fitLN: Fit Log Normal Mixture Model

In SIBER: Systematic Identification of Bimodally Expressed Genes Using RNAseq Data

Description

The function fits a two-component log normal mixture model.

Usage

fitLN(y, base=10, eps=10, d=NULL, model='E', zeroPercentThr=0.2, logLikToLN=TRUE)

Arguments

y	A vector representing the RNAseq raw count.
base	The logarithm base defining the parameter estimates in the logarithm scale. This is also the base of log transformation applied to the data.
eps	A scalar to be added to the count data to avoid taking logarithm of zero.
d	A vector of the same length as y representing the normalization constant to be applied to the data. For the LN model, the original data would be devided by this vector.
model	Character specifying E or V model. E model fits the mixture model with equal variance while V model doesn't put any constraint.
zeroPercentThr	A scalar specifying the minimum percent of zero counts needed when fitting a zero-inflated log normal model. This parameter is used to deal with zero-inflation in RNAseq count data. When the percent of zero exceeds this threshold, 1-comp mixture LN model is used to estimate mu and sigma from nonzero count.
logLikToLN	logical indicating if the log likelihod is defined on the transformed value or the orginal value from log noral distribution.

Details

The parameter estimates from log normal mixture is obtained by taking logarithm and fit normal mixture. We use mclust package to obtain parameter estimates of normal mixture model. In particular, log_{base}(\frac{y+eps}{d}) is used to fit to normal mixture model.

With this function, three models can be fitted: (1) log normal mixture with equal variance (E model); (2) Generalized Poisson mixture with unequal variance (V model); (3) 0-inflated log normal model. The 0-inflated log normal has the following density function:

P(Y=y)=π D(y) + (1-π)LN(μ, σ) where D is the point mass at 0 while LN(μ, σ) is the density of log normal distribution with mean μ and variance σ^2.

The rule to fit 0-inflated model is that the observed percentage of count exceeds the user specified threshold. This rule overrides the model argument (E or V) when observed percentae of zero count exceeds the threshold.

Value

A vector consisting parameter estimates of mu1, mu2, sigma1, sigma2, pi1, logLik and BIC. For 0-inflated model, mu1=sigma1=0.

Author(s)

Pan Tong ([e-mail]), Kevin R Coombes ([e-mail])

References

Wang, J.,Wen, S., Symmans,W., Pusztai, L., and Coombes, K. (2009). The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data. Cancer informatics, 7, 199.

Tong, P., Chen, Y., Su, X. and Coombes, K. R. (2012). Systematic Identification of Bimodally Expressed Genes Using RNAseq Data. Bioinformatics, submitted.

See Also

SIBER fitNB fitGP fitNL

Examples

# artificial RNAseq data from negative binomial distribution
set.seed(1000)
dat <- rnbinom(100, mu=1000, size=1/0.2)
fitLN(y=dat)

SIBER documentation built on May 2, 2019, 5:20 p.m.