maSigPro (version 1.44.0)

get.siggenes: 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

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

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.

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")