LMGene {LMGene} | R Documentation |
LMGene calls function genediff
to calculate the unadjusted gene-specific and posterior p-values of all genes
and then calculates the FDR-adjusted p-values of all genes.
Significant genes for each factor in model
(based on either the gene-specific or posterior FDR-adjusted p-values) are output.
LMGene(eS, model = NULL, level = 0.05, posterior = FALSE, method = c("MLE", "MOM", "MOMlog"))
eS |
An |
model |
Specifies model to be used. Default is to use all variables from eS without interactions. See details. |
level |
Significance level |
posterior |
If |
method |
Method by which the posterior p-values are calculated. Default is |
If you have data in a matrix
and information about experimental design factors, then you
can use neweS
to convert the data into an ExpressionSet
object. Please see
neweS
for more detail.
The level
argument indicates the False Discovery Rate, e.g. level=0.05 means a 5 percent FDR.
The model
argument is an optional character string, constructed like the right-hand
side of a formula for lm
. It specifies which of the variables in the ExpressionSet
will
be used in the model and whether interaction terms will be included. If model=NULL
,
it uses all variables from the ExpressionSet
without interactions. Be careful of using
interaction terms with factors; this often leads to overfitting, which will yield an error.
See genediff
for details of method
.
lmres |
A list with one component for each factor in |
David Rocke and Geun-Cheol Lee
Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society, Series B, 57, 289–300.
David M. Rocke (2004) Design and analysis of experiments with high throughput biological assay data, Seminars in Cell & Developmental Biology, 15, 703–713.
library(Biobase) library(LMGene) #data data(sample.mat) data(vlist) raw.eS <- neweS(sample.mat, vlist) # glog transform data trans.eS <- transeS(raw.eS, lambda = 727, alpha = 56) # Identify significant genes, using an FDR of 1 percent LMGene(trans.eS, level = 0.01)