问题描述
我有一个映射数据框和一个大数据框，其中每一行代表其序列的蛋白质。
我想要一种有效的方法，根据映射数据帧将序列映射到对应于氨基酸的值。
我能够遍历序列，并用以下代码替换它：
calcStickiness <- function(seq) {
seq_iter <- strsplit(unlist(seq),"")[[1]]
transformed_seq <- c()
for (c in seq_iter) {
transformed_seq <- c(transformed_seq,stickiness_tabel[stickiness_tabel["X"] == c][2])
}
print(transformed_seq)
}
# calling the function
calcStickiness(row["sequence_full"][1])
stickiness_tabel在哪里：
structure(list(X = c("K","E","D","N","Q","S","P","R","T","H","A","G","M","V","L","I","F","C","Y","W"),x = c(-1.25639466063649,-0.928687786101206,-0.700106643211895,-0.356971499674196,-0.295054350932285,-0.209468209138379,-0.177787659972006,-0.0892949396458573,0.0576667944592403,0.215277407729333,0.263739398989502,0.556792734365241,0.7448899445842,0.900506232741908,1.06680680601946,1.18416532767113,1.68723510186035,1.70109173545121,1.70150269278206,2.01452547017961
)),class = "data.frame",row.names = c(NA,-20L))
我想知道是否有更快的方法，因为序列的数据帧中有很多条目。
数据框的简单行是：
structure(list(X = 1L,code = "12as_1",nsub2 = 2L,pdb_error2 = "NO",QSBIO_err_prob = 3.5,chain_name = "B",sequence_full = "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDEDRLELEWHQALLRGEMPQTIGGGIGQSRLTMLLLQLPHIGQVQAGVWPAAVRESVPsll"),row.names = 1L,class = "data.frame")
我对sequence_full感兴趣的地方。
对于以下行：
MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDEDRLELEWHQALLRGEMPQTIGGGIGQSRLTMLLLQLPHIGQVQAGVWPAAVRESVPsll
我想得到类似的东西：
[1] " 0.74488994" "-1.25639466" " 0.05766679" " 0.26373940" " 1.70150269" " 1.18416533" " 0.26373940" "-1.25639466" "-0.29505435"
[10] "-0.08929494" "-0.29505435" " 1.18416533" "-0.20946821" " 1.68723510" " 0.90050623" "-1.25639466" "-0.20946821" " 0.21527741"
[19] " 1.68723510" "-0.20946821" "-0.08929494" "-0.29505435" " 1.06680681" "-0.92868779" "-0.92868779" "-0.08929494" " 1.06680681"
[28] " 0.55679273" " 1.06680681" " 1.18416533" "-0.92868779" " 0.90050623" "-0.29505435" " 0.26373940" "-0.17778766" " 1.18416533"
[37] " 1.06680681" "-0.20946821" "-0.08929494" " 0.90050623" " 0.55679273" "-0.70010664" " 0.55679273" " 0.05766679" "-0.29505435"
[46] "-0.70010664" "-0.35697150" " 1.06680681" "-0.20946821" " 0.55679273" " 0.26373940" "-0.92868779" "-1.25639466" " 0.26373940"
[55] " 0.90050623" "-0.29505435" " 0.90050623" "-1.25639466" " 0.90050623" "-1.25639466" " 0.26373940" " 1.06680681" "-0.17778766"
[64] "-0.70010664" " 0.26373940" "-0.29505435" " 1.68723510" "-0.92868779" " 0.90050623" " 0.90050623" " 0.21527741" "-0.20946821"
[73] " 1.06680681" " 0.26373940" "-1.25639466" " 2.01452547" "-1.25639466" "-0.08929494" "-0.29505435" " 0.05766679" " 1.06680681"
[82] " 0.55679273" "-0.29505435" " 0.21527741" "-0.70010664" " 1.68723510" "-0.20946821" " 0.26373940" " 0.55679273" "-0.92868779"
