maSigPro {maSigPro} | R Documentation

## Wrapping function for identifying significant differential gene expression profiles in micorarray time course experiments

### Description

`maSigPro` performs a whole maSigPro analysis for a times series gene expression experiment. The function sucesively calls the functions `make.design.matrix`(optional), `p.vector`, `T.fit`, `get.siggenes` and `see.genes`.

### Usage

    maSigPro(data, edesign, matrix = "AUTO", groups.vector = NULL,
        degree = 2, time.col = 1, repl.col = 2, group.cols = c(3:ncol(edesign)),
        Q = 0.05, alfa = Q, nvar.correction = FALSE, step.method = "backward", rsq = 0.7,
        min.obs = 3, vars = "groups", significant.intercept = "dummy", cluster.data = 1,
        add.IDs = FALSE, IDs = NULL, matchID.col = 1, only.names = FALSE, k = 9,
        cluster.method = "hclust", distance = "cor", agglo.method = "ward.D", iter.max = 500,
        summary.mode = "median", color.mode = "rainbow", trat.repl.spots = "none",
        index = IDs[, (matchID.col + 1)], match = IDs[, matchID.col], rs = 0.7,
        show.fit = TRUE, show.lines = TRUE, pdf = TRUE, cexlab = 0.8,
        legend = TRUE, main = NULL, ...)

### Arguments

`data` | matrix with normalized gene expression data. Genes must be in rows and arrays in columns. Row names must contain geneIDs (argument of `p.vector`)
`edesign` | matrix of experimental design. Row names must contain arrayIDs (argument of `make.design.matrix` and `see.genes`)
`matrix` | design matrix for regression analysis. By default design is calculated with make.design.matrix (argument of `p.vector` and `T.fit`, by default computed by `make.design.matrix`)
`groups.vector` | vector indicating experimental group of each variable (argument of `get.siggenes` and `see.genes`, by default computed by `make.design.matrix`)
`degree` | the degree of the regression fit polynome. `degree` = 1 returns lineal regression, `degree` = 2 returns quadratic regression, etc... (argument of `make.design.matrix`)
`time.col` | column in edesign containing time values. Default is first column (argument of `make.design.matrix` and `see.genes`)
`repl.col` | column in edesign containing coding for replicates arrays. Default is second column (argument of `make.design.matrix` and `see.genes`)
`group.cols` | columns in `edesign` indicating the coding for each group of the experiment (see `make.design.matrix`) (argument of `make.design.matrix` and `see.genes`)
`Q` | level of false discovery rate (FDR) control (argument of `p.vector`)
`alfa` | significance level used for variable selection in the stepwise regression (argument of `T.fit`)
`nvar.correction` | logical for indicating correcting of stepwise regression significance level (argument of `T.fit`)
`step.method` | argument to be passed to the step function. Can be either `"backward"`, `"forward"`, `"two.ways.backward"` or `"two.ways.forward"`
`rsq` | cut-off level at the R-squared value for the stepwise regression fit. Only genes with R-squared greater than `rsq` are selected
`min.obs` | genes with less than this number of true numerical values will be excluded from the analysis (argument of `p.vector` and `T.fit`)
`vars` | variables for which to extract significant genes (argument of `get.siggenes`)
`significant.intercept` | experimental groups for which significant intercept coefficients are considered (argument of `get.siggenes`)
`cluster.data` | Type of data used by the cluster algorithm (argument of `see.genes`)
`add.IDs` | logical indicating whether to include additional gene id's in the significant genes result (argument of `get.siggenes`)
`IDs` | matrix contaning additional gene id information (required when `add.IDs` is TRUE) (argument of `get.siggenes`)
`matchID.col` | number of matching column in matrix IDs for adding genes ids (argument of`get.siggenes`)
`only.names` | logical. If TRUE, expression values are ommited in the significant genes result (argument of `get.siggenes`)
`k` | number of clusters (argument of `see.genes`)
`cluster.method` | clustering method for data partioning (argument of `see.genes`)
`distance` | distance measurement function used when `cluster.method` is `"hclust"` (argument of `see.genes`)
`agglo.method` | aggregation method used when `cluster.method` is `"hclust"` (argument of `see.genes`)
`iter.max` | number of iterations when `cluster.method` is `"kmeans"` (argument of `see.genes`)
`summary.mode` | the method to condensate expression information when more than one gene is present in the data. Possible values are `"representative"` and `"median"` (argument of `PlotGroups`)
`color.mode` | color scale for plotting profiles. Can be either `"rainblow"` or `"gray"` (argument of `PlotProfiles`)
`trat.repl.spots` | treatment givent to replicate spots. Possible values are `"none"` and `"average"` (argument of `get.siggenes`)
`index` | argument of the `average.rows` function to use when `trat.repl.spots` is `"average"` (argument of `get.siggenes`)
`match` | argument of the `link{average.rows}` function to use when `trat.repl.spots` is `"average"` (argument of `get.siggenes`)
`rs` | minimun pearson correlation coefficient for replicated spots profiles to be averaged (argument of `get.siggenes`)
`show.fit` | logical indicating whether regression fit curves must be plotted (argument of `see.genes`)
`show.lines` | logical indicating whether a line must be drawn joining plotted data points for reach group (argument of `see.genes`)
`pdf` | logical indicating whether a pdf results file must be generated (argument of `see.genes`)
`cexlab` | graphical parameter maginfication to be used for x labels in plotting functions
`legend` | logical indicating whether legend must be added when plotting profiles (argument of `see.genes`)
`main` | title for pdf results file
`...` | other graphical function arguments

