Dplyr：仅当行值 > 时才使用汇总跨来取列的平均值0

发布于 2025-01-16 19:26:56 字数 5578 浏览 1 评论 0原文

我有一个基因表达分数的数据框（细胞x基因）。我还将每个单元格所属的簇存储为一列。

我想计算一组基因（列）的每个簇的平均表达值，但是，我只想包含值 >这些计算中的 0。

我的尝试如下：

test <- gene_scores_df2 %>% 
  select(all_of(gene_list), Clusters) %>%
  group_by(Clusters) %>%
  summarize(across(c(1:13), ~mean(. > 0)))

这会产生以下小标题：

# A tibble: 16 x 14
   Clusters SLC17A7  GAD1  GAD2 SLC32A1  GLI3   TNC PROX1  SCGN   LHX6 NXPH1 MEIS2 ZFHX3     C3
   <chr>      <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
 1 C1         0.611 0.605 0.817   0.850 0.979 0.590 0.725 0.434 0.275  0.728 0.949 0.886 0.332 
 2 C10        0.484 0.401 0.434   0.401 0.791 0.387 0.431 0.362 0.204  0.652 0.715 0.580 0.186 
 3 C11        0.495 0.5   0.538   0.412 0.847 0.437 0.516 0.453 0.187  0.764 0.804 0.640 0.160 
 4 C12        0.807 0.626 0.559   0.703 0.942 0.448 0.644 0.366 0.403  0.702 0.917 0.859 0.228 
 5 C13        0.489 0.578 0.709   0.719 0.796 0.409 0.565 0.371 0.367  0.773 0.716 0.776 0.169 
 6 C14        0.541 0.347 0.330   0.388 0.731 0.281 0.438 0.279 0.198  0.577 0.777 0.633 0.128 
 7 C15        0.152 0.306 0.337   0.198 0.629 0.304 0.331 0.179 0.132  0.496 0.509 0.405 0.0556
 8 C16        0.402 0.422 0.542   0.418 0.813 0.514 0.614 0.287 0.267  0.729 0.574 0.737 0.279 
 9 C2         0.152 0.480 0.458   0.297 0.883 0.423 0.511 0.195 0.152  0.722 0.692 0.598 0.0632
10 C3         0.585 0.679 0.659   0.711 0.996 0.886 0.801 0.297 0.305  0.789 0.992 0.963 0.346 
11 C4         0.567 0.756 0.893   0.940 0.892 0.334 0.797 0.750 0.376  0.686 0.897 0.885 0.240 
12 C5         0.220 0.516 0.560   0.625 0.673 0.250 0.466 0.275 0.358  0.590 0.571 0.641 0.112 
13 C6         0.558 0.908 0.836   0.973 0.725 0.280 0.830 0.642 0.871  0.927 0.830 0.916 0.202 
14 C7         0.380 0.743 0.749   0.772 0.825 0.415 0.480 0.211 0.199  0.614 0.860 0.901 0.135 
15 C8         0.616 0.348 0.312   0.334 0.749 0.271 0.451 0.520 0.129  0.542 0.743 0.735 0.147 
16 C9         0.406 0.381 0.400   0.265 0.679 0.266 0.465 0.233 0.0820 0.648 0.565 0.557 0.119

但是，当我对照（我假设的）单列上的类似过程进行检查时，我会得到不同的平均值。

这是 SLC1747 的代码：

gene_scores_df2 %>% 
  select(SLC17A7, Clusters) %>%
  group_by(Clusters) %>%
  filter(SLC17A7 > 0) %>%
  summarize(SLC17A7 = mean(SLC17A7))

结果：

# A tibble: 16 x 2
   Clusters SLC17A7
   <chr>      <dbl>
 1 C1         0.780
 2 C10        1.42 
 3 C11        1.21 
 4 C12        1.64 
 5 C13        1.09 
 6 C14        1.83 
 7 C15        1.61 
 8 C16        0.968
 9 C2         1.09 
10 C3         0.512
11 C4         0.920
12 C5         1.53 
13 C6         0.814
14 C7         1.22 
15 C8         2.24 
16 C9         1.72

