pickgene {pickgene}R Documentation

Plot and Pick Genes based on Differential Expression


The function picks plots the average intensity versus linear contrasts (currently linear, quadratic up to cubic) across experimental conditions. Critical line is determine according to Bonferroni-like multiple comparisons, allowing SD to vary with intensity.


pickgene(data, geneID=1:nrow(x), overalllevel=0.05, npickgene=-1,
  marginal = FALSE, rankbased=TRUE, allrank=FALSE, meanrank=FALSE,
  offset=0, modelmatrix = <<see below>>, faclevel=ncol(x),
  facnames=<<see below>>, contrasts.fac="contr.poly",
  show=<<see below>>, main="", renorm=1, drop.negative=F,
  plotit, mfrow=<<see below>>, mfcol=<<see below>>, ylab, ... )


data data matrix
geneID gene identifier (default 1:nrow(x))
overalllevel overall significance level (default 0.05)
npickgene number of genes to pick (default -1 allows automatic selection)
marginal additive model if TRUE, include interactions if FALSE
rankbased use ranks if TRUE, log tranform if FALSE
allrank rank all chips together if true, otherwise rank separately
meanrank show mean abundance as rank if TRUE
offset offset for log transform
modelmatrix model matrix with first row all 1's and other rows corresponding to design contrasts; automatically created by call to model.pickgene if omitted
faclevel number of factor levels for each factor
facnames factor names
contrasts.fac type of contrasts
show vector of contrast numbers to show (default is all)
main vector of main titles for plots (default is none)
renorm vector to renormalize contrasts (e.g. use sqrt(2) to turn two-condition contrast into fold change)
drop.negative drop negative values in log transform
plotit plot if TRUE
mfrow par() plot arrangement by rows (default up to 6 per page; set to NULL to not change)
mfcol par() plot arrangement by columns (default is NULL)
ylab vertical axis labels
... parameters for robustscale


Infer genes that differentially express across conditions using a robust data-driven method. Adjusted gene expression levels A are replaced by qnorm(rank(A)), followed by robustscale estimation of center and spread. Then Bonferroni-style gene by gene tests are performed and displayed graphically.


Data frame containing significant genes with the following information:

pick data frame with picked genes
score data frame with center and spread for plotting
probe gene identifier
average average gene intensity
fold1 positive fold change
fold2 negative fold change
pvalue Bonferroni-corrected p-value
x mean expression level (antilog scale)
y contrast (antilog scale)
center center for contrast
scale scale (spread) for contrast
lower lower test limit
upper upper test limit


Yi Lin and Brian Yandell


Y Lin, BS Yandell and ST Nadler (2000) ``Robust Data-Driven Inference for Gene Expression Microarray Experiments,'' Technical Report, Department of Statistics, UW-Madison.

See Also



## Not run: 
pickgene( data )
## End(Not run)

[Package pickgene version 1.0.0 Index]