Accessor function for the 'adjacencyMatrix' information in a mRMRe.Network object.

Description

The adjency matrix is a directed matrix of 0's and 1's indicating if there is a link between features.

Usage

## S4 method for signature 'mRMRe.Network'
adjacencyMatrix(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))

# Build an mRMR-based network and display adjacency matrix (topology)
network <- new("mRMRe.Network", data = feature_data, target_indices = c(1, 2),
			levels = c(2, 1), layers = 1)
adjacencyMatrix(network)

Accessor function for the 'causality' information in a mRMRe.Filter and mRMRe.Network object.

Description

The causality data is compute using the co-information lattice algorithm on each V-structure (feature, target, feature). Given that this procedure is computed for each pair of features, the minimum result is kept. A negative score indicates putative causality of the feature to the target.

Usage

## S4 method for signature 'mRMRe.Filter'
causality(object)
## S4 method for signature 'mRMRe.Network'
causality(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
causality(filter)

Part of the large pharmacogenomic dataset published by Garnett et al. within the Cancer Genome Project (CGP)

Description

This dataset contains gene expression of 200 cancer cell lines for which sensitivity (IC50) to Camptothecin was measured (release 2).

Usage

data(cgps)

Format

The cgps dataset is composed of three objects

cgps.annot

Dataframe containing gene annotations

cgps.ge

Matrix containing expressions of 1000 genes; cell lines in rows, genes in columns

cgps.ic50

Drug sensitivity measurements (IC50) for Camptothecin

Details

Camptothecin is a drug mainly used in colorectal cancer.

Source

http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-783

http://www.nature.com/nature/journal/v483/n7391/extref/nature11005-s2.zip

References

Garnett MJ et al. "Systematic identification of genomic markers of drug sensitivity in cancer cells", Nature, 483:570-575, 2012.

Examples

set.thread.count(2)
data(cgps)

message("Gene expression data:")
print(cgps.ge[1:3, 1:3])

message("Gene annotations:")
print(head(cgps.annot))

message("Drug sensitivity (IC50) values:")
print(head(cgps.ic50))

Function to compute various correlation measures between two variables

Description

Correlate is a function that cestimates correlation between two variables, which can be either continuous, categorical (ordered factor) or censored (survival data).

Usage

correlate(X, Y, method = c("pearson", "spearman", "kendall", "frequency", "cramersv",
		"cindex"),strata, weights, outX = TRUE, bootstrap_count = 0, alpha = 0.05,
 		alternative = c("two.sided", "less", "greater"))

Arguments

Details

The correlate function could be used to measure correlation between any types of variables:

numeric vs. numeric

Pearson, Spearman, Kendall or concordance index

numeric vs. ordered factor

concordance index (Somers' Dxy)

numeric vs. survival data

concordance index (Somers' Dxy)

ordered factor vs. ordered factor

Carmer's V

ordered factor vs. survival data

concordance index (Somers' Dxy)

survival data vs. survival data

concordance index (Somers' Dxy)

Part of the code underlying correlate is also used in mim method of the mRMRe.Data object because correlations are used to build the mutual information matrix in order for feature selection to take place. This is why these two functions have many argiuments in common.

Value

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

See Also

mRMRe.Data-class

Examples

set.thread.count(2)

## load data
data(cgps)
   
## spearman correlation coefficent between the first gene and Camptothecin IC50
correlate(X=cgps.ge[ ,1], Y=cgps.ic50, method="spearman")
   
## concordance index between the first gene and Camptothecin IC50
correlate(X=cgps.ge[ ,1], Y=cgps.ic50, method="cindex")

Export concordance index

Description

Export the concordance index

Arguments

Value

No return. Modifies the ratio argument by reference.

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Export filters

Description

Export the filters

Arguments

Value

Exhaustively computes the minimum redudancy maximim relevance features from the mutual information matrix, returing a list of solutions where each item is a numeric index of selected features.

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Export filters bootstrap

Description

Export the filters

Arguments

Value

Bootstraps and estimate of the minimum redundancy maximim relevance features from the mutual information, returning a list where each item is a numeric vector of selected feature indices.

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Export mim

Description

Export mim

Arguments

Value

Returns the mutual information matrix.

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Accessor function for the 'featureCount' information in a mRMRe.Data, mRMRe.Filter and mRMRe.Network object.