我不确定上面的第一次尝试到底出了什么问题。

任何建议将不胜感激。

原始 df 的代码片段

# First 20 cols of:
gene_scores_df2 %>% 
       select(all_of(gene_list), Clusters) %>%
       group_by(Clusters)

structure(list(SLC17A7 = c(0.273, 0.722, 0.699, 0.71, 0.333, 
0.674, 0.63, 0.481, 0.274, 0.981, 0.586, 0.401, 0.325, 0.583, 
0, 0.348, 0.287, 0, 0.295, 0.351), GAD1 = c(0.355, 0.392, 0.455, 
0.34, 0.108, 1.169, 0, 0.426, 2.219, 0.099, 1.16, 0.332, 0.404, 
0.284, 0, 5.297, 0.518, 0.027, 1.19, 0.346), GAD2 = c(0.12, 0.562, 
0.337, 0.49, 0.095, 0.958, 0.09, 1.518, 1.464, 0.175, 0.419, 
0.536, 0.501, 1.103, 0.343, 0, 0.247, 0, 0.635, 0.906), SLC32A1 = c(0, 
0.97, 0.067, 0.999, 0.224, 1.04, 0, 2.569, 1.544, 0.059, 2.177, 
3.227, 3.603, 1.229, 0.102, 2.421, 0.055, 0.826, 2.646, 0.228
), GLI3 = c(1.527, 0.487, 0.341, 3.352, 0.346, 0.694, 1.395, 
0.767, 1.334, 1.373, 1.7, 2.216, 0.394, 1.029, 1.235, 0.55, 2.043, 
4.469, 2.901, 4.139), TNC = c(0, 0, 0.448, 0.03, 1.377, 0.045, 
0, 0.169, 0.123, 0, 0.188, 0.075, 0, 1.074, 0, 1.272, 0.124, 
0.505, 0.173, 0.889), PROX1 = c(0, 0.075, 0.167, 0.782, 0.802, 
0.561, 0.098, 0.734, 0.448, 1.645, 0.735, 0.795, 0.102, 0.317, 
0.124, 0.324, 0.352, 0.236, 0.826, 0.308), SCGN = c(0.696, 0.234, 
0, 0.202, 0.059, 0.162, 0, 0.653, 0.383, 0.42, 0.094, 0.779, 
0.228, 0.248, 0.171, 0.089, 0.081, 0.026, 0.159, 0), LHX6 = c(0, 
0, 0.134, 0.1, 0.829, 1.489, 0, 0.38, 0.526, 0.117, 0, 0.205, 
0.299, 2.235, 0, 1.335, 0, 0.115, 0.454, 0.108), NXPH1 = c(0.792, 
0.143, 0.175, 0.658, 0, 1.034, 1.798, 0.219, 0.896, 0.249, 1.336, 
1.507, 0.26, 0.242, 1.235, 2.16, 0.235, 0.349, 1.297, 2.234), 
    MEIS2 = c(4.337, 0.559, 0.978, 1.972, 0.964, 0.657, 0.162, 
    0.827, 0.882, 0.157, 1.494, 1.171, 2.524, 2.458, 0.205, 0.448, 
    2.027, 4.767, 1.514, 2.077), ZFHX3 = c(1.48, 1.38, 2.323, 
    1.039, 1.343, 1.354, 0.238, 1.224, 1.676, 0.811, 0.316, 2.012, 
    2.298, 1.869, 0.201, 0.176, 1.829, 1.081, 0.522, 0.959), 
    C3 = c(0.52, 0.527, 0, 0.073, 0, 0.15, 0.094, 0.315, 0.174, 
    0, 0, 0.17, 0.165, 0, 0.237, 0, 0.091, 0.095, 0, 0.081), 
    Clusters = c("C12", "C5", "C13", "C4", "C12", "C13", "C13", 
    "C4", "C6", "C8", "C4", "C4", "C4", "C12", "C5", "C6", "C1", 
    "C3", "C4", "C3")), row.names = c(NA, -20L), groups = structure(list(
    Clusters = c("C1", "C12", "C13", "C3", "C4", "C5", "C6", 
    "C8"), .rows = structure(list(17L, c(1L, 5L, 14L), c(3L, 
    6L, 7L), c(18L, 20L), c(4L, 8L, 11L, 12L, 13L, 19L), c(2L, 
    15L), c(9L, 16L), 10L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -8L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

原文

I have a dataframe of gene expression scores (cells x genes). I also have the cluster that each cell belongs to in stored as a column.

I want to calculate the mean expression values per cluster for a group of genes (columns), however, I only want to include values > 0 in these calculations.

