To Display Two Heatmaps in Same PDF Side by Side in R

Heatmap and forest plot on the same pdf file

I can't give a complete answer without a reproducible example. However, one nice way to do it when you're using the grid-derived ggplot2 package is with viewports in grid:

First, define your plots without printing them:

heatmap <- ggplot(...
forestplot <- ggplot(...

Next, define viewports.

require(grid)
vp1 <- viewport(x=0.25, y=0.5, width=0.5, height=0.5)
vp2 <- viewport(x=0.75, y=0.5, width=0.5, height=0.5)

x and y represent the position of the center of the plot, along the x- and y-axes (from the left and bottom, respectively).

Then, use the pdf device to save the plot

pdf("my_file.pdf")
print(heatmap, vp=vp1)
print(forestplot, vp=vp2)
dev.off

Another possibility is to use grid.arrange, which will come up with a nice arrangement for you automatically:

require(gridExtra)
pdf("myfile.pdf")
grid.arrange(heatmap, forestplot)
dev.off()

How can I do a multiple plot with a somSC type graph?

Heatmaps will probably not work with par(). Maybe these will help:

R: arranging multiple plots together using gridExtra

to display two heatmaps in same pdf side by side in R

Combining multiple complex plots as panels in a single figure

Okay. I suppose this question has been sitting unanswered for enough time that the long answer should be written up.

The answer to most difficult graphics issues is (as @backlin suggests) the raw use of the 'grid' package. Many prebuilt graphics packages override all current viewports and plot device settings, so if you want something done a very specific way, you have to build it yourself.

I recommend picking up Paul Murrell's book "R Graphics" and going over the chapter on the 'grid' package. It's a crazy useful book, and a copy sits on my desk all the time.

For your heatmap, I've written up a quick primer that will get you started quickly.

Functions to know

grid.newpage() This initializes the plotting device. Use it without parameters.
grid.rect() This draws a rectangle. Your heatmap is basically just a giant set of colored rectangles, so this will be bulk of your graphic. It works like so: grid.rect(x=x_Position, y=y_Position, width=width_Value, height=height_Value, gp=gpar(col=section_Color, fill=section_Color), just=c("left", "bottom"), default.units="native") The 'just' argument specifies which point of the rectangle will sit on your specified (x, y) coordinates.
grid.text() This draws text. It works like so: grid.text("Label Text", x_Value, y_Value, gp=gpar(col=color_Value, cex=font_Size), just=c("right","center"), rot=rot_Degrees, default.units="native")
grid.lines() This draws a line. It works like so: grid.lines(c(x_Start,x_End), c(y_Start, y_End), gp=gpar(col=color_Value), default.units="native")
dataViewport() This defines the attributes of a plotting window, which 'grid' refers to as a "viewport." Use it like so: pushViewport(dataViewport(xData=x_Data, yData=y_Data, xscale=c(x_Min, x_Max), yscale=c(y_Min, y_Max), x=x_Value, y=y_Value, width=width_Value, height=height_Value, just=c("left","center"))) There is some stuff to keep in mind here... see the more detailed explanation of viewports.
pushViewport() This is used to initialize a veiwport. You wrap this around a viewport definition to actually execute the viewport, like so: pushViewport(dataViewport([stuff in here]))
popViewport() This finalizes a viewport and moves you up one level in the hierarchy of viewports. See the more detailed explanation of viewports.

Viewports in a nutshell

Viewports are temporary drawing spaces that define where and how 'grid' objects will be drawn. Everything inside the viewport is drawn relative to the viewport. If the viewport is rotated, everything inside will be rotated. Viewports can be nested, can overlap, and are almost infinitely flexible, with one exception: they are always a rectangle.

