# Comparing scatterplots with lots of points

I have two sets of data of protein-protein interactions in a matrices entitled: s1m and s2m. Each DB and AD pair make an interaction and the one matrix looks like:

> head(s1m)
[1,]      2   8153
[2,]      7   3553
[3,]      8   4812
[4,]     13   7838
[5,]     24   3315
[6,]     24   6012


I can then plot the density of the points basically showing where the points are the most concentrated:

s1m:

s2m:

The code I used in R to make these plots was:

z <- kde2d(s1m[,1], s1m[,2], n=50)
filled.contour(z)
z <- kde2d(s2m[,1], s2m[,2], n=50)
filled.contour(z)


I want to be able to somehow compare how simiarly these plots are rather than just looking at them by eye. Is there someway to do this? By the way, I know very little about statistics. These are very large datasets also, something like 10,000 points among a matrix of 15k by 15k.

• Would it be possible for you to upload at least a part of the dataset somewhere and maybe better explain what you are analysing? Anyway, I would start by plotting the histograms on each dimension, which would be probably cleaner to look at then the image. You could then maybe try to create a linear(?) model for each dataset and compare those. It is difficult to say without knowing exactly what you are looking at. – nico Aug 1 '13 at 7:21
• @nico What do you mean by "plotting the histograms on each dimension?" And, how would I upload the data? Is there a feature somewhere to do that? – Kerpal Jenkiens Aug 1 '13 at 7:45
• @KerpalJenkiens There is a FAQ on Stack Overflow which explains how to share data for R-related questions. – Roland Aug 1 '13 at 8:58
• @Kerpal Jenkiens: try something like hist(s1m[,1]) or hist(s2m[,1]) to see the different distributions of the two variables. Or you can do something like this or this – nico Aug 1 '13 at 10:03
• @nico would hist(s1m[,1]) or hist(s2m[,1]), just display the distribution of DBs within each set? Would that really tell me anything about how the pairs of DBs and ADs compare within "clustered groups", i.e.(something like what a contour map produces)? – Kerpal Jenkiens Aug 2 '13 at 17:05

You could look at the distribution of the differences between the z values returned by kde2d (i.e., z$z). Let's create some example data: set.seed(42) x <- 1:100 y <- 1:100 Z1 <- outer(x, y, function(a,b) rnorm(length(a))) Z2 <- outer(x, y, function(a,b) rnorm(length(a))) filled.contour(Z1-Z2)  summary(as.vector(Z1-Z2)) # Min. 1st Qu. Median Mean 3rd Qu. Max. #-6.844000 -0.973200 -0.003553 -0.012130 0.942800 5.598000 sd(Z1-Z2) #[1] 1.429194 Z3 <- outer(x, y, function(a,b) a+b-mean(a+b)+rnorm(length(a))) filled.contour(Z1-Z3)  summary(as.vector(Z1-Z3)) # Min. 1st Qu. Median Mean 3rd Qu. Max. #-99.49000 -29.37000 0.08199 -0.01940 29.46000 101.00000 sd(Z1-Z3) #[1] 40.83703  • This all looks very cool, but I don't exactly understand what Z1 <- outer(x, y, function(a,b) rnorm(length(a))) Z2 <- outer(x, y, function(a,b) rnorm(length(a))) does/means. Is it basically grabbing the topography of one map, subtracting it from the other, and displaying the difference between each area on the plot in a graph form? If so, then what does Z3 <- outer(x, y, function(a,b) a+b-mean(a+b)+rnorm(length(a)))do/mean? And, what is the set.seed for? – Kerpal Jenkiens Aug 2 '13 at 16:59 • Also would this method work correctly if the x & y points on the plot were listed as one x column and one y column in a data frame, or would they need to be placed and arranged into a matrix of dimensions: (highest x or y) by (highest x or y)? – Kerpal Jenkiens Aug 2 '13 at 17:10 • Since you didn't provide data, I created some dummy data. You can use Z1 <- kde2d(s1m[,1], s1m[,2], n=50)$z and Z2 <- kde2d(s2m[,1], s2m[,2], n=50)$z. Z3 is just an additional dataset that is actually different, while Z1 and Z2 are pretty much equal in my example. I don't think it makes sense to try and teach you R. Read some introductions and tutorials and study the help files. – Roland Aug 2 '13 at 20:18 What about something like this (using the "geyser" data set for illustrative purposes) From the example: attach(geyser) f1 <- kde2d(duration, waiting, n = 50, lims = c(0.5, 6, 40, 100))  Make up new data: geyser2 <- geyser*rnorm(1) f2 <- with(geyser2, kde2d(duration, waiting, n = 50, lims = c(0.5, 6, 40, 100)))  Create differences and plot them: zdiff <- f1$z - f2$z contour(zdiff)  • How would you go to test whether those differences are significant or merely due to chance? I guess you could do some simulation by randomly shuffling the data a large number of times and have some sort of 95% confidence bands of the differences. Would that make sense? – nico Aug 2 '13 at 8:49 • To simulate, you'd have to figure out how to "shuffle" the data properly. I don't know if that's been done for this type of problem – Peter Flom - Reinstate Monica Aug 2 '13 at 10:21 You could compare contour line plots directly on the same graph.  library(ggplot2) ggplot(mpg, aes(x = displ, y = cyl)) + stat_density2d () + stat_density_2d(mapping = aes(x = mpg$displ[sample(1:length(mpg$displ))], y = mpg$cyl),