My attempt at this is as follows:

test <- gene_scores_df2 %>% 
  select(all_of(gene_list), Clusters) %>%
  group_by(Clusters) %>%
  summarize(across(c(1:13), ~mean(. > 0)))

This produces the following tibble:

# A tibble: 16 x 14
   Clusters SLC17A7  GAD1  GAD2 SLC32A1  GLI3   TNC PROX1  SCGN   LHX6 NXPH1 MEIS2 ZFHX3     C3
   <chr>      <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
 1 C1         0.611 0.605 0.817   0.850 0.979 0.590 0.725 0.434 0.275  0.728 0.949 0.886 0.332 
 2 C10        0.484 0.401 0.434   0.401 0.791 0.387 0.431 0.362 0.204  0.652 0.715 0.580 0.186 
 3 C11        0.495 0.5   0.538   0.412 0.847 0.437 0.516 0.453 0.187  0.764 0.804 0.640 0.160 
 4 C12        0.807 0.626 0.559   0.703 0.942 0.448 0.644 0.366 0.403  0.702 0.917 0.859 0.228 
 5 C13        0.489 0.578 0.709   0.719 0.796 0.409 0.565 0.371 0.367  0.773 0.716 0.776 0.169 
 6 C14        0.541 0.347 0.330   0.388 0.731 0.281 0.438 0.279 0.198  0.577 0.777 0.633 0.128 
 7 C15        0.152 0.306 0.337   0.198 0.629 0.304 0.331 0.179 0.132  0.496 0.509 0.405 0.0556
 8 C16        0.402 0.422 0.542   0.418 0.813 0.514 0.614 0.287 0.267  0.729 0.574 0.737 0.279 
 9 C2         0.152 0.480 0.458   0.297 0.883 0.423 0.511 0.195 0.152  0.722 0.692 0.598 0.0632
10 C3         0.585 0.679 0.659   0.711 0.996 0.886 0.801 0.297 0.305  0.789 0.992 0.963 0.346 
11 C4         0.567 0.756 0.893   0.940 0.892 0.334 0.797 0.750 0.376  0.686 0.897 0.885 0.240 
12 C5         0.220 0.516 0.560   0.625 0.673 0.250 0.466 0.275 0.358  0.590 0.571 0.641 0.112 
13 C6         0.558 0.908 0.836   0.973 0.725 0.280 0.830 0.642 0.871  0.927 0.830 0.916 0.202 
14 C7         0.380 0.743 0.749   0.772 0.825 0.415 0.480 0.211 0.199  0.614 0.860 0.901 0.135 
15 C8         0.616 0.348 0.312   0.334 0.749 0.271 0.451 0.520 0.129  0.542 0.743 0.735 0.147 
16 C9         0.406 0.381 0.400   0.265 0.679 0.266 0.465 0.233 0.0820 0.648 0.565 0.557 0.119

However, when I check this against (what I assume is) a similar procedure on a single column I get different mean values.

Here is the code for SLC1747:

gene_scores_df2 %>% 
  select(SLC17A7, Clusters) %>%
  group_by(Clusters) %>%
  filter(SLC17A7 > 0) %>%
  summarize(SLC17A7 = mean(SLC17A7))

And the result:

# A tibble: 16 x 2
   Clusters SLC17A7
   <chr>      <dbl>
 1 C1         0.780
 2 C10        1.42 
 3 C11        1.21 
 4 C12        1.64 
 5 C13        1.09 
 6 C14        1.83 
 7 C15        1.61 
 8 C16        0.968
 9 C2         1.09 
10 C3         0.512
11 C4         0.920
12 C5         1.53 
13 C6         0.814
14 C7         1.22 
15 C8         2.24 
16 C9         1.72

I'm unsure what exactly is wrong with the first attempt above.

Any suggestions would be greatly appreciated.

