How can I create a boxplot of a single row of a matrix

Question

I have a large matrix of 4775 rows of genes and 37 columns (individual samples). The data in each cell is the logCPM value for each gene.

Is it possible to create a boxplot of just a single row (gene) that I am interested in? I have a corresponding metadata dataframe which has groupings for the individual samples.

I have been trying something like this which is not working:

boxplot(logcpm~pd_2$PEDIS, data=logcpm["K11040",], ylab="LogCPM", xlab="PEDIS Group") 

If I do this code:

boxplot(logcpm["K11040",], ylab="LogCPM", xlab="PEDIS Group") 

I get something like this:

However this obviously doesn't seperate the values based on patient group. How do I plot the logcpm values from a matrix while also including the groupings from a separate metadata dataframe?

Example data of what I mean:

I have a matrix similar to the below structure:

	1	2	3	4	5
gene1	0.3	0.4	0.8	1.3	3.3
gene2	0.0	0.1	0.8	0.9	0.9
gene3	0.4	0.6	0.9	0.1	0.1

And metadata dataframe:

Sample	PEDIS
1	PEDIS1
2	PEDIS1
3	PEDIS2
4	PEDIS2
5	PEDIS2

What I want to do is create a boxplot of only gene2 but group the values based on the pedis column in my metadata dataframe

Answer 1

This question has been answered in the comments by @thelatemail, but I've added some extra details for clarity.

One approach (as suggested by @thelatemail) is to subset the matrix to only include the gene you're interested in, e.g.

matrix_1 <- structure(c(0.3, 0, 0.4, 0.4, 0.1, 0.6, 0.8, 0.8, 0.9,
                   1.3, 0.9, 0.1, 3.3, 0.9, 0.1),
                 .Dim = c(3L, 5L),
                 .Dimnames = list(c("gene_1", "gene_2", "gene_3"),
                                  c("X1", "X2", "X3", "X4", "X5")))
matrix_1
#>         X1  X2  X3  X4  X5
#> gene_1 0.3 0.4 0.8 1.3 3.3
#> gene_2 0.0 0.1 0.8 0.9 0.9
#> gene_3 0.4 0.6 0.9 0.1 0.1

df_1 <- structure(list(Sample = 1:5,
                      PEDIS = c("PEDIS1", "PEDIS1", "PEDIS2", 
                                "PEDIS2", "PEDIS2")),
                 class = c("tbl_df", "tbl", "data.frame"),
                 row.names = c(NA, -5L))
df_1
#> # A tibble: 5 × 2
#>   Sample PEDIS 
#>    <int> <chr> 
#> 1      1 PEDIS1
#> 2      2 PEDIS1
#> 3      3 PEDIS2
#> 4      4 PEDIS2
#> 5      5 PEDIS2

# Boxplot
boxplot(matrix_1[2,] ~ df_1$PEDIS)

# Or, using the gene name, rather than the row number
boxplot(matrix_1["gene_2",] ~ df_1$PEDIS)

^{Created on 2021-09-09 by the reprex package (v2.0.1)}

--

Another approach is to combine and transpose your data into the 'tidy' format (each row is a sample, each column is a variable), e.g.

df_2 <- as.data.frame(t(matrix_1))
df_2$group <- df_1$PEDIS
df_2
#>    gene_1 gene_2 gene_3  group
#> X1    0.3    0.0    0.4 PEDIS1
#> X2    0.4    0.1    0.6 PEDIS1
#> X3    0.8    0.8    0.9 PEDIS2
#> X4    1.3    0.9    0.1 PEDIS2
#> X5    3.3    0.9    0.1 PEDIS2

boxplot(gene_2 ~ group, data = df_2)

The tidy format facilitates the use of the ggplot2 package (for details on why this is a good thing, see: https://r-graphics.org/) which enables you to add 'features' to your plot, e.g. the individual logCPM values (dots) for each sample:

library(tidyverse)

df_2 %>%
  pivot_longer(cols = -group, names_to = "genes") %>%
  filter(genes == "gene_2") %>%
  ggplot(aes(x = group, y = value)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color = genes), width = 0.2) +
  theme_minimal()

You can also make more complicated figures, for example, comparing logCPM values for two genes 'side-by-side':

df_2 %>%
  pivot_longer(cols = -group, names_to = "genes") %>%
  filter(genes %in% c("gene_1", "gene_2")) %>%
  ggplot(aes(x = group, y = value)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color = genes), width = 0.2) +
  theme_minimal() +
  facet_wrap(~ genes)

^{Created on 2021-09-09 by the reprex package (v2.0.1)}