從R中的資料框列值均值和標準差創建表

我是 R 新手，所以不知道代碼。我有兩個資料框。一個資料框看起來像這樣。

df

另一個資料框如下所示：

df1

我想制作一個表格，包括每個基因的 mean_AML、sd_AML（標準差）、min_AML、max_AML、mean_Control、sd_Control、min_Control、max_Control 和 Fold_change（即 mean_AML – mean_Control）。使用內置函式很好。

無法弄清楚我該怎么做。請幫忙。

謝謝。

- - 更新 - -

提示：將資料集拆分為 AML 資料和正常資料集，然后對于每個基因/探針集，分別計算其跨樣本的均值、標準導數、最小和最大表達值（使用內置函式），并進一步合并這些統計資料每個基因的值放在一張表中。應用 data.frame() 并為創建的表賦予與基因表達資料表相同的行名。

uj5u.com熱心網友回復：

我們可以結合起來pivot_longer使用right_join，然后使用summarise該組：

library(dplyr)
library(tidyr)
df1 %>% 
  pivot_longer(
    everything(),
    names_to = "ID", 
    values_to = "value"
  ) %>% 
  right_join(df, by="ID") %>% 
  group_by(Disease) %>% 
  summarise(Min = min(value), Mean = mean(value), Max = max(value), Sd = sd(value)) %>%
  ungroup()

  Disease   Min  Mean   Max    Sd
  <chr>   <dbl> <dbl> <dbl> <dbl>
1 AML      2.13  4.91  9.53  2.04
2 Control  2.13  4.92 11.4   2.12

uj5u.com熱心網友回復：

使用以下命令將第二個資料幀以長格式放置會有所幫助pivot_longer()：

library(tidyr)
newdf <- pivot_longer(df1, 
    cols = everything(),
    names_to = 'ID',
    values_to = 'value')

然后merge()將兩個資料框合二為一：

df.all <- merge(df, newdf, by = 'ID', all = T)

然后你可以group_by(gene)and summarise()，添加你需要的任何值：

library(dplyr)
df.all %>% group_by(gene) %>% summarise(sd = sd(value), min = min(value), max = max(value), mean = mean(value))

uj5u.com熱心網友回復：

基本 R方法。

df_new <- data.frame(t(df1), ID=colnames(df1))
df_new <- merge(df, df_new, by = 'ID')
out <- apply(df_new[, grep('^X', names(df_new))], 1, function(x) {
    data.frame(min=min(x), IQR_low=quantile(x, .25),
          mean=mean(x), median=median(x),IQR_high=quantile(x, .75),
          max=max(x), sd=sd(x))
})
out <- round(do.call(rbind, out), 2L)
out <- cbind(Disease=df_new$Disease, out)
rownames(out) <- df_new$ID

輸出

> out
          Disease  min IQR_low mean median IQR_high   max   sd
GSM239170 Control 2.20    2.97 4.56   4.24     6.10  7.98 1.91
GSM239323 Control 2.33    3.22 5.04   4.98     6.49  8.82 1.98
GSM239324 Control 2.13    2.91 4.93   4.52     6.48 11.42 2.45
GSM239326 Control 2.18    2.91 4.97   4.66     6.57 10.75 2.34
GSM239328     AML 2.13    2.88 4.91   4.69     6.56  9.30 2.15
GSM239329     AML 2.27    3.11 4.95   4.57     6.48  9.53 2.13
GSM239331     AML 2.26    2.97 4.88   4.78     6.43  8.29 1.96
GSM239332 Control 2.25    3.08 4.97   4.63     6.39  9.77 2.16
GSM239333 Control 2.29    3.04 5.01   4.75     6.47  9.77 2.14

現在我們需要找到您定義為 mean_AML - mean_Control 的所有 18 倍變化。這可以通過以下函式來完成，該函式fold_change_fun()根據mean或回傳折疊更改median。

fold_change_fun <- function(x, statistic = c('mean', 'median')) {
  AML_genes <- rownames(x[x$Disease %in% 'AML', ])
  Control_genes <- rownames(x[x$Disease %in% 'Control', ])
  AML <- x[x$Disease %in% 'AML', statistic]
  Control <- x[x$Disease %in% 'Control', statistic]
  nrow_AML <- nrow(out[out$Disease %in% 'AML', ])
  nrow_Control <- nrow(out[out$Disease %in% 'Control', ])
  out <- matrix(c(prod(nrow_AML, nrow_Control)),
                nrow = nrow_AML, ncol = nrow_Control)
  for(i in seq_len(nrow_AML)) {
    for(j in seq_len(nrow_Control)) {
      out[i, j] <- AML[i] - Control[j]
    }
  }
  out <- t(out)
  colnames(out) <- paste(AML_genes, 'AML')
  rownames(out) <- paste(Control_genes, 'Control')
  return(out)
}

