# Estimates Pseudotimes for Single Cell Expression Data

## Documentation for package ‘DeLorean’ version 1.5.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A C D E F G H I K M O P R S T W

DeLorean-package | The 'DeLorean' package.

## -- A --

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.

## -- C --

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.

## -- D --

de.lorean | Initialise DeLorean object
de.lorean.stylesheet | The filename of the R markdown stylesheet
default.num.cores | Default number of cores to use.
DeLorean | The 'DeLorean' package.
dim.de.lorean | Dimensions of DeLorean object

## -- E --

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

## -- F --

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

## -- G --

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.cell.meta | Single cell expression data and meta data from Guo et al. (2012). They investigated the expression of 48 genes in 500 mouse embryonic cells.
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.
guo.gene.meta | Single cell expression data and meta data from Guo et al. (2012). They investigated the expression of 48 genes in 500 mouse embryonic cells.

## -- H --

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

## -- I --

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?

## -- K --

knit.report | Knit a report, the file inst/Rmd/<report.name>.Rmd must exist in the package directory.
kouno.cell.meta | 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.
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.
kouno.gene.meta | 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.

## -- M --

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

## -- O --

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

## -- P --

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.

## -- R --

report.file | The filename of the R markdown report.
Rhat.plot | Plot the Rhat convergence statistics. 'examine.convergence' must be called before this plot can be made.
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

## -- S --

seriation.find.orderings | Use seriation package to find good orderings

## -- T --

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

## -- W --

windram.cell.meta | 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.
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.
windram.gene.meta | 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.