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Patterns (version 1.1)

Deciphering Biological Networks with Patterned Heterogeneous Measurements

Description

A modeling tool dedicated to biological network modeling. It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks. Example of use with microarray or RNA-Seq data are provided.

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Install

install.packages('Patterns')

Monthly Downloads

532

Version

1.1

License

GPL (>= 2)

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Maintainer

Frederic Bertrand

Last Published

August 24th, 2019

Functions in Patterns (1.1)

micro_array-class

Class "micro_array"
micropredict-class

Class "micropred"
evolution

See the evolution of the network with change of cutoff
dim

Dimension of the data
CascadeFinit

Create initial F matrices for cascade networks inference.
network_random

Generates a network.
clustInference

A function to explore a dataset and cluster its rows.
infos

Details on some probesets of the affy_hg_u133_plus_2 platform.
clustExploration

A function to explore a dataset and cluster its rows.
print-methods

~~ Methods for Function print ~~
inference

Reverse-engineer the network
M

Simulated microarray.
probeMerge

Function to merge probesets
predict

Methods for Function predict
cutoff

Choose the best cutoff
position

Retrieve network position for consistent plotting.
compare-methods

Some basic criteria of comparison between actual and inferred network.
plot-methods

Plot
gene_expr_simulation

Simulates microarray data based on a given network.
head

Overview of a micro_array object
analyze_network

Analysing the network
as.micro_array

Coerce a matrix into a micro_array object.
network-class

Class "network"
network

A example of an inferred network (4 groups case).
geneNeighborhood

Find the neighborhood of a set of nodes.
plotF

Plot functions for the F matrices.
genePeakSelection

Methods for selecting genes
unsupervised_clustering_auto_m_c

Cluster a micro_array object: determine optimal fuzzification parameter and number of clusters.
unionMicro-methods

Makes the union between two micro_array objects.
unsupervised_clustering

Cluster a micro_array object: performs the clustering.
position-methods

Returns the position of edges in the network
network2gp

A example of an inferred cascade network (2 groups case).
replaceBand

Replace matrix values by band.
networkCascade

A example of an inferred cascade network (4 groups case).
replaceDown

Replace matrix values triangular lower part and by band for the upper part.
summary-methods

~~ Methods for Function summary ~~
replaceUp

Replace matrix values triangular upper part and by band for the lower part.
CascadeFshape

Create F matrices shaped for cascade networks inference.
IndicFshape

Create F matrices using specific intergroup actions for network inference.
Net

Simulated network for examples.
Patterns-package

The Patterns Package
Net_inf_PL

Reverse-engineered network of the M and Net simulated data.
IndicFinit

Create initial F matrices using specific intergroup actions for network inference.
CLL

Expression data from healthy and malignant (chronic lymphocytic leukemia, CLL) human B-lymphocytes after B-cell receptor stimulation (GSE 39411 dataset)
Selection

Selection of genes.