Kwrigh35 Week 8 - Revision history

Kwrigh35: /* Summary Paragraph */ wrote summary paragraph

2017-10-24T06:59:44Z

‎Summary Paragraph: wrote summary paragraph

Kwrigh35: /* Electronic Notebook */ took out unnecessary sections

2017-10-24T06:46:54Z

‎Electronic Notebook: took out unnecessary sections

Kwrigh35: /* Assignment Deliverables */ formatting change

2017-10-24T02:56:23Z

‎Assignment Deliverables: formatting change

Kwrigh35: /* Assignment Deliverables */ added link to p-value ppt slide

2017-10-24T02:54:26Z

‎Assignment Deliverables: added link to p-value ppt slide

Kwrigh35: /* Acknowledgements */ added italicized disclaimer

2017-10-24T02:50:48Z

‎Acknowledgements: added italicized disclaimer

Kwrigh35: /* Acknowledgements */ started acknowledgements page

2017-10-24T02:46:37Z

‎Acknowledgements: started acknowledgements page

Kwrigh35: /* Sanity Check: Number of genes significantly changed */ added p-value/avg log fold change data for "my favorite gene" ASP1

2017-10-24T02:34:53Z

‎Sanity Check: Number of genes significantly changed: added p-value/avg log fold change data for "my favorite gene" ASP1

Kwrigh35: /* Sanity Check: Number of genes significantly changed */ added the final step of the procedure, which was previously missing

2017-10-24T02:26:31Z

‎Sanity Check: Number of genes significantly changed: added the final step of the procedure, which was previously missing

Kwrigh35: /* Sanity Check: Number of genes significantly changed */ Added values for NSR1 data

2017-10-23T22:28:51Z

‎Sanity Check: Number of genes significantly changed: Added values for NSR1 data

Kwrigh35: /* Sanity Check: Number of genes significantly changed */ added p value data for Bonferroni and BH corrected p values

2017-10-23T22:17:45Z

‎Sanity Check: Number of genes significantly changed: added p value data for Bonferroni and BH corrected p values

← Older revision		Revision as of 06:59, 24 October 2017
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	===Summary Paragraph===		===Summary Paragraph===
		+	While completing this assignment, I learned a number of things. I learned a few new tricks in Excel, as well as why it is so important to manage data well. If data is mis-managed, it can create a whole host of frustrating (and potentially life threatening, in the case of Dr. Potti's fraud) problems down the road. While analyzing the data set from this week, I determined the p-values for the log-fold changes of genes in the dZAP1 deleted strain of yeast. I found that just over 40% of the genes have p < 0.05. Two more stringent p-value corrections, "Bonferroni" and "Benjamini and Hochberg," put the number of genes having p < 0.05 at 3.38% and 28.53% respectively. These two corrections are good for times when we want more certainty in our results, but are probably unnecessary for this set of data since it is not being used to justify clinical trials.

	==Acknowledgements==		==Acknowledgements==

← Older revision		Revision as of 06:46, 24 October 2017
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	==Electronic Notebook==		==Electronic Notebook==
−	~~===Background===~~
−	~~===Experimental Design===~~
	===Part 1: Statistical Analysis Part 1===		===Part 1: Statistical Analysis Part 1===

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 ==Assignment Deliverables==
-Excel Workbook: [[media:DZAP1_ANOVA_KWRIGH35.zip]]
+::Excel Workbook: [[media:DZAP1_ANOVA_KWRIGH35.zip]]
-P-Value PowerPoint Slide: [[media:Kwrigh35_dZAP1_p-value_slide.zip]]
+::P-Value PowerPoint Slide: [[media:Kwrigh35_dZAP1_p-value_slide.zip]]
 ==Electronic Notebook==

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 ==Assignment Deliverables==
-[[media:DZAP1_ANOVA_KWRIGH35.zip]]
+Excel Workbook: [[media:DZAP1_ANOVA_KWRIGH35.zip]]
-===Summary Paragraph===
+P-Value PowerPoint Slide: [[media:Kwrigh35_dZAP1_p-value_slide.zip]]
 ==Electronic Notebook==

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 *I'd like to thank my homework partner, [[User:Aporras1|Antonio Porras]].  We worked together/checked answers during class on Thursday October 19th, and nearly finished the assignment.
 *Thanks also to Dr. Dionisio, who helped me figure out how to upload my PowerPoint slide.
 [[User:Kwrigh35|Kwrigh35]] ([[User talk:Kwrigh35|talk]]) 19:46, 23 October 2017 (PDT)

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 ==Acknowledgements==
 ==Refereneces==
 LMU BioDB 2017. (2017). Week 8. Retrieved October 17, 2017, from [[Week 8]]

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 ***t=120: 0.054215477
 * 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_F17_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. Upload your slide to the wiki.
-*For fun, find "your favorite gene" (from your web page) 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?
+*For fun, find "your favorite gene" (from your web page) 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?
 ===Summary Paragraph===

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 * 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.
 * 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 "dZAP1_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis.
 * 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_F17_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. Upload your slide to the wiki.

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 * 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.
 * 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 "dZAP1_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis.