R Expr VI: Results

The topTable() function can be used to look at the top-ranked genes. You can use the number argument to extract more rows. The nrow() function usually gives the number of rows of a data frame or matrix, but can actually be used on your linear fit to extract the number of probes.

1. Use the nrow() function to find out the number of probes in your data.

2. Use the number of probes together with your linear fit as arguments to the topTable() function to create a top table with all probes.

3. Write in your report how many probes that have significant adjusted p-values on the 0.05 level.

4. The decideTests() function returns a 0 for each non-significant test, a 1 if upregulated, and -1 if downregulated. Use the decideTests() function on your fit together with the table() function, to learn how many probes are up- and down regulated.

The volcanoplot() function can be used to create a vulcano plot. Significant genes can be highlighted in the plot by adding the number of genes to print with the highlight parameter. The imported gene names have been saved in a data frame called genes which you can access through writing $genes after the name of your fitted models.

5. Do a volcano plot where you highlight all genese that are significant after adjusting for multiple tests. You should use the names arguments together with the Symbol vector from the genes data frame stored in your model fit. Save this this plot and include it in your report

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