Plot and Pick Genes based on Differential Expression

Share:

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
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

1
2
3
4
## Not run: 
pickgene( data )

## End(Not run)