[91] " 0.55679273" " 1.06680681" " 1.70150269" " 0.05766679" " 0.21527741" " 0.74488994" "-1.25639466" " 0.26373940" " 1.06680681"
[100] "-0.08929494" "-0.17778766" "-0.70010664" "-0.92868779" "-0.70010664" "-0.08929494" " 1.06680681" "-0.20946821" "-0.17778766"
[109] " 1.06680681" " 0.21527741" "-0.20946821" " 0.90050623" " 1.70150269" " 0.90050623" "-0.70010664" "-0.29505435" " 2.01452547"
[118] "-0.70010664" " 2.01452547" "-0.92868779" "-0.08929494" " 0.90050623" " 0.74488994" " 0.55679273" "-0.70010664" " 0.55679273"
[127] "-0.92868779" "-0.08929494" "-0.29505435" " 1.68723510" "-0.20946821" " 0.05766679" " 1.06680681" "-1.25639466" "-0.20946821"
[136] " 0.05766679" " 0.90050623" "-0.92868779" " 0.26373940" " 1.18416533" " 2.01452547" " 0.26373940" " 0.55679273" " 1.18416533"
[145] "-1.25639466" " 0.26373940" " 0.05766679" "-0.92868779" " 0.26373940" " 0.26373940" " 0.90050623" "-0.20946821" "-0.92868779"
[154] "-0.92868779" " 1.68723510" " 0.55679273" " 1.06680681" " 0.26373940" "-0.17778766" " 1.68723510" " 1.06680681" "-0.17778766"
[163] "-0.70010664" "-0.29505435" " 1.18416533" " 0.21527741" " 1.68723510" " 0.90050623" " 0.21527741" "-0.20946821" "-0.29505435"
[172] "-0.92868779" " 1.06680681" " 1.06680681" "-0.20946821" "-0.08929494" " 1.70150269" "-0.17778766" "-0.70010664" " 1.06680681"
[181] "-0.70010664" " 0.26373940" "-1.25639466" " 0.55679273" "-0.08929494" "-0.92868779" "-0.08929494" " 0.26373940" " 1.18416533"
[190] " 0.26373940" "-1.25639466" "-0.70010664" " 1.06680681" " 0.55679273" " 0.26373940" " 0.90050623" " 1.68723510" " 1.06680681"
[199] " 0.90050623" " 0.55679273" " 1.18416533" " 0.55679273" " 0.55679273" "-1.25639466" " 1.06680681" "-0.20946821" "-0.70010664"
[208] " 0.55679273" " 0.21527741" "-0.08929494" " 0.21527741" "-0.70010664" " 0.90050623" "-0.08929494" " 0.26373940" "-0.17778766"
[217] "-0.70010664" " 1.70150269" "-0.70010664" "-0.70010664" " 2.01452547" "-0.20946821" " 0.05766679" "-0.17778766" "-0.20946821"
[226] "-0.92868779" " 1.06680681" " 0.55679273" " 0.21527741" " 0.26373940" " 0.55679273" " 1.06680681" "-0.35697150" " 0.55679273"
[235] "-0.70010664" " 1.18416533" " 1.06680681" " 0.90050623" " 2.01452547" "-0.35697150" "-0.17778766" " 0.90050623" " 1.06680681"
[244] "-0.92868779" "-0.70010664" " 0.26373940" " 1.68723510" "-0.92868779" " 1.06680681" "-0.20946821" "-0.20946821" " 0.74488994"
[253] " 0.55679273" " 1.18416533" "-0.08929494" " 0.90050623" "-0.70010664" " 0.26373940" "-0.70010664" " 0.05766679" " 1.06680681"
[262] "-1.25639466" " 0.21527741" "-0.29505435" " 1.06680681" " 0.26373940" " 1.06680681" " 0.05766679" " 0.55679273" "-0.70010664"
[271] "-0.92868779" "-0.70010664" "-0.08929494" " 1.06680681" "-0.92868779" " 1.06680681" "-0.92868779" " 2.01452547" " 0.21527741"
