Adjustments to heatmap (geom_tile)?

Question

I'm very new to R and am trying to make a heatmap for a colleague of mine.

I've been able to generate a heatmap, but there are a few things wrong.

This is what my plot currently looks like:

What I would like to change from my existing code:

• I would like to figure out someway to order the sample names in my table so they're grouped together. For example, for the sample NC_rep1_nogel_wash10_IgG_p150 , I would like this to be grouped with all IgG samples.

In other words, I was thinking of something like this for ordering on the x and y axis, but not sure how to implement it:

    sample_order <-c(
"NC_rep1_nogel_wash10_IgG_p150",
"NC_rep2_nogel_wash10_IgG_p150",
"XL_rep2_nogel_wash10_IgG_p150",
"NC_rep1_nogel_wash10_p150",
"NC_rep2_nogel_wash10_p150",
"XL_rep2_nogel_wash10_p150"

    )

• I'm also trying to change the dynamic range of colors. Right now it's hard to see the difference between 20% and 50% on the plot. Is it possible to invert the colors as well?

My code:

library(readxl)
library(tidyverse); library(scales)

best_peak <- read_csv("/Users/kylestangline/OneDrive - Eclipse Bioinnovations Inc/Projects/20210427-eclip-adar005/overlaps/clusters/heatmap.csv")


best_peak %>%
  separate(`Peak Comparison`, into = c("peak1", "peak2"), sep = " vs ") %>%
  mutate(label = paste(percent(Rate, accuracy = 1), comma(Intersection.Value, accuracy = 1), sep = "\n")) %>%
  ggplot(aes(peak1, peak2, fill = Rate)) +
  geom_tile() +
  geom_text(aes(label = label), size = 1.50, color = "gray80", lineheight = 0.7) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Table

One extra thing about the table: my code grabs the sample names from the Peak Comparison column, where it is separated by " vs "

