Elizabeth Polidan Week9: Difference between revisions
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(Setting up questions to be answered) |
(Added table formatting in preparation for adding results later.) |
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Begin by recording in your wiki the number of replicates for each time point in your data. | |||
{| | |||
! t15 | |||
! t30 | |||
! t60 | |||
! t90 | |||
! t120 | |||
|- | |||
| A | |||
| B | |||
| C | |||
| D | |||
| E | |||
|} | |||
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 | |||
| A | |||
| B | |||
| C | |||
| D | |||
| E | |||
| .01 | |||
| A | |||
| B | |||
| C | |||
| D | |||
| E | |||
| .001 | |||
| A | |||
| B | |||
| C | |||
| D | |||
| E | |||
| .0001 | |||
| A | |||
| B | |||
| C | |||
| D | |||
| E | |||
|} | |||
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. | |||
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? |
Revision as of 10:41, 2 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 |
---|---|---|---|---|
A | B | C | D | E |
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 | A | B | C | D | E | .01 | A | B | C | D | E | .001 | A | B | C | D | E | .0001 | A | B | C | D | E |
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.
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?