Description Usage Arguments Details Value Author(s) References See Also Examples
T.fit
selects the best regression model for each gene using stepwise regression.
1 2 3 4 
data 
can either be a 
design 
design matrix for the regression fit such as that generated by the 
step.method 
argument to be passed to the step function. Can be either 
min.obs 
genes with less than this number of true numerical values will be excluded from the analysis 
alfa 
significance level used for variable selection in the stepwise regression 
nvar.correction 
argument for correcting T.fit significance level. See details 
family 
the distribution function to be used in the glm model. It must be the same used in p.vector 
epsilon 
argument to pass to 
item 
Name of the analysed item to show in the screen while T.fit is in process 
In the maSigPro approach p.vector
and T.fit
are subsequent steps, meaning that significant genes are
first selected on the basis of a general model and then the significant variables for each gene are found by stepwise regression.
The step regression can be "backward"
or "forward"
indicating whether the step procedure starts from the
model with all or none variables. With the "two.ways.backward"
or "two.ways.forward"
options the variables are both allowed to get in and out.
At each step the pvalue of each variable is computed and variables get in/out the model when this pvalue is
lower or higher than given threshold alfa. When nva.correction is TRUE the given significance level is corrected by the number of variables in the model
sol 
matrix for summary results of the stepwise regression. For each selected gene the following values are given:

sig.profiles 
expression values for the genes contained in 
coefficients 
matrix containing regression coefficients for the adjusted models 
groups.coeffs 
matrix containing the coefficients of the impiclit models of each experimental group 
variables 
variables in the complete regression model 
G 
total number of input genes 
g 
number of genes taken in the regression fit 
dat 
input analysis data matrix 
dis 
regression design matrix 
step.method 
imputed step method for stepwise regression 
edesign 
matrix of experimental design 
influ.info 
data frame of genes containing influencial data 
Ana Conesa and Maria Jose Nueda, mj.nueda@ua.es
Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2006. maSigPro: a Method to Identify Significant Differential Expression Profiles in TimeCourse Microarray Experiments. Bioinformatics 22, 10961102
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60  #### GENERATE TIME COURSE DATA
## generate n random gene expression profiles of a data set with
## one control plus 3 treatments, 3 time points and r replicates per time point.
tc.GENE < function(n, r,
var11 = 0.01, var12 = 0.01,var13 = 0.01,
var21 = 0.01, var22 = 0.01, var23 =0.01,
var31 = 0.01, var32 = 0.01, var33 = 0.01,
var41 = 0.01, var42 = 0.01, var43 = 0.01,
a1 = 0, a2 = 0, a3 = 0, a4 = 0,
b1 = 0, b2 = 0, b3 = 0, b4 = 0,
c1 = 0, c2 = 0, c3 = 0, c4 = 0)
{
tc.dat < NULL
for (i in 1:n) {
Ctl < c(rnorm(r, a1, var11), rnorm(r, b1, var12), rnorm(r, c1, var13)) # Ctl group
Tr1 < c(rnorm(r, a2, var21), rnorm(r, b2, var22), rnorm(r, c2, var23)) # Tr1 group
Tr2 < c(rnorm(r, a3, var31), rnorm(r, b3, var32), rnorm(r, c3, var33)) # Tr2 group
Tr3 < c(rnorm(r, a4, var41), rnorm(r, b4, var42), rnorm(r, c4, var43)) # Tr3 group
gene < c(Ctl, Tr1, Tr2, Tr3)
tc.dat < rbind(tc.dat, gene)
}
tc.dat
}
## Create 270 flat profiles
flat < tc.GENE(n = 270, r = 3)
## Create 10 genes with profile differences between Ctl and Tr1 groups
twodiff < tc.GENE (n = 10, r = 3, b2 = 0.5, c2 = 1.3)
## Create 10 genes with profile differences between Ctl, Tr2, and Tr3 groups
threediff < tc.GENE(n = 10, r = 3, b3 = 0.8, c3 = 1, a4 = 0.1, b4 = 0.8, c4 = 1.2)
## Create 10 genes with profile differences between Ctl and Tr2 and different variance
vardiff < tc.GENE(n = 10, r = 3, a3 = 0.7, b3 = 1, c3 = 1.2, var32 = 0.03, var33 = 0.03)
## Create dataset
tc.DATA < rbind(flat, twodiff, threediff, vardiff)
rownames(tc.DATA) < paste("feature", c(1:300), sep = "")
colnames(tc.DATA) < paste("Array", c(1:36), sep = "")
tc.DATA [sample(c(1:(300*36)), 300)] < NA # introduce missing values
#### CREATE EXPERIMENTAL DESIGN
Time < rep(c(rep(c(1:3), each = 3)), 4)
Replicates < rep(c(1:12), each = 3)
Control < c(rep(1, 9), rep(0, 27))
Treat1 < c(rep(0, 9), rep(1, 9), rep(0, 18))
Treat2 < c(rep(0, 18), rep(1, 9), rep(0,9))
Treat3 < c(rep(0, 27), rep(1, 9))
edesign < cbind(Time, Replicates, Control, Treat1, Treat2, Treat3)
rownames(edesign) < paste("Array", c(1:36), sep = "")
## run T.fit from a p.vector object
tc.p < p.vector(tc.DATA, design = make.design.matrix(edesign), Q = 0.01)
tc.tstep < T.fit(data = tc.p , alfa = 0.05)
## run T.fit from a data matrix and a design matrix
dise < make.design.matrix(edesign)
tc.tstep < T.fit (data = tc.DATA[271:300,], design = dise$dis,
step.method = "two.ways.backward", min.obs = 10, alfa = 0.05)
tc.tstep$sol # gives the p.values of the significant
# regression coefficients of the optimized models

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