In this post, I describe how to create interactive Q-Q plots using the manhattanly package. Q-Q plots tell us about the distributional assumptions of the observed test statistics and are common visualisation tools in statistical analyses.
Visit the package website for full details and example usage.
The following three lines of code will produce the Q-Q plot below
install.packages("manhattanly") library(manhattanly) qqly(HapMap, snp = "SNP", gene = "GENE")
Notice that we have added two annotations (the SNP and nearest GENE), that are revealed when hovering the mouse over a point. This feature of interactive Q-Q plots adds a great deal of information to the plot without cluttering it with text.
Inspired by the heatmaply package by Tal Galili, we split the tasks into data pre-processing and plot rendering. Therefore, we can use the
manhattanly::qqr function to get the data used to produce a Q-Q plot. This allows flexibility in the rendering of the plot, since any graphics package, such as
plot in base R can make used to create the plot.
The plot data is derived using the
qqrObject <- qqr(HapMap) str(qqrObject)
## List of 6
## $ data :'data.frame': 14412 obs. of 3 variables:
## ..$ P : num [1:14412] 6.75e-10 3.41e-09 3.95e-09 4.71e-09 5.02e-09 ...
## ..$ OBSERVED: num [1:14412] 9.17 8.47 8.4 8.33 8.3 ...
## ..$ EXPECTED: num [1:14412] 4.46 3.98 3.76 3.61 3.51 ...
## $ pName : chr "P"
## $ snpName : logi NA
## $ geneName : logi NA
## $ annotation1Name: logi NA
## $ annotation2Name: logi NA
## - attr(*, "class")= chr "qqr"
## P OBSERVED EXPECTED
## 4346 6.75010e-10 9.170690 4.459754
## 4347 3.41101e-09 8.467117 3.982633
## 4344 3.95101e-09 8.403292 3.760784
## 4338 4.70701e-09 8.327255 3.614656
## 4342 5.02201e-09 8.299122 3.505512
## 4341 6.22801e-09 8.205651 3.418362
qqrObject which is of class
qqr can also be passed to the
manhattanly::qqly function to produce the inteactive Q-Q plot above:
This work is based on the qqman package by Stephen Turner. It produces similar manhattan and Q-Q plots as the
qqman::qq functions; the main difference here is being able to interact with the plot, including extra annotation information and seamless integration with HTML.