, c(keep, intersect(names(NE), x))]，但是由于data.table处理括号内内容的方式，这将给出奇怪的结果(尝试使用data.table，然后使用常规数据框NE[, names(NE)[1:2]]，以了解我的意思)。
打印输出
# $`Brain - Amygdala`
# #Chr start end ID GTEX-NPJ8-0011-R4a-SM-2HML3
# 1: 13 20244503 20244980 13:20244503:20244980:clu_1587_NA -0.4373220
# 2: 13 20244503 20245346 13:20244503:20245346:clu_1587_NA 0.5077547
# 3: 13 20249124 20251864 13:20249124:20251864:clu_1588_NA -0.1507162
# 4: 13 20249793 20251864 13:20249793:20251864:clu_1588_NA 0.2597159
# 5: 13 20251963 20304379 13:20251963:20304379:clu_1589_NA -1.2599646
# 6: 13 20283739 20304379 13:20283739:20304379:clu_1589_NA 1.1824779
# 7: 13 20803888 20804837 13:20803888:20804837:clu_1590_NA -1.1686460
# 8: 13 20803888 20805005 13:20803888:20805005:clu_1590_NA 0.3159457
# 9: 13 20803888 20805521 13:20803888:20805521:clu_1590_NA -0.3288013
# 10: 13 20804946 20805521 13:20804946:20805521:clu_1590_NA -1.2321289
# GTEX-Q2AG-0011-R4A-SM-2HMKA
# 1: -0.3757792
# 2: 0.4462926
# 3: -0.7109780
# 4: 0.6629016
# 5: -1.3077109
# 6: 1.1971567
# 7: -0.7477408
# 8: 0.1601717
# 9: -0.4874609
# 10: -0.8164861
#
# $`Brain - Caudate (basal ganglia)`
# #Chr start end ID GTEX-N7MT-0011-R5a-SM-2I3G6
# 1: 13 20244503 20244980 13:20244503:20244980:clu_1587_NA -0.73425796
# 2: 13 20244503 20245346 13:20244503:20245346:clu_1587_NA 0.83044440
# 3: 13 20249124 20251864 13:20249124:20251864:clu_1588_NA -0.02207567
# 4: 13 20249793 20251864 13:20249793:20251864:clu_1588_NA 0.16780741
# 5: 13 20251963 20304379 13:20251963:20304379:clu_1589_NA -0.96313998
# 6: 13 20283739 20304379 13:20283739:20304379:clu_1589_NA 1.00445387
# 7: 13 20803888 20804837 13:20803888:20804837:clu_1590_NA -1.10454772
# 8: 13 20803888 20805005 13:20803888:20805005:clu_1590_NA 0.87244684
# 9: 13 20803888 20805521 13:20803888:20805521:clu_1590_NA -1.47490518
# 10: 13 20804946 20805521 13:20804946:20805521:clu_1590_NA -1.69929164
#
# $`Brain - Spinal cord (cervical c-1)`
# #Chr start end ID GTEX-Q2AG-0011-R9A-SM-2HMJ6
# 1: 13 20244503 20244980 13:20244503:20244980:clu_1587_NA -1.09599155
# 2: 13 20244503 20245346 13:20244503:20245346:clu_1587_NA 0.92548695
# 3: 13 20249124 20251864 13:20249124:20251864:clu_1588_NA -0.40406824
# 4: 13 20249793 20251864 13:20249793:20251864:clu_1588_NA 0.46501441
# 5: 13 20251963 20304379 13:20251963:20304379:clu_1589_NA 0.08697363
# 6: 13 20283739 20304379 13:20283739:20304379:clu_1589_NA -0.04966781
# 7: 13 20803888 20804837 13:20803888:20804837:clu_1590_NA -0.15193551
# 8: 13 20803888 20805005 13:20803888:20805005:clu_1590_NA -0.14492768
# 9: 13 20803888 20805521 13:20803888:20805521:clu_1590_NA 2.26125149
# 10: 13 20804946 20805521 13:20804946:20805521:clu_1590_NA 0.32149405
#
# $Lung
# #Chr start end ID GTEX-PW2O-0526-SM-2I3DX
