acde-package {acde} | R Documentation |
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).
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
Juan Pablo Acosta, Liliana Lopez-Kleine
Maintainer: Juan Pablo Acosta <jpacostar@unal.edu.co>
Acosta, J. P. (2015) Strategy for Multivariate Identification of Differentially Expressed Genes in Microarray Data. Unpublished MS thesis. Universidad Nacional de Colombia, Bogot\'a.
## 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)