Code snippet for original df for

# First 20 cols of:
gene_scores_df2 %>% 
       select(all_of(gene_list), Clusters) %>%
       group_by(Clusters)

structure(list(SLC17A7 = c(0.273, 0.722, 0.699, 0.71, 0.333, 
0.674, 0.63, 0.481, 0.274, 0.981, 0.586, 0.401, 0.325, 0.583, 
0, 0.348, 0.287, 0, 0.295, 0.351), GAD1 = c(0.355, 0.392, 0.455, 
0.34, 0.108, 1.169, 0, 0.426, 2.219, 0.099, 1.16, 0.332, 0.404, 
0.284, 0, 5.297, 0.518, 0.027, 1.19, 0.346), GAD2 = c(0.12, 0.562, 
0.337, 0.49, 0.095, 0.958, 0.09, 1.518, 1.464, 0.175, 0.419, 
0.536, 0.501, 1.103, 0.343, 0, 0.247, 0, 0.635, 0.906), SLC32A1 = c(0, 
0.97, 0.067, 0.999, 0.224, 1.04, 0, 2.569, 1.544, 0.059, 2.177, 
3.227, 3.603, 1.229, 0.102, 2.421, 0.055, 0.826, 2.646, 0.228
), GLI3 = c(1.527, 0.487, 0.341, 3.352, 0.346, 0.694, 1.395, 
0.767, 1.334, 1.373, 1.7, 2.216, 0.394, 1.029, 1.235, 0.55, 2.043, 
4.469, 2.901, 4.139), TNC = c(0, 0, 0.448, 0.03, 1.377, 0.045, 
0, 0.169, 0.123, 0, 0.188, 0.075, 0, 1.074, 0, 1.272, 0.124, 
0.505, 0.173, 0.889), PROX1 = c(0, 0.075, 0.167, 0.782, 0.802, 
0.561, 0.098, 0.734, 0.448, 1.645, 0.735, 0.795, 0.102, 0.317, 
0.124, 0.324, 0.352, 0.236, 0.826, 0.308), SCGN = c(0.696, 0.234, 
0, 0.202, 0.059, 0.162, 0, 0.653, 0.383, 0.42, 0.094, 0.779, 
0.228, 0.248, 0.171, 0.089, 0.081, 0.026, 0.159, 0), LHX6 = c(0, 
0, 0.134, 0.1, 0.829, 1.489, 0, 0.38, 0.526, 0.117, 0, 0.205, 
0.299, 2.235, 0, 1.335, 0, 0.115, 0.454, 0.108), NXPH1 = c(0.792, 
0.143, 0.175, 0.658, 0, 1.034, 1.798, 0.219, 0.896, 0.249, 1.336, 
1.507, 0.26, 0.242, 1.235, 2.16, 0.235, 0.349, 1.297, 2.234), 
    MEIS2 = c(4.337, 0.559, 0.978, 1.972, 0.964, 0.657, 0.162, 
    0.827, 0.882, 0.157, 1.494, 1.171, 2.524, 2.458, 0.205, 0.448, 
    2.027, 4.767, 1.514, 2.077), ZFHX3 = c(1.48, 1.38, 2.323, 
    1.039, 1.343, 1.354, 0.238, 1.224, 1.676, 0.811, 0.316, 2.012, 
    2.298, 1.869, 0.201, 0.176, 1.829, 1.081, 0.522, 0.959), 
    C3 = c(0.52, 0.527, 0, 0.073, 0, 0.15, 0.094, 0.315, 0.174, 
    0, 0, 0.17, 0.165, 0, 0.237, 0, 0.091, 0.095, 0, 0.081), 
    Clusters = c("C12", "C5", "C13", "C4", "C12", "C13", "C13", 
    "C4", "C6", "C8", "C4", "C4", "C4", "C12", "C5", "C6", "C1", 
    "C3", "C4", "C3")), row.names = c(NA, -20L), groups = structure(list(
    Clusters = c("C1", "C12", "C13", "C3", "C4", "C5", "C6", 
    "C8"), .rows = structure(list(17L, c(1L, 5L, 14L), c(3L, 
    6L, 7L), c(18L, 20L), c(4L, 8L, 11L, 12L, 13L, 19L), c(2L, 
    15L), c(9L, 16L), 10L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -8L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

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拔了角的鹿 2025-01-23 19:26:56

您想要的是：

library(tidyverse)
df %>%
  group_by(Clusters) %>%
  summarize(across(everything(), ~mean(.[. > 0])))

~mean(. > 0) 检查某个元素是否大于 0，从而返回 TRUE/FALSE，然后给出基础 0/1 的平均值。相反，您想要过滤每一列，这可以通过通常的 [] 方法来实现

What you want is:

library(tidyverse)
df %>%
  group_by(Clusters) %>%
  summarize(across(everything(), ~mean(.[. > 0])))

~mean(. > 0) checks if an element is greater 0 or not and thus returns TRUE/FALSE and then gives you the mean of the underlying 0/1's. Instead you want to filter each column which can be achieved with the usual [] approach

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