# Elizabeth Polidan Week9

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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 13:41, 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?