Package 'acde' reference manual

Title:	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 vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication).
Authors:	Juan Pablo Acosta, Liliana Lopez-Kleine
Maintainer:	Juan Pablo Acosta <[email protected]>
License:	GPL-3
Version:	1.37.0
Built:	2024-11-29 03:07:18 UTC
Source:	https://github.com/bioc/acde

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 <[email protected]>

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)
## 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)

Artificial Components for Gene Expression Data

Description

Computes the artificial components for gene expression data between two conditions for a single time point.

Usage

ac(Z, design)

ac2(Z, design)
ac(Z, design)

ac2(Z, design)

Arguments

`Z`	a numeric matrix or data.frame with $n$ rows and $p$ columns representing genes' expression levels. The rows of $Z$ correspond to the genes in the experiment, and the columns correspond to the replicates. Treatment replicates are to the left, control replicates to the right.
`design`	a vector of length $p$ with 1's for the treatment replicates and 2's for the control replicates $(1, \ldots, 1, 2, \ldots, 2)$ .

Details

This function computes the artificial components of Z, based on the specified design vector. First, the function scales $Z$ so that its columns have zero mean and unit variance. Then computation of the artificial components $\mathbf{\psi}_1$ and $\mathbf{\psi}_2$ is performed as $\psi_1 = Z\mathbf{v_1}$ , where $\mathbf{v_1} = \left(1, \ldots, 1\right) / \sqrt{p}$ , and $\psi_2 = Z\mathbf{v_2}$ , where $\mathbf{v_2} = \left(1, \ldots, 1, -1, \ldots, -1 \right) / \sqrt{pp_1(p-p_1)}$ . Here, $p_1$ is the number of treatment replicates, and $\mathbf{v_2}$ has $p_1$ positive and $p-p_1$ negative entries.

Value

ac returns a matrix with the artificial components $\psi_1$ and $\psi_2$ in the columns.

ac2 returns a matrix with the second artificial component $\psi_2$ in the only column.

Author(s)

Juan Pablo Acosta ([email protected]).

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

## Computes the artificial components for the 
## phitophthora infestans data at 60 hai.
psi <- ac(phytophthora[[4]], c(rep(1,8), rep(2,8)))
plot(x=psi[,1], y=psi[,2])
## Computes the artificial components for the 
## phitophthora infestans data at 60 hai.
psi <- ac(phytophthora[[4]], c(rep(1,8), rep(2,8)))
plot(x=psi[,1], y=psi[,2])

BCa Confidence Upper Bound for the FDR

Description

For internal use in function stp. Computes a BCa confidence upper bound for the FDR following Algorithm 2 in the vignette.

Usage

bcaFDR(Z, design, th = NULL, B = 100, 
    lambda = 0.5, PER = FALSE, R = 1000, 
    gamma = 0.95, Q = NULL, ...)
bcaFDR(Z, design, th = NULL, B = 100, 
    lambda = 0.5, PER = FALSE, R = 1000, 
    gamma = 0.95, Q = NULL, ...)

Arguments

`Z`	a matrix or data.frame representing genes' expression levels. The rows of $Z$ correspond to the genes in the experiment, and the columns correspond to the replicates. Treatment replicates are to the left, control replicates to the right.
`design`	a vector of length equal to the number of columns in `Z` with 1's for the treatment replicates and 2's for the control replicates $(1, \ldots, 1, 2, \ldots, 2)$ .
`th`	Threshold values for estimating the FDR. If `NULL`, the values from `abs(ac2(Z,design))` are used.
`B`	Number of bootstrap or permutation replications for estimating the FDR at each iteration (as passed from `stp`).
`lambda`	Parameter for the estimation of $\pi_0$ and the FDR as passed from `stp` (see Storey, 2002).
`R`	Number of bootstrap replications for the computation of the FDR's BCa confidence upper bound (as passed from `stp`).
`gamma`	Confidence level for the FDR's BCa upper confidence bound (as passed from `stp`).
`PER`	If `FALSE` (default), bootstrap replications are used to estimate the FDR. If `TRUE`, permutation replications are used instead (as passed from `stp`).
`Q`	Estimated FDR as returned in object `\$Q` from `fdr` function (passed from call to `stp`). For internal use.
`...`	additional arguments for parallel computation in `boot` function as passed from `stp` (see `stp` help page for details).

Value

`cbound`	BCa upper confidence bound for the FDR for each threshold value in `th`.
`warnings`	warning messages generated from use of `boot.ci` function from package `boot`.