Description

The feature count is simply the total number of feature considered in the mRMRe procedure.

Usage

## S4 method for signature 'mRMRe.Data'
featureCount(object)
## S4 method for signature 'mRMRe.Filter'
featureCount(object)
## S4 method for signature 'mRMRe.Network'
featureCount(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))
featureCount(feature_data)
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
featureCount(filter)

Accessor function for the 'featureData' information in a mRMRe.Data object

Description

the featureData consists of the numerical value of each feature for each sample considered

Usage

## S4 method for signature 'mRMRe.Data'
featureData(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))
featureData(feature_data)

Accessor function for the 'featureNames' information in a mRMRe.Data, mRMRe.Filter and mRMRe.Network object

Description

featureNames are the names of the features given as input to the mRMRe procedure.

Usage

## S4 method for signature 'mRMRe.Data'
featureNames(object)
## S4 method for signature 'mRMRe.Filter'
featureNames(object)
## S4 method for signature 'mRMRe.Network'
featureNames(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data = data.frame(cgps.ge))
featureNames(feature_data)
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
featureNames(filter)

openMP Thread Count

Description

This methods allows you to retrieve the number of cores currently accessible to openMP

Usage

get.thread.count()

Value

Return the number of cores accessible to openMP for C level parallelization.

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

get.thread.count() 

openMP Thread Count

Description

This methods allows you to get the number of cores currently accessible to openMP

Arguments

Value

Return the current number of cores accessible to openMP for C level parallelization

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Class "mRMRe.Data"

Description

mRMRe.Data is the class containing datasets. Most if not all of the routines in the mRMRe package use mRMRe.Data objects as primary input.

Such an object is instantiated with a data frame containing the sample sets and optionally, stratum, weight vectors and a prior matrix. In addition to basic accession functions, we describe several methods which serve to manipulate the contents of the dataset.

Note that mRMR.data function is a wrapper to easily create mRMRe.Data objects.

Instantiation

Objects are created via calls of the form new("mRMRe.Data", data, strata, weights, priors).

data: is expected to be a data frame with samples and features respectively organized as rows and columns. The columns have to be of type :numeric, ordered factor, Surv and respectively interpreted as :continuous, discrete and survival variables.

strata: is expected to be a vector of type :ordered factor with the strata associated to the samples provided in data.

weights: is expected to be a vector of type :numeric with the weights associated to the samples provided in data.

priors: is expected to be a matrix of type :numeric where priors[i, j]: denotes an forced association between features i and j in data. The latter takes into consideration the directionality of the relationship and must be a value between 0 and 1.

Mutual Information Matrix

The mim method computes and returns a mutual information matrix. A correlation between continuous features is estimated using an estimator specified in continuous_estimator; currently, :pearson, spearman, kendall, frequency are supported. The estimator for discrete features is Cramer's V and for all other combinations, concordance index.

When outX is set to TRUE, ties are ignored when computing the concordance index and otherwise, these are considered. The correlations are first computed per strata and these are then combined by the inverse variance weight mean of the estimates using a bootstrap_count number of bootstraps if the former parameter is greater than 0, and by the relative weights of each strata otherwise. The resulting correlation is then summated with the corresponding value in the priors matrix with the latter being weighed for a proportion prior_weight of a final, biased correlation.

Slots

sample_names:

Object of class "character" containing the sample names.

feature_names:

Object of class "character" containing the feature names.

feature_types:

Object of class "numeric" containing the internal representation of features/variables: 1 for numeric, 2 for ordered factor, and 3 for survival data

data:

Object of class "matrix" containing the internal representation of the data set.

strata:

Object of class "numeric" containing the feature strata.

weights:

Object of class "numeric" containing sample weights.

priors:

Object of class "matrix" containing the priors.

