pickgene: Plot and Pick Genes based on Differential Expression

In pickgene: Adaptive Gene Picking for Microarray Expression Data Analysis

Description

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.

Usage

pickgene(data, geneID = 1:nrow(data), overalllevel = 0.05,
         npickgene = -1, marginal = FALSE, rankbased = TRUE,
         allrank = FALSE, meanrank = FALSE, offset = 0,
         modelmatrix = model.pickgene(faclevel, facnames,
         contrasts.fac, collapse, show, renorm), faclevel =
         ncol(data), facnames =
         letters[seq(length(faclevel))], contrasts.fac =
         "contr.poly", show = NULL, main = "", renorm = 1,
         drop.negative = FALSE, plotit = npickgene < 1, mfrow
         = c(nr, nc), mfcol = NULL, ylab = paste(shownames,
         "Trend"), ...)

Arguments

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

Details

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.

Value

Data frame containing significant genes with the following information:

pick	data frame with picked genes
score	data frame with center and spread for plotting

Each of these is a list with elements for each contrast. The pick data frame elements have the following information:

probe	gene identifier
average	average gene intensity
fold1	positive fold change
fold2	negative fold change
pvalue	Bonferroni-corrected p-value

The score data frame elements have the following:

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

Author(s)

Yi Lin and Brian Yandell

References

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

pickgene

Examples

## Not run: 
pickgene( data )

## End(Not run)

pickgene documentation built on Nov. 8, 2020, 6:50 p.m.