我正在尝试创建一个脚本来自动进行分析。特定分析物具有用于检测它的不同通道（通道 1-4），但机器会输出包含所有 4 个通道结果的文件。对于我的分析，我想根据通道进行子集化，因此我想在脚本开始时提供该通道，这样这就是我接下来需要更改的全部内容。但是当我使用 if else 语句根据通道向量对 df 进行子集化时，它会返回一个列表：
Channel <- "CH1"
head(WG1)
X img_nr class cycle id x y x_glb y_glb width yolk_x yolk_y yolk_width adjust adjust_suppl CH1..yolk_mean
1 0 1 wellA1_col1_row1 1 1 1332.9 1951.6 1332.9 1951.6 43.1 1336.5 1951.8 21.4 1 2 46.3
2 1 1 wellA1_col1_row1 1 2 1774.8 1950.0 1774.8 1950.0 47.9 1775.2 1948.3 24.9 1 2 50.1
3 2 1 wellA1_col1_row1 1 3 708.1 1949.7 708.1 1949.7 47.6 710.0 1949.0 27.2 1 2 45.8
4 3 1 wellA1_col1_row1 1 4 405.6 1948.7 405.6 1948.7 44.9 405.6 1948.7 28.0 1 0 32.8
5 4 1 wellA1_col1_row1 1 5 1832.3 1946.4 1832.3 1946.4 45.9 1834.9 1947.1 22.8 1 2 33.5
6 5 1 wellA1_col1_row1 1 6 2042.6 1946.8 2042.6 1946.8 47.6 2044.6 1946.8 21.5 1 2 46.5
CH1..yolk_sd CH1..yolk_sat CH1..yolk_mean_hist CH1..yolk_sd_hist CH1..confi CH1..mean_hist CH1..sd_hist CH1..bg_hist CH1..contrast
1 2.261 128.8 45.8 1.442 1 41.2 2.570 25.1 17.1
2 2.200 139.5 49.6 1.378 1 42.0 3.605 26.7 16.7
3 2.650 127.2 45.2 1.640 1 40.3 2.974 26.6 14.9
4 1.943 90.9 32.3 1.218 1 31.5 1.402 22.9 8.9
5 1.847 93.1 33.1 1.227 1 31.2 1.259 24.6 7.1
6 2.454 129.3 46.0 1.559 1 42.3 2.712 26.0 17.4
CH1..mean CH1..sd CH1..sat CH2..yolk_mean CH2..yolk_sd CH2..yolk_sat CH2..yolk_mean_hist CH2..yolk_sd_hist CH2..confi CH2..mean_hist
1 42.0 3.647 116.0 21.65 1.640 113.9 21.33 1.194 1 15.96
2 43.0 4.956 118.1 21.47 1.538 112.8 21.12 1.038 1 16.42
3 41.1 4.260 113.3 19.79 1.989 103.2 19.32 1.164 1 15.14
4 32.1 2.257 88.5 17.28 1.565 90.2 16.89 1.009 1 13.91
5 31.7 2.093 87.7 21.04 1.380 110.7 20.73 0.884 1 16.38
6 43.5 4.545 119.2 21.71 1.358 114.4 21.42 0.883 1 17.50
CH2..sd_hist CH2..bg_hist CH2..contrast CH2..mean CH2..sd CH2..sat CH3..yolk_mean CH3..yolk_sd CH3..yolk_sat CH3..yolk_mean_hist
1 1.092 9.02 7.49 16.38 1.669 85.3 24.27 0.903 210.4 24.06
2 1.220 10.27 6.74 16.82 1.783 87.7 24.45 0.885 212.1 24.25
3 1.164 9.49 5.99 15.49 1.701 80.9 23.60 0.942 204.4 23.37
4 1.115 5.14 9.17 14.42 1.884 74.3 23.47 0.953 203.1 23.22
5 1.066 9.70 7.11 16.77 1.615 87.5 24.50 0.924 212.4 24.29
6 1.156 10.78 7.11 17.99 1.929 93.5 24.92 0.942 216.1 24.70
CH3..yolk_sd_hist CH3..confi CH3..mean_hist CH3..sd_hist CH3..bg_hist CH3..contrast CH3..mean CH3..sd CH3..sat CH4..yolk_mean
1 0.556 1 23.93 0.569 21.75 2.52 24.15 0.900 209.3 238.1
2 0.582 1 24.08 0.588 21.97 2.40 24.31 0.924 210.6 251.4
3 0.545 1 23.18 0.586 21.15 2.17 23.42 0.919 202.8 197.5
