Volcano Plot of Gene Expression Utilizing
initial exercise with GEOdatasets, 5 genes were selected that were differentially expressed between normal brain tissue and brain tissue of people affected by MS.
In this exercise you have the opportunity to visualize genes of interest in context of the entire set of human genes. A volcano plot is a scatter plot that can be
used when analysing microarray data sets to give an overview of interesting genes. The log fold change is plotted on the x-axis and the negative log10 p-value is
plotted on the y-axis. You will operate on the set of all genes globally for the selected brain samples, and then highlight selected genes of interest. To begin the analysis, you will need to import the data to plot from the NCBI repository. This information for the samples has been downloaded for you and stored as a
matrix labeled M.txt which you can retrieve from Blackboard and save on your desktop as a txt file. Using Rstudio, select Import Data from the right side panel and identify the location where you have stored M.txt. Next you should see the Read statement appear by
the R interactive cursor. To arrange the data format, select Headings button and you should be able to see the contents of M arranged by headings in the window. Before you list the required 10 program statements (boldface below) add annotation comments to your command lines statements that include the following: # Author # Title # DateUsing # symbol before your name, title, and date will incorporate but not execute these lines.
Next define the subgroups for comparison: Control samples 12,13 columns from Matrix M CAP samples 15,16 columns from Matrix M
control <- as.matrix(M[,c(“GSM931812″,”GSM931813”)]) CAP <- as.matrix(M[,c(“GSM931815″,”GSM931816”)])
able to see the successful creation of these groups in the viewer window of R console.
CALCULATIONS ON THE DATASET M
To plot all the genes for this analysis on x,y axes, the best approach is a log scale conversion for the Y axis to condense the widespread range of p values you
observed.
A useful comparison tool for microarray data is calculating the fold change by dividing the CAP expression for each gene by the control expression for each gene. The
following statement will divide the row means in Matrix M for CAP by the row means in Matrix M for control. fold_change <- log2(rowMeans(CAP) / rowMeans(control)) pval <- sapply(1:nrow(M), function(i) t.test(CAP[i,],control[i,])$p.value)
Now you are ready to plot the fold change versus p value for your data in a graphical form. The dataframe will hold the information and establish the background color
pattern.
plot_df <- data.frame(FC=fold_change,P=-log(pval),GENE=M[,2],colors=rep(“grey”,nrow(M)))
To highlight the genes you included in your report from Ex 3, place their gene symbols in the statement below, inside each paranthesis.
genes <- c(“gene 1”, “gene 2 “, ” gene 3 “, ” gene 4 “, “gene 5 “)
The syntax for the plot statements is: plot_df$colors <- as.character(plot_df$colors)
To distinguish your selected genes on the graph, change the color for these using the following statement. plot_df[plot_df$GENE %in% genes,”colors”] <- “insert color of your choice here”
To see the possible colors: Demo(“colors”)
Then construct the graph with the following plot commands plot_df <- plot_df[order(plot_df$colors,decreasing=T),] plot(x=plot_df$FC,y=plot_df$P,col=plot_df$colors,xlab=”log2 fold change”,ylab=”-log10 p value”,pch=19,cex=.8)
Last write a sum command to determine the number of genes that have a fold change greater than 2, and a p-value less than .001. How many genes are in this set?Save your command file in Notepad, and your graph as a pdf file. Submit your pdf file and txt file to Blackboard to complete Exercise #4.
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