R code

$*$ Reads in the data file you created using Python called $`$all$\_$VarX$\_$TwoTimePoints.csv$`$ and assigns it to a data frame called $`$var$\_$x$\_$all$`$

$*$ Reads in the data file you created using Python called $`$all$\_$VarY$\_$TwoTimePoints.csv$`$ and assigns it to a data frame called $`$var$\_$y$\_$all$`$

$*$ Find out how many genes are in your dataset and assign the result to a variable called $`$num$\_$genes$`.$

$*$ Reads in the data file you created using Python called $`$Leaf$\_$DEGs$\_$VarX.csv$`$ and assigns it to a data frame called $`$var$\_$x$\_$degs$`$

$*$ Reads in the data file you created using Python called $`$Leaf$\_$DEGs$\_$VarY.csv$`$ and assigns it to a data frame called $`$var$\_$y$\_$degs$`$

$4-$ our data is currently in WIDE FORMAT, with a column for each variable $($in this case, each sample$).$ we want to have our data in LONG FORMAT, with a column for each variable type and column for the values. Run the following cell $```\{$r$\}$ var$\_$x$\_$all.long $<-$ pivot$\_$longer$($var$\_$x$\_$all,cols$=$VarXCRep$\mathrm{.}1$:VarX$1$Rep$\mathrm{.}3,$names$\_$to $=$ "sample", values$\_$to $=$ "expression"$)$ View$($var$\_$x$\_$all.long$)```$

$*$ Create a suitable plot to look at the distribution of expression values for all the genes as a function of the sample, for Variety X$.$

$5-*$ Now you can repeat the above process for Variety Y$.$ Use the $`$tidyr$`$ long$\_$format$()$ to transform your $`$var$\_$y$\_$all$`$ data frame into a long format and call the data frame $`$var$\_$y$\_$all.long$`.$

$*$ Create a suitable plot to look at the distribution of expression values for all the genes as a function of the sample, for Variety Y$.$

$6-$ INVESTIGATE THE DISTRIBUTION OF EXPRESSION VALUES FOR THE DEGs IN EACH SAMPLE $($Variety X$).*$ Use the $`$tidyr$`$ long$\_$format$()$ to transform your $`$var$\_$x$\_$degs$`$ data frame into a long format and call the data frame $`$var$\_$x$\_$degs.long$`.*$ Create a suitable plot to look at the distribution of expression values for DEGs as a function of the sample, for Variety X$.$

$7-*$ Use the $`$tidyr$`$ long$\_$format$()$ to transform your $`$var$\_$y$\_$degs$`$ data frame into a long format and call the data frame $`$var$\_$y$\_$degs.long$`.$

$*$ Create a suitable plot to look at the distribution of expression values for DEGs as a function of the sample, for Variety Y$.$

$8-*$ Find out how many duplicate Soltu gene names there are in the $`$var$\_$x$\_$degs$`$ data frame and assign the result to a variable called $`$var$\_$x$\_$dup$`$

$*$ Find out how many duplicate Soltu gene names there are in the $`$var$\_$y$\_$degs$`$ data frame and assign the result to a variable called $`$var$\_$y$\_$dup$`$

$9-*$ Create a suitable plot to look at the overlap in the DEGs between the two Varieties.

By looking at the gene expression data in the $`$var$\_$x$\_$degs$`$ and $`$var$\_$y$\_$degs$`$ data frames, you can see that some genes have a positive log $2$ fold change and others have a negative log $2$ fold change. $*$ Create a data frame called $`$var$\_$x$\_$degs.up$`$ containing only genes that are upregulated in Stress Treatment compared to control in Variety X$.$

$*$ Create a data frame called $`$var$\_$x$\_$degs.down$`$ containing only genes that are downregulated in Stress Treatment compared to control in Variety X$.$

$*$ Create a data frame called $`$var$\_$y$\_$degs.up$`$ containing only genes that are upregulated in Stress Treatment compared to control in Variety Y$.$

$*$ Create a data frame called $`$var$\_$y$\_$degs.down$`$ containing only genes that are downregulated in Stress Treatment compared to control in Variety Y$.$

$*$ Create a box plot to show the distribution of log$2$ fold change for all DEGs by variety. Hint: the base R boxplot$()$ command and the abs$()$ function could be helpful here.

$*$ Create a box plot to show the distribution of log$2$ fold change for upregulated DEGs by variety. Hint: the base R boxplot$()$ command could be helpful here.

$*$ Create a box plot to show the distribution of log$2$ fold change for downregulated DEGs by variety. Hint: the base R boxplot$()$ command could be helpful here.

$*$ Find out the function of the bottom most upregulated gene in Variety X $($lowest fold change$)$ and assign the result to variable called $`$bottom$\_$gene.x$`.$

$*$ Find out the function of the bottom most upregulated gene in Variety Y $($lowest fold change$)$ and assign the result to variable called $`$bottom$\_$gene.y$`.$

$*$ Create a set of scatterplots to visually inspect how well the different replicates agree$/$correlate for the DEGs in Variety X in the treatment time point.

$*$ Create a set of scatterplots to visually inspect how well the different replicates agree$/$correlate for the DEGs in Variety X in the control time point.

$*$ Modify your data frame $`$var$\_$x$\_$degs$`$ to include two new $($additional$)$ columns as follows: The first new column should be named $`$control$\_$mean$`$ and contain the mean expression value for the three control replicates.

$*$ The second new column should be named $`$stress$\_$mean$`$ and contain the mean expression value for the three stress treatment replicates.

$*$ Create a data frame called $`$var$\_$y$\_$degs.up$.$big$`$ containing only genes in Variety y that are upregulated in Stress Treatment compared to control, have at least an $2$ fold absolute change in expression and have a p value less than $1$e$-06.*$Hint: remember you are dealing with log $2$ fold change