QL.fit: Fit quasi-likelihood models to matrix of RNA-seq expression...

In gitlongor/QuasiSeq: Analyzing RNA Sequencing Count Tables Using Quasi-Likelihood

Description

Fit a quasi-likelihood model to RNA-seq expression count data using the methods detailed in Lund, Nettleton, McCarthy, and Smyth (2012).

Usage

QL.fit(counts, design.list, test.mat=cbind(1L, 2:length(design.list)), 
	   log.offset=NULL, Model="NegBin", print.progress=TRUE, NBdisp="trend", 
	   bias.fold.tolerance=1.10, ...)

Arguments

counts	RNA-seq data matrix of integer expression counts. Each row contains observations from a single gene. Each column contains observations from a single experimental unit.
design.list	List of design matrices for the full model and reduced model(s). The first element of design.list must describe the overall full model, as this design is used to compute deviance residuals for estimating dispersion. One-factor designs may be specified as vectors. The number of rows in each design matrix (or the length of each design vector) must be ncol(counts). Means are modeled with a log link function.
test.mat	T by 2 matrix dictating which designs are to be compared, where T is the total number of desired hypothesis tests for each gene. Each row contains two integers, which provide the indices within design.list of the two designs to be compared. If test.mat is not specified, the default is compare the first design (the full model) to each of the other designs provided in design.list in order (i.e. first design compared to second design, first design compared to third design, first design compared to fourth design, etc.). If length(design.list)=1, then test.mat is ignored.
log.offset	A vector of log-scale, additive factors used to adjust estimated log-scale means for differences in library sizes across samples. Commonly used offsets include log.offset=log(colSums(counts)) and log.offset=log(apply(counts[rowSums(counts)!=0,],2,quantile,.75)). The default setting makes no adjustment for library sizes (i.e. log.offset=0).
Model	Must be one of "Poisson" or "NegBin", specifying use of a quasi-Poisson or quasi-negative binomial model, respectively.
print.progress	logical. If TRUE, a text progress bar will be displayed during the fitting of each models in the design.list.
NBdisp	Used only when Model="NegBin". Must be one of "trend", "common" or a vector of non-negative real numbers with length equal to nrow(counts). Specifying NBdisp="trend" or NBdisp="common" will use estimateGLMTrendedDisp or estimateGLMCommonDisp, respectively, from the package edgeR to estimate negative binomial dispersion parameters for each gene. Estimates obtained from other sources can be used by entering NBdisp as a vector containing the negative binomial dispersion value to use for each gene when fitting quasi-likelihood model.
bias.fold.tolerance	A numerical value no smaller than 1. If the bias reduction of maximum likelihood estimates of (log) fold change is likely to result in a ratio of fold changes greater than this value, then bias reduction will be performed on such genes. Setting bias.fold.tolerance=Inf will completely disable bias reduction; setting bias.fold.tolerance=1 will always perform bias reduction. See NBDev for details. Currently, this option is ignored unless Model="NegBin".
...	arguments to be passed to the function estimateGLMTrendedDisp or estimateGLMCommonDisp from the package edgeR.

Value

list containing:

"LRT"	matrix providing unadjusted likelihood ratio test statistics. Each column contains statistics from a single hypothesis test, applied separately to each gene.
"LRT.Bart"	The same as LRT, except that the LRT test statistics are adjusted by the Bartlett factor to better match their asymptotic moments.
"phi.hat.dev"	vector providing unshrunken, deviance-based estimates of quasi-dispersion (phi) for each gene.
"phi.hat.pearson"	vector providing unshrunken, Pearson estimates of quasi-dispersion (phi) for each gene.
"mn.cnt"	vector providing average count for each gene.
"den.df"	denominator degrees of freedom. Equal to the number of samples minus the number of fitted parameters in the full model, which is specified by the first element of design.list
"num.df"	vector of numerator degrees of freedom for each test, computed as the difference in the number of fitted parameters between the full and reduced models for each test.
"Model"	Either "Poisson" or "NegBin", specifying which model (quasi-Poisson or quasi-negative binomial, respectively) was used.
"nb.disp"	Only appears when Model="NegBin". Vector providing negative binomial dispersion parameter estimate used during fitting of quasi-negative binomial model for each gene.
fitted.values	matrix of fitted mean values
coefficients	matrix of estimated coefficients for each gene. Note that these are given on the log scale. (i.e. intercept coefficient reports log(average count) and non-intercept coefficients report estimated (and bias reduced, when appropriate) log fold-changes.)

Author(s)

Originally authored by Steve Lund [e-mail]; later modified by Long Qu [e-mail]

References

Kosmidis and Firth (2009) "Bias reduction in exponential family nonlinear models" Biometrika, 96, 793–804.

Lund, Nettleton, McCarthy and Smyth (2012) "Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates" SAGMB, 11(5).

McCarthy, Chen and Smyth (2012) "Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation" Nucleic Acids Res. 40(10), 4288–97.

Cordeiro (1983) "Improved Likelihood Ratio Statistics for Generalized Linear Models" Journal of the Royal Statistical Society. Series B (Methodological), 45, 404–413.

See Also

QL.results, NBDev, PoisDev, mockRNASeqData

Examples

 
data(mockRNASeqData)

### Create list of designs describing model under null and alternative hypotheses
design.list=list(
	mockRNASeqData$design.matrix, 
	matrix(1, nrow=rep(mockRNASeqData$nsamples))
)

## Not run: 
### Analyze using QL, QLShrink and QLSpline methods applied to quasi-Poisson model
fit = 
with(mockRNASeqData,
	QL.fit(counts, design.list,
	log.offset=log(estimated.normalization),
	 Model="Poisson", bias.fold.tolerance=Inf)
)
results = QL.results(fit)

### How many significant genes at FDR=.05 from QLSpline method?
apply(results$Q.values[[3]]<.05,2,sum)

### Indexes for Top 10 most significant genes from QLSpline method
head(order(results$P.values[[3]]), 10)

## End(Not run)

## Not run: 
### Analyze using QL, QLShrink and QLSpline methods 
### applied to quasi-negative binomial model
fit2 = 
with(mockRNASeqData,
	QL.fit(counts, design.list,
		log.offset=log(estimated.normalization),
		Model="NegBin")
)
##########################################################
## Note: 'NBdisp' typically will not be specified when  ##
## calling QL.fit while analyzing real data. Providing  ##
## numeric values for 'NBdisp' prevents neg binomial    ##
## dispersions from being estimated from the data.      ##
##########################################################
results2<-QL.results(fit2)

### How many significant genes at FDR=.05 for QLSpline method?
apply(results2$Q.values[[3]]<.05,2,sum)

### Indexes for Top 10 most significant genes from QLShrink method
head(order(results2$P.values[[2]]), 10)


## End(Not run)

gitlongor/QuasiSeq documentation built on May 17, 2019, 5:28 a.m.