Methods

featureCount

signature(object = "mRMRe.Data"): Returns the number of features.

featureData

signature(object = "mRMRe.Data"): Returns a data frame corresponding to the data set.

featureNames

signature(object = "mRMRe.Data"): Returns a vector containing the feature names.

mim

signature(object = "mRMRe.Data", prior_weight = 0, continuous_estimator = c("pearson", "spearman", "kendall", "frequency"), outX = TRUE, bootstrap_count = 0): Computes and returns the mutual information matrix.

priors

signature(object = "mRMRe.Data"): Returns a matrix containing the priors.

priors<-

signature(object = "mRMRe.Data", value): Sets the prior matrix.

sampleCount

signature(object = "mRMRe.Data"): Returns the number of samples.

sampleNames

signature(object = "mRMRe.Data"): Returns a vector containing sample names.

sampleStrata

signature(object = "mRMRe.Data"): Returns a vector containing sample strata.

sampleStrata<-

signature(object = "mRMRe.Data", value): Sets the sample strata.

sampleWeights

signature(object = "mRMRe.Data"): Returns a vector containing sample weights.

sampleWeights<-

signature(object = "mRMRe.Data"): Sets the sample weights.

subsetData

signature(object = "mRMRe.Data", row_indices, column_indices): Returns another data object containing only the specified samples and features (rows and columns, respectively.)

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

See Also

mRMRe.Filter-class, mRMRe.Network-class

Examples

showClass("mRMRe.Data")

set.thread.count(2)

## load data
data(cgps)

## equivalent ways of building an mRMRe.Data object
ge <- mRMR.data(data = data.frame(cgps.ge[ , 1:10, drop=FALSE]))
ge <- new("mRMRe.Data", data = data.frame(cgps.ge[ , 1:10, drop=FALSE]))

## print data
print(featureData(ge)[1:3, 1:3])

## print feature names
print(featureNames(ge))

## print the first sample names
print(head(sampleNames(ge)))

## print the first sample weights
print(head(sampleWeights(ge)))

Class "mRMRe.Filter"

Description

mRMRe.Filter is a wrapper for various variants of the maximum relevance minimum redundancy (mRMR) feature selection/filter.

Note that mRMR.classic and mRMR.ensemble functions are wrappers to easily perform classical (single) and ensemble mRMR feature selection.

Instantiation

Objects are created via calls of the form new("mRMRe.Filter", data, prior_weight, target_indices, levels, method, continuous_estimator, outX, bootstrap_count).

data: is expected to be a mRMRe.Data object.

target_indices: is expected to be a vector of type integer containing the indices of the features that will serve as targets for the feature selections.

levels: is expected to be a vector of type integer containing the number of children of each element at each level of the resulting filter tree.

method: is expected to be either exhaustive or bootstrap. The former uses the whole dataset to pick siblings in the tree according to the mRMR metric, while the latter perform the classical mRMR feature selection on several bootrstap selections of the dataset.

continuous_estimator: it specifies the estimators for correlation between two continuous variables; value is either pearson, spearman, kendall, frequency,

outX: set to TRUE (default value) to not count pairs of observations tied on x as a relevant pair. This results in a Goodman-Kruskal gamma type rank correlation.

bootstrap_count: Number of bootstraps to statistically compare the mRMR scores of each solution.

Since a mutual information matrix must be computed in order for feature selection to take place, the remaining arguments are identical to those required by the mim method of the mRMRe.Data object.

Slots

filters:

Object of class "list" containing for each target a solutions matrix.

mi_matrix:

Object of class "matrix" containing the combined mutual information matrix of the relevant targets.

causality_list:

Object of class "list" containing for each target a vector of causality coefficients between the target and its predictors.

sample_names:

Object of class "character" containing the sample names.

feature_names:

Object of class "character" containing the feature names.

target_indices:

Object of class "integer" containing the target indices.

fixed_feature_count:

Object of class "integer" containing the number of fixed features.

levels:

Object of class "integer" containing the desired topology of the tree.

scores:

Object of class "list" containing the mRMR score of selected features, respective to filters.

Methods

causality

signature(object = "mRMRe.Filter"): ...

featureCount

signature(object = "mRMRe.Filter"): Returns the number of features.

featureNames

signature(object = "mRMRe.Filter"): Returns a vector containing the feature names.

mim

signature(object = "mRMRe.Filter"): Returns the potentially partial mutual information matrix used for feature selection.

sampleCount

signature(object = "mRMRe.Filter"): Returns the number of samples.

sampleNames

signature(object = "mRMRe.Filter"): Returns a vector containing sample names.

solutions

signature(object = "mRMRe.Filter", mi_threshold = -Inf, causality_threshold = Inf): Returns a matrix in which each column represents a different solution (path from root of the tree to a leaf.)

target

signature(object = "mRMRe.Filter"): Returns a vector containing the target indices.

