T.fit {maSigPro} | R Documentation

## 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, item="gene")

### 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
`item` | Name of the analysed item to show in the screen while T.fit is in process

### 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

### 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

### Author(s)

Ana Conesa and Maria Jose Nueda, mj.nueda@ua.es

### 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

[Package maSigPro version 1.64.0 Index]