In this post I show how groupScatterPlot(), function of the rnatoolbox R package can be used for plotting the individual values in several groups together with their mean (or other statistics). I think this is a useful function for plotting grouped data when some groups (or all groups) have few data points ! You may be wondering why to include such function in the rnatoolbox package ?! Well ! I happen to use it quit a bit for plotting expression values of different groups of genes/transcripts in a sample or expression levels of a specific gene/transcript in several sample groups. These expression value are either FPKM, TPM, LCPM, or PSI values (Maybe I should go through these different normalizations later in a different post 😐!). But of course its application is not restricted to gene expression or RNAseq data analysis.

For the test, I first generate a list with three random values. The values are generated randomly using normal distribution, featuring different means and standard deviations.

library(rnatoolbox)
datList<- list(
  l1=rnorm(n=30, mean = 10, sd = 3),
  l2=rnorm(n=20, mean = 0, sd = 1),
  l3=rnorm(n=25, mean = 10, sd = 1)
)


Then I plot the grouped values. By default the mean function is used to add a summary for the values. However, other functions (e.g. median) can be defined as the FUN parameter.


png(
  "/proj/pehackma/ali/test/test_rnatoolbox/test_groupedScatterPlot_3.png",
  width=500, height=500, pointsize=21)
groupScatterPlot(l=datList, col=rainbow(3),
                 lty=1, lwd=1.5,
                 ylab="Test values")
dev.off()



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In this post I show how groupScatterPlot(), function of the rnatoolbox R package can be used for plotting the individual values in several groups toge

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Many times, in our projects, we may need to compare different measured factors in our samples to one another, and study whether they are linearly depe
Many times, in our projects, we may need to compare different measured factors in our samples to one another, and study whether they are linearly depe
Many times, in our projects, we may need to compare different measured factors in our samples to one another, and study whether they are linearly depe
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@SRR3117565.1.1 1 length=100

NCAAAACAGCTCTCCCTCCTTTGATCTGATGGTCTGCAGAGG

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Yesterday (on the opening day of the new Batman movie) I search the Internet for the Batman formula and it's implementations in R.
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I am a Postdoc researcher at the Neuromuscular Disorders Research lab and Genetic Determinants of Osteoporosis Research lab, in University of Helsinki and Folkhälsan RC. I specialize in Bioinformatics. I am interested in Machine learning and multi-omics data analysis. My go-to programming language is R.
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