r - dplyr - 根据条件从两个不同的数据帧中减去

Question

我有一个如下所示的数据框:

quant <- structure(list(Name = structure(c(158L, 159L, 160L, 161L, 162L, 
163L, 164L, 165L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 98L, 
99L, 100L, 101L), .Label = c("abc_02_NEHC_025_100_A", "abc_02_NEHC_025_100_B", 
"abc_02_NEHC_025_100_C", "abc_02_NEHC_025_100_D", "abc_02_NEHC_025_100_E", 
"abc_02_NEHC_025_100_F", "abc_02_NEHC_025_100_G", "abc_02_NEHC_025_100_H", 
"abc_02_NEHC_05_100_A", "abc_02_NEHC_05_100_B", "abc_02_NEHC_05_100_C", 
"abc_02_NEHC_05_100_D", "abc_02_NEHC_05_100_E", "abc_02_NEHC_05_100_F", 
"abc_02_NEHC_05_100_G", "abc_02_NEHC_05_100_H", "abc_02_NEHC_100_1_A", 
"abc_02_NEHC_100_1_B", "abc_02_NEHC_100_1_C", "abc_02_NEHC_100_1_D", 
"abc_02_NEHC_100_1_E", "abc_02_NEHC_100_1_F", "abc_02_NEHC_100_1_G", 
"abc_02_NEHC_100_1_H", "abc_02_VL_025_100_A", "abc_02_VL_025_100_B", 
"abc_02_VL_025_100_C", "abc_02_VL_025_100_D", "abc_02_VL_025_100_E", 
"abc_02_VL_025_100_F", "abc_02_VL_025_100_G", "abc_02_VL_025_100_H", 
"abc_02_VL_05_100_A", "abc_02_VL_05_100_B", "abc_02_VL_05_100_C", 
"abc_02_VL_05_100_D", "abc_02_VL_05_100_E", "abc_02_VL_05_100_F", 
"abc_02_VL_05_100_G", "abc_02_VL_05_100_H", "abc_02_VL_1_100_A", 
"abc_02_VL_1_100_B", "abc_02_VL_1_100_C", "abc_02_VL_1_100_D", 
"abc_02_VL_1_100_E", "abc_02_VL_1_100_F", "abc_02_VL_1_100_G", 
"abc_02_VL_1_100_H", "BACKGROUND_NEHC_0125_100_A", "BACKGROUND_NEHC_0125_100_B", 
"BACKGROUND_NEHC_0125_100_C", "BACKGROUND_NEHC_0125_100_D", "BACKGROUND_NEHC_0125_100_E", 
"BACKGROUND_NEHC_0125_100_F", "BACKGROUND_NEHC_0125_100_G", "BACKGROUND_NEHC_025_100_A", 
"BACKGROUND_NEHC_025_100_B", "BACKGROUND_NEHC_025_100_C", "BACKGROUND_NEHC_025_100_D", 
"BACKGROUND_NEHC_025_100_F", "BACKGROUND_NEHC_025_100_G", "BACKGROUND_NEHC_05_100_A", 
"BACKGROUND_NEHC_05_100_B", "BACKGROUND_NEHC_05_100_C", "BACKGROUND_NEHC_05_100_D", 
"BACKGROUND_NEHC_05_100_F", "BACKGROUND_NEHC_05_100_G", "BACKGROUND_NEHC_05_100_H", 
"BACKGROUND_NEHC_1_100_A", "BACKGROUND_NEHC_1_100_B", "BACKGROUND_NEHC_1_100_C", 
"BACKGROUND_NEHC_1_100_D", "BACKGROUND_NEHC_1_100_E", "BACKGROUND_NEHC_1_100_F", 
"BACKGROUND_NEHC_1_100_G", "BACKGROUND_VL_0125_100_A", "BACKGROUND_VL_0125_100_B", 
"BACKGROUND_VL_0125_100_C", "BACKGROUND_VL_0125_100_D", "BACKGROUND_VL_0125_100_E", 
"BACKGROUND_VL_0125_100_F", "BACKGROUND_VL_025_100_A", "BACKGROUND_VL_025_100_B", 
"BACKGROUND_VL_025_100_C", "BACKGROUND_VL_025_100_D", "BACKGROUND_VL_025_100_E", 
"BACKGROUND_VL_025_100_F", "BACKGROUND_VL_025_100_G", "BACKGROUND_VL_025_100_H", 
"BACKGROUND_VL_05_100_A", "BACKGROUND_VL_05_100_B", "BACKGROUND_VL_05_100_C", 
"BACKGROUND_VL_05_100_D", "BACKGROUND_VL_05_100_E", "BACKGROUND_VL_05_100_F", 
"BACKGROUND_VL_05_100_G", "BACKGROUND_VL_05_100_H", "BACKGROUND_VL_1_100_A", 
"BACKGROUND_VL_1_100_B", "BACKGROUND_VL_1_100_C", "BACKGROUND_VL_1_100_D", 
"BACKGROUND_VL_1_100_E", "BACKGROUND_VL_1_100_F", "BACKGROUND_VL_1_100_G", 
"BACKGROUND_VL_1_100_H", "Epq_11_NEHC_0125_100_a", "Epq_11_NEHC_0125_100_B", 
"Epq_11_NEHC_0125_100_C", "Epq_11_NEHC_0125_100_D", "Epq_11_NEHC_0125_100_E", 
"Epq_11_NEHC_0125_100_F", "Epq_11_NEHC_0125_100_G", "Epq_11_NEHC_025_100_a", 
"Epq_11_NEHC_025_100_B", "Epq_11_NEHC_025_100_C", "Epq_11_NEHC_025_100_D", 
"Epq_11_NEHC_025_100_E", "Epq_11_NEHC_05_100_a", "Epq_11_NEHC_05_100_B", 
"Epq_11_NEHC_05_100_C", "Epq_11_NEHC_05_100_D", "Epq_11_NEHC_05_100_E", 
"Epq_11_NEHC_05_100_F", "Epq_11_NEHC_05_100_G", "Epq_11_NEHC_05_100_H", 
"Epq_11_NEHC_1_100_a", "Epq_11_NEHC_1_100_B", "Epq_11_NEHC_1_100_C", 
"Epq_11_NEHC_1_100_D", "Epq_11_NEHC_1_100_E", "Epq_11_NEHC_1_100_F", 
"Epq_11_NEHC_1_100_G", "Epq_11_NEHC_1_100_H", "Epq_11_VL_0125_100_A", 
"Epq_11_VL_0125_100_B", "Epq_11_VL_0125_100_C", "Epq_11_VL_0125_100_D", 
"Epq_11_VL_0125_100_E", "Epq_11_VL_0125_100_F", "Epq_11_VL_0125_100_G", 
"Epq_11_VL_0125_100_H", "Epq_11_VL_025_100_A", "Epq_11_VL_025_100_B", 
"Epq_11_VL_025_100_C", "Epq_11_VL_025_100_D", "Epq_11_VL_025_100_E", 
"Epq_11_VL_025_100_F", "Epq_11_VL_025_100_G", "Epq_11_VL_025_100_H", 
"Epq_11_VL_05_100_A", "Epq_11_VL_05_100_B", "Epq_11_VL_05_100_C", 
"Epq_11_VL_05_100_D", "Epq_11_VL_05_100_E", "Epq_11_VL_05_100_F", 
"Epq_11_VL_05_100_G", "Epq_11_VL_05_100_H", "Epq_11_VL_1_100_A", 
"Epq_11_VL_1_100_B", "Epq_11_VL_1_100_C", "Epq_11_VL_1_100_D", 
"Epq_11_VL_1_100_E", "Epq_11_VL_1_100_F", "Epq_11_VL_1_100_G", 
"Epq_11_VL_1_100_H"), class = "factor"), conc_factor = structure(c(4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L), .Label = c("pep_0.125", "pep_0.25", "pep_0.5", "pep_1.0"
), class = "factor"), peptide_factor = structure(c(3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L), .Label = c("ABC", "Background", "EpQ_11"), class = "factor"), 
    serum_factor = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("NEHC", 
    "VL"), class = "factor"), mean_fluorescence = c(65535, 65535, 
    65534.93359, 65535, 65535, 65535, 65535, 65535, 21322.06055, 
    22704.08594, 22546.32617, 21801.30664, 21668.2168, 22054.40234, 
    21621.54688, 21516.33984, 17760.80273, 17886.12891, 18382.7832, 
    17531.80273)), class = "data.frame", row.names = c(NA, -20L
), .Names = c("Name", "conc_factor", "peptide_factor", "serum_factor", 
"mean_fluorescence"))

