nullModelLogLike: Calculation of loglikelihood for null model in differential...
In HTSDiff: Differential Analysis of RNA-Seq Data with Poisson Mixture Models

Description Usage Arguments Details Value Author(s) References Examples

This function calculates the loglikelihood, BIC, and ICL for the null model, i.e., a single cluster of non-differentially expressed genes.

1	nullModelLogLike(counts, conds, norm="TMM")

`counts`	(n x q) matrix of observed counts for n genes and q samples
`conds`	Vector of length q defining the condition (treatment group) for each variable (column) in `counts`
`norm`	The estimator to be used for the library size parameter: “`TC`” for total count, “`UQ`” for upper quantile, “`Med`” for median, “`DESeq`” for the normalization method in the DESeq package, and “`TMM`” for the TMM normalization method.

This function implements the calculation of the loglikelihood, BIC, and ICL for the model containing a single cluster of non-differentially expressed genes. Its main utility is to enable model selection between this null model and a model containing at least one potential cluster of differentially expressed genes.

`logLike`	Loglikelihood value
`BIC`	Value of BIC
`ICL`	Value of ICL criterion

Andrea Rau <andrea.rau@jouy.inra.fr>

S. Balzergue, G. Rigaill, V. Brunaud, E. Blondet, A. Rau, O. Rogier, J. Caius, C. Maugis-Rabusseau, L. Soubigou-Taconnat, S. Aubourg, C. Lurin, E. Delannoy, and M.-L. Martin-Magniette. (2013) HTSDiff: A Model-Based Clustering Alternative to Test-Based Methods in Differential Gene Expression Analyses by RNA-Seq Benchmarked on Real and Synthetic Datasets (submitted).

set.seed(12345)

#### Generate synthetic data: 2000 genes under H0
## syn <- syntheticData(H0number = 2000)

#### Calculate criteria for null model
## nulltest <- nullModelLogLike(syn, conds=c(1,1,2,2),
##	norm="DESeq")