Difference between revisions of "Elizabeth Polidan Week9"

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(Formatting corrections)
(Formatting corrections)
Line 4: Line 4:
 
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.
 
{| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000"
 
{| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000"
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t15
 
! t15
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t30
 
! t30
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t60
 
! t60
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t90
 
! t90
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t120
 
! t120
 
|-
 
|-
 
| 4
 
| 4
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
| 4
 
| 4
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
| 4
 
| 4
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
| 5
 
| 5
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
| 5
 
| 5
 
|}
 
|}
Line 31: Line 22:
 
{| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000"
 
{| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000"
 
! p
 
! p
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t15
 
! t15
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t30
 
! t30
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t60
 
! t60
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t90
 
! t90
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t120
 
! t120
 
|-
 
|-
 
! <.05
 
! <.05
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  802
 
|  802
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
| 1213
 
| 1213
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
| 1046
 
| 1046
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  672
 
|  672
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  288
 
|  288
 
|-
 
|-
 
! <.01
 
! <.01
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  202
 
|  202
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  415
 
|  415
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  276
 
|  276
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  162
 
|  162
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  36
 
|  36
 
|-
 
|-
 
! <.001
 
! <.001
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  24
 
|  24
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  69
 
|  69
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  33
 
|  33
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|  14
 
|  14
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    5
 
|    5
 
|-
 
|-
 
! <.0001
 
! <.0001
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    2
 
|    2
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    8
 
|    8
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    4
 
|    4
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    0
 
|    0
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    2
 
|    2
 
|-
 
|-
Line 95: Line 61:
 
*Perform this correction and determine whether and how many of the genes are still significantly changed at p < 0.05 after the 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.
 
{| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000"
 
{| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000"
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! p
 
! p
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t15
 
! t15
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t30
 
! t30
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t60
 
! t60
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t90
 
! t90
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! t120
 
! t120
 
|-
 
|-
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
! <.05
 
! <.05
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    0
 
|    0
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    1
 
|    1
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    0
 
|    0
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    0
 
|    0
style="border-style: solid; border-width: 0 1px 1px 0"|
 
 
|    0
 
|    0
 
|-
 
|-

Revision as of 20:17, 2 April 2013

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
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?
  • 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?