### Details

maSigPro finds and display genes with significant profile differences in time series gene expression experiments. The main, compulsory, input parameters for this function are a matrix of gene expression data (see `p.vector` for details) and a matrix describing experimental design (see `make.design.matrix` or `p.vector` for details). In case extended gene ID information is wanted to be included in the result of significant genes, a third IDs matrix containing this information will be required (see `get.siggenes` for details).

Basiscally in the function calls subsequent steps of the maSigPro approach which is:

- Make a general regression model with dummies to indicate different experimental groups.

- Select significant genes on the basis of this general model, applying fdr control.

- Find significant variables for each gene, using stepwise regression.

- Extract and display significant genes for any set of variables or experimental groups.

### 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.The cluster assingment of each gene is given in the last column `coefficients`: regression coefficients for significant genes `t.score`: value of the t statistics of significant genes `sig.pvalues`: p-values of the regression coefficients for significant genes `g`: number of genes ... :arguments passed by previous functions
`input.data` | input analysis data
`G` | number of input genes
`edesign` | matrix of experimental design
`dis` | regression design matrix
`min.obs` | imputed value for minimal number of true observations
`p.vector` | vector containing the computed p-values of the general regression model for each gene
`variables` | variables in the general regression model
`g` | number of signifant genes
`p.vector.alfa` | p-vlaue at FDR = `Q` control
`step.method` | imputed step method for stepwise regression
`Q` | imputed value for false discovery rate (FDR) control
`step.alfa` | inputed significance level in stepwise regression
`influ.info` | data frame of genes containing influencial data

### 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. 2005. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments.

### See Also

`make.design.matrix`, `p.vector`, `T.fit`, `get.siggenes`, `see.genes`

### 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 maSigPro
    tc.test <- maSigPro (tc.DATA, edesign, degree = 2, vars = "groups", main = "Test")

    tc.test$g  # gives number of total significant genes
    tc.test$summary  # shows significant genes by experimental groups
    tc.test$sig.genes$Treat1$sig.pvalues  # shows pvalues of the significant coefficients
                                          # in the regression models of the significant genes
                                          # for Control.vs.Treat1 comparison

[Package maSigPro version 1.60.0 Index]