rmaMicroRna {AgiMicroRna} | R Documentation

## Getting the Total Gene Signal by RMA algorithm

### Description

The function creates an uRNAList containing the TotalGeneSignal computed by the RMA algorithm. This signal can be used for the statistical analysis.

### Usage

    rmaMicroRna(dd, normalize, background)

### Arguments

`dd` | uRNAList, containing the output from `readMicroRnaAFE`
`normalize` | logical, if `TRUE` the signal is normalized between arrays using the 'quantile' method
`background` | logical, if `TRUE` the signal is background corrected by fitting a normal + exponential convolution model to a vector of observed intensities

### Details

The function creates an uRNAList output that contains in the uRNAList$TGS, uRNAList$TPS, uRNAList$meanS & uRNAList$procS slots the Total Gene Signal (TGS) computed by the RMA algorithm. The function uses the robust multiarray average (RMA) method from the 'affy' package. RMA obtains an estimate of the expression measure for each gene using all the replicated probes for that gene. First, RMA obtains a background corrected intensity by fitting a normal + exponential convolution model to a vector of observed intensities. The normal part represents the background and the exponential part represents the signal intensities. Then the arrays are normalized using 'quantile' normalization. Finally, for each probe set that interrogates the same microRNA, RMA fits a linear model to the background-corrected, normalized and log2 transformed probe intensities. This model produces an estimate of the gene signal taking into account the probe effect. The model parameters estimates are obtained by median polish. The estimates of the gene expression signals are referred as RMA estimates. Normally, each microRNA is interrogated by 16 probes either using 2 different probes, each of them replicated 8 times, or using 4 differnt probes replicated 4 times. First, function 'rmaMicroRna' obtains a background corrected signal using the 'rma.background.correct' function of the package 'preprocessCore' , then the signal is normalized bewtween arrays using the 'limma' function 'normalizeBetweenArrays' with the 'quantile' method. Then, the median of the replicated probes is obtained, leading to either 2 or 4 different measures for each gene. These measures correspond to different probes for the same genes that are summarized into a single RMA linear model described above.

### Value

uRNAList containing the Total Gene Signal computed by the RMA algorithm in log 2 scale.

### Author(s)

Pedro Lopez-Romero

### References

Irizarry, R., Hobbs,B., Collin,F., Beazer-Barclay,Y., Antonellis,K., Scherf,U., Speed,T. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4, 249-264

Gautier, L., Cope, L., Bolstad, B. M., and Irizarry, R. A.(2004). affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 3, 307-315.

Bolstad B. M. (). preprocessCore: A collection of pre-processing functions. R package version 1.4.0

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

### Examples

    data(dd.micro)
    ddTGS.rma=rmaMicroRna(dd.micro, normalize=TRUE, background=TRUE)
    dim(ddTGS.rma)
    RleMicroRna(ddTGS.rma$TGS,"RLE TGS.rma","blue")

[Package AgiMicroRna version 2.32.0 Index]