Now the shading of the points reflects the extent to which the transcripts are enriched in the mitochondria relative to the total cellular fraction (lighter blue indicates more enrichment). singlePlot2 = ggplot (data=RNAseq_chrom21, aes(x=Pos, y=RCM, color=Enrichment)) + geom_point() Let’s try that by defining a new plot entirely. For example, instead of putting them into two categories (genic and intergenic) we could have used a “heat map” to color them based on a continuous variable such as “Enrichment” (see above). Note that we could have used an alternative color scheme for our points. But there are also highly expressed regions in uncharacterized intergenic sequence.

Now we see that the region of highest expression is an annotated gene. singlePlot + scale_colour_manual(values=c('red', 'black')) If you are not fond of the color scheme, you can modify it by adding an additional statement to the existing plot. singlePlot = ggplot (data=RNAseq_chrom21, aes(x=Pos, y=RCM, color=Region)) + geom_point() singlePlot = ggplot (data=RNAseq_chrom21, aes(x=Pos, y=RCM)) + geom_point()īut what if we want to distinguish between “genic” and “intergenic” regions as defined in the input file? We can re-define our plot by adding a color statement. In this case, we must at least specify that we want to generate a scatter plot by adding the geom_point() statement. For example: plot (RNAseq_chrom21$Pos, RNAseq_chrom21$RCM)īut this is not sufficient for ggplot. In the native plot function in R, simply defining the x- and y-variables is sufficient to generate a plot. This is a common mistake (and source of frustration) with ggplot. Now, try viewing that plot by entering its name on the command line. singlePlot = ggplot (data=RNAseq_chrom21, aes(x=Pos, y=RCM)) We will work from the data subset for chromosome 21 only, and we will make nucleotide position our x-variable and read depth our y-variable. Let’s begin by defining a new plot with the ggplot function. Generate plots of read depth along the length of chromosome 21 with ggplot Enrichment – Proportional enrichment of coverage in a mitochondrial fraction relative to total cellular RNA (log scale)Ĥ.RCM – Average read depth in the corresponding window expressed as read count per million.Region – Identification of the corresponding window as either “genic” or “intergenic”.Pos – Nucleotide position for a 250-bp window used to calculated average read depth.Note that there are five data columns, containing the following.

Print the first five rows of data with the following command: RNAseq # Chrom Pos Region RCM Enrichment

Read in the data with the following command (you may need to provide the path to where you stored this data file): RNAseq = lim ('SlidingWindow.txt') It is called SlidingWindow.txt and can be downloaded here. We are going to work from a data set that summarizes read depth from RNA-seq reads that were mapped onto a genome that consists of multiple chromosomes.

R multipanel plot