Marmas Week 8 - Revision history

Marmas: /* References */ added reference for week 8

2019-10-24T02:29:10Z

‎References: added reference for week 8

Marmas: /* Acknowledgements */ added acknowledgements

2019-10-24T02:25:06Z

‎Acknowledgements: added acknowledgements

Marmas: /* Data and Files */ added stem analysis data

2019-10-23T23:25:45Z

‎Data and Files: added stem analysis data

Marmas: Problem with saving after closing my laptop, should be all up to date now

2019-10-23T23:18:09Z

Problem with saving after closing my laptop, should be all up to date now

Marmas: added answer for sanity check

2019-10-23T22:04:27Z

added answer for sanity check

Marmas: /* Sanity Check: Number of genes significantly changed */ added answer to sanity check

2019-10-23T21:53:00Z

‎Sanity Check: Number of genes significantly changed: added answer to sanity check

Marmas: /* Data and Files */ added line break

2019-10-22T23:01:52Z

‎Data and Files: added line break

Marmas: /* Data and Files */ added p-value slide

2019-10-22T23:01:40Z

‎Data and Files: added p-value slide

Marmas: /* Sanity Check: Number of genes significantly changed */ added answers for bonferoni sanity check and B-H sanity check

2019-10-22T22:38:21Z

‎Sanity Check: Number of genes significantly changed: added answers for bonferoni sanity check and B-H sanity check

Marmas: /* Sanity Check: Number of genes significantly changed */ fixed a type

2019-10-22T22:20:16Z

‎Sanity Check: Number of genes significantly changed: fixed a type

← Older revision		Revision as of 02:29, 24 October 2019
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	==References==		==References==
		+	Dahlquist, K. (2019, October 17). Week 8 [Wikipedia Page Post]. Retrieved from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_8.

← Older revision		Revision as of 02:25, 24 October 2019
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	==Acknowledgements==		==Acknowledgements==
		+	*I would like to acknowledge Joey Nimmers and Iliana Crespin for helping with the data analysis throughout this week.
		+	*I would like to acknowledge Dr. Dahlquist for assisting with the spreadsheet analysis and calculations.

	==References==		==References==

@@ Line 127: / Line 127: @@
 ==Data and Files==
 [[Media: Marmas_data_dGLN3_102219.zip | ∆GLN3 Data]]<br>
-[[Media: Marmas_BIOL367_F19_p-value_slide.pptx | p-value Slide]]
+[[Media: Marmas_BIOL367_F19_p-value_slide.pptx | p-value Slide]]<br>
 ==Conclusion==

@@ Line 95: / Line 95: @@
 **''Average Log fold change at t15: -0.2466 ; Average Log fold change at t30: -0.5757 ; Average Log fold change at t60: -0.4961 ; Average Log fold change at t90: -0.5305 ; Average Log fold change at t120: -0.4613''
 **''No, ASP1 does not change expression due to cold shock.''
 ==Data and Files==
 [[Media: Marmas_data_dGLN3_102219.zip | ∆GLN3 Data]]<br>

@@ Line 91: / Line 91: @@
 **''Yes, NSR1 changes expression due to cold shock.''
 * For fun, find "your favorite gene" (from your [[Week 3]] assignment) in the 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?'''''  Does your favorite gene change expression due to cold shock in this experiment?
 ==Data and Files==

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 ** Note that since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for your strain.  We will do the comparison with wild type at a later date.
 * Comparing results with known data:  the expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. '''''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?'''''  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?
 * For fun, find "your favorite gene" (from your [[Week 3]] assignment) in the 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?'''''  Does your favorite gene change expression due to cold shock in this experiment?

@@ Line 79: / Line 79: @@
 * We have just performed 6189 hypothesis tests.  Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times.  Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed.  However, we don't know ''which'' ones.  To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values.  The Bonferroni correction is very stringent.  The Benjamini-Hochberg correction is less stringent.  To see this relationship, filter your 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)?'''''
 ** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?'''''
 * In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant".  Instead, it is a moveable confidence level.  If we want to be very confident of our data, use a small p value cut-off.  If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
 * We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table.  Use this [[Media:BIOL367_F19_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. '''''Upload your slide to the wiki.'''''

@@ Line 75: / Line 75: @@
 *** ''There are 514 genes (8.31%) that have a p-value < 0.001.''
 ** '''''How many genes have p < 0.0001? and what is the percentage (out of 6189)?'''''
-*** ''There are 180 (2.90% genes that have a p-value < 0.0001.''
+*** ''There are 180 genes (2.90%) that have a p-value < 0.0001.''
 * When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
 * We have just performed 6189 hypothesis tests.  Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times.  Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed.  However, we don't know ''which'' ones.  To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values.  The Bonferroni correction is very stringent.  The Benjamini-Hochberg correction is less stringent.  To see this relationship, filter your data to determine the following: