DeLorean-package        The 'DeLorean' package.
Rhat.plot               Plot the Rhat convergence statistics.
                        'examine.convergence' must be called before
                        this plot can be made.
adjust.by.cell.sizes    Adjust the expression by the estimated cell
                        sizes.
alpha.for.rug           Calculate a suitable value for a rug plot given
                        the number of points
analyse.noise.levels    Analyse noise levels and assess which genes
                        have the greatest ratio of temporal variance to
                        noise. This are labelled as the 'gene.high.psi'
                        genes.
analyse.variance        Analyse variance of expression between and
                        within capture times.
anders.huber.cell.sizes
                        Estimate the cell sizes according to Anders &
                        Huber Differential expression analysis for
                        sequence count data
aov.dl                  Perform an analysis of variance to select genes
                        for the DeLorean model.
avg.par.samples         Average across a parameters samples.
calc.inducing.pseudotimes
                        Calculate inducing pseudotimes for sparse
                        approximation
calc.roughness          Calculate the roughness of the vector. The
                        roughness is the RMS of the differences between
                        consecutive points.
centralise              Centralises a periodic position into [period/2,
                        period) by shifting by n*period, where n is an
                        integer
cmp.profiles.plot       Plot a comparison of the profiles from several
                        de.lorean objects
cov.all.genes.conditioned
                        Calculate covariances for all genes when
                        conditioned on data at estimated pseudotimes.
cov.calc.dists          Calculate distances between vectors of time
                        points
cov.calc.dl.dists       Calculate distances over estimated pseudotimes
                        and test inputs.
cov.calc.gene           Calculate covariance structure for gene over
                        pseudotimes and test inputs.
cov.calc.gene.conditioned
                        Calculate covariance for gene over test inputs
                        when conditioned on data at estimated
                        pseudotimes.
cov.matern.32           Matern 3/2 covariance function
cov.periodise           Makes a distance periodic
create.ordering.ll.fn   Calculate the covariance structure of evenly
                        spread tau and create a function that
                        calculates the log likelihood of orderings.
de.lorean               Initialise DeLorean object
de.lorean.stylesheet    The filename of the R markdown stylesheet
default.num.cores       Default number of cores to use.
dim.de.lorean           Dimensions of DeLorean object
estimate.cell.sizes     Estimate the cell sizes. We only consider genes
                        that are expressed in a certain proportion of
                        cells.
estimate.hyper          Estimate hyperparameters for model using
                        empirical Bayes.
examine.convergence     Analyse the samples and gather the convergence
                        statistics. Note this only makes sense if a
                        sampling method was used to fit the model as
                        opposed to variational Bayes.
expected.sample.var     The expected within sample variance of a
                        Gaussian with the given covariance.
expr.data.plot          Plot the expression data by the capture points
filter_cells            Filter cells
filter_genes            Filter genes
find.best.tau           Find best tau to initialise chains with by
                        sampling tau from the prior and using empirical
                        Bayes parameter estimates for the other
                        parameters.
find.good.ordering      Run a find good ordering method and append
                        results to existing orderings
find.smooth.tau         Find best order of the samples assuming some
                        smooth GP prior on the expression profiles over
                        this ordering.
fit.dl                  Perform all the steps necessary to fit the
                        model: 1. prepare the data 2. find suitable
                        initialisations 3. fit the model using the
                        specified method (sampling or variational
                        Bayes) 4. process the posterior
fit.held.out            Fit held out genes
fit.model               Fit the model using specified method (sampling
                        or variational Bayes).
fit.model.sample        Fit the model using Stan sampler
fit.model.vb            Fit the model using Stan variational Bayes
gaussian.condition      Condition a Gaussian on another. See Eqn. A.6
                        on page 200 of Rasmussen and Williams' book.
gene.covariances        Calculate the covariance structure of the tau
get.posterior.mean      Get posterior mean of samples
get_model               Get the Stan model for a DeLorean object.
gp.log.marg.like        The log marginal likelihood. See "2.3 Varying
                        the Hyperparameters" on page 19 of Rasmussen
                        and Williams' book.
gp.predict              Predictive mean, variance and log marginal
                        likelihood of a GP. See "2.3 Varying the
                        Hyperparameters" on page 19 of Rasmussen and
                        Williams' book.
gp.predictions.df       Convert the output of gp.predict() into a
                        data.frame.
guo.expr                Single cell expression data and meta data from
                        Guo et al. (2012). They investigated the
                        expression of 48 genes in 500 mouse embryonic
                        cells.
