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"))
CodePudding user response：
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