CRIT/workflow - Revision history

Tara: /* Example Code */

2011-02-21T15:50:51Z

Example Code

← Older revision		Revision as of 15:50, 21 February 2011
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	#Determine set of targets sensitive to this feature		#Determine set of targets sensitive to this feature
	#This step can be quite slow, be patient		#This step can be quite slow, be patient (on average about 2-3 minutes)
	DC<-discriminator(C, tLabel, multCorrect=TRUE)		DC<-discriminator(C, tLabel, multCorrect=TRUE)

2011-02-21T15:49:37Z

Example Code

← Older revision		Revision as of 15:49, 21 February 2011
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	#Determine set of targets sensitive to this feature		#Determine set of targets sensitive to this feature
			#This step can be quite slow, be patient
	DC<-discriminator(C, tLabel, multCorrect=TRUE)		DC<-discriminator(C, tLabel, multCorrect=TRUE)

2011-02-21T15:21:26Z

Example Code

← Older revision		Revision as of 15:21, 21 February 2011
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	source(file="CRIT.R")		source(file="CRIT.R")

	#Generate label for feature of interest ~~- set~~ x for column variable		#Generate label for feature of interest
			#Specify x for column variable of interest
	tLabel<-initializer(T[,x], type="median")		tLabel<-initializer(T[,x], type="median")

2011-02-21T15:20:05Z

Example Code

← Older revision		Revision as of 15:20, 21 February 2011
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	<pre>		<pre>

			#The below code snippet needs to be altered to contain the appropriate file path for both the dataset and CRIT package.
	#Load Data		#Load Data
	~~data~~(TFExample.RData)		load(file="TFExample.RData")

	#Load CRIT functions		#Load CRIT functions
	~~library~~(CRIT.R)		source(file="CRIT.R")

	#Generate label for feature of interest - set x for column variable		#Generate label for feature of interest - set x for column variable

2011-02-21T15:12:12Z

Example Code

← Older revision		Revision as of 15:12, 21 February 2011
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	#Load Data		#Load Data
	~~load~~(~~file="~~TFExample.RData")		data(TFExample.RData)

	#Load CRIT functions		#Load CRIT functions
	~~source~~(~~file="~~CRIT.R")		library(CRIT.R)

	#Generate label for feature of interest - set x for column variable		#Generate label for feature of interest - set x for column variable

2011-02-21T15:10:09Z

Motivation and Problem Set Up

← Older revision		Revision as of 15:10, 21 February 2011
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	Cis regulatory elements as a means of regulating gene expression have		Cis regulatory elements as a means of regulating gene expression have
	been extensively studied. However, beyond such motifs, are ~~there~~		been extensively studied. However, beyond such motifs, are their
	inherent properties of the targets themselves that make them more or		inherent properties of the targets themselves that make them more or
	less likely to be regulated by a given class of transcription factors?		less likely to be regulated by a given class of transcription factors?
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	regulate essential targets? Are there genome composition features		regulate essential targets? Are there genome composition features
	such as GC or codon bias that influence which targets are regulated by		such as GC or codon bias that influence which targets are regulated by
	which TFs?		which TFs?

	===Input Data===		===Input Data===

2011-01-30T21:21:15Z

Output

← Older revision		Revision as of 21:21, 30 January 2011
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	[http://tyna.gersteinlab.org/tyna/ tYNA] or [http://www.cytoscape.org/ Cytoscape].		[http://tyna.gersteinlab.org/tyna/ tYNA] or [http://www.cytoscape.org/ Cytoscape].

			[[File:results.png]]

2011-01-30T21:17:00Z

Output

← Older revision		Revision as of 21:17, 30 January 2011
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	===Output===		===Output===

	Cross Patterns have a natural X relationship Y representation making a network representation ~~and~~ ideal way to visualize results.		Cross Patterns have a natural X relationship Y representation making a network representation an ideal way to visualize results.

	Cross patterns can easily be formatted in .sif for loading into various network browsers including		Cross patterns can easily be formatted in .sif for loading into various network browsers including

	[http://tyna.gersteinlab.org/tyna/ tYNA] or [http://www.cytoscape.org/ Cytoscape].		[http://tyna.gersteinlab.org/tyna/ tYNA] or [http://www.cytoscape.org/ Cytoscape].

2011-01-30T21:16:41Z

← Older revision		Revision as of 21:16, 30 January 2011
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	DG<-discriminator(t(G), gLabel, multCorrect=TRUE)		DG<-discriminator(t(G), gLabel, multCorrect=TRUE)
	</pre>		</pre>

			===Output===

			Cross Patterns have a natural X relationship Y representation making a network representation and ideal way to visualize results.

			Cross patterns can easily be formatted in .sif for loading into various network browsers including

			[http://tyna.gersteinlab.org/tyna/ tYNA] or [http://www.cytoscape.org/ Cytoscape].

2011-01-30T21:12:38Z

Example Code