A C D E F G H I K M O P R S T W
DeLorean-package | The 'DeLorean' package. |
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. |
DeLorean | The 'DeLorean' package. |
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.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. |
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.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. |
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. |
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 |
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.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. |