Author(s)

Juan Pablo Acosta ([email protected]).

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.

Storey, J. D. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3): 479–498.

Efron B. and Tibshirani R. J. (1994) An Introduction to the Bootstrap. Chapman & Hall/CRC, 1993.

Examples

## Single time point analysis for 50 genes with 10 treatment 
## replicates and 10 control replicates
n <- 50; 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] + 5

resFdr <- fdr(Z, des)
bca <- bcaFDR(Z, des, Q=resFdr$Q, B=50, R=500)
plot(resFdr$th, resFdr$Q, type="l", col="blue")
lines(resFdr$th, bca$cbound, col="green")
legend(x="topright", legend=c("FDR", "BCa upper bound"), 
    lty=c(1,1), col=c("blue", "green"))
## Note: Discontinuities in the BCa upper bound are due to warnings
## generated during computations with function \code{boot.ci} 
## from package \code{boot}.
## Single time point analysis for 50 genes with 10 treatment 
## replicates and 10 control replicates
n <- 50; 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] + 5

resFdr <- fdr(Z, des)
bca <- bcaFDR(Z, des, Q=resFdr$Q, B=50, R=500)
plot(resFdr$th, resFdr$Q, type="l", col="blue")
lines(resFdr$th, bca$cbound, col="green")
legend(x="topright", legend=c("FDR", "BCa upper bound"), 
    lty=c(1,1), col=c("blue", "green"))
## Note: Discontinuities in the BCa upper bound are due to warnings
## generated during computations with function \code{boot.ci} 
## from package \code{boot}.

False Discovery Rate Computation

Description

For internal use in functions stp and bcaFDR. Computes steps 2.1 to 2.4 from Algorithm 1 in the vignette.

Usage

fdr(Z, design, th = NULL, B = 100, lambda = 0.5, PER = FALSE, ...)
fdr(Z, design, th = NULL, B = 100, lambda = 0.5, PER = FALSE, ...)

Arguments

`Z`	a matrix or data.frame representing genes' expression levels. The rows of $Z$ correspond to the genes in the experiment, and the columns correspond to the replicates. Treatment replicates are to the left, control replicates to the right.
`design`	a vector of length equal to the number of columns in `Z` with 1's for the treatment replicates and 2's for the control replicates $(1, \ldots, 1, 2, \ldots, 2)$ .
`th`	Threshold values for estimating the FDR. If `NULL`, the values from `abs(ac2(Z,design))` are used.
`B`	Number of bootstrap or permutation replications for estimating the FDR (as passed from `stp` and `bcaFDR`).
`lambda`	Parameter for the estimation of $\pi_0$ and the FDR as passed from `stp` and `bcaFDR` (see Storey, 2002).
`PER`	If `FALSE` (default), bootstrap replications are used to estimate the FDR. If `TRUE`, permutation replications are used instead (as passed from `stp` and `bcaFDR`).
`...`	additional arguments for parallel computation in `boot` function as passed from `stp` (see `stp` help page for details).

Value

`Q`	Estimations of the FDR using each value in `th` as the threshold.
`th`	Threshold values used for estimating the FDR.
`pi0`	Estimation of $\pi_0$ , the true proportion of non differentially expressed genes in the experiment.
`B`	Number of bootstrap or permutation replications used for estimating the FDR.
`lambda`	Parameter used for the estimation of $\pi_0$ and the FDR.
`call`	The matched call.

Author(s)

Juan Pablo Acosta ([email protected]).

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.

Storey, J. D. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3): 479–498.

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

res <- fdr(Z, des)
plot(res$th, res$Q, type="l", col="blue")
legend(x="topright", legend="FDR", lty=1, col="blue")
## 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

res <- fdr(Z, des)
plot(res$th, res$Q, type="l", col="blue")
legend(x="topright", legend="FDR", lty=1, col="blue")

Gene Expression Data for Tomato Plants Inoculated with Phytophthora infestans

Description

Gene expression data for 16 tomato plants (line IL6-2) in field conditions. 8 of these plants were inoculated with Phytophthora infestans, and the other 8 were mock-inoculated with sterile water. Leaf tissue samples from each replicate were taken at 12 hours before and 12, 36 and 60 hours after inoculation. We refer to 12 hours before inoculation as the h0 time point. Expression levels were obtained for 13440 genes.