輸出

> fold_change_fun(x = out, statistic = 'mean')
                  GSM239328 AML GSM239329 AML GSM239331 AML
GSM239170 Control          0.35          0.39          0.32
GSM239323 Control         -0.13         -0.09         -0.16
GSM239324 Control         -0.02          0.02         -0.05
GSM239326 Control         -0.06         -0.02         -0.09
GSM239332 Control         -0.06         -0.02         -0.09
GSM239333 Control         -0.10         -0.06         -0.13

> fold_change_fun(x = out, statistic = 'median')
                  GSM239328 AML GSM239329 AML GSM239331 AML
GSM239170 Control          0.45          0.33          0.54
GSM239323 Control         -0.29         -0.41         -0.20
GSM239324 Control          0.17          0.05          0.26
GSM239326 Control          0.03         -0.09          0.12
GSM239332 Control          0.06         -0.06          0.15
GSM239333 Control         -0.06         -0.18          0.03

請注意，這些只是點估計。我們不知道這些樣本倍數變化作為總體倍數變化的估計值有多精確。

資料

df <- structure(list(ID = c("GSM239170", "GSM239323", "GSM239324", 
"GSM239326", "GSM239328", "GSM239329", "GSM239331", "GSM239332", 
"GSM239333"), Disease = c("Control", "Control", "Control", "Control", 
"AML", "AML", "AML", "Control", "Control")), row.names = c(NA, 
-9L), class = "data.frame")

df1 <- structure(list(GSM239170 = c(3.016704177, 7.977735461, 4.207280707, 
7.25609471, 2.204955099, 7.28437278, 4.265792764, 2.6943516, 
3.646303109, 3.040292001, 2.357625231, 5.378494673, 2.833814735, 
6.192932281, 4.543042482, 6.069437462), GSM239323 = c(3.285669072, 
6.532514237, 4.994965767, 7.420807337, 2.331625217, 6.983593721, 
4.970684112, 2.916324936, 8.817891552, 3.38486713, 2.444753172, 
6.065038394, 3.038434511, 6.478439634, 4.786227217, 6.232738284
), GSM239324 = c(2.929482692, 6.388007183, 4.40159671, 6.999340125, 
2.133305231, 6.86337111, 4.595545125, 2.578130269, 11.4248793, 
2.958851115, 2.340767158, 5.134842087, 2.837711812, 6.180169144, 
4.445131477, 6.74644117), GSM239326 = c(2.922820483, 6.466679556, 
4.747114589, 7.094488581, 2.18332885, 6.865970678, 4.575545289, 
2.659717988, 10.74738082, 3.047880699, 2.32143889, 5.367342376, 
2.859800224, 6.151689376, 4.51471011, 7.04995802), GSM239328 = c(3.15950317, 
6.432795021, 4.830045513, 7.024332721, 2.12778313, 7.219840938, 
4.547957809, 2.567436676, 9.296043108, 2.878562717, 2.282608342, 
5.682051149, 2.866040813, 6.238949956, 4.491645167, 6.938928532
), GSM239329 = c(3.163327169, 6.407321524, 4.213762092, 7.17928981, 
2.269697813, 7.181113053, 4.68215122, 2.8095128, 9.53150669, 
3.209319974, 2.401218719, 5.712072512, 2.969167906, 6.708196123, 
4.460114204, 6.348253102), GSM239331 = c(2.985901308, 6.426470803, 
4.884418365, 7.159898654, 2.264705552, 7.392230178, 4.674495889, 
2.790110381, 8.285160496, 3.20260379, 2.385568421, 5.57179966, 
2.929449968, 6.441437631, 4.602482637, 6.080950712), GSM239332 = c(3.122708843, 
6.376394357, 4.4318876, 7.009977785, 2.253940441, 7.484052914, 
4.675841709, 2.795882913, 9.769919327, 3.195993624, 2.375334953, 
5.72082395, 2.963530689, 6.448280595, 4.587221948, 6.324619355
), GSM239333 = c(3.070948463, 6.469070308, 4.849665444, 6.830979234, 
2.287924323, 7.52498281, 4.643311767, 2.884588792, 9.774610531, 
3.3004227, 2.432634747, 5.656674512, 2.931065261, 6.413562269, 
4.623125028, 6.472893789)), row.names = c(NA, -16L), class = "data.frame")

uj5u.com熱心網友回復：

舊函式的另一個選項tidyr::gather有一個列 df：

library(tidyverse)

df2_spread <- df1 %>% 
  tidyr::gather(ID, val) %>% 
  left_join(df, by = 'ID')
  
result_1 <- df2_spread %>% 
  group_by(Disease, gene = ID) %>% 
  summarise(n = n(),
            mean = mean(val),
            sd = sd(val),
            min = min(val),
            max = max(val), .groups = "drop")

