# 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.''' |

{| 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" | ||

! t15 | ! t15 | ||

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Data errors replaced by single space: 108 occurences | Data errors replaced by single space: 108 occurences | ||

− | 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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Only one gene was still significantly changed under this stringent correction. | Only one gene was still significantly changed under this stringent correction. | ||

− | Magnitude and direction of gene expression | + | '''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 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, 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? | *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.) | *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. | + | |

+ | '''Check expression of NSR1. Find NSR1 in your dataset.''' | ||

*Is its expression significantly changed at any timepoint? | *Is its expression significantly changed at any timepoint? | ||

*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. |

## Revision as of 21:18, 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?