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tigre (version 1.26.0)

Transcription factor Inference through Gaussian process Reconstruction of Expression

Description

The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF.

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Version

Version

1.26.0

License

AGPL-3

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Maintainer

Antti Honkela

Last Published

February 15th, 2017

Functions in tigre (1.26.0)

GPRankTargets

Ranking possible target genes or regulators
tigre-package

tigre - Transcription factor Inference through Gaussian process Reconstruction of Expression
ExpressionTimeSeries-class

Class to contain time series expression assays
GPModel-class

A container for gpsim models
SCGoptim

Optimise the given function using (scaled) conjugate gradients.
gpsimCreate

Create a GPSIM/GPDISIM model.
drosophila_gpsim_fragment

Fragment of 12 time point Drosophila embryonic development microarray gene expression time series
GPPlot

Plot GP(DI)SIM models
export.scores

Export results to an SQLite database
expTransform

Constrains a parameter.
lnDiffErfs

Helper function for computing the log of difference
drosophila_mmgmos_fragment

Fragment of 12 time point Drosophila embryonic development microarray gene expression time series
processData

Processing expression time series
generateModels

Generating models with the given data
kernDiagGradX

Compute the gradient of the kernel wrt X.
GPLearn

Fit a GP model
kernGradient

Compute the gradient wrt the kernel parameters.
modelDisplay

Display a model.
plotTimeseries

Plot ExpressionTimeSeries data
modelExtractParam

Extract the parameters of a model.
modelGradient

Model log-likelihood/objective error function and its gradient.
kernCompute

Compute the kernel given the parameters and X.
modelExpandParam

Update a model structure with new parameters or update the posterior processes.
kernCreate

Initialise a kernel structure.
optimiDefaultConstraint

Returns function for parameter constraint.
scoreList-class

Class "scoreList"
modelTieParam

Tie parameters of a model together.