Estimates Pseudotimes for Single Cell Expression Data


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Documentation for package ‘DeLorean’ version 1.5.0

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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.