 A tibble: 9 × 7
  Disease gene          n  mean    sd   min   max
  <chr>   <chr>     <int> <dbl> <dbl> <dbl> <dbl>
1 AML     GSM239328    16  4.91  2.15  2.13  9.30
2 AML     GSM239329    16  4.95  2.13  2.27  9.53
3 AML     GSM239331    16  4.88  1.96  2.26  8.29
4 Control GSM239170    16  4.56  1.91  2.20  7.98
5 Control GSM239323    16  5.04  1.98  2.33  8.82
6 Control GSM239324    16  4.93  2.45  2.13 11.4 
7 Control GSM239326    16  4.97  2.34  2.18 10.7 
8 Control GSM239332    16  4.97  2.16  2.25  9.77
9 Control GSM239333    16  5.01  2.14  2.29  9.77

在任何情況下，我都無法找到一種方法來計算每個基因的 Fold_change，因為這里似乎只有一種基因疾病。

這是資料

df <- tibble::tribble(
          ~ID,  ~Disease,
  "GSM239170", "Control",
  "GSM239323", "Control",
  "GSM239324", "Control",
  "GSM239326", "Control",
  "GSM239328",     "AML",
  "GSM239329",     "AML",
  "GSM239331",     "AML",
  "GSM239332", "Control",
  "GSM239333", "Control"
  )



df1 <- tibble::tribble(
   ~GSM239170,  ~GSM239323,  ~GSM239324,  ~GSM239326,  ~GSM239328,  ~GSM239329,  ~GSM239331,  ~GSM239332,  ~GSM239333,
  3.016704177, 3.285669072, 2.929482692, 2.922820483,  3.15950317, 3.163327169, 2.985901308, 3.122708843, 3.070948463,
  7.977735461, 6.532514237, 6.388007183, 6.466679556, 6.432795021, 6.407321524, 6.426470803, 6.376394357, 6.469070308,
  4.207280707, 4.994965767,  4.40159671, 4.747114589, 4.830045513, 4.213762092, 4.884418365,   4.4318876, 4.849665444,
   7.25609471, 7.420807337, 6.999340125, 7.094488581, 7.024332721,  7.17928981, 7.159898654, 7.009977785, 6.830979234,
  2.204955099, 2.331625217, 2.133305231,  2.18332885,  2.12778313, 2.269697813, 2.264705552, 2.253940441, 2.287924323,
   7.28437278, 6.983593721,  6.86337111, 6.865970678, 7.219840938, 7.181113053, 7.392230178, 7.484052914,  7.52498281,
  4.265792764, 4.970684112, 4.595545125, 4.575545289, 4.547957809,  4.68215122, 4.674495889, 4.675841709, 4.643311767,
    2.6943516, 2.916324936, 2.578130269, 2.659717988, 2.567436676,   2.8095128, 2.790110381, 2.795882913, 2.884588792,
  3.646303109, 8.817891552,  11.4248793, 10.74738082, 9.296043108,  9.53150669, 8.285160496, 9.769919327, 9.774610531,
  3.040292001,  3.38486713, 2.958851115, 3.047880699, 2.878562717, 3.209319974,  3.20260379, 3.195993624,   3.3004227,
  2.357625231, 2.444753172, 2.340767158,  2.32143889, 2.282608342, 2.401218719, 2.385568421, 2.375334953, 2.432634747,
  5.378494673, 6.065038394, 5.134842087, 5.367342376, 5.682051149, 5.712072512,  5.57179966,  5.72082395, 5.656674512,
  2.833814735, 3.038434511, 2.837711812, 2.859800224, 2.866040813, 2.969167906, 2.929449968, 2.963530689, 2.931065261,
  6.192932281, 6.478439634, 6.180169144, 6.151689376, 6.238949956, 6.708196123, 6.441437631, 6.448280595, 6.413562269,
  4.543042482, 4.786227217, 4.445131477,  4.51471011, 4.491645167, 4.460114204, 4.602482637, 4.587221948, 4.623125028,
  6.069437462, 6.232738284,  6.74644117,  7.04995802, 6.938928532, 6.348253102, 6.080950712, 6.324619355, 6.472893789
  )

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