Usage

data("phytophthora")data("phytophthora")

Format

A list with four matrices representing expression levels for 13440 genes (rows) in 16 replicates (columns) at time points "h0", "h12", "h36" and "h60". At each time point, the first 8 columns correspond to treatment (inoculated) replicates and the last 8 columns correspond to control (mock-inoculated) replicates. The names of the genes are the names of the rows in each matrix.

Details

For details about experimental conditions, see Restrepo et al. (2005) and Cai et al. (2013).

Source

Tomato Expression Database website (http://ted.bti.cornell.edu/), experiment E022 (Restrepo et al., 2005).

References

Restrepo, S., Cai, G., Fry, W. E. and Smart, C. D. (2005) Gene expression profiling of infection of tomato by Phytophthora infestans in the field. Phytopathology, 95(S88).

Cai, G., Restrepo, S., Myers, K., Zuluaga, P., Danies, G., Smart, C. and Fry W.E. (2013) Gene profiling in partially resistant and susceptible near-isogenic tomatoes in response to late blight in the field. Molecular plant pathology, 14(2): 171–184.

Examples

for(tp in 1:4){
    cat(paste("Time Point:", names(phytophthora[tp]), "\n"))
    print(phytophthora[[tp]][1:10,])
    cat("...\n \n")
}
for(tp in 1:4){
    cat(paste("Time Point:", names(phytophthora[tp]), "\n"))
    print(phytophthora[[tp]][1:10,])
    cat("...\n \n")
}

Plot Method for Single Time Point Analysis

Description

a method for the plot generic. It is designed for displaying plots of the estimated FDR and the genes' classification when performing a Single Time Point Analysis for detecting differentially expressed genes in gene expression data.

Usage

## S3 method for class 'STP'
plot(x, FDR=TRUE, AC=TRUE, WARNINGS=FALSE, tp=NULL, ...)
## S3 method for class 'STP'
plot(x, FDR=TRUE, AC=TRUE, WARNINGS=FALSE, tp=NULL, ...)

Arguments

`x`	an object of class '`STP`' as returned by function `stp`.
`FDR`	if `TRUE`, a plot of the estimated FDR is displayed.
`AC`	if `TRUE`, a plot of the differentially expressed genes in the artificial components is displayed.
`WARNINGS`	if `TRUE` and if a BCa confidence upper bound was computed for obtaining `x`, the threshold values for which an extreme order statistic was used in the BCa computations are shown (these warnings are produced in calls to `boot.ci`).
`tp`	a character string to be added at the end of the plot's title (used for adding time points in `plot.TC`).
`...`	further arguments passed to or from other methods.

Author(s)

Juan Pablo Acosta ([email protected]).

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)
## 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)

Plot Method for Time Course Analysis

Description

a method for the plot generic. It is designed for displaying plots of the estimated FDR and the genes' classification when performing a Time Course Analysis for detecting differentially expressed genes in gene expression data.

Usage

## S3 method for class 'TC'
plot(x, iRatios=TRUE, FDR = TRUE, AC = TRUE, 
    WARNINGS = FALSE, ...)
## S3 method for class 'TC'
plot(x, iRatios=TRUE, FDR = TRUE, AC = TRUE, 
    WARNINGS = FALSE, ...)

Arguments

`x`	if `TRUE`, a plot of inertia ratios for all the time points is displayed.
`iRatios`	an object of class '`TC`' as returned by function `tc`.
`FDR`	if `TRUE`, a plot of the estimated FDRs are displayed for each time point.
`AC`	if `TRUE`, a plot of the differentially expressed genes in the artificial components is displayed for each time point.
`WARNINGS`	if `TRUE` and if a BCa confidence upper bound was computed for obtaining `x`, the threshold values for which an extreme order statistic was used in the BCa computations are shown (these warnings are produced in calls to `boot.ci`).
`...`	further arguments passed to or from other methods.

Author(s)

Juan Pablo Acosta ([email protected]).

Examples

## 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)

## Not run: 
## Phytophthora Infestans Time Course Analysis (takes time...)
dataPI <- phytophthora
desPI <- vector("list", 4)
for(tp in 1:4){ desPI[[tp]] <- c(rep(1, 8), rep(2, 8)) }
resPI <- tc(dataPI, desPI)
resPI
summary(resPI)
plot(resPI)

## End(Not run)
## 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)

## Not run: 
## Phytophthora Infestans Time Course Analysis (takes time...)
dataPI <- phytophthora
desPI <- vector("list", 4)
for(tp in 1:4){ desPI[[tp]] <- c(rep(1, 8), rep(2, 8)) }
resPI <- tc(dataPI, desPI)
resPI
summary(resPI)
plot(resPI)

## End(Not run)

Print Method for Single Time Point Analysis

Description

a method for the print generic. It prints relevant results when performing a Single Time Point Analysis for detecting differentially expressed genes in gene expression data.

