get.siggenes {maSigPro} | R Documentation

## Extract significant genes for sets of variables in time series gene expression experiments

### Description

This function creates lists of significant genes for a set of variables whose significance value has been computed with the `T.fit` function.

### Usage

    get.siggenes(tstep, rsq = 0.7, add.IDs = FALSE, IDs = NULL, matchID.col = 1,
                 only.names = FALSE, vars = c("all", "each", "groups"),
    	     significant.intercept = "dummy",

                 groups.vector = NULL, trat.repl.spots = "none",
                 index = IDs[, (matchID.col + 1)], match = IDs[, matchID.col],
    	     r = 0.7)

### Arguments

`tstep` | a `T.fit` object
`rsq` | cut-off level at the R-squared value for the stepwise regression fit. Only genes with R-squared more than rsq are selected
`add.IDs` | logical indicating whether to include additional gene id's in the result
`IDs` | matrix contaning additional gene id information (required when `add.IDs` is `TRUE`)
`matchID.col` | number of matching column in matrix IDs for adding genes ids
`only.names` | logical. If `TRUE`, expression values are ommited in the results
`vars` | variables for which to extract significant genes (see details)
`significant.intercept` | experimental groups for which significant intercept coefficients are considered (see details)
`groups.vector` | required when `vars` is `"groups"`.
`trat.repl.spots` | treatment given to replicate spots. Possible values are `"none"` and `"average"`
`index` | argument of the `average.rows` function to use when `trat.repl.spots` is `"average"`
`match` | argument of the `average.rows` function to use when `trat.repl.spots` is `"average"`
`r` | minimun pearson correlation coefficient for replicated spots profiles to be averaged

### Details

There are 3 possible values for the vars argument:

`"all"`: generates one single matrix or gene list with all significant genes.

`"each"`: generates as many significant genes extractions as variables in the general regression model. Each extraction contains the significant genes for that variable.

`"groups"`: generates a significant genes extraction for each experimental group.

The difference between `"each"` and `"groups"` is that in the first case the variables of the same group (e.g. `"TreatmentA"` and `"time*TreatmentA"` ) will be extracted separately and in the second case jointly.

When `add.IDs` is `TRUE`, a matrix of gene ids must be provided as argument of IDs, the `matchID.col` column of which having same levels as in the row names of `sig.profiles`. The option `only.names` is `TRUE` will generate a vector of significant genes or a matrix when `add.IDs` is set also to `TRUE`.

When `trat.repl.spots` is `"average"`, `match` and `index` vectors are required for the `average.rows` function. In gene expression data context, the `index` vector would contain geneIDs and indicate which spots are replicates. The `match` vector is used to match these genesIDs to rows in the significant genes matrix, and must have the same levels as the row names of `sig.profiles`.

The argument `significant.intercept` modulates the treatment for intercept coefficients to apply for selecting significant genes when `vars` equals `"groups"`. There are three possible values: `"none"`, no significant intercept (differences) are considered for significant gene selection, `"dummy"`, includes genes with significant intercept differences between control and experimental groups, and `"all"` when both significant intercept coefficient for the control group and significant intercept differences are considered for selecting significant genes.

`add.IDs` = TRUE and `trat.repl.spots` = `"average"` are not compatible argumet values. `add.IDs` = TRUE and `only.names` = `TRUE` are compatible argumet values.

### Value

`summary` | a vector or matrix listing significant genes for the variables given by the function parameters
`sig.genes` | a list with detailed information on the significant genes found for the variables given by the function parameters. Each element of the list is also a list containing: `sig.profiles`: expression values of significant genes `coefficients`: regression coefficients of the adjusted models `groups.coeffs`: regression coefficients of the impiclit models of each experimental group `sig.pvalues`: p-values of the regression coefficients for significant genes `g`: number of genes `...`: arguments passed by previous functions

### 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

### 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 = "")

    tc.p <- p.vector(tc.DATA, design = make.design.matrix(edesign), Q = 0.01)
    tc.tstep <- T.fit(data = tc.p , alfa = 0.05)

    ## This will obtain sigificant genes per experimental group
    ## which have a regression model Rsquared > 0.9
    tc.sigs <- get.siggenes (tc.tstep, rsq = 0.9, vars = "groups")

    ## This will obtain all sigificant genes regardless the Rsquared value.
    ## Replicated genes are averaged.
    IDs <- rbind(paste("feature", c(1:300), sep = ""),
           rep(paste("gene", c(1:150), sep = ""), each = 2))
    tc.sigs.ALL <- get.siggenes (tc.tstep, rsq = 0, vars = "all", IDs = IDs)
    tc.sigs.groups <- get.siggenes (tc.tstep, rsq = 0, vars = "groups", significant.intercept="dummy")

[Package maSigPro version 1.58.0 Index]