Elizabeth Polidan Week9

Elizabeth Polidan

BIOL 398.03 / MATH 388

• Loyola Marymount University

• Los Angeles, CA, USA

• Email me through OpenWetWare

Elizabeth Polidan Home

Course Home

---

Begin by recording in your wiki the number of replicates for each time point in your data.

style="border-style: solid; border-width: 0 1px 1px 0"|

t15 style="border-style: solid; border-width: 0 1px 1px 0"|	t30 style="border-style: solid; border-width: 0 1px 1px 0"|	t60 style="border-style: solid; border-width: 0 1px 1px 0"|	t90 style="border-style: solid; border-width: 0 1px 1px 0"|	t120
4 style="border-style: solid; border-width: 0 1px 1px 0"|	4 style="border-style: solid; border-width: 0 1px 1px 0"|	4 style="border-style: solid; border-width: 0 1px 1px 0"|	5 style="border-style: solid; border-width: 0 1px 1px 0"|	5

Data errors replaced by single space: 108 occurences Sanity Check

• Check the number of genes significantly changed. How many genes have p value < 0.05? p < 0.01? p < 0.001? p < 0.0001?

p style="border-style: solid; border-width: 0 1px 1px 0"|	t15 style="border-style: solid; border-width: 0 1px 1px 0"|	t30 style="border-style: solid; border-width: 0 1px 1px 0"|	t60 style="border-style: solid; border-width: 0 1px 1px 0"|	t90 style="border-style: solid; border-width: 0 1px 1px 0"|	t120
<.05 style="border-style: solid; border-width: 0 1px 1px 0"|	802 style="border-style: solid; border-width: 0 1px 1px 0"|	1213 style="border-style: solid; border-width: 0 1px 1px 0"|	1046 style="border-style: solid; border-width: 0 1px 1px 0"|	672 style="border-style: solid; border-width: 0 1px 1px 0"|	288
<.01 style="border-style: solid; border-width: 0 1px 1px 0"|	202 style="border-style: solid; border-width: 0 1px 1px 0"|	415 style="border-style: solid; border-width: 0 1px 1px 0"|	276 style="border-style: solid; border-width: 0 1px 1px 0"|	162 style="border-style: solid; border-width: 0 1px 1px 0"|	36
<.001 style="border-style: solid; border-width: 0 1px 1px 0"|	24 style="border-style: solid; border-width: 0 1px 1px 0"|	69 style="border-style: solid; border-width: 0 1px 1px 0"|	33 style="border-style: solid; border-width: 0 1px 1px 0"|	14 style="border-style: solid; border-width: 0 1px 1px 0"|	5
<.0001 style="border-style: solid; border-width: 0 1px 1px 0"|	2 style="border-style: solid; border-width: 0 1px 1px 0"|	8 style="border-style: solid; border-width: 0 1px 1px 0"|	4 style="border-style: solid; border-width: 0 1px 1px 0"|	0 style="border-style: solid; border-width: 0 1px 1px 0"|	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.

style="border-style: solid; border-width: 0 1px 1px 0"| style="border-style: solid; border-width: 0 1px 1px 0"|

p style="border-style: solid; border-width: 0 1px 1px 0"|	t15 style="border-style: solid; border-width: 0 1px 1px 0"|	t30 style="border-style: solid; border-width: 0 1px 1px 0"|	t60 style="border-style: solid; border-width: 0 1px 1px 0"|	t90 style="border-style: solid; border-width: 0 1px 1px 0"|	t120
<.05 style="border-style: solid; border-width: 0 1px 1px 0"|	0 style="border-style: solid; border-width: 0 1px 1px 0"|	1 style="border-style: solid; border-width: 0 1px 1px 0"|	0 style="border-style: solid; border-width: 0 1px 1px 0"|	0 style="border-style: solid; border-width: 0 1px 1px 0"|	0

Only one gene was still significantly changed under this stringent correction.

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.)

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.

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?