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.

In this post I show how classifySex(), function of the rnatoolbox R package can be used for inferring the sex of  the studied subjects from their binary alignment bam files. The sex can be a source of unwanted variation within the data, for which you may want to adjust your differential gene expression or splicing analysis. However, complete  metadata are unfortunately not always available. Furthermore, sometimes details within metadata are incorrect or have been misplaced due to manual error. Therefore, it is a good practice to quickly double check some details within the data to either complete the missing metadata information or to make sure that the prior stages have been performed without any accidental mix-ups. For muscle tissues, this showed to be useful on our ribo-depleted RNAseq data. NOTE! Earlier the function referred to in this post was named differently(i.e. getGender). Since version 0.2.1 classifySex() is used.

Recently I have started to organize my commonly used functions related to quality assessment and analyzing RNAseq data into an R package. It is called rnatoolbox and it is available here. In this post I introduce getMappedReadsCount(), i.e. a function that can be used for checking the number of aligned/mapped fragments in several bam files and detecting the outliers. The outliers are the bam files with oddly high (i.e. exceeding1.5 times the interquartile) and oddly low (i.e. lower than 1.5 times the interquartile) number of mapped fragments.