DM Notes 2.04.16 - Revision history

Dymaghini at 18:45, 11 February 2016

2016-02-11T18:45:28Z

Dymaghini at 19:50, 4 February 2016

2016-02-04T19:50:51Z

Dymaghini at 19:34, 4 February 2016

2016-02-04T19:34:19Z

Dymaghini at 19:33, 4 February 2016

2016-02-04T19:33:21Z

Dymaghini at 19:32, 4 February 2016

2016-02-04T19:32:06Z

Dymaghini at 19:31, 4 February 2016

2016-02-04T19:31:29Z

Dymaghini at 19:26, 4 February 2016

2016-02-04T19:26:32Z

Dymaghini at 19:12, 4 February 2016

2016-02-04T19:12:09Z

Dymaghini at 19:02, 4 February 2016

2016-02-04T19:02:57Z

Dymaghini: Created page with "Exploring correlation: correl_explore.pdf, correl_explore_scenarios.xls"

2016-02-04T18:41:28Z

Created page with "Exploring correlation: correl_explore.pdf, correl_explore_scenarios.xls"

New page

Exploring correlation: correl_explore.pdf, correl_explore_scenarios.xls

← Older revision		Revision as of 18:45, 11 February 2016
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	Correlation: not necessarily directional. Two variables/treatments seeing change together. In our heat maps, how do we measure correlation? R^2: how well data fits the linear regression. Slope tells positive/negative relationship. Same slope can have tightly correlated points, wide points. Slope does not indicate good correlation.		Correlation: not necessarily directional. Two variables/treatments seeing change together. In our heat maps, how do we measure correlation? R^2: how well data fits the linear regression. Slope tells positive/negative relationship. Same slope can have tightly correlated points, wide points. Slope does not indicate good correlation.
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		+	Back to home [[Dylan Maghini]]

← Older revision		Revision as of 19:50, 4 February 2016
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	Exploring correlation: correl_explore.pdf, correl_explore_scenarios.xls		Exploring correlation: correl_explore.pdf, correl_explore_scenarios.xls
		+
		+	Correlation: not necessarily directional. Two variables/treatments seeing change together. In our heat maps, how do we measure correlation? R^2: how well data fits the linear regression. Slope tells positive/negative relationship. Same slope can have tightly correlated points, wide points. Slope does not indicate good correlation.

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 *We can also use R to generate a heat map, but it'd be interesting to set our own parameters.
 *How do we account for genetic differences between individual snakes?
 '''From class discussion:'''

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 *What tissues do we have, specifically? Validate that we sampled as expected. Are our samples comparable? Look for housekeeping genes in those tissues.Ex: serosa won't have proteins involved in amino acid uptake. So, do we have mucosa, serosa, and from proximal, distal...
 *What exactly is the heat map representing? How was it calculated? How does it select genes? We gave it a p-value.
 *From the genes in our R heat map, can we bin those by function to see if any one cellular function is more prominent?
 *Why does intestine_fed_4 show up as more closely related to intestine_no_1 than it does to other fed samples?

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-Questions to work on in our groups of three (capture electronically when applicable):
+'''What do we want out of our research? What's the perfect outcome? How do we get there?'''
 Goal: find all of the differentially expressed genes between samples. Perfect outcome is having a few genes that look like they're setting this digestive process in motion. What is this gene encoding for? Why is this the trigger? What kind of protein starts atrophy/hyperplasia?
-What do we need to do with each of our twelve data sets, to evaluate it/know how to treat it downstream?
+'''What do we need to do with each of our twelve data sets, to evaluate it/know how to treat it downstream?'''
 So far, we've made comparisons in R: liver vs intestine, fed vs not fed. Dustin is going to compare all fed versus all non fed organs to get common expression across all organs (essentially, fed vs not fed, irregardless of tissue). What exactly is this heat map representing? Go into DESeq documentation and read up on exactly how it calculated log change, how it selects which genes to represent in the heat map.

@@ Line 4: / Line 4: @@
 What do we want out of our research? What's the perfect outcome? How do we get there?
-Goal: find all of the differentially expressed genes between samples. Perfect outcome is having a few genes that look like they're setting this digestive process in motion. What is this gene encoding for? Why is this the trigger?
+Goal: find all of the differentially expressed genes between samples. Perfect outcome is having a few genes that look like they're setting this digestive process in motion. What is this gene encoding for? Why is this the trigger? What kind of protein starts atrophy/hyperplasia?
 What do we need to do with each of our twelve data sets, to evaluate it/know how to treat it downstream?
-So far, we've looked at liver vs intestine, fed vs not fed. Dustin is going to compare all fed versus all non fed organs to get common expression across all organs. What exactly is this heat map representing? Go into DESeq documentation and read up on exactly how it calculated log change.
+So far, we've made comparisons in R: liver vs intestine, fed vs not fed. Dustin is going to compare all fed versus all non fed organs to get common expression across all organs (essentially, fed vs not fed, irregardless of tissue). What exactly is this heat map representing? Go into DESeq documentation and read up on exactly how it calculated log change, how it selects which genes to represent in the heat map.
 Some things to do: get the list of genes from the heat map, bin those genes by function. For six intestine samples, verify that we have the tissue we need (hopefully mucosa, proximal, but we might have serosa, distal, etc.) Find genes specific to mucosa, other portions, look at that in each of the 6 samples. Another question: how do we account for genetic differences between snakes?
-Perhaps write our own script normalizing read numbers, then comparing across samples.
+'''Starting questions we have'''
 Exploring correlation: correl_explore.pdf, correl_explore_scenarios.xls

← Older revision		Revision as of 19:02, 4 February 2016
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		+	Questions to work on in our groups of three (capture electronically when applicable):
		+
		+
		+	What do we want out of our research? What's the perfect outcome? How do we get there?
		+
		+
		+	Goal: find all of the differentially expressed genes between samples. Perfect outcome is having a few genes that look like they're setting this digestive process in motion. What is this gene encoding for? Why is this the trigger?
		+
		+
		+	What do we need to do with each of our twelve data sets, to evaluate it/know how to treat it downstream?
		+
		+	So far, we've looked at liver vs intestine, fed vs not fed. Dustin is going to compare all fed versus all non fed organs to get common expression across all organs.
		+	Some things to do: get the list of genes from the heat map, bin those genes by function. For six intestine samples, verify that we have the tissue we need (hopefully mucosa, but we might have serosa, proximal, distal, etc.) Find genes specific to mucosa, other portions, look at that in each of the 6 samples.
		+
		+	Perhaps write our own script normalizing read numbers, then comparing across samples.
		+
	Exploring correlation: correl_explore.pdf, correl_explore_scenarios.xls		Exploring correlation: correl_explore.pdf, correl_explore_scenarios.xls