Peak Comparison	Param -a	Param -b	Intersection.Value	Total -a	Total -b	Rate
NC_rep1_nogel_wash10_IgG_p150 vs NC_rep1_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_IgG_p150	12770	12770	12770	1
NC_rep1_nogel_wash10_IgG_p150 vs NC_rep1_nogel_wash10_p150	NC_rep1_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_p150	4370	12770	94156	0.342208301
NC_rep1_nogel_wash10_IgG_p150 vs NC_rep2_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_IgG_p150	2260	12770	10012	0.225729125
NC_rep1_nogel_wash10_IgG_p150 vs NC_rep2_nogel_wash10_p150	NC_rep1_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_p150	4034	12770	81936	0.315896633
NC_rep1_nogel_wash10_IgG_p150 vs XL_rep2_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_IgG_p150	2352	12770	11470	0.20505667
NC_rep1_nogel_wash10_IgG_p150 vs XL_rep2_nogel_wash10_p150	NC_rep1_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_p150	5358	12770	253054	0.419577134
NC_rep2_nogel_wash10_IgG_p150 vs NC_rep1_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_IgG_p150	2242	10012	12770	0.223931282
NC_rep2_nogel_wash10_IgG_p150 vs NC_rep1_nogel_wash10_p150	NC_rep2_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_p150	3220	10012	94156	0.321614063
NC_rep2_nogel_wash10_IgG_p150 vs NC_rep2_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_IgG_p150	10012	10012	10012	1
NC_rep2_nogel_wash10_IgG_p150 vs NC_rep2_nogel_wash10_p150	NC_rep2_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_p150	3046	10012	81936	0.304234918
NC_rep2_nogel_wash10_IgG_p150 vs XL_rep2_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_IgG_p150	1813	10012	11470	0.181082701
NC_rep2_nogel_wash10_IgG_p150 vs XL_rep2_nogel_wash10_p150	NC_rep2_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_p150	3850	10012	253054	0.384538554
XL_rep2_nogel_wash10_IgG_p150 vs NC_rep1_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_IgG_p150	2276	11470	12770	0.198430689
XL_rep2_nogel_wash10_IgG_p150 vs NC_rep1_nogel_wash10_p150	XL_rep2_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_p150	3463	11470	94156	0.301918047
XL_rep2_nogel_wash10_IgG_p150 vs NC_rep2_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_IgG_p150	1772	11470	10012	0.176987615
XL_rep2_nogel_wash10_IgG_p150 vs NC_rep2_nogel_wash10_p150	XL_rep2_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_p150	3219	11470	81936	0.280645161
XL_rep2_nogel_wash10_IgG_p150 vs XL_rep2_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_IgG_p150	11470	11470	11470	1
XL_rep2_nogel_wash10_IgG_p150 vs XL_rep2_nogel_wash10_p150	XL_rep2_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_p150	4439	11470	253054	0.38700959
NC_rep1_nogel_wash10_p150 vs NC_rep1_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_p150	NC_rep1_nogel_wash10_IgG_p150	4668	94156	12770	0.365544244
NC_rep1_nogel_wash10_p150 vs NC_rep1_nogel_wash10_p150	NC_rep1_nogel_wash10_p150	NC_rep1_nogel_wash10_p150	94156	94156	94156	1
NC_rep1_nogel_wash10_p150 vs NC_rep2_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_p150	NC_rep2_nogel_wash10_IgG_p150	3362	94156	10012	0.335797044
NC_rep1_nogel_wash10_p150 vs NC_rep2_nogel_wash10_p150	NC_rep1_nogel_wash10_p150	NC_rep2_nogel_wash10_p150	16636	94156	81936	0.203036516
NC_rep1_nogel_wash10_p150 vs XL_rep2_nogel_wash10_IgG_p150	NC_rep1_nogel_wash10_p150	XL_rep2_nogel_wash10_IgG_p150	3714	94156	11470	0.323801221
NC_rep1_nogel_wash10_p150 vs XL_rep2_nogel_wash10_p150	NC_rep1_nogel_wash10_p150	XL_rep2_nogel_wash10_p150	28631	94156	253054	0.304080462
NC_rep2_nogel_wash10_p150 vs NC_rep1_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_p150	NC_rep1_nogel_wash10_IgG_p150	4273	81936	12770	0.334612373
NC_rep2_nogel_wash10_p150 vs NC_rep1_nogel_wash10_p150	NC_rep2_nogel_wash10_p150	NC_rep1_nogel_wash10_p150	16572	81936	94156	0.202255419
NC_rep2_nogel_wash10_p150 vs NC_rep2_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_p150	NC_rep2_nogel_wash10_IgG_p150	3154	81936	10012	0.315021974
NC_rep2_nogel_wash10_p150 vs NC_rep2_nogel_wash10_p150	NC_rep2_nogel_wash10_p150	NC_rep2_nogel_wash10_p150	81936	81936	81936	1
NC_rep2_nogel_wash10_p150 vs XL_rep2_nogel_wash10_IgG_p150	NC_rep2_nogel_wash10_p150	XL_rep2_nogel_wash10_IgG_p150	3438	81936	11470	0.299738448
NC_rep2_nogel_wash10_p150 vs XL_rep2_nogel_wash10_p150	NC_rep2_nogel_wash10_p150	XL_rep2_nogel_wash10_p150	26501	81936	253054	0.323435364
XL_rep2_nogel_wash10_p150 vs NC_rep1_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_p150	NC_rep1_nogel_wash10_IgG_p150	5899	253054	12770	0.461942052
XL_rep2_nogel_wash10_p150 vs NC_rep1_nogel_wash10_p150	XL_rep2_nogel_wash10_p150	NC_rep1_nogel_wash10_p150	30344	253054	94156	0.322273673
XL_rep2_nogel_wash10_p150 vs NC_rep2_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_p150	NC_rep2_nogel_wash10_IgG_p150	4072	253054	10012	0.406711946
XL_rep2_nogel_wash10_p150 vs NC_rep2_nogel_wash10_p150	XL_rep2_nogel_wash10_p150	NC_rep2_nogel_wash10_p150	28397	253054	81936	0.346575376
XL_rep2_nogel_wash10_p150 vs XL_rep2_nogel_wash10_IgG_p150	XL_rep2_nogel_wash10_p150	XL_rep2_nogel_wash10_IgG_p150	4930	253054	11470	0.429816914
XL_rep2_nogel_wash10_p150 vs XL_rep2_nogel_wash10_p150	XL_rep2_nogel_wash10_p150	XL_rep2_nogel_wash10_p150	253054	253054	253054	1

Answer 1

If you convert your discrete x and y axis mappings to a factor you can control the order in which they appear.

sample_order <- c(
"NC_rep1_nogel_wash10_IgG_p150",
"NC_rep2_nogel_wash10_IgG_p150",
"XL_rep2_nogel_wash10_IgG_p150",
"NC_rep1_nogel_wash10_p150",
"NC_rep2_nogel_wash10_p150",
"XL_rep2_nogel_wash10_p150" )

best_peak %>%
  separate(`Peak Comparison`, into = c("peak1", "peak2"), sep = " vs ") %>%
  mutate(
   label = paste(percent(Rate, accuracy = 1),
                 comma(Intersection.Value, accuracy = 1), sep = "\n"),
   across(c(peak1,peak2),
          ~factor(., levels = sample_order))
    ) %>%
  ggplot(aes(peak1, peak2, fill = Rate)) +
  geom_tile() +
  geom_text(aes(label = label), size = 1.50, color = "gray80", lineheight = 0.7) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))