[280] "-0.29505435" " 0.26373940" " 1.06680681" " 1.06680681" "-0.08929494" " 0.55679273" "-0.92868779" " 0.74488994" "-0.17778766"
[289] "-0.29505435" " 0.05766679" " 1.18416533" " 0.55679273" " 0.55679273" " 0.55679273" " 1.18416533" " 0.55679273" "-0.29505435"
[298] "-0.20946821" "-0.08929494" " 1.06680681" " 0.05766679" " 0.74488994" " 1.06680681" " 1.06680681" " 1.06680681" "-0.29505435"
[307] " 1.06680681" "-0.17778766" " 0.21527741" " 1.18416533" " 0.55679273" "-0.29505435" " 0.90050623" "-0.29505435" " 0.26373940"
[316] " 0.55679273" " 0.90050623" " 2.01452547" "-0.17778766" " 0.26373940" " 0.26373940" " 0.90050623" "-0.08929494" "-0.92868779"
[325] "-0.20946821" " 0.90050623" "-0.17778766" "-0.20946821" " 1.06680681" " 1.06680681"
解决方法
我以与您相同的方式调用数据：
setState()现在，您可以做的是这样：
stickiness_tabel <- structure(list(X = c("K","E","D","N","Q","S","P","R","T","H","A","G","M","V","L","I","F","C","Y","W"),x = c(-1.25639466063649,-0.928687786101206,-0.700106643211895,-0.356971499674196,-0.295054350932285,-0.209468209138379,-0.177787659972006,-0.0892949396458573,0.0576667944592403,0.215277407729333,0.263739398989502,0.556792734365241,0.7448899445842,0.900506232741908,1.06680680601946,1.18416532767113,1.68723510186035,1.70109173545121,1.70150269278206,2.01452547017961
)),class = "data.frame",row.names = c(NA,-20L))
row <- structure(list(X = 1L,code = "12as_1",nsub2 = 2L,pdb_error2 = "NO",QSBIO_err_prob = 3.5,chain_name = "B",sequence_full = "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDEDRLELEWHQALLRGEMPQTIGGGIGQSRLTMLLLQLPHIGQVQAGVWPAAVRESVPSLL"),row.names = 1L,class = "data.frame")
它返回数字矢量列表。列表中的每个元素都对应于您转换的行，每个向量都是由相应字母命名的具有粘性级别的命名向量。
这是您期望的输出吗？因为您的问题对我来说还不清楚。
,也许data.table解决方案会满足您的需求。
我通过重复提供的行创建了一个包含1000行的样本数据集。
library(data.table)
df <- row[rep(1,1000),] #repeat row
df_dt <- setDT(df) # convert to data.table
value <- setNames(stickiness_tabel$x,stickiness_tabel$X)
start <- Sys.time()
df_dt[,sequence_full := lapply(sequence_full,function(x) value[unlist(strsplit(x,split = ""))])]
end <- Sys.time()
end - start
Time difference of 0.03744602 secs
df_dt[1,sequence_full]
[[1]]
M K T A Y I A K Q
0.74488994 -1.25639466 0.05766679 0.26373940 1.70150269 1.18416533 0.26373940 -1.25639466 -0.29505435
R Q I S F V K S H
-0.08929494 -0.29505435 1.18416533 -0.20946821 1.68723510 0.90050623 -1.25639466 -0.20946821 0.21527741
F S R Q L E E R L
1.68723510 -0.20946821 -0.08929494 -0.29505435 1.06680681 -0.92868779 -0.92868779 -0.08929494 1.06680681
G L I E V Q A P I
0.55679273 1.06680681 1.18416533 -0.92868779 0.90050623 -0.29505435 0.26373940 -0.17778766 1.18416533 ...
它将粘性表转变为向量，并为每行中的每个sequence_full编制索引。
要输出它，您可以执行以下操作：
write.csv(stack(unlist(df_dt[1,sequence_full])),file = "~/sequence_output.csv",row.names = F)
哪个会返回csv，其中一列包含粘性值，另一列包含序列元素。