JP Feb 09 16 - Revision history

Jupreziosi at 18:41, 11 February 2016

2016-02-11T18:41:56Z

2016-02-11T18:41:46Z

2016-02-09T19:35:48Z

2016-02-09T19:33:20Z

2016-02-09T19:25:54Z

2016-02-09T19:25:46Z

2016-02-09T19:07:18Z

2016-02-09T19:03:40Z

2016-02-09T18:53:40Z

2016-02-09T18:39:57Z

Created page with "Julia Preziosi"

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[[Julia Preziosi]]

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 . Removes these genes from consideration (no longer a part of anyone's group), and repeat.
 (next best cluster gets cut, etc).
 [http://gcat.davidson.edu/DGPB/clust/clustering.htm Clustering Resource]

← Older revision		Revision as of 18:41, 11 February 2016
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	3. Removes these genes from consideration (no longer a part of anyone's group), and repeat.		3. Removes these genes from consideration (no longer a part of anyone's group), and repeat.
	(next best cluster gets cut, etc).		(next best cluster gets cut, etc).
		+	[http://gcat.davidson.edu/DGPB/clust/clustering.htm Clustering Resource]

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 Supervised Clustering:
-Can pull out clusters to suit your situation. Pull out each gene(s) individually that you want to find all genes which are similar to it / to the average. "similar" controlled by whatever method you chose. Iterative - join two most similar, then find the next most similar to that cluster (by whatever method).
+Can pull out clusters to suit your situation. Pull out each gene(s) individually that you want to find all genes which are similar (by correlation coefficient perchance) to it / to the average. "similar" controlled by whatever method you chose. Iterative - join two most similar, then find the next most similar to that cluster (by whatever method).
 * Want to know every gene that tracks with a transcription factor - even though relative increased expression of that factor may be low.
 * Need to filter data and get rid of the noise first.

← Older revision		Revision as of 19:33, 9 February 2016
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	We could set k to 2 and see if data supports fed / nonfed clusters.		We could set k to 2 and see if data supports fed / nonfed clusters.
	Clustering genes? Hard to pick k.		Clustering genes? Hard to pick k.
		+
		+	Supervised Clustering:
		+	Can pull out clusters to suit your situation. Pull out each gene(s) individually that you want to find all genes which are similar to it / to the average. "similar" controlled by whatever method you chose. Iterative - join two most similar, then find the next most similar to that cluster (by whatever method).
		+	* Want to know every gene that tracks with a transcription factor - even though relative increased expression of that factor may be low.
		+	* Need to filter data and get rid of the noise first.
		+
		+	Quality Clustering - QT Clust:
		+	1. Each gene builds a supervised cluster.
		+	(Some groups overlap)
		+	2. Gene with "best" list, and genes in its list, becomes a cluster.
		+	(best cluster rule set - could be biggest)
		+	3. Removes these genes from consideration (no longer a part of anyone's group), and repeat.
		+	(next best cluster gets cut, etc).

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 [[Julia Preziosi]]
-'''Clustering & Correlation Coefficient:'''
+'''Correlation Coefficient:'''
 Statistically, we see significant changes. Biologically, looking at changes in the genes, we may not think they're that significant.

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 Gene expression can be made into a ratio between fed and nonfed expression levels. Generates "gene induction / repression" values.
 However, 1/16th looks less significant than 16 fold. *Use a log scale instead: ratio of 16 becomes value of 4. Lets you visualize repression and induction between genes - direct and indirect relationships can be assumed (coregulation).
 *Analysis techniques - how to pull out negative correlations.
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 Clustering by gene expression profiles- "Guilt by association".
 Compare expression levels of the genes over samples. *Pattern less meaningful since we are not doing 'over time'.

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 Small fluctuations can really change correlations, especially if the gene is clustered around a very tight line.
 *Do we include all three fastings and feedings if little differences change clustering?
 '''Clustering:'''
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 Gene expression can be made into a ratio between fed and nonfed expression levels. Generates "gene induction / repression" values.
 However, 1/16th looks less significant than 16 fold. *Use a log scale instead: ratio of 16 becomes value of 4. Lets you visualize repression and induction between genes - direct and indirect relationships can be assumed (coregulation).

← Older revision		Revision as of 18:53, 9 February 2016
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	[[Julia Preziosi]]		[[Julia Preziosi]]
		+
		+	Correlation Coefficient:
		+	Statistically, we see significant changes. Biologically, looking at changes in the genes, we may not think they're that significant.
		+
		+	Genes are correlated when multiple samples display a trend in expression for the genes. For instance, if Gene 1 is upregulated and Gene 2 is upregulated (magnitude doesn't matter) in the same sample, they're correlated for that sample. Over multiple samples, a correlation coefficient can be produced, especially if it's more upregulated in other samples. Negative correlations indicate that as one gene increases, the other gene decreases.
		+	They don't have to be parallel lines.
		+	Small fluctuations can really change correlations, especially if the gene is clustered around a very tight line.
		+	*Do we include all three fastings and feedings if little differences change clustering?