4 0.582 1 23.19 0.596 20.87 2.63 23.42 0.931 202.8 173.7
5 0.590 1 24.18 0.595 21.96 2.40 24.40 0.947 211.5 251.5
6 0.586 1 24.47 0.584 22.09 2.63 24.69 0.919 214.0 243.5
CH4..yolk_sd CH4..yolk_sat CH4..yolk_mean_hist CH4..yolk_sd_hist CH4..confi CH4..mean_hist CH4..sd_hist CH4..bg_hist CH4..contrast
1 9.395 203.8 235.8 5.996 1 235.4 5.664 215.5 20.8
2 9.316 215.2 249.1 6.067 1 249.3 5.993 226.3 24.3
3 8.171 168.9 195.5 4.997 1 195.0 5.075 175.3 22.0
4 7.831 148.5 171.9 5.060 1 172.2 5.046 152.0 22.0
5 9.639 215.2 249.1 6.137 1 248.8 5.948 225.5 25.5
6 9.336 208.5 241.3 5.793 1 241.5 5.709 219.1 24.3
CH4..mean CH4..sd CH4..sat masked well pos relevant..double_bead relevant..well_circle width_0 width_muem yolk_width_muem
1 237.5 9.031 203.3 1 A1 col1_row1 1 1 43 82.9675 41.1950
2 251.6 9.370 215.4 1 A1 col1_row1 1 1 48 92.2075 47.9325
3 196.9 7.965 168.5 1 A1 col1_row1 1 1 48 91.6300 52.3600
4 174.0 8.020 148.7 0 A1 col1_row1 1 0 45 86.4325 53.9000
5 250.9 9.137 215.0 1 A1 col1_row1 1 1 46 88.3575 43.8900
6 243.8 9.314 208.7 1 A1 col1_row1 1 1 48 91.6300 41.3875
ratio_yolk_to_bead_width CH1..yolk_cv_hist CH2..yolk_cv_hist CH3..yolk_cv_hist CH4..yolk_cv_hist CH1..yolk_contrast
1 0.4965197 0.031 0.056 0.023 0.025 5.1
2 0.5198330 0.028 0.049 0.024 0.024 8.1
3 0.5714286 0.036 0.060 0.023 0.026 5.5
4 0.6236080 0.038 0.060 0.025 0.029 1.3
5 0.4967320 0.037 0.043 0.024 0.025 2.3
6 0.4516807 0.034 0.041 0.024 0.024 4.2
CH2..yolk_contrast CH3..yolk_contrast CH4..yolk_contrast valid..image_seg valid..image_yolk_seg CH1..bg_hist_norm CH2..bg_hist_norm
1 5.69 0.34 2.7 1 1 25.1 9.02
2 5.05 0.37 2.1 1 1 26.7 10.27
3 4.65 0.42 2.5 1 1 26.6 9.49
4 3.37 0.28 1.5 1 0 22.9 5.14
5 4.66 0.32 2.7 1 1 24.6 9.70
6 4.21 0.45 2.0 1 1 26.0 10.78
CH3..bg_hist_norm CH4..bg_hist_norm valid..bead_background valid..yolk_width_ratio CH1..yolk_contrast_norm CH2..yolk_contrast_norm
1 21.75 215.5 0 NaN 5.1 5.69
2 21.97 226.3 0 NaN 8.1 5.05
3 21.15 175.3 1 1 5.5 4.65
4 20.87 152.0 NaN NaN 1.3 3.37
5 21.96 225.5 0 NaN 2.3 4.66
6 22.09 219.1 0 NaN 4.2 4.21
CH3..yolk_contrast_norm CH4..yolk_contrast_norm CH1..contrast_norm CH2..contrast_norm CH3..contrast_norm CH4..contrast_norm
1 0.34 2.7 17.1 7.49 2.52 20.8
2 0.37 2.1 16.7 6.74 2.40 24.3
3 0.42 2.5 14.9 5.99 2.17 22.0
4 0.28 1.5 8.9 9.17 2.63 22.0
5 0.32 2.7 7.1 7.11 2.40 25.5
6 0.45 2.0 17.4 7.11 2.63 24.3
bead_type Bead.Type.Name valid..bead_width valid..yolk_width Validity CH1..level CH3..level validJT
1 1 Analyte_1_Analyte_2 1 NaN valid::bead_background invalid invalid invalid
