pdeg: Detection of differentially expressed genes.

In degenes: Detection of differentially expressed genes.

Description

Calculated gene-specific unadjusted p-values for the comparison of samples from two distinct groups.

Usage

pdeg(treatment, control, alpha = 0.05)

Arguments

treatment	Matrix of normalized expression levels in the first group. Rows represent genes, columns represent samples.
control	Matrix of normalized expression levels in the second group. Rows represent genes, columns represent samples.
alpha	Global significance level.

Value

A vector with the gene-specific unadjusted p-values.

Author(s)

Klaus Jung

References

Jung K., Quast K., Gannoun A. and Urfer W. (2006) A renewed approach to the nonparametric analysis of replicated microarray experiments. Biometrical Journal, 48, 245-254.

Examples

X1 = matrix(rnorm(2000, 0, 1), 200, 10)
X2 = matrix(rnorm(2000, 0, 1), 200, 10)
index = sample(1:200, 5, replace=FALSE)
X2[index,] = X2[index,] + 5
D = deg(X1, X2)
PD = pdeg(X1, X2)
PDa = p.adjust(PD, method="bonferroni")
sort(index)
D
which(PDa<0.05)
which(PD<0.05)

Example output

Wed Jan  1 10:53:41 2020
Calculation of Z
Calculation of z
Calculation of the kernel estimator
Calculation of the kernel estimator
Determination of the rejection region
Wed Jan  1 10:53:41 2020
Wed Jan  1 10:53:41 2020
Calculation of Z
Calculation of z
Calculation of the kernel estimator
Calculation of the kernel estimator
Wed Jan  1 10:53:42 2020
[1]   8  11 108 117 129
[1]   8  11 108 117 129
[1]   8  11 108 117 129
 [1]   8  11  26  29  75  88 105 108 117 123 129 139 165

degenes documentation built on May 30, 2017, 5:08 a.m.