Usage

## S3 method for class 'STP'
print(x, headerSTP = TRUE, ...)
## S3 method for class 'STP'
print(x, headerSTP = TRUE, ...)

Arguments

`x`	an object of class '`STP`' as returned by function `stp`.
`headerSTP`	if `FALSE`, the header is omitted (used for `print.TC`).
`...`	further arguments passed to or from other methods.

Details

If the desired FDR level was achieved (i.e. x$astar <= x$alpha), the results are printed for the differentially expressed genes and 10 more rows only. If the desired FDR level was not achieved, only ten rows are displayed.

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)
## 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)

Print Method for Time Course Analysis

Description

methods for the print and summary generics that print relevant results when performing a Time Course Analysis for detecting differentially expressed genes in gene expression data.

Usage

## S3 method for class 'TC'
print(x, ...)

## S3 method for class 'TC'
summary(object, ...)
## S3 method for class 'TC'
print(x, ...)

## S3 method for class 'TC'
summary(object, ...)

Arguments

`x`	an object of class '`TC`' as returned by function `tc`.
`object`	an object of class '`TC`' as returned by function `tc`.
`...`	further arguments passed to or from other methods.

Details

With print, at each time point, if the desired FDR level was achieved (i.e. x$astar <= x$alpha), the results are printed for the differentially expressed genes and 10 more rows only. If the desired FDR level was not achieved, only ten rows are displayed.

summary prints a more concise version of the results.

Examples

## 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)

## Not run: 
## Phytophthora Infestans Time Course Analysis (takes time...)
dataPI <- phytophthora
desPI <- vector("list", 4)
for(tp in 1:4){ desPI[[tp]] <- c(rep(1, 8), rep(2, 8)) }
resPI <- tc(dataPI, desPI)
resPI
summary(resPI)
plot(resPI)

## End(Not run)
## 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)

## Not run: 
## Phytophthora Infestans Time Course Analysis (takes time...)
dataPI <- phytophthora
desPI <- vector("list", 4)
for(tp in 1:4){ desPI[[tp]] <- c(rep(1, 8), rep(2, 8)) }
resPI <- tc(dataPI, desPI)
resPI
summary(resPI)
plot(resPI)

## End(Not run)

Q-Values Computation

Description

For internal use in function stp. Computes the genes' Q-Values in the Single Time Point Analysis according to Algorithm 3 in the vignette.

Usage

qval(Q, psi2)
qval(Q, psi2)

Arguments

`Q`	vector with the estimated FDRs when the threshold values used are `abs(ac2(Z, design))`.
`psi2`	vector with the second artificial component as returned by `ac2`.

Value

returns a vector with the computed Q-Values for each gene in the experiment.

Author(s)

Juan Pablo Acosta ([email protected]).

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.

Storey, J. D. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3): 479–498.

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

res <- fdr(Z, des)
qValues <- qval(res$Q, ac2(Z, des))
plot(res$th, res$Q, type="l", col="blue")
lines(res$th, qValues[order(abs(ac2(Z, des)))], col="green")
legend(x="topright", legend=c("FDR", "Q Values"), lty=c(1,1), 
    col=c("blue", "green"))
## 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

res <- fdr(Z, des)
qValues <- qval(res$Q, ac2(Z, des))
plot(res$th, res$Q, type="l", col="blue")
lines(res$th, qValues[order(abs(ac2(Z, des)))], col="green")
legend(x="topright", legend=c("FDR", "Q Values"), lty=c(1,1), 
    col=c("blue", "green"))

Single Time Point Analysis for Detecting Differentially Expressed Genes

Description

Performs the Single Time Point Analysis for detecting differentially expressed genes following Acosta (2015).

Usage

stp(Z, design, alpha = 0.05, B = 100, lambda = 0.5, 
    th = NULL, PER = FALSE, BCa = FALSE, gamma = 0.95, 
    R = 1000, ...)
stp(Z, design, alpha = 0.05, B = 100, lambda = 0.5, 
    th = NULL, PER = FALSE, BCa = FALSE, gamma = 0.95, 
    R = 1000, ...)

