# Elizabeth Polidan Week9

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 Revision as of 00:20, 3 April 2013 (view source) (Formatting corrections)← Previous diff Revision as of 00:34, 3 April 2013 (view source) (Periodic save while entering results)Next diff → Line 10: Line 10: ! t120 ! t120 |- |- - | 4 | 4| 4| 5| 5 + | 4 + | 4 + | 4 + | 5 + | 5 |} |} Line 77: Line 81: '''Magnitude and direction of gene expression''' '''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 greater than zero. How many meet these two criteria? + {| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000" + ! p + ! t15 + ! t30 + ! t60 + ! t90 + ! t120 + |- + ! <.05 & ALFC > 0 + |  449 + |  681 + |  621 + |  418 + |  221 + |- + ! <.05 & ALFC > 0.25 + |  439 + |  668 + |  609 + |  398 + |  191 + |- + ! <.05 & ALFC < -0.25 + |  331 + |  517 + |  413 + |  249 + |  10 + |- + |} *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, 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? *Keeping the "Pval" filter at p < 0.05, How many have an average log fold change of > 0.25 and p < 0.05?

## Revision as of 00:34, 3 April 2013

Elizabeth Polidan

BIOL 398.03 / MATH 388

• Loyola Marymount University
• Los Angeles, CA, USA

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

t15 t30 t60 t90 t120
4 4 4 5 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 t15 t30 t60 t90 t120
<.05 802 1213 1046 672 288
<.01 202 415 276 162 36
<.001 24 69 33 14 5
<.0001 2 8 4 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.
p t15 t30 t60 t90 t120
<.05 0 1 0 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?
p t15 t30 t60 t90 t120
<.05 & ALFC > 0 449 681 621 418 221
<.05 & ALFC > 0.25 439 668 609 398 191
<.05 & ALFC < -0.25 331 517 413 249 10
• 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?