held.out.melt           Melt held out genes
held.out.posterior      Calculate posterior covariance and estimate
                        parameters for held out genes given pseudotimes
                        estimated by DeLorean model.
held.out.posterior.by.variation
                        Order the genes by the variation of their
                        posterior mean
held.out.posterior.filter
                        Filter the genes
held.out.posterior.join
                        Join with another data frame. Useful for adding
                        gene names etc..
held.out.select.genes   Select held out genes by those with highest
                        variance
inducing.covariance     Calculate the covariance structure of the
                        inducing points
init.orderings.vs.pseudotimes.plot
                        Plot the orderings for initialisation against
                        the estimated pseudotime.
is.de.lorean            Is a DeLorean object?
knit.report             Knit a report, the file
                        inst/Rmd/<report.name>.Rmd must exist in the
                        package directory.
kouno.expr              Kouno et al. investigated the transcriptional
                        network controlling how THP-1 human myeloid
                        monocytic leukemia cells differentiate into
                        macrophages. They provide expression values for
                        45 genes in 960 single cells captured across 8
                        distinct time points.
make.fit.valid          Make a fit valid by running one iteration of
                        the sampler.
make.init.fn            Returns a function that constructs parameter
                        settings with good tau.
make.predictions        Make predictions
marg.like.plot          Plot posterior for marginal log likelihoods of
                        individual gene's expression profiles
melt.expr               Melt an expression matrix.
mutate.profile.data     Mutate the profile data into shape compatible
                        with GP plot function
optimise.best.sample    Optimise the best sample and update the
                        best.sample index.
ordering.block.move     Move a block in an ordering and shift the other
                        items.
ordering.improve        Improve the ordering in the sense that some
                        function is maximised.
ordering.invert         Invert the ordering
ordering.is.valid       Check that it is a valid ordering
ordering.maximise       Find a good ordering in the sense that some
                        function is locally maximised.
ordering.metropolis.hastings
                        Metropolis-Hastings on orderings.
ordering.move           Move one item in an ordering and shift the
                        other items.
ordering.random.block.move
                        Randomly move a block in an ordering to another
                        location
ordering.random.move    Randomly move one item in an ordering to
                        another location
ordering.test.score     Test ordering score: sum every time consecutive
                        items are in order.
orderings.plot          Plot likelihoods of orderings against elapsed
                        times taken to generate them
partition.de.lorean     Partition de.lorean object by cells
permute.df              Permute a data frame, x. If group.col is given
                        it should name an ordered factor that the order
                        of the permutation should respect.
permuted.roughness      Permute cells and test roughness of expression.
plot.add.expr           Add expression data to a plot
plot.add.mean.and.variance
                        Add posterior representation to a plot.
plot.de.lorean          Various DeLorean object plots
plot.held.out.posterior
                        Plot the posterior of held out genes
prepare.for.stan        Prepare for Stan
print.de.lorean         Print details of DeLorean object
process.posterior       Process the posterior, that is extract and
                        reformat the samples from Stan. We also
                        determine which sample has the highest
                        likelihood, this is labelled as the 'best'
                        sample.
profiles.plot           Plot best sample predicted expression.
pseudotime.plot         Plot pseudotime (tau) against observed capture
                        time.
pseudotimes.from.orderings
                        Convert best orderings into initialisations
pseudotimes.pair.plot   Plot two sets of pseudotimes against each
                        other.
report.file             The filename of the R markdown report.
roughness.of.permutations
                        Apply permutation based roughness test to held
                        out genes
roughness.of.sample     Calculate the roughness of the held out genes
                        given the sample.
roughness.test          Calculate roughnesses under fit samples and
                        also under random permutations
roughnesses.plot        Plot results of roughness test
seriation.find.orderings
                        Use seriation package to find good orderings
tau.offsets.plot        Plot the tau offsets, that is how much the
                        pseudotimes (tau) differ from their prior means
                        over the full posterior.
test.fit                Test fit for log normal and gamma
test.mh                 Test ordering Metropolis-Hastings sampler.
test.robustness.de.lorean
                        Test robustness of pseudotime estimation on
                        subsets of de.lorean object
windram.expr            Windram et al. investigated the defense
                        response in Arabidopsis thaliana to the
                        necrotrophic fungal pathogen Botrytis cinerea.
                        They collected data at 24 time points in two
                        conditions for 30336 genes.
