| p.vector {maSigPro} | R Documentation |
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.
p.vector(data, design = NULL, Q = 0.05, MT.adjust = "BH", min.obs = 3)
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. Default is 3 (minimun value for a quadratic fit) |
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.
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 |
BH.alfa |
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 |
Ana Conesa, aconesa@ivia.es; Maria Jose Nueda, mj.nueda@ua.es
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
#### 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$BH.alfa # p.value at FDR control
tc.p$p.adjusted# adjusted p.values