PECASI: PECA splicing index
In PECA: Probe-level Expression Change Averaging

Description Usage Arguments Details Value References See Also

Calculates the PECA splicing index to determine differentially spliced exons between two groups of samples in Affymetrix exon array studies.

1	PECASI(path, dataFolder, chipType, cdfTag=NULL, samplenames1, samplenames2, test="t")

`path`	A character string specifying the path of the working directory containing the expression and annotation data.
`dataFolder`	A character string specifying the name of the directory containing the raw expression data (.CEL-files).
`chipType`	A character string specifying the microarray (chip) type.
`cdfTag`	A character string indicating an optional suffix added to the name of the particular chip definition file (CDF).
`samplenames1`	A character vector containing the names of the .CEL-files in the first group without the extension .CEL.
`samplenames2`	A character vector containing the names of the .CEL-files in the second group without the extension .CEL. The paired samples are assumed to be in the same order in both of the vectors `samplenames1` and `samplenames2`.
`test`	A character string indicating whether the ordinary ("`t`") or modified ("`modt`") t-test is performed.

PECASI determines differential alternative splicing using directly the probe-level measurements from Affymetrix exon microarrays. Differential splicing between two groups of samples is first calculated for each probe on the array. The exon-level differential splicing is then defined as the median over the probe-level differences. For more details about the probe-level expression change averaging (PECA) procedure, see Elo et al. (2005), Elo et al. (2006) and Laajala et al.

The current implementation of PECASI calculates the probe-level differential splicing using the ordinary or modified t-statistic over splicing index values. The ordinary t-statistic is calculated using the function rowttests in the Bioconductor genefilter package. The modified t-statistic is calculated using the linear modeling approach in the Bioconductor limma package. The samples are assumed to be paired. For more details about the PECA splicing index procedure, see Laajala et al.

PECASI uses the aroma.affymetrix package to normalize and extract the probe-level data from the .CEL-files (Bengtsson et al. 2008). Therefore, it is important that the naming and structure of the data files follow exactly the rules specified in the aroma.affymetrix package.

The raw expression data (.CEL-files) need to be in the directory rawData/<dataFolder>/<chipType>, where rawData is a directory under the current working directory specified by the path, dataFolder is the name of the dataset given by the user, and chipType indicates the type of the microarray used in the experiment.

In addition to the expression data, a chip definition file (CDF) is required. The CDF-file(s) for a particular microarray type chipType need to be in the directory annotationData/chipTypes/<chipType>, where annotationData is a directory under the current working directory specified by the path. Besides the CDF-files provided by Affymetrix, various custom CDF-files are available for a particular microarray type. The different versions can be separated by adding a suffix cdfTag to the name of the CDF-file: <chipType>,<cdfTag>.cdf

The quality control and filtering of the data (e.g. based on low intensity or probe specificity) is left to the user.

PECASI returns a matrix which rows correspond to the exons under analysis and columns indicate the corresponding splicing index (si), t-statistic, p-value and FDR adjusted p-value.

L.L. Elo, L. Lahti, H. Skottman, M. Kylaniemi, R. Lahesmaa and T. Aittokallio: Integrating probe-level expression changes across generations of Affymetrix arrays. Nucleic Acids Research 33(22), e193, 2005.

L.L. Elo, M. Katajamaa, R. Lund, M. Oresic, R. Lahesmaa and T. Aittokallio: Improving identification of differentially expressed genes by integrative analysis of Affymetrix and Illumina arrays. OMICS A Journal of Integrative Biology 10(3), 369–380, 2006.

E. Laajala, T. Aittokallio, R. Lahesmaa and L.L. Elo: Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies. Genome Biology 10(7), R77, 2009.

H. Bengtsson, K. Simpson, J. Bullard and K. Hansen: aroma.affymetrix: A generic framework in R for analyzing small to very large Affymetrix data sets in bounded memory. Tech Report \#745, Department of Statistics, University of California, Berkeley, 2008.

PECA

PECA documentation built on Nov. 8, 2020, 7:24 p.m.