cov.calc.gene.conditioned
Calculate covariance for gene over test inputs when conditioned on
data at estimated pseudotimes.
cov.all.genes.conditioned
Calculate covariances for all genes when conditioned on data at
estimated pseudotimes.
Initialise DeLorean object
Calculate a suitable value for a rug plot given the
number of points
calc.inducing.pseudotimes
Calculate inducing pseudotimes for sparse approximation
Melt held out genes
Filter cells
Calculate posterior covariance and estimate parameters for
held out genes given pseudotimes estimated by DeLorean model.
Select held out genes by those with highest variance
Invert the ordering
Plot a comparison of the profiles from several de.lorean objects
Calculate distances over estimated pseudotimes and
test inputs.
Filter genes
The filename of the R markdown stylesheet
Calculate distances between vectors of time points
Fit the model using Stan sampler
Run a find good ordering method and append results to existing orderings
Test fit for log normal and gamma
Fit the model using specified method (sampling or variational Bayes).
Get posterior mean of samples
Knit a report, the file inst/Rmd/.Rmd must exist in
the package directory.
ordering.metropolis.hastings
Metropolis-Hastings on orderings.
Fit held out genes
Plot the tau offsets, that is how much the pseudotimes (tau) differ
from their prior means over the full posterior.
Matern 3/2 covariance function
Calculate roughnesses under fit samples and also under random
permutations
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.
Default number of cores to use.
Compile the model and cache the DSO to avoid unnecessary recompilation.
Check that it is a valid ordering
Test ordering score: sum every time consecutive items are in
order.
ordering.random.block.move
Randomly move a block in an ordering to another location
Makes a distance periodic
Plot the expression data by the capture points
Estimate hyperparameters for model using empirical Bayes.
Perform an analysis of variance to select genes for the DeLorean model.
Centralises a periodic position into [period/2, period)
by shifting by n*period, where n is an integer
Mutate the profile data into shape compatible with GP plot function
Partition de.lorean object by cells
Find best order of the samples assuming some smooth GP prior on the
expression profiles over this ordering.
Print details of DeLorean object
held.out.posterior.filter
Filter the genes
Optimise the best sample and update the best.sample index.
Move one item in an ordering and shift the other items.
test.robustness.de.lorean
Test robustness of pseudotime estimation on subsets of de.lorean
object
Improve the ordering in the sense that some function is
maximised.
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.
Find a good ordering in the sense that some function is
locally maximised.
Perform all the steps necessary to fit the model.
- prepare the data
- compile the model
- find suitable initialisations
- fit the model using the specified method (sampling or variational Bayes)
- process the posterior.
Calculate the covariance structure of the inducing points
Add expression data to a plot
Permute a data frame, x. If group.col is given it should name an ordered
factor that the order of the permutation should respect.
Plot posterior for marginal log likelihoods of individual gene's
expression profiles
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.
roughness.of.permutations
Apply permutation based roughness test to held out genes
Calculate the covariance structure of the tau
Dimensions of DeLorean object
Plot two sets of pseudotimes against each other.
Analyse variance of expression between and within capture times.
Calculate the covariance structure of evenly spread tau and
create a function that calculates the log likelihood of
orderings.
held.out.posterior.by.variation
Order the genes by the variation of their posterior mean
Single cell expression data and meta data from Guo et al. (2012).
They investigated the expression of 48 genes in 500 mouse embryonic cells.
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.
Is a DeLorean object?
Test ordering Metropolis-Hastings sampler.
Calculate the roughness of the vector. The roughness is the RMS
of the differences between consecutive points.
Fit the model using Stan variational Bayes
Estimate the cell sizes according to Anders & Huber
Differential expression analysis for sequence count data
Randomly move one item in an ordering to another location
Plot likelihoods of orderings against elapsed times taken
to generate them
plot.add.mean.and.variance
Add posterior representation to a plot.
pseudotimes.from.orderings
Convert best orderings into initialisations
Plot results of roughness test
DeLorean.
estimate.capture.cell.sizes
Estimate the cell sizes per capture. Only uses genes that are expressed
in more than half the cells.
Join with another data frame. Useful for adding gene names etc..
Plot the Rhat convergence statistics. 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.
Find best tau to initialise chains with by sampling tau from the prior
and using empirical Bayes parameter estimates for the other parameters.
Plot best sample predicted expression.
Estimate the cell sizes and adjust the expression by cell size.
The expected within sample variance of a Gaussian with the given covariance.
The filename of the R markdown report.
Calculate covariance structure for gene over pseudotimes and test
inputs.
The log marginal likelihood. See "2.3 Varying the Hyperparameters"
on page 19 of Rasmumssen and Williams' book.
Prepare for Stan
Plot pseudotime (tau) against observed capture time.
Use Magda's code to find good orderings
Melt an expression matrix.
Average across a parameters samples.
Convert the output of gp.predict() into a data.frame.
Make predictions
Predictive mean, variance and log marginal likelihood of a GP.
See "2.3 Varying the Hyperparameters"
on page 19 of Rasmumssen and Williams' book.
Move a block in an ordering and shift the other items.
Returns a function that constructs parameter settings with good tau.
Plot the posterior of held out genes
Permute cells and test roughness of expression.
Condition a Guassian on another.
See Eqn. A.6
on page 200 of Rasmumssen and Williams' book.
Various DeLorean object plots
Calculate the roughness of the held out genes given the sample.
Use seriation package to find good orderings