maSigPro (version 1.44.0)

T.fit: Makes a stepwise regression fit for time series gene expression experiments

Description

T.fit selects the best regression model for each gene using stepwise regression.

Usage

T.fit(data, design = data$dis, step.method = "backward",  min.obs = data$min.obs, alfa = data$Q, nvar.correction = FALSE, family = gaussian(), epsilon=0.00001)

Arguments

data

can either be a p.vector object or a matrix containing expression data with the same requirements as for the p.vector function

design

design matrix for the regression fit such as that generated by the make.design.matrix function. If data is a p.vector object, the same design matrix is used by default

step.method

argument to be passed to the step function. Can be either "backward", "forward", "two.ways.backward" or "two.ways.forward"

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 glm.control, convergence tolerance in the iterative process to estimate de glm model

Value

sol

matrix for summary results of the stepwise regression. For each selected gene the following values are given:

• p-value of the regression ANOVA
• R-squared of the model
• p-value of the regression coefficients of the selected variables

sig.profiles

expression values for the genes contained in sol

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

Details

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 step-wise 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 p-value of each variable is computed and variables get in/out the model when this p-value 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

References

Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2006. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102

See Also

p.vector, step

Examples

#### 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