entropyML: Maximum Likelihood Entropy Estimate

In lucgar/synRNASeqNet: Synthetic RNA-Seq Network Generation and Mutual Information Estimates

Description

Computing the Maximul Likelihood Entropy Estimate of cellCounts.

Usage

entropyML(cellCounts, unit = unit)

Arguments

cellCounts	an integer vector (or matrix) representing the number of times each particular count is obtained.
unit	the unit in which entropy is measured. One of "bit" (log2, default), "ban" (log10) or "nat" (natural units).

Value

The entropyML function returns the value of the entropy of that gene H(X) (or pair of genes H(X,Y)).

Author(s)

Luciano Garofano [e-mail], Stefano Maria Pagnotta, Michele Ceccarelli

References

Paniski L. (2003). Estimation of Entropy and Mutual Information. Neural Computation, vol. 15 no. 6 pp. 1191-1253.

Meyer P.E., Laffitte F., Bontempi G. (2008). minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information. BMC Bioinformatics 9:461.

Antos A., Kontoyiannis I. (2001). Convergence properties of functional estimates for discrete distributions. Random Structures and Algorithms, vol. 19 pp. 163-193.

Strong S., Koberle R., de Ruyter van Steveninck R.R., Bialek W. (1998). Entropy and Information in Neural Spike Trains. Physical Review Letters, vol. 80 pp. 197-202.

See Also

entropyMM, entropyBayes, entropyCS, entropyShrink

Examples

simData <- simulatedData(p = 50, n = 100, mu = 100, sigma = 0.25,
                        ppower = 0.73, noise = FALSE)
cellCounts <- table(simData$counts[1, ])
eML <- entropyML(cellCounts, unit = "nat")

lucgar/synRNASeqNet documentation built on May 21, 2019, 8:54 a.m.