# Difference between revisions of "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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Sanity Check | 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? | *Check the number of genes significantly changed. How many genes have p value < 0.05? p < 0.01? p < 0.001? p < 0.0001? | ||

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Bonferroni correction | 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. | *Perform this correction and determine whether and how many of the genes are still significantly changed at p < 0.05 after the Bonferroni correction. | ||

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## Revision as of 20:08, 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 |
---|---|---|---|---|

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?