Arguments

`Z`	a matrix or data.frame representing genes' expression levels. The rows of $Z$ correspond to the genes in the experiment, and the columns correspond to the replicates. Treatment replicates are to the left, control replicates to the right.
`design`	a vector of length equal to the number of columns in `Z` with 1's for the treatment replicates and 2's for the control replicates $(1, \ldots, 1, 2, \ldots, 2)$ .
`alpha`	between 0 and 1. Desired level for controlling the false discovery rate (FDR).
`B`	Number of bootstrap or permutation replications for estimating the FDR.
`lambda`	Parameter for the estimation of $\pi_0$ and of the FDR (see Storey, 2002).
`th`	Threshold values for estimating the FDR. If `NULL`, the values from `abs(ac2(Z,design))` are used.
`PER`	If `FALSE` (default), bootstrap replications are used to estimate the FDR. If `TRUE`, permutation replications are used instead.
`BCa`	If `TRUE`, a BCa confidence upper bound for the FDR is computed (see Efron and Tibshirani, 1994).
`gamma`	Confidence level for the FDR's BCa confidence upper bound.
`R`	Number of bootstrap replications for the computation of the FDR's BCa confidence upper bound.
`...`	additional arguments for parallel computation in `boot` function (see Details).

Details

For details on the computations performed in this function, see Acosta (2015).

Additional parameters in the '...' argument are used for parallel computation in bootstrap calculations. These are supplied to calls to the boot function in package boot. With this in mind, the use of additional arguments must be restricted to arguments parallel and ncpus from function boot.

Value

stp returns an object of class 'STP', which is a list with components:

`dgenes`	factor with the classification of each gene in `Z`. Classes: "up-reg.", "down-reg.", "no-diff.".
`tstar`	Threshold value used to identify differentially expressed genes.
`astar`	Achieved FDR level.
`Q`	Estimations of the FDR using each value in `th` as threshold.
`th`	Threshold values used for estimating the FDR.
`qvalues`	Estimated Q-Values for the genes in the analysis. If argument `th!=NULL`, these are not computed.
`pi0`	Estimation of $\pi_0$ , the true proportion of non differentially expressed genes in the experiment.
`B`	Number of bootstrap or permutation replications used for estimating the FDR.
`lambda`	Parameter used for the estimation of $\pi_0$ and the FDR.
`ac`	Artificial components of `Z`.
`gNames`	Gene names (by default the row names in `Z`).
`iRatio`	Inertia ratio $Var(\psi_2) / \lambda_1$ , where $\lambda_1$ is the first eigenvalue of `Z`'s Principal Components Analysis.
`bca`	BCa upper confidence bounds for the FDR using each value in `th` as the threshold.
`gamma`	Confidence level used in the computation of the BCa upper bounds.
`alpha`	Desired FDR level.
`call`	The matched call.

Warning

If argument BCa=TRUE, computations may take a considerable amount of time.

Author(s)

Juan Pablo Acosta ([email protected]).

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.

Storey, J. D. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3): 479–498.

Efron B. and Tibshirani R. J. (1994) An Introduction to the Bootstrap. Chapman & Hall/CRC, 1993.

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)


## Not run: 
## Phytophthora Infestans Single Time Point Analysis (takes time...)
dataPI <- phytophthora[[4]]
desPI <- c(rep(1,8), rep(2,8))
resPI <- stp(dataPI, desPI)
resPI
plot(resPI, tp="60 hai")

## End(Not run)
## 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)


## Not run: 
## Phytophthora Infestans Single Time Point Analysis (takes time...)
dataPI <- phytophthora[[4]]
desPI <- c(rep(1,8), rep(2,8))
resPI <- stp(dataPI, desPI)
resPI
plot(resPI, tp="60 hai")

## End(Not run)

Time Course Analysis for Detecting Differentially Expressed Genes

Description

Performs the Time Course Analysis from Acosta (2015) for detecting differentially expressed genes in time course experiments for gene expression data.

Usage

tc(data, designs, tPoints = NULL, 
    method = c("active vs complementary", "groups conformation"), 
    activeTP = NULL, alpha = 0.05, B = 100, lambda = 0.5, 
    PER = FALSE, BCa = FALSE, gamma = 0.95, R = 1000, ...)
tc(data, designs, tPoints = NULL, 
    method = c("active vs complementary", "groups conformation"), 
    activeTP = NULL, alpha = 0.05, B = 100, lambda = 0.5, 
    PER = FALSE, BCa = FALSE, gamma = 0.95, R = 1000, ...)

