First, you need to format your data frame from wide format to long format. The following is an example using tidyverse to format the data frame.

library(tidyverse)

dt2 <- dt %>%
  rownames_to_column() %>%
  gather(colname, value, -rowname)
head(dt2)
#          rowname colname        value
# 1        tension tension           NA
# 2        cluster tension 1.943113e+08
# 3 migraineNoAura tension 8.462798e+00
# 4   migraineAura tension 2.833333e+00
# 5        tension cluster 1.500000e+00
# 6        cluster cluster           NA

Now we are ready to use the ggplot2 to plot the heatmap using geom_tile.

ggplot(dt2, aes(x = rowname, y = colname, fill = value)) +
  geom_tile()

First I would transform your dataframe as you needed with your precedent code, and then using your ggplot code to plot the heatmap :

library(tidyr)
library(plyr)

#Create an ID column to keep the row line number
DF.heatmap$ID = rownames(DF.heatmap)

#Reformat your dataframe with only 3 columns
DF = pivot_longer(DF.heatmap,cols=1:5,names_to="Col",values_to="Value")
colnames(DF)=c("X","Y","Z")

#Your ggplot code
DF$Z <- factor(round(DF$Z))
DF$Z <- revalue(DF$Z, c('-1'='negative'))
DF$Z <- revalue(DF$Z, c('0' = 'no'))
DF$Z <- revalue(DF$Z, c('1' = 'yes'))
DF$Z <- revalue(DF$Z, c('2' = 'other'))

#Reorder the rows in descending order
DF$X=as.numeric(DF$X)
DF$X=reorder(DF$X,desc(DF$X))

    
ggplot(DF, aes(Y, X, fill= Z)) + 
  geom_tile(color = "white",
            lwd = 0.5,
            linetype = 1)+
  scale_fill_manual(values = c('red', 'green', 'yellow', 'blue'), name = 'Z')

After you can arrange your plot to make it readable, as there is here a lot of Y-axis labels

You may do either of these 2

df <- read.table(header = T, text = "protein           value    treatment
EPN1              0.986    treat1
LAMB1             0.881    treat2
PKP4              0.827    treat2     
PKP2              0.739    treat3     
BAIAP2            0.519    treat2     
UTRN              0.502    treat4     
REPS2             0.481    treat2     
PKP4              0.365    treat1      
LAMC1            -0.529    treat2     
PPIB              2.86     treat4 ")

library(tidyverse)
df %>% ggplot(aes(x= treatment, y = protein, fill = value)) +
  geom_tile()

OR

library(echarts4r)

df |> 
  e_charts(protein) |> 
  e_heatmap(treatment, value) |> 
  e_visual_map(value)

library(tidyr)
long_df <- df |> pivot_longer(cols = c('black', 'white'), names_to = "X", values_to = "Z")

Slightly reorganised to keep the "Name" column name.

To produce a heatmap:

library(ggplot2)
long_df |> ggplot(aes(Name, X, fill = Z)) + geom_tile()

which is very skewed by the white milk value of 999.

Data:

df <- read.table(text = "allPoints.xLocs allPoints.yLocs allPoints.patchStatus
1       67.30330212       87.857495                     0
                 2       69.60800088       77.959314                     0
                 3       74.63313295       93.059260                     0
                 4       92.59099136       77.732215                     1
                 5       18.05288289       61.200910                     1
                 6       55.83499856       50.993785                     0
                 7       12.15664148       58.220179                     1
                 8       41.50413859       92.529054                     0
                 9       83.08209025       24.567501                     0
                 10      53.50615149       46.339927                     0", header = TRUE, stringsAsFactors = FALSE)

listofdfs <- list(df, df)

Code:

library('data.table')
listofdfs <- lapply(seq_len(length(listofdfs)), function(i){
  x <- listofdfs[[i]]
  # assign id and combine x and y coordinates
  setDT(x)[, `:=` ( id = i, coords = paste0(allPoints.xLocs, ",", allPoints.yLocs)) ]
} )

# combine list into a data table.
df2 <- rbindlist(l = listofdfs)

Plot

library('ggplot2')
ggplot( data = df2, mapping = aes( x = coords, y = factor(id) ) ) +  # draw heatmap
  geom_tile( aes( fill = factor(allPoints.patchStatus) ),  colour = "white") +
  coord_flip() + 
  scale_fill_discrete(name = "Patch Status") +
  labs( x = "Coordinates", y = "Data Frame Number")

Graph:

You can loop through the list of data frames and create heatmap for each dataframe. Below, I am showing how to get a heatmap for one data frame.

Plot-2

ggplot( data = df, mapping = aes( x = factor(allPoints.xLocs), y = factor(allPoints.yLocs) ) ) + 
  geom_tile( aes( fill = factor(allPoints.patchStatus) ),  colour = "white") +
  scale_fill_discrete(name = "Patch Status") +
  labs( x = "X-Coordinate", y = "Y-Coordinate") +
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Graph-2

Plot-3 df data is used from above - see Data section at the top.

library('data.table')
listofdfs <- list(df, df)
df2 <- rbindlist(l = listofdfs)
df2 <- df2[, .(sum_patch = sum(allPoints.patchStatus)), by = .(allPoints.xLocs, allPoints.yLocs)]

library('ggplot2')
ggplot( data = df2, mapping = aes( x = factor(allPoints.xLocs), y = factor(allPoints.yLocs) ) ) + 
  geom_tile( aes( fill = sum_patch ),  colour = "white") +
  labs( x = "X-Coordinate", y = "Y-Coordinate") +
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Graph-3: