AgiMicroRna (version 2.22.0)

readMicroRnaAFE: Read Agilent Feature Extraction txt data files

Description

Read the data files generated by the Agilent Feature Extraction image analysis software

Usage

readMicroRnaAFE(targets,verbose=FALSE)

Arguments

targets

A data frame that specifies experimental conditions under which each sample has been obtained.

verbose

logical, if TRUE prints out output

Value

A uRNAList containing the following elements:

uRNAList\$TGS

matrix, 'gTotalGeneSignal'

uRNAList\$TPS

matrix, 'gTotalProbeSignal'

uRNAList\$meanS

matrix, 'gMeanSignal'

uRNAList\$procS

matrix, 'gProcessedSignal'

uRNAList\$targets

data.frame, 'FileName'

uRNAList\$genes\$ProbeName

character, 'AGilent Probe Name'

uRNAList\$genes\$GeneName

character, 'microRNA Name'

uRNAList\$genes\$ControlType

integer, '0'= Feature, '1'= Positive control, '-1'= Negative control

uRNAList\$other\$gIsGeneDetected

matrix, FLAG to classify signal if 'IsGeneDetected=1' or 'not=0'

uRNAList\$other\$gIsSaturated

matrix, FLAG to classify signal if 'IsSaturated = 1' or 'not=0'

uRNAList\$other\$gIsFeatPopnOL

matrix, FLAG to classify signal if 'IsFeatPopnOL = 0' or 'not=1'

uRNAList\$other\$gIsFeatNonUnifOL

matrix, FLAG to classify signal if 'gIsFeatNonUnifOL = 0' or 'not=1'

uRNAList\$other\$gBGMedianSignal

matrix, gBGMedianSignal

uRNAList\$other\$gBGUsed

matrix, gBGUsed

Details

The function reads the *.txt files generated by the AFE Software using the 'read.maimages' function of 'limma' package.

Data, colected with the Agilent Feature Extraction Software, are stored in a uRNAList object with the following components:

- dd.micro\$TGS 'gTotalGeneSignal' - dd.micro\$TPS 'gTotalProbeSignal' - dd.micro\$meanS 'gMeanSignal' - dd.micro\$procS 'gProcessedSignal' - dd.micro\$targets 'targets' - dd.micro\$genes\$ProbeName 'Probe Name' - dd.micro\$genes\$GeneName 'microRNA Name' - dd.micro\$genes\$ControlType 'FLAG to specify the sort of feature' - dd.micro\$other\$gIsGeneDetected 'FLAG IsGeneDetected' - dd.micro\$other\$gIsSaturated 'FLAG IsSaturated' - dd.micro\$other\$gIsFeatNonUnifOL 'FLAG IsFeatNonUnifOL' - dd.micro\$other\$gIsFeatPopnOL 'FLAG IsFeatPopnOL' - dd.micro\$other\$gBGMedianSignal 'gBGMedianSignal' - dd.micro\$other\$gBGUsed 'gBGUsed'

References

Agilent Feature Extraction Reference Guide http://www.Agilent.com Smyth, G. K. (2005). Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397--420.

See Also

A data example can be found in dd.micro See also readTargets to see how to build the target file and the example given in targets.micro

Examples

## Not run: 
# data(targets.micro)
# dd.micro = readMicroRnaAFE(targets.micro)
# ## End(Not run)