# Difference between revisions of "Elizabeth Polidan Week9"

Elizabeth Polidan

BIOL 398.03 / MATH 388

• Loyola Marymount University
• Los Angeles, CA, USA

1. Begin by recording in your wiki the number of replicates for each time point in your data.
2. Sanity Check: Number of genes significantly changed
1. Check the number of genes significantly changed
• How many genes have p value < 0.05?
• What about p < 0.01?
• What about p < 0.001?
• What about p < 0.0001?
2. Bonferroni correction
• Perform this correction and determine whether and how many of the genes are still significantly changed at p < 0.05 after the Bonferroni correction.
3. Magnitude and direction of gene expression
• Keeping the "Pval" filter at p < 0.05, filter the "AvgLogFC" column to show all genes with an average log fold change greater than zero. How many meet these two criteria?
• Keeping the "Pval" filter at p < 0.05, filter the "AvgLogFC" column to show all genes with an average log fold change less than zero. How many meet these two criteria?
• Keeping the "Pval" filter at p < 0.05, How many have an average log fold change of > 0.25 and p < 0.05?
• How many have an average log fold change of < -0.25 and p < 0.05? (These are more realistic values for the fold change cut-offs because it represents about a 20% fold change which is about the level of detection of this technology.)
4. Check expression of NSR1. Find NSR1 in your dataset.
• Is its expression significantly changed at any timepoint?
• Record the average fold change and p value for NSR1 for each timepoint in your dataset.
5. Check for gene with smallest p-value. You can find this by sorting your data based on p value (but be careful that you don't cause a mismatch in the rows of your data!)
• Which gene has the smallest p value in your dataset (at any timepoint)?
• Look up the function of this gene at the Saccharomyces Genome Database and record it in your notebook.
• Why do you think the cell is changing this gene's expression upon cold shock?