# 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.''' | ||

- | {| 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" border="1" cellpadding="2" |

! t15 | ! t15 | ||

! 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. | ||

- | Check for gene with smallest p-value. | + | **See the table above. |

+ | |||

+ | '''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

**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?

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?

- 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.

t15 | t30 | t60 | t90 | t120 | |
---|---|---|---|---|---|

p value | .0042 | .0019 | .0462 | .1821 | .5056 |

Avg LFC | 1.57 | 2.06 | 1.66 | -0.66 | -0.14 |