p.vector {maSigPro} | R Documentation

## Make regression fit for time series gene expression experiments

### Description

`p.vector` performs a regression fit for each gene taking all variables present in the model given by a regression matrix and returns a list of FDR corrected significant genes.

### Usage

    p.vector(data, design, Q = 0.05, MT.adjust = "BH", min.obs = 6, counts=FALSE, family=NULL, theta=10, epsilon=0.00001, item="gene")

### Arguments

`data` | matrix containing normalized gene expression data. Genes must be in rows and arrays in columns
`design` | design matrix for the regression fit such as that generated by the `make.design.matrix` function
`Q` | significance level
`MT.adjust` | argument to pass to `p.adjust` function indicating the method for multiple testing adjustment of p.value
`min.obs` | genes with less than this number of true numerical values will be excluded from the analysis. Minimum value to estimate the model is (degree+1)xGroups+1. Default is 6.
`counts` | a logical indicating whether your data are counts
`family` | the distribution function to be used in the glm model. It must be specified as a function: gaussian(), poisson(), negative.binomial(theta)... If NULL family will be negative.binomial(theta) when counts=TRUE or gaussian() when counts=FALSE
`theta` | theta parameter for negative.binomial family
`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 p.vector is in process

### Details

`rownames(design)` and `colnames(data)` must be identical vectors and indicate array naming.

`rownames(data)` should contain unique gene IDs.

`colnames(design)` are the given names for the variables in the regression model.

### Value

`SELEC` | matrix containing the expression values for significant genes
`p.vector` | vector containing the computed p-values
`G` | total number of input genes
`g` | number of genes taken in the regression fit
`FDR` | p-value at FDR `Q` control when Benajamini & Holderberg (BH) correction is used
`i` | number of significant genes
`dis` | design matrix used in the regression fit
`dat` | matrix of expression value data used in the regression fit
`...` | additional values from input parameters

### Author(s)

Ana Conesa, aconesa@cipf.es; 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

`T.fit`, `lm`

### Examples

    #### GENERATE TIME COURSE DATA
    ## generates 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, c2 = 1.3, 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 = "")

    tc.p <- p.vector(tc.DATA, design = make.design.matrix(edesign), Q = 0.05)
    tc.p$i # number of significant genes
    tc.p$SELEC # expression value of signficant genes
    tc.p$FDR # p.value at FDR control
    tc.p$p.adjusted# adjusted p.values

[Package maSigPro version 1.56.0 Index]