# 1: 13 20244503 20244980 13:20244503:20244980:clu_1587_NA 0.32721133
# 2: 13 20244503 20245346 13:20244503:20245346:clu_1587_NA -0.08213476
# 3: 13 20249124 20251864 13:20249124:20251864:clu_1588_NA -1.68168241
# 4: 13 20249793 20251864 13:20249793:20251864:clu_1588_NA 1.55309040
# 5: 13 20251963 20304379 13:20251963:20304379:clu_1589_NA 0.96385901
# 6: 13 20283739 20304379 13:20283739:20304379:clu_1589_NA -0.89044547
# 7: 13 20803888 20804837 13:20803888:20804837:clu_1590_NA 0.12667894
# 8: 13 20803888 20805005 13:20803888:20805005:clu_1590_NA 0.13549352
# 9: 13 20803888 20805521 13:20803888:20805521:clu_1590_NA 0.12652705
# 10: 13 20804946 20805521 13:20804946:20805521:clu_1590_NA 0.14416639
# GTEX-OHPK-0526-SM-2HMJB GTEX-OXRL-0526-SM-2I3EZ GTEX-OXRK-0926-SM-2HMKP
# 1: -0.05645774 0.06747821 0.53607153
# 2: 0.23888779 0.15864565 -0.32498668
# 3: 1.69042151 1.78738099 0.28614696
# 4: -1.83313242 -1.88842810 -0.05841975
# 5: 0.40292406 0.50895489 0.24524915
# 6: -0.24664686 -0.31594565 -0.02313067
# 7: 0.09136061 0.11969509 0.13412507
# 8: 0.09930709 0.12698272 0.14104599
# 9: 0.09120931 0.11954333 0.13397303
# 10: 0.10756295 0.13458119 0.14766901
#
# $`Skin - Sun Exposed (Lower leg)`
# #Chr start end ID GTEX-Q2AG-0126-SM-2HMLB
# 1: 13 20244503 20244980 13:20244503:20244980:clu_1587_NA 0.01122552
# 2: 13 20244503 20245346 13:20244503:20245346:clu_1587_NA 0.09151192
# 3: 13 20249124 20251864 13:20249124:20251864:clu_1588_NA 0.94156107
# 4: 13 20249793 20251864 13:20249793:20251864:clu_1588_NA -1.00545250
# 5: 13 20251963 20304379 13:20251963:20304379:clu_1589_NA -0.17101732
# 6: 13 20283739 20304379 13:20283739:20304379:clu_1589_NA 0.26907797
# 7: 13 20803888 20804837 13:20803888:20804837:clu_1590_NA 2.26711789
# 8: 13 20803888 20805005 13:20803888:20805005:clu_1590_NA -2.79253862
# 9: 13 20803888 20805521 13:20803888:20805521:clu_1590_NA -0.73248347
# 10: 13 20804946 20805521 13:20804946:20805521:clu_1590_NA 1.14279010
#
# $`Whole Blood`
# #Chr start end ID GTEX-OIZH-0005-SM-2HMJN
# 1: 13 20244503 20244980 13:20244503:20244980:clu_1587_NA 2.0066926
# 2: 13 20244503 20245346 13:20244503:20245346:clu_1587_NA -2.5313974
# 3: 13 20249124 20251864 13:20249124:20251864:clu_1588_NA -1.1797539
# 4: 13 20249793 20251864 13:20249793:20251864:clu_1588_NA 0.8340660
# 5: 13 20251963 20304379 13:20251963:20304379:clu_1589_NA 1.4722278
# 6: 13 20283739 20304379 13:20283739:20304379:clu_1589_NA -1.9188667
# 7: 13 20803888 20804837 13:20803888:20804837:clu_1590_NA 0.5305132
# 8: 13 20803888 20805005 13:20803888:20805005:clu_1590_NA 0.9691472
# 9: 13 20803888 20805521 13:20803888:20805521:clu_1590_NA 0.3785295
# 10: 13 20804946 20805521 13:20804946:20805521:clu_1590_NA 1.1931558