AgiMicroRna (version 2.22.0)

filterMicroRna: Filtering Genes

Description

Filter genes out according to their Quality Flag

Usage

filterMicroRna(ddNORM, 
	dd,
	control,
	IsGeneDetected,
	wellaboveNEG,
	limIsGeneDetected,
	limNEG,
	makePLOT,
	targets,
	verbose, 	writeout)

Arguments

ddNORM

uRNAList with the Total Gene Signal in log2 scale to be FILTERED out according to a Quality FLAG

dd

uRNAList, containing the output from readMicroRnaAFE

control

logical, if TRUE it removes controls

IsGeneDetected

logical, if TRUE it filters genes according to gIsGeneDetected Flag. Flag = 1, then gene is detected

wellaboveNEG

logical, if TRUE it filter genes whose expression is not above a limit value defined by the expression of negative controls. Limit= Mean(negative) + 1.5 x sd(negative)

limIsGeneDetected

for a given feature xi accros samples, is the minimum in at least one experimental condition with a IsGeneDetected-FLAG = 1 (Is Detected)

limNEG

for a given feature xi accros samples, is the minimum in at least one experimental condition with intensity > Limit established for negative controls (Mean + 1.5 x SD)

makePLOT

logical, if TRUE makes QC plots with the remaining signals

targets

data.frame with the targets structure

verbose

logical, if TRUE prints out output

writeout

logical, if TRUE writes out output files

Value

The function returns a uRNAList containing the FILTERED data. In order to allow the tracking of those microRNAs that may have been filtered out from the original raw data, the following files are given:NOCtrl\_exprs.txt: Log2 Normalized Total Gene Signals for the Non Control Genes NOCtrl\_FlagIsGeneDetected.txt: IsGeneDetected Flag for the Non Control Genes, 1 = detected IsNOTGeneDetected.txt: Genes that not are not detected according to IsGeneDetected Flag

Details

Agilent Feature Extraction software provides a flag for each spot that identifies different quantification errors of the signal. Quantification flags were used to filter out signals that did not reach a minimum established criterion of quality.

References

Agilent Feature Extraction Reference Guide http://www.Agilent.com

Examples

data(dd.micro,verbose=FALSE)
data(targets.micro,verbose=FALSE)
ddTGS=tgsMicroRna(dd.micro,half=TRUE,makePLOT=FALSE,verbose=FALSE)
ddNORM=tgsNormalization(ddTGS,'quantile',
                      makePLOTpre=FALSE,makePLOTpost=TRUE,targets.micro,verbose=FALSE)
ddPROC=filterMicroRna(ddNORM,
		      dd.micro,
                      control=TRUE,
                      IsGeneDetected=TRUE,
                      wellaboveNEG=FALSE,
                      limIsGeneDetected=50,
                      limNEG=25,
                      makePLOT=FALSE,
                      targets.micro,
		      verbose=FALSE,
		      writeout=FALSE)