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