Elizabeth Polidan Week9

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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.
+
Begin by recording in your wiki the number of replicates for each time point in your data.
-
#Sanity Check: Number of genes significantly changed
+
{|
-
##Check the number of genes significantly changed
+
! t15
-
##*How many genes have p value < 0.05?
+
! t30
-
##*What about p < 0.01?
+
! t60
-
##*What about p < 0.001?
+
! t90
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##*What about p < 0.0001?
+
! t120
-
##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.
+
| A
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##Magnitude and direction of gene expression
+
| B
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##*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?
+
| C
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##*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?
+
| D
-
##*Keeping the "Pval" filter at p < 0.05, How many have an average log fold change of > 0.25 and p < 0.05?
+
| E
-
##*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.)  
+
|}
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##Check expression of NSR1.  Find NSR1 in your dataset.  
+
 
-
##*Is its expression significantly changed at any timepoint?  
+
Sanity Check
-
##*Record the average fold change and p value for NSR1 for each timepoint in your dataset.
+
*Check the number of genes significantly changedHow many genes have p value < 0.05? p < 0.01? p < 0.001? p < 0.0001?
-
##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)?  
+
| p
-
##*Look up the function of this gene at the Saccharomyces Genome Database and record it in your notebook.  
+
| t15
-
##*Why do you think the cell is changing this gene's expression upon cold shock?
+
| 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 12:41, 2 April 2013

alt=My children

Elizabeth Polidan

BIOL 398.03 / MATH 388

  • Loyola Marymount University
  • Los Angeles, CA, USA

Elizabeth Polidan Home

Course Home



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?
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