我正在尝试整理几个样本中已鉴定肽序列的数据框：
Sample_Elu_HN，Sample_LW_HN，Sample_Elu_HM，Sample_LW_HM，Sample_Elu_M1，Sample_LW_M1，Sample_Elu_M2，Sample_LW_M2 ，Sample_Elu_N1，Sample_LW_N1，Sample_Elu_N2，Sample_LW_N2和Control_Preload_None。
数据框包含每个肽段的信息，包括每个样品中它们的丰度以及其鉴定的信心。
names <- c("Sequence", "Modifications", "Master Protein Accessions","Missed Cleavages",
"Abundance: Mean: Control, None, Preload","Abundance: SD: Control, None, Preload","Abundance: CV: Control, None, Preload",
"Abundance: Mean: Sample, HM, Elu","Abundance: SD: Sample, HM, Elu","Abundance: CV: Sample, HM, Elu",
"Abundance: Mean: Sample, HN, Elu","Abundance: SD: Sample, HN, Elu","Abundance: CV: Sample, HN, Elu",
"Abundance: Mean: Sample, M1, Elu","Abundance: SD: Sample, M1, Elu","Abundance: CV: Sample, M1, Elu",
"Abundance: Mean: Sample, M2, Elu","Abundance: SD: Sample, M2, Elu","Abundance: CV: Sample, M2, Elu",
"Abundance: Mean: Sample, N1, Elu","Abundance: SD: Sample, N1, Elu","Abundance: CV: Sample, N1, Elu",
"Abundance: Mean: Sample, N2, Elu","Abundance: SD: Sample, N2, Elu","Abundance: CV: Sample, N2, Elu",
"Abundance: Mean: Sample, HM, LW","Abundance: SD: Sample, HM, LW","Abundance: CV: Sample, HM, LW",
"Abundance: Mean: Sample, HN, LW","Abundance: SD: Sample, HN, LW","Abundance: CV: Sample, HN, LW",
"Abundance: Mean: Sample, M1, LW","Abundance: SD: Sample, M1, LW","Abundance: CV: Sample, M1, LW",
"Abundance: Mean: Sample, M2, LW","Abundance: SD: Sample, M2, LW","Abundance: CV: Sample, M2, LW",
"Abundance: Mean: Sample, N1, LW","Abundance: SD: Sample, N1, LW","Abundance: CV: Sample, N1, LW",
"Abundance: Mean: Sample, N2, LW","Abundance: SD: Sample, N2, LW","Abundance: CV: Sample, N2, LW",
"Found in Sample Group: Control, Preload, None","Found in Sample Group: Sample, Elu, HM",
"Found in Sample Group: Sample, Elu, HN","Found in Sample Group: Sample, Elu, M1",
"Found in Sample Group: Sample, Elu, M2","Found in Sample Group: Sample, Elu, N1",
"Found in Sample Group: Sample, Elu, N2","Found in Sample Group: Sample, LW, HM",
"Found in Sample Group: Sample, LW, HN","Found in Sample Group: Sample, LW, M1",
"Found in Sample Group: Sample, LW, M2","Found in Sample Group: Sample, LW, N1",
"Found in Sample Group: Sample, LW, N2")
peptide1 <- c("FQSEEQQQTEDELQDK","1xPhospho [S3(100)]","P02666",0,591079706.5,129831141.4,21.96508186,92078374.7,5559797.773,6.038114585,130764801.6,11101742.04,8.489854991,304661843.6,89701289.78,29.44290257,100024065.8,174405.3367,0.174363375,20777445.26,7953029.115,38.27722329,43696929.72,10030935.24,22.95569805,496031039,260945694.4,52.60672697,111323285.3,32961482.23,29.60879402,329268465.6,243189584.2,73.85753864,478737037.1,153121463.4,31.98446151,701372889.6,20000942.58,2.851684585,847417746,84344510.23,9.953120599,"High","High","High","High","High","High","High","High","High","Found","High","High","High")
peptide2 <- c("HPGDFGADAQGAMTK","1xPhospho [H1(100)]","P68082",0,295017576,49088902.73,16.63931464,2845912.875,709262.9265,24.92215882,3659951.5,215619.485,5.891320828,41946172,301640.4391,0.719113151,9336196.75,1507110.776,16.14266298,1469308.375,434213.7682,29.55225572,1607320,498424.3673,31.00965379,191151516,137956380.3,72.17121954,236416096,97608884.31,41.28690303,119327816,55998433.41,46.92823123,152802424,9555841.041,6.253723462,147086456,33874815.85,23.03054732,255244232,75472108.91,29.56858548,"High","High","High","High","High","High","High","High","High","Not Found","High","Not Found","High")
peptide3 <- c("IEKFQSEEQQQTEDELQDK","","P02666",1,75099003,12104439.14,16.11797582,18015945.88,6770542.657,37.58083369,7913736.75,4197999.975,53.04700053,46005954.5,8581332.638,18.65265645,14313846.5,4426286.925,30.92311298,5085692.75,528187.9059,10.38576123,7676983.313,3681526.619,47.95538129,24546758.5,13126407.14,53.47511419,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,9098671.051,22.39353163,31170934,NA,NA,"High","High","High","High","High","High","High","Not Found","Not Found","Not Found","High","Not Found","High")
example.data <- as.data.frame(rbind(peptide1, peptide2, peptide3))
colnames(example.data) <- names
example.data
我想要的是将列"Abundance: Mean: ..."，"Abundance: SD: ..."，"Abundance: CV:..."和"Found in Sample Group: ..."收集到"Mean"，"SD"，{{1 }}和"CV"。
这是我尝试过的：
"Found"但是，它在不同的行中将平均值，标准差和CV值与“找到的”值分开，留下了很多NA值...
什么是固定的，以便所有变量的所有值都包含在同一行中？
一如既往地感谢您的帮助！
答案 0 :(得分：1)
您需要将要堆叠的名称更改为单个模式。
library(dplyr)
library(tidyr)
example.data %>%
rename_with(~ sub(".+?:\\s", "", .), starts_with("Abundance")) %>%
rename_with(~ sub(".+:(.+),(.+),(.+)", "Found:\\1,\\3,\\2", .), starts_with("Found")) %>%
pivot_longer(-(1:4), names_to = c(".value", "Set"), names_sep = ":\\s") %>%
separate(Set, c("Sample", "Polymer", "Fraction"))
# # A tibble: 39 x 11
# Sequence Modifications `Master Protein Accessi… `Missed Cleavages` Sample Polymer Fraction Mean SD CV Found
# <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
# 1 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Contr… None Preload 59107970… 12983114… 21.96508… High
# 2 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample HM Elu 92078374… 5559797.… 6.038114… High
# 3 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample HN Elu 13076480… 11101742… 8.489854… High
# 4 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample M1 Elu 30466184… 89701289… 29.44290… High
# 5 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample M2 Elu 10002406… 174405.3… 0.174363… High
# 6 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample N1 Elu 20777445… 7953029.… 38.27722… High
# 7 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample N2 Elu 43696929… 10030935… 22.95569… High
# 8 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample HM LW 496031039 26094569… 52.60672… High
# 9 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample HN LW 11132328… 32961482… 29.60879… High
# 10 FQSEEQQQTEDEL… 1xPhospho [S3(100… P02666 0 Sample M1 LW 32926846… 24318958… 73.85753… Found
# # … with 29 more rows
您的数据中存在陷阱。以Found开头的那些列应重命名以匹配其他列的模式。例如
Found in Sample Group: Sample, Elu, HM
应重命名为
Found in Sample Group: Sample, HM, Elu
以此类推。