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In this respect, the R platform offers an extremely large and versatile statistical environment for mining analyte matrices produced in a first step by packages such as XCMS.
Locally Adaptive Procedure (LAP) is a bioinformatic tool developed under R statistical environment for the identification of differentially expressed chromosomal regions, which accounts for variations in gene distance and density [ 20].
The resultant spike times were then imported into the R statistical environment for further processing, statistical analysis and production of final graphics (our open source R code is available for download at http://jefferislab.org/si/frulhns ).
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The functions superpc.cv and superpc.plotcv from the superpc library (created by one of the authors of [22]) from the R statistical environment were used for this purpose.
The R statistical environment was used for analysis.
The R statistical environment was used for all calculations [ 28].
Quality control, differential expression, and cross-platform microarray analysis were performed using Bioconductor packages in the R statistical environment (see Supplementary Material for a detailed description).
We used generalized linear mixed models (GLMM) using the MCMCglmm package [[ 42]] of the R statistical environment [[ 43]] for all of our statistical analyses.
PGLS analyses were conducted using the CAPER package for the statistical environment R v.2.10.1 (R Development Core Team, 2011).
The resulting raw data (IDATs) were normalized in the R statistical environment, using the minfi package for SWAN normalization.
Availability and implementation: The package h5vc for the statistical environment R is available through the Bioconductor project.
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