wfm.inference: Perform transcriptome analysis on fitted wavelet-based...

In waveTiling: Wavelet-Based Models for Tiling Array Transcriptome Analysis

Description

Main function to perform trancriptome analysis on a fitted wavelet-based functional model of class WfmFit.

Usage

wfm.inference(object, contrast.matrix=NULL, contrasts=c("compare","means","effects","overallMean"), delta=NULL, two.sided=NULL, minRunPos=90, minRunProbe=1, alpha=0.05, nsim=1000, rescale=NULL)

Arguments

object	object of class WfmFit
contrast.matrix	custom contrasts matrix
contrasts	character indicating the type of transcriptome analysis is to be applied. Currently the following types are implemented: compare for doing a pairwise differential expression analysis between any combination of two groups; effects, which corresponds to a circadian rhythm analysis if a circadian design is used for the fit, and to a time effects analysis (linear, quadratic,...) if a time-course design is used for the fit; means for doing a group-wise transcript discovery analysis.
delta	threshold value to be used in the inference procedure. This should be a numeric vector with as first element the threshold for the overall mean transcript discovery and the other elements the threshold for the differential expression, the effects analysis or group-wise mean analysis. If the threshold should be equal for all comparisons, effects or group-wise means only a vector of length 2 is needed. Otherwise, the vector must be of length r+1 with r the number of pairwise comparisons, effects or group-wise means.
two.sided	logical indicating if one-sided or two-sided tests are desired
minRunPos	minrun by position. An integer to indicate the minimum number of basepairs a significant genomic region should contain.
minRunProbe	minrun by probes. An integer to indicate the minimum number of probes the significant genomic region should map to.
alpha	significance level
nsim	number of simulations used when doing circadian rhythm inference
rescale	rescale matrix

Value

object of class WfmFit

Author(s)

Kristof De Beuf <[e-mail]>

References

[1] Clement L, De Beuf K, Thas O, Vuylsteke M, Irizarry RA and Crainiceanu CM. (2012) Fast wavelet based functional models for transcriptome analysis with tiling arrays. Statistical Applications in Genetics and Molecular Biology 11: Iss. 1, Article 4.

[2] De Beuf K, Andriankaja, M, Thas O, Inze D, Crainiceanu CM and Clement L (2012) Model-based analysis of tiling array expression studies with flexible designs. Technical document.

Examples

  library(waveTilingData)
  data(leafdevFit)
  delta <-  log(1.2,2)
  leafdevInfCompare <- wfm.inference(leafdevFit,contrasts="compare",delta=c("median",delta))

waveTiling documentation built on Oct. 31, 2019, 9:23 a.m.