我有一個基因表達分數的資料框（細胞 x 基因）。我還將每個單元格所屬的集群存盤為一列。
我想計算一組基因（列）的每個簇的平均表達值，但是，我只想在這些計算中包含 > 0 的值。
我的嘗試如下：
test <- gene_scores_df2 %>%
select(all_of(gene_list), Clusters) %>%
group_by(Clusters) %>%
summarize(across(c(1:13), ~mean(. > 0)))
這會產生以下小標題：
# 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，當我對照（我假設是）在單個列上進行類似程式檢查時，我得到不同的平均值。
這是 SLC1747 的代碼：
gene_scores_df2 %>%
select(SLC17A7, Clusters) %>%
group_by(Clusters) %>%
filter(SLC17A7 > 0) %>%
summarize(SLC17A7 = mean(SLC17A7))
結果：
# 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
我不確定上面的第一次嘗試到底出了什么問題。
任何建議將不勝感激。
原始df的代碼片段
# 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"))
uj5u.com熱心網友回復：
你想要的是：
library(tidyverse)
df %>%
group_by(Clusters) %>%
summarize(across(everything(), ~mean(.[. > 0])))
~mean(. > 0)檢查元素是否大于 0，因此回傳 TRUE/FALSE，然后為您提供底層 0/1 的平均值。相反，您想過濾可以通過通常[]方法實作的每一列
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