Arguments

`data`	a list with matrices or data.frames representing genes' expression levels at each time point. The rows of each matrix correspond to the genes in the experiment, and the columns correspond to the replicates. Treatment replicates are to the left, control replicates to the right.
`designs`	a list with a vector for each time point, of length equal to the number of columns in the respective matrix or data.frame in `data`, with 1's for the treatment replicates and 2's for the control replicates.
`tPoints`	a character vector with the names of the timepoints.
`method`	if "active vs complementary", an analysis following the active vs complementary time points approach (Acosta, 2015) is performed. If "groups conformation", an analysis following the groups conformation through time approach (Acosta, 2015) is performed. The default is both.
`activeTP`	numeric. The index of the active timepoint in `tPoints` for the active vs complementary time points approach. If `NULL` (default), the active time point is selected via inertia ratios.
`alpha`	between 0 and 1. Desired level for controlling the false discovery rate (FDR).
`B`	Number of bootstrap or permutation replications for estimating the FDR.
`lambda`	Parameter for the estimation of $\pi_0$ and, further, the estimation of the FDR (see Storey, 2002).
`PER`	If `FALSE` (default), bootstrap replications are used to estimate the FDR. If `TRUE`, permutation replications are used instead.
`BCa`	If TRUE, a BCa confidence upper bound for the FDR is computed (see Efron and Tibshirani, 1994).
`gamma`	Confidence level for the FDR's BCa upper confidence bound.
`R`	Number of bootstrap replications for the computation of the FDR's BCa upper confidence bound.
`...`	additional arguments for parallel computation in `boot` function (see Details).

Details

In the active vs complementary time points approach, the time point that maximizes the inertia ratio is selected as the active time point. Then, a Single Time Point Analysis (stp) is performed on this time point and plots of the behavior throughout the time course of the differentially expressed genes identified in this time point are displayed.

In the groups conformation through time approach, a Single Time Point Analysis (stp) is performed at each time point and plots are displayed showing the behaviour of the differential expression process throughout the time course; that is, how many genes are differentially expressed and how strong is the differential expression at each time point.

For details on the computations performed in this function, see Acosta (2015).

Value

tc returns an object of class 'TC', which is a list with components:

`iRatios`	inertia ratios for each time point.
`gct`	results for the groups conformation through time approach. A list with an object of class '`STP`' for each timepoint.
`act`	results for the active vs complementary time points approach. A list with an object of class '`STP`' for each timepoint.
`activeTP`	the index of the active timepoint in `tPoints` used in the active vs complementary time points approach.
`tPoints`	a character vector with the names of the timepoints.
`call`	The matched call.

Warning

If argument BCa=TRUE, computations may take a considerable amount of time.

Author(s)

Juan Pablo Acosta ([email protected]).

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.

Storey, J. D. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3): 479–498.

Efron B. and Tibshirani R. J. (1994) An Introduction to the Bootstrap. Chapman & Hall/CRC, 1993.

Examples

## 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)

## Not run: 
## Phytophthora Infestans Time Course Analysis (takes time...)
dataPI <- phytophthora
desPI <- vector("list", 4)
for(tp in 1:4){ desPI[[tp]] <- c(rep(1, 8), rep(2, 8)) }
resPI <- tc(dataPI, desPI)
resPI
summary(resPI)
plot(resPI)

## End(Not run)
## 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)

## Not run: 
## Phytophthora Infestans Time Course Analysis (takes time...)
dataPI <- phytophthora
desPI <- vector("list", 4)
for(tp in 1:4){ desPI[[tp]] <- c(rep(1, 8), rep(2, 8)) }
resPI <- tc(dataPI, desPI)
resPI
summary(resPI)
plot(resPI)

## End(Not run)

Package 'acde'

Help Index

Artificial Components Detection of Differentially Expressed Genes

Description

Details

Author(s)

References

Examples

Artificial Components for Gene Expression Data

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

BCa Confidence Upper Bound for the FDR

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

False Discovery Rate Computation

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Gene Expression Data for Tomato Plants Inoculated with Phytophthora infestans

Description

Usage

Format

Details

Source

References

Examples

Plot Method for Single Time Point Analysis

Description

Usage

Arguments

Author(s)

See Also

Examples

Plot Method for Time Course Analysis

Description

Usage

Arguments

Author(s)

See Also

Examples

Print Method for Single Time Point Analysis

Description

Usage

Arguments

Details

See Also

Examples

Print Method for Time Course Analysis

Description

Usage

Arguments

Details

See Also

Examples

Q-Values Computation

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples