acde-package {acde} | R Documentation

## Artificial Components Detection of Differentially Expressed Genes

### Description

This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the article Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments by Acosta (2015).

### Details

Package: | acde
Type: | Package
Version: | 1.0
Date: | 2015-02-25
License: | GLP-3
LazyData: | yes
Depends: | R(>= 3.1), ade4(>= 1.6), boot(>= 1.3)
Encoding: | UTF-8
Built: | R 3.1.2; 2015-05-01; unix

Index:

    ac              Artificial Components for Gene
                    Expression Data
    acde-package    Artificial Components Detection of
                    Differentially Expressed Genes
    bcaFDR          BCa Confidence Upper Bound for the FDR.
    fdr             False Discovery Rate Computation
    phytophthora    Gene Expression Data for Tomato Plants
                    Inoculated with _Phytophthora infestans_
    plot.STP        Plot Method for Single Time Point Analysis
    plot.TC         Plot Method for Time Course Analysis
    print.STP       Print Method for Single Time Point Analysis
    print.TC        Print Method for Time Course Analysis
    qval            Q-Values Computation
    stp             Single Time Point Analysis for Detecting
                    Differentially Expressed Genes
    tc              Time Course Analysis for Detecting
                    Differentially Expressed Genes

### Author(s)

Juan Pablo Acosta, Liliana Lopez-Kleine

Maintainer: Juan Pablo Acosta <jpacostar@unal.edu.co>

### References

Acosta, J. P. (2015) Strategy for Multivariate Identification of Differentially Expressed Genes in Microarray Data. Unpublished MS thesis. Universidad Nacional de Colombia, Bogot\'a.

### Examples

    ## Single time point analysis for 500 genes with 10 treatment
    ## replicates and 10 control replicates
    n <- 500; p <- 20; p1 <- 10
    des <- c(rep(1, p1), rep(2, (p-p1)))
    mu <- as.matrix(rexp(n, rate=1))
    Z <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
    ### 5 up regulated genes
    Z[1:5,1:p1] <- Z[1:5,1:p1] + 5
    ### 10 down regulated genes
    Z[6:15,(p1+1):p] <- Z[6:15,(p1+1):p] + 4

    resSTP <- stp(Z, des)
    resSTP
    plot(resSTP)

    ## Time course analysis for 500 genes with 10 treatment
    ## replicates and 10 control replicates
    tPts <- c("h0", "12h", "24h")
    n <- 500; p <- 20; p1 <- 10
    Z <- vector("list", 3)
    des <- vector("list", 3)
    for(tp in 1:3){ des[[tp]] <- c(rep(1, p1), rep(2, (p-p1))) }
    mu <- as.matrix(rexp(n, rate=1))
    ### h0 time point (no diff. expr.)
    Z[[1]] <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
    ### h12 time point (diff. expr. begins)
    Z[[2]] <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
    #### Up regulated genes
    Z[[2]][1:5,1:p1] <- Z[[2]][1:5,1:p1] +
        matrix(runif(5*p1, 1, 3), nrow=5)
    #### Down regulated genes
    Z[[2]][6:15,(p1+1):p] <- Z[[2]][6:15,(p1+1):p] +
        matrix(runif(10*(p-p1), 1, 2), nrow=10)
    ### h24 time point (maximum differential expression)
    Z[[3]] <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
    #### 5 up regulated genes
    Z[[3]][1:5,1:p1] <- Z[[3]][1:5,1:p1] + 5
    #### 10 down regulated genes
    Z[[3]][6:15,(p1+1):p] <- Z[[3]][6:15,(p1+1):p] + 4

    resTC <- tc(Z, des)
    resTC
    summary(resTC)
    plot(resTC)

[Package acde version 1.18.0 Index]