maSigPro: Wrapping function for identifying significant differential...

In maSigPro: Significant Gene Expression Profile Differences in Time Course Gene Expression Data

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 ofget.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, [e-mail]; Maria Jose Nueda, [e-mail]

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

maSigPro documentation built on Nov. 8, 2020, 6:51 p.m.