LMGene {LMGene} | R Documentation |
LMGene calls function genediff
to calculate the raw p-values of all genes
and then calls function pvadjust
to calculate the adjusted p-values of all genes.
Finally, calls function rowlist
to list the names of genes that are selected as significant under the specified significance level.
LMGene(eS, model=NULL, level = 0.05)
eS |
Array data. must be an ExpressionSet object and the log-transformation and
the normalization of exprs(eS) are recommended. |
model |
Specifies model to be used. Default is to use all variables from eS without interactions. See details. |
level |
Significance level |
The argument eS
must be an ExpressionSet
object from the Biobase package.
If you have a data in a matrix
and information about the considered factors, then you
can use neweS
to convert the data into an ExpressionSet
object. Please see
neweS
in more detail.
The level
argument indicates False Discovery Rate, e.g. level=0.05 means 5
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.
lmres |
A list which contains significant gene names for each considered factor. |
David Rocke and Geun-Cheol Lee
David M. Rocke (2004), Design and analysis of experiments with high throughput biological assay data, Seminars in Cell & Developmental Biology, 15, 703-713.
http://www.idav.ucdavis.edu/~dmrocke/
#library library(Biobase) library(LMGene) #data data(sample.mat) data(vlist) LoggedSample <- neweS(lnorm(log(sample.mat)),vlist) siggeneslist <- LMGene(LoggedSample, 'patient + dose', 0.01)