MAST (version 0.931)

logFC: Calculate log-fold changes from hurdle model components

Description

Using the delta method, estimate the log-fold change from a state given by a vector contrast0 and the state(s) given by contrast1.

Usage

logFC(zlmfit, contrast0, contrast1)
getLogFC(zlmfit, contrast0, contrast1)

Arguments

zlmfit

ZlmFit output

contrast0

vector of coefficients giving baseline contrast, or a Hypothesis. If missing, then the '(Intercept)' is used as baseline.

contrast1

matrix of coefficients giving comparison contrasts, or a Hypothesis. If missing, then all non-(Intercept) coefficients are compared.

Value

list of matrices `logFC` and `varLogFC`, giving the log-fold-changes for each contrast (columns) and genes (rows) and the estimated sampling variance thereof

Functions

• getLogFC: Return results as a perhaps friendlier data.table

Details

The log-fold change is defined as follows. For each gene, let \(u(x)\) be the expected value of the continuous component, given a covariate x and the estimated coefficients coefC, ie, \(u(x)=\) crossprod(x, coefC). Likewise, Let \(v(x)=\) 1/(1+exp(-crossprod(coefD, x))) be the expected value of the discrete component. The log fold change from contrast0 to contrast1 is defined as $$u(contrast1)v(contrast1)-u(contrast0)v(contrast0).$$ Note that for this to be a log-fold change, then the regression for u must have been fit on the log scale. This is returned in the matrix logFC. An approximation of the variance of logFC (applying the delta method to formula defined above) is provided in varLogFC.

See Also

Hypothesis

Examples

# NOT RUN {
data(vbetaFA)
zz <- zlm.SingleCellAssay( ~ Stim.Condition+Population, vbetaFA[,1:5])
##log-fold changes in terms of intercept (which is Stim(SEB) and CD154+VbetaResponsive)
lfcStim <- logFC(zz)
##If we want to compare against unstim, we can try the following
coefnames <- colnames(coef(zz, 'D'))
contrast0 <- setNames(rep(0, length(coefnames)), coefnames)
contrast0[c('(Intercept)', 'Stim.ConditionUnstim')] <- 1
contrast1 <- diag(length(coefnames))
rownames(contrast1)<-colnames(contrast1)<-coefnames
contrast1['(Intercept)',]<-1
lfcUnstim <- logFC(zz, contrast0, contrast1)
##log-fold change with itself is 0
stopifnot(all(lfcUnstim$logFC[,2]==0))
##inverse of log-fold change with Stim as reference
stopifnot(all(lfcStim$logFC[,1]==(-lfcUnstim$logFC[,1])))
##As a data.table:
getLogFC(zz)
# }