Reference no: EM132375913
Steps:
1.) Use Spellman yeast cell cycle dataset zip file.
2.) Unzip the text file, and read into R (using the read.table() function with a "header=T" argument and "row.names=1" argument).
3.) Look at the dimensions of the data frame and make sure that there are 6,178 genes and 77 arrays/sample.
4.) Isolate only the cdc15 experiment (samples 23-46).
5) calculate a correlation matrix between the time points (use Pearson's correlation).
* Make sure to title the plot, label the axes, and provide a legend of the color gradient.
* In the correlation calculation, make sure to use the argument ‘use' and value=pairwise.complete.obs since all of these arrays have at least one missing value.
6.) Select the gene YAL002W, which is VPS8, a membrane-binding component of the CORVET complex; involved in endosomal vesicle tethering and fusion in the endosome to vacuole protein targeting pathway.
* Impute the missing values with the row mean (save as something).
• Make sure to cast the gene to numeric.
7.) Generate a profile plot of the same gene. Title the plot, label the axes, and on the x-axis, provide the time points only for each array (no "cdc15_" prefix) so we can visualize the transcript pattern over time. Use lwd in the plot command (lwd=line width).
8.) create a simple shiny app which allows the user to select and correlate any time point verse another time point across all genes.
*To do this, create a server and ui function within the same file, paste both into the R session, then call them with:
>shinyApp(ui = ui, server = server)
Hints:
The ui function can start with:
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
selectInput('xcol', 'X Variable', dimnames(dat)[[2]]),
The server function can start with:
server <- function(input, output) {
selectedData <- reactive({
dat[, c(input$xcol, input$ycol)]
})
Attachment:- correlation matrix.rar