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Patterns (version 1.2)

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

1.2

License

GPL (>= 2)

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Maintainer

Frederic Bertrand

Last Published

November 13th, 2019

Functions in Patterns (1.2)

dim

Dimension of the data
predict

Methods for Function predict
network

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

Analysing the network
as.micro_array

Coerce a matrix into a micro_array object.
network-class

Class "network"
position

Retrieve network position for consistent plotting.
CascadeFinit

Create initial F matrices for cascade networks inference.
genePeakSelection

Methods for selecting genes
geneNeighborhood

Find the neighborhood of a set of nodes.
evolution

See the evolution of the network with change of cutoff
unsupervised_clustering_auto_m_c

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

Some basic criteria of comparison between actual and inferred network.
micro_array-class

Class "micro_array"
print-methods

~~ Methods for Function print ~~
probeMerge

Function to merge probesets
micropredict-class

Class "micropred"
cutoff

Choose the best cutoff
unsupervised_clustering

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

Makes the union between two micro_array objects.
head

Overview of a micro_array object
gene_expr_simulation

Simulates microarray data based on a given network.
replaceBand

Replace matrix values by band.
replaceUp

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

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

Details on some probesets of the affy_hg_u133_plus_2 platform.
inference

Reverse-engineer the network
clustInference

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

Plot functions for the F matrices.
clustExploration

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

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

Generates a network.
summary-methods

~~ Methods for Function summary ~~
network2gp

A example of an inferred cascade network (2 groups case).
position-methods

Returns the position of edges in the network
plot-methods

Plot
CLL

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

Simulated network for examples.
IndicFinit

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

Selection of genes.
Net_inf_PL

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

Create F matrices shaped for cascade networks inference.
Patterns-package

The Patterns Package
M

Simulated microarray.
IndicFshape

Create F matrices using specific intergroup actions for network inference.