Difference between revisions of "Knguye66 Week 7"
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* ANOVA --> is the gene expression significantly different than zero at any time point? | * ANOVA --> is the gene expression significantly different than zero at any time point? | ||
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| + | This week we will only be doing: experimental design and getting ready | ||
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| + | * starting file: excel (reference this as a link) | ||
| + | * record file name and time point for data | ||
== Microrray Data Analysis (wild type data) == | == Microrray Data Analysis (wild type data) == | ||
Revision as of 14:56, 15 October 2019
Contents
Notes before starting
- T-test -> is this gene expression change significantly different than zero?
- p<0.05: probability that you would have at least this big of a change by chance
- ie. when we have 6189 genes, there are 6189 t-tests, are they significant? when we say there's a p<0.05, there's a 5% chance it's due to chance... meaning, if we have 6189 (5% of these are significant just by chance)
- magnitude of difference
- variation is sample measurements
- number of samples/measurements
- ANOVA --> is the gene expression significantly different than zero at any time point?
This week we will only be doing: experimental design and getting ready
- starting file: excel (reference this as a link)
- record file name and time point for data
Microrray Data Analysis (wild type data)
Purpose
Methods/Results
- The number of genes that are significantly changed at p value cut-off of p < 0.05 -
- How many genes have p < 0.05? and what is the percentage (out of 6189)?
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- How many genes have p < 0.01? and what is the percentage (out of 6189)?
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- How many genes have p < 0.001? and what is the percentage (out of 6189)?
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- How many genes have p < 0.0001? and what is the percentage (out of 6189)?
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- Filter data to determine the following -
- How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?
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- How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
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- Compare your results with known data. Note that the average Log fold change is what we called "STRAIN)_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does NSR1 change expression due to cold shock in this experiment? -
- Find NSR1 in your dataset. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment?
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- Find your favorite gene from Week 3. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment? Does your favorite gene change expression due to cold shock in this experiment? -
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Data and Files
Conclusion
Acknowledgements
This section is in acknowledgement to partner Christina Dominguez (User:Cdomin12), as well as, Marcus Avila (User:Mavila9) and Jonar Cowan (User:Jcowan4).
"Except for what is noted above, this individual journal entry was completed by me and not copied from another source."
User Page
User:knguye66
Template Page
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