Something that messes a lot of people up initially is the coordinate system. Every viewport, including the initial 'grid.newpage()' viewport, goes from 0 to 1 on both the x and y axes. The origin (0,0) is the far lower left corner, and the max (1,1) is the far upper right corner. This is the "npc" unit system, and everything that doesn't have a set of units specified will likely end up being drawn according to this system. This means two things for you:

Use the "npc" system when specifying viewport sizes and locations. Just assume that your viewports have to use the "npc" coordinates, and you'll save yourself a LOT of hassle. This means if I want to draw two plots next to each other, the definitions for the two viewports would look something like:
- viewport(x=0, y=0, width=0.5, height=1, just=c("left","lower")) and
- viewport(x=0.5, y=0, width=0.5, height=1, just=c("left","lower"))
If your viewport has a different coordinate system (for example a viewport for plotting a graph), then you will need to specify the 'default.units' argument for every 'grid' object you draw. For instance, if you tried to plot a point at (2,4) you would never see the point, because it would be far off-screen. Specifying default.units="native" would tell that point to use the viewport's own coordinate system and would draw the point correctly.

Viewports can be navigated and written to directly, but unless you're doing something very automated, it is easier to specify a viewport, draw inside it, and then "pop" (finalize) the viewport. This returns you to the parent viewport, and you can start on the next viewport. Popping each viewport is a clutter-free approach and will suit most purposes (and make it easier to debug!).

The 'dataViewport' function is all important when plotting a graph. This is a special type of viewport that handles all of the coordinates and scales for you, as long as you tell it what data you are using. This is the one I use for any plotting area. When I first started using the 'grid' package, I adjusted all of my values to fit the "npc" coordinate system, but that was a mistake! The 'dataViewport' function makes is all easy as long as you remember to use the "native" units for each drawing item.

Disclaimer

Data visualization is my forte, and I don't mind spending half a day scripting up a good visual. The 'grid' package allows me to create quite sophisticated visuals faster than anything else I found. I script up my visuals as functions, so I can load various data quickly. I couldn't be happier.

However, if you don't like to script things, 'grid' will be your enemy. Also, if you consider half a day to be too much time for a visual, then 'grid' won't help you too much. The (in)famous 'ggplot2' package is what most people settle on, and I heartily recommend it, even though I don't personally find it useful.

If someone wants help learning 'grid' graphics, I'm more than willing to help teach. It has completely revolutionized my ability to create fast, intelligent, and good-looking data visuals.

Combining pheatmaps in R

This will work.

library(gridExtra); library(pheatmap)

m <- matrix(c(1:4), ncol=2)
n <- matrix(c(1,1,1,2), ncol=2)
a <- list(pheatmap(m)[[4]])
a[[2]] <- pheatmap(n)[[4]]
z <- do.call(grid.arrange,a)
plot(z)

Based on one of the comments. If you have many single plots; you can use a loop like this.

mn <- list(m, n)

a <- list()
for(i in 1:length(mn)){
  a[i] <- list(pheatmap(mn[[i]])[[4]])
}

z <- do.call(grid.arrange,a)
plot(z)

The point is it to add all the data for your single plots in a list. You can then loop over the list, applying pheatmap.

Is it possible to do in the loop ? Clustering/heatmaps

You can achieve this with a simple for loop. The dynamic variable naming may require use of assign.

k=dim(tbl_cluster)
for(i in 1:k){
   cluster <- tbl_cluster[tbl_cluster$fit.cluster == i,]
   cluster <- cluster1[,-25]

   cluster <- as.matrix(cluster[,2:24])
   mode(cluster)<-"numeric"

   heatmap.2(cluster,
      Colv = NA,
      scale = "none",
      dendrogram = "row",
      col=rainbow(256, s = 1, v = 1, start = 0, end = 2/6, alpha = 1),
      density.info = "none",
      trace = "none",
      key = FALSE,
      cexCol=0.5,
      cexRow=1)

  mtext(paste("Cluster ",i,"_"), side=3, line=-1, cex=2, col="black") 
  mtext("Size in kDa", side=1, line=3.5, cex=1.3, col="black")
  ## at this point you can dump the cluster object
}