2 1 Analyte_1_Analyte_2 1 NaN valid::bead_background invalid invalid invalid
3 1 Analyte_1_Analyte_2 1 1 valid positive NaN invalid
4 1 Analyte_1_Analyte_2 1 NaN relevant::well_circle invalid invalid invalid
5 1 Analyte_1_Analyte_2 1 NaN valid::bead_background invalid invalid invalid
6 1 Analyte_1_Analyte_2 1 NaN valid::bead_background invalid invalid invalid
WG1Raw <- ifelse(Channel == "CH1", subset(WG1[,c('class','CH1..contrast','validJT','well')]),
ifelse(Channel == "CH2", subset(WG1[,c('class','CH2..contrast','validJT','well')]),
ifelse(Channel == "CH3", subset(WG1[,c('class','CH3..contrast','validJT','well')]),subset(WG1[,c('class','CH4..contrast','validJT','well')]))))
这返回一个列表，而不是我想要的 df 。
谢谢提前
# mockup data:
req_channel <- "CH1"
WG1 <- data.frame(class = "WellA1_col1_row1",
cycle = 1,
id = 1:6,
x = runif(6),
y= runif(6),
CH1..yolk_sd = runif(6),
CH1..yolk_sat = runif(6),
CH1..yolk_mean_hist = runif(6),
CH2..yolk_sd = runif(6),
CH2..yolk_sat = runif(6),
CH2..yolk_mean_hist = runif(6),
CH3..yolk_sd = runif(6),
CH3..yolk_sat = runif(6),
CH3..yolk_mean_hist = runif(6),
CH4..yolk_sd = runif(6),
CH4..yolk_sat = runif(6),
CH4..yolk_mean_hist = runif(6)
)
IMO 解决这个问题的最简洁（或最简洁）的方法是扩大范围，将频道数字分开：
WG1 |> pivot_longer(cols = starts_with("CH"),
names_to = c("Channel", "Type"),
names_sep = "\\.+") |>
filter(Channel %in% req_channel)
给出：
# A tibble: 18 × 8
class cycle id x y Channel Type value
<chr> <dbl> <int> <dbl> <dbl> <chr> <chr> <dbl>
1 WellA1_col1_row1 1 1 0.399 0.139 CH1 yolk_sd 0.855
2 WellA1_col1_row1 1 1 0.399 0.139 CH1 yolk_sat 0.576
3 WellA1_col1_row1 1 1 0.399 0.139 CH1 yolk_mean_hist 0.347
4 WellA1_col1_row1 1 2 0.934 0.152 CH1 yolk_sd 0.140
5 WellA1_col1_row1 1 2 0.934 0.152 CH1 yolk_sat 0.497
6 WellA1_col1_row1 1 2 0.934 0.152 CH1 yolk_mean_hist 0.718
7 WellA1_col1_row1 1 3 0.421 0.582 CH1 yolk_sd 0.381
8 WellA1_col1_row1 1 3 0.421 0.582 CH1 yolk_sat 0.00176
9 WellA1_col1_row1 1 3 0.421 0.582 CH1 yolk_mean_hist 0.875
10 WellA1_col1_row1 1 4 0.704 0.401 CH1 yolk_sd 0.416
11 WellA1_col1_row1 1 4 0.704 0.401 CH1 yolk_sat 0.0469
12 WellA1_col1_row1 1 4 0.704 0.401 CH1 yolk_mean_hist 0.376
13 WellA1_col1_row1 1 5 0.230 0.373 CH1 yolk_sd 0.287
14 WellA1_col1_row1 1 5 0.230 0.373 CH1 yolk_sat 0.804
15 WellA1_col1_row1 1 5 0.230 0.373 CH1 yolk_mean_hist 0.274
16 WellA1_col1_row1 1 6 0.880 0.320 CH1 yolk_sd 0.363
17 WellA1_col1_row1 1 6 0.880 0.320 CH1 yolk_sat 0.941
18 WellA1_col1_row1 1 6 0.880 0.320 CH1 yolk_mean_hist 0.955