如何使用 tidyr 包对不同时间点的重复数据进行平均并分别绘制？

Question

我有超过 100 个不同样本的数据集。样本来自不同的基因型（例如 X、Y、Z）和 4 个不同的时间点（T0、1、2、3），具有 3 个生物学重复（R1、2、3）。我正在测量 50 个不同基因的值（按行）。

structure(list(Gene = structure(1:2, .Label = c("A", "B"), class = "factor"), 
X_T0_R1 = c(1.46559502, 0.220140568), X_T0_R2 = c(1.087642983, 
0.237500819), X_T0_R3 = c(1.424945196, 0.21066267), X_T1_R1 = c(1.289943948, 
0.207778662), X_T1_R2 = c(1.376535013, 0.488774258), X_T1_R3 = c(1.833390311, 
0.182798731), X_T2_R1 = c(1.450753714, 0.247576125), X_T2_R2 = c(1.3094609, 
0.390028842), X_T2_R3 = c(0.5953716, 1.007079177), X_T3_R1 = c(0.7906009, 
0.730242116), X_T3_R2 = c(1.215333041, 1.012914813), X_T3_R3 = c(1.069312467, 
0.780421013), Y_T0_R1 = c(0.053317766, 3.316414959), Y_T0_R2 = c(0.506623748, 
3.599442788), Y_T0_R3 = c(0.713670106, 2.516735845), Y_T1_R1 = c(0.740998252, 
1.444496448), Y_T1_R2 = c(0.648231834, 0.097957459), Y_T1_R3 = c(0.780499252, 
0.187840968), Y_T2_R1 = c(0.35344654, 1.190274584), Y_T2_R2 = c(0.220223951, 
1.367784148), Y_T2_R3 = c(0.432856978, 1.403057729), Y_T3_R1 = c(0.234963735, 
1.232129062), Y_T3_R2 = c(0.353770497, 0.885122768), Y_T3_R3 = c(0.396091395, 
1.333921747), Z_T0_R1 = c(0.398000559, 1.286528398), Z_T0_R2 = c(0.384759325, 
1.122251177), Z_T0_R3 = c(1.582230097, 0.697419716), Z_T1_R1 = c(1.136843842, 
0.804552001), Z_T1_R2 = c(1.275683837, 1.227821594), Z_T1_R3 = c(0.963349308, 
0.968589683), Z_T2_R1 = c(3.765036263, 0.477443352), Z_T2_R2 = c(1.901023385, 
0.832736132), Z_T2_R3 = c(1.407713024, 0.911920317), Z_T3_R1 = c(0.988333629, 
1.095130142), Z_T3_R2 = c(0.618606729, 0.497458337), Z_T3_R3 = c(0.429823986, 
0.471389536)), .Names = c("Gene", "X_T0_R1", "X_T0_R2", "X_T0_R3", 
"X_T1_R1", "X_T1_R2", "X_T1_R3", "X_T2_R1", "X_T2_R2", "X_T2_R3", 
"X_T3_R1", "X_T3_R2", "X_T3_R3", "Y_T0_R1", "Y_T0_R2", "Y_T0_R3", 
"Y_T1_R1", "Y_T1_R2", "Y_T1_R3", "Y_T2_R1", "Y_T2_R2", "Y_T2_R3", 
"Y_T3_R1", "Y_T3_R2", "Y_T3_R3", "Z_T0_R1", "Z_T0_R2", "Z_T0_R3", 
"Z_T1_R1", "Z_T1_R2", "Z_T1_R3", "Z_T2_R1", "Z_T2_R2", "Z_T2_R3", 
"Z_T3_R1", "Z_T3_R2", "Z_T3_R3"), class = "data.frame", row.names = 
c(NA, -2L))

对于每个基因（即每一列），我想绘制一个图表，其中包含每个基因型 + SE 的平均重复次数

expected final graph look

我想通过创建一个新的数据框来做到这一点；包含每组复制的平均值和标准误差。 这怎么可能使用 tidyr 包？如何包含标准错误？ 我该如何改进这种编码？

data.mean<- data.frame(matrix(nrows=50))
for(col in seq(1,length(colnames(data)), by=3))
{data.mean <-cbind(data.mean,apply(subset(data, select=seq(col,length.out =   3)),1,mean, na.rm = TRUE))
colnames(data.mean)[ncol(data.mean)] <- colnames(data)[col]}

Answer 1

首先，我们需要将数据鞭打成合理的格式。这样就可以了：

(longdata <- dataset %>% 
  gather(key, value, -Gene) %>% 
  separate(key, c("Genotype", "Time", "Replicate")))
#> # A tibble: 72 x 5
#>    Gene  Genotype Time  Replicate value
#>    <fct> <chr>    <chr> <chr>     <dbl>
#>  1 A     X        T0    R1        1.47 
#>  2 B     X        T0    R1        0.220
#>  3 A     X        T0    R2        1.09 
#>  4 B     X        T0    R2        0.238
#>  5 A     X        T0    R3        1.42 
#>  6 B     X        T0    R3        0.211
#>  7 A     X        T1    R1        1.29 
#>  8 B     X        T1    R1        0.208
#>  9 A     X        T1    R2        1.38 
#> 10 B     X        T1    R2        0.489
#> # ... with 62 more rows

预先计算我们想要绘制的度量会变得更容易：

(longdata <- longdata %>% 
  group_by(Gene, Genotype, Time) %>% 
  summarize(Ave = mean(value), SE = sd(value)/sqrt(n())) %>% 
  ungroup() %>% 
  mutate(Time = factor(Time), Genotype = factor(Genotype)))
#> # A tibble: 24 x 5
#>    Gene  Genotype Time    Ave     SE
#>    <fct> <fct>    <fct> <dbl>  <dbl>
#>  1 A     X        T0    1.33  0.120 
#>  2 A     X        T1    1.50  0.169 
#>  3 A     X        T2    1.12  0.265 
#>  4 A     X        T3    1.03  0.125 
#>  5 A     Y        T0    0.425 0.195 
#>  6 A     Y        T1    0.723 0.0392
#>  7 A     Y        T2    0.336 0.0620
#>  8 A     Y        T3    0.328 0.0482
#>  9 A     Z        T0    0.788 0.397 
#> 10 A     Z        T1    1.13  0.0903
#> # ... with 14 more rows

我将一些变量更改为因子，因为绘图需要这样。

现在绘图，在这种情况下，“误差条”定义为 +/- 一个标准误差，因为您没有指定，但它可以更改。

longdata %>% 
  ggplot(aes(x = Time, y = Ave, fill = Genotype)) + 
  geom_bar(position = position_dodge(), stat = "identity") + 
  geom_errorbar(aes(ymin = Ave - SE, ymax = Ave + SE), 
                width = 0.1, 
                position = position_dodge(0.9)) + 
  facet_wrap(~ Gene)