joda (version 1.20.0)

differential.probs: Calculating probabilities of differential expression in perturbation experiments

Description

Returns probabilities of differential expression for genes under perturbation of a set of regulators. Takes as input perturbation data and beliefs about known genes.

Usage

differential.probs(data, beliefs, verbose, plot.it)

Arguments

data

A matrix of log expression ratios perrturbation vs control, for the genes (rows), in the perturbations of the regulators (columns). The data has to have row and colnames specified by the user.

beliefs

A list with names being a subset of the regulators (i.e., the names of beliefs have to be a subset of the columns of the data). Each list entry for a given regulator is a matrix with rows corresponding to the genes that are known to respond in some way to the perturbation of this regulator. The rownames of the matrix must be a subset of the rows in the data. The matrix can have either two or three columns. Each row is a distribution over the differential and unchanged cluster (2 columns) or over down, up-regulated and unchanged cluster of genes (3 columns). This distribution reflects the certainties with which a gene that corresponds to this row belongs to each of those clusters.

verbose

When TRUE, for each regulator and its perturbation data, the execution prints out the parameters of the fitted model(s), indicating which components are differential and which are unchanged.

plot.it

When TRUE, for each regulator and its perturbation data, the execution plots the Gaussian components of the fitted model(s), indicating which components are differential and which are unchanged.

Value

and entries giving the signed probabilities of differential expression.

Details

For each regulator, a belief-based mixture model is fitted to the observations in the data. The fitted models have the number of model components equal to the number of columns in the corresponding beliefs. If no beliefs are given, unsupervised two-component mixture modeling is applied.

References

http://joda.molgen.mpg.de

See Also

regulation.scores, deregulation.scores

Examples

	
data(damage)
	
# Get the probabilities of differential expression
# for the knockout of p53 in healthy cells
probs.healthy.p53= differential.probs(data.healthy[,"p53",FALSE],
					beliefs.healthy["p53"], TRUE,TRUE)
	
# Get the probabilities of differential expression
# for the knockout of Ste12 under pheromone treatment 	
library(bgmm)
data(Ste12)
data=as.matrix(Ste12Data)
colnames(data)="Ste12"
beliefs=list(Ste12=Ste12Beliefs)
diff.p=differential.probs(data,beliefs,TRUE,TRUE)
	
## Not run: 
# probs.healthy= differential.probs(data.healthy, beliefs.healthy, TRUE,TRUE)
# ## End(Not run)