pickgene {pickgene} | R Documentation |
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(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"), ...)
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 |
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 |
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 |
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.
## Not run: pickgene( data ) ## End(Not run)