Elizabeth Polidan Week9

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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.'''
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{| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000"
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{| class="wikitable" style="text-align: right; color: green; border-collapse: collapse; border: 1px solid #000" border="1" cellpadding="2"
! t15  
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! t30
! t30
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*Record the average fold change and p value for NSR1 for each timepoint in your dataset.
*Record the average fold change and p value for NSR1 for each timepoint in your dataset.
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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!)
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**See the table above.
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'''Check for gene with smallest p-value.  '''
*Which gene has the smallest p value in your dataset (at any timepoint)?  
*Which gene has the smallest p value in your dataset (at any timepoint)?  
**YOL159C has the smallest p-value of .0000024114 at t=30.
**YOL159C has the smallest p-value of .0000024114 at t=30.

Revision as of 01:27, 3 April 2013

alt=My children

Elizabeth Polidan

BIOL 398.03 / MATH 388

  • Loyola Marymount University
  • Los Angeles, CA, USA

Elizabeth Polidan Home

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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
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 59
  • 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? See table above, row 1.
  • Keeping the "Pval" filter at p < 0.05, How many have an average log fold change of > 0.25 and p < 0.05? See table above, row 2.
  • How many have an average log fold change of < -0.25 and p < 0.05? See table above, row 3.

Check expression of NSR1. Find NSR1 in your dataset.

  • Is its expression significantly changed at any timepoint?
There was significant change at t=15, 30, and 60
  • Record the average fold change and p value for NSR1 for each timepoint in your dataset.
    • See the table above.
Check for gene with smallest p-value.
  • Which gene has the smallest p value in your dataset (at any timepoint)?
    • YOL159C has the smallest p-value of .0000024114 at t=30.
  • Look up the function of this gene at the Saccharomyces Genome Database and record it in your notebook.
    • YOL159C is listed in the Yeast Genome Database, but its function is listed as unknown.
  • Why do you think the cell is changing this gene's expression upon cold shock?
    • I don't know how to answer this.
Values for NSR1
t15 t30 t60 t90 t120
p value .0042 .0019 .0462 .1821 .5056
Avg LFC 1.57 2.06 1.66 -0.66 -0.14
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