这实际上只是我完整数据框的一部分 (1:20)。为了更好地了解我的完整数据框，我将粘贴到变量 conc_factor、peptide_factor 和 serum_factor 的下方:

levels(quant$conc_factor)
[1] "pep_0.125" "pep_0.25"  "pep_0.5"   "pep_1.0"  
levels(quant$peptide_factor)
[1] "ABC"        "Background" "EpQ_11"    
levels(quant$serum_factor)
[1] "NEHC" "VL"  

使用以下命令:

summary_backgrounds <- quant %>% filter(peptide_factor=="Background") %>% group_by(conc_factor, serum_factor) %>% summarise(avg_fluorescence_grouped = mean(mean_fluorescence))

  conc_factor serum_factor avg_fluorescence_grouped
       <fctr>       <fctr>                    <dbl>
1   pep_0.125         NEHC                 18439.70
2   pep_0.125           VL                 16985.60
3    pep_0.25         NEHC                 18666.52
4    pep_0.25           VL                 17577.98
5     pep_0.5         NEHC                 18300.47
6     pep_0.5           VL                 18010.99
7     pep_1.0         NEHC                 16103.50
8     pep_1.0           VL                 17710.50

对于每个 conc_factor 和 serum_factor，我都获得了背景的 mean_fluorescence 值。我现在要做的是:我想向数据框 quant 添加一个新变量(名为 avg_fluorescence_minus_background)，我将在其中减去背景值( summary_backgrounds$avg_fluorescence_grouped，考虑 quant$mean_fluorescence 上每个单独值的 conc_factor 和 serum_factor。

例如，对于 quant[1, ]，假设我有 conc_factor=="pep_1.0" 和 serum_factor=="VL"，我的结果是 65535.00 - 17710.50 = 47824.5。等等。

Answer 1

阅读有关联接的内容，您会发现它们使这类问题非常容易解决:

quant <- left_join(quant, summary_backgrounds, by = c("conc_factor", "serum_factor"))
mutate(quant, avg_flourescence_minus_bg = mean_fluorescence - avg_fluorescence_grouped)