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

References

Ding, C. and Peng, H. (2005). "Minimum redundancy feature selection from microarray gene expression data". Journal of bioinformatics and computational biology, 3(2):185–205.

See Also

mRMRe.Data-class

Examples

showClass("mRMRe.Filter")

set.thread.count(2)

## load data
data(cgps)

## build an mRMRe.Data object
ge <- mRMR.data(data = data.frame(cgps.ge[ , 1:100, drop=FALSE]))

## perform a classic (single) mRMR to select the 10 genes the most correlated with 
## the first gene but the less correlated between each other
exect <- system.time(fs <- new("mRMRe.Filter", data = ge, target_indices = 1,
					levels = c(8, 1, 1, 1, 1)))
print(exect)

## print the index of the selected features for each distinct mRMR solutions
print(solutions(fs)[[1]])

## print the names of the selected features for each distinct mRMR solutions
print(apply(solutions(fs)[[1]], 2, function(x, y) { return(y[x]) }, y=featureNames(ge)))

Class "mRMRe.Network"

Description

mRMRe.Network is a wrapper for inferring a network of features based on mRMR feature selection.

Instantiation

Objects are created via calls of the form new("mRMRe.Network", data, prior_weight, target_indices, levels, layers, ..., mi_threshold, causality_threshold).

layers: is expected to be an integer specifying the number of layers of network inference desired. When multiple layers are desired, the elements of the solutions found in the last step of feature selection are used as the targets of the next step.

Since networking involves filter processing, the remaining arguments are identical to those required by solutions method of the mRMRe.Filter object and mim method of the mRMRe.Data object.

Slots

topologies:

Object of class "list" ~~

mi_matrix:

Object of class "matrix" containing the combined mutual information matrix of the network elements.

causality_list:

Object of class "list" containing for each target a vector of causality coefficients between the target and its predictors.

sample_names:

Object of class "character" containing the sample names.

feature_names:

Object of class "character" containing the feature names.

target_indices:

Object of class "integer" containing the target indices.

Methods

adjacencyMatrix

signature(object = "mRMRe.Network"): Returns a matrix describing the topology of the network.

adjacencyMatrixSum

signature(object = "mRMRe.Network"): ...

causality

signature(object = "mRMRe.Network"): Returns a list containing vectors containing causality coefficients between targets and predictors.

featureNames

signature(object = "mRMRe.Network"): Returns a vector containing the feature names.

mim

signature(object = "mRMRe.Network"): ...

sampleNames

signature(object = "mRMRe.Network"): Returns a vector containing sample names.

solutions

signature(object = "mRMRe.Network"): ...

visualize

signature(object = "mRMRe.Network"): ...

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

See Also

mRMRe.Filter-class, mRMRe.Data-class

Examples

showClass("mRMRe.Network")

set.thread.count(2)

## load data
data(cgps)

## build an mRMRe.Data object
ge <- mRMR.data(data = data.frame(cgps.ge[ , 1:100, drop=FALSE]))

## build a network object with the 10 first genes and their children,
## 8 distinct mRMR feature selections of 5 genes for each gene
exect <- system.time(netw <- new("mRMRe.Network", data = ge, target_indices = 1:10,
		levels = c(8, 1, 1, 1, 1), layers = 2))
print(exect)

## plot network using igraph
## Not run: visualize(netw)

Accessor function for the 'mim' information in a mRMRe.Data, mRMRe.Filter and mRMRe.Network object

Description

In both mRMRe.Filter and mRMRe.Network objects, a sparse mutual information matrix is computed for the mRMRe procedure and this lazy-evaluated matrix is returned. In the context of a a mRMRe.Data 'mim', the full pairwise mutual information matrix is computed and returned.

Usage

## S4 method for signature 'mRMRe.Data'
mim(object, prior_weight, continuous_estimator, outX, bootstrap_count)
## S4 method for signature 'mRMRe.Filter'
mim(object, method)
## S4 method for signature 'mRMRe.Network'
mim(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))

# Calculate the pairwise mutual information matrix
mim(feature_data)
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)

# Obtain the sparse (lazy-evaluated) mutual information matrix.
mim(filter)

Accessor function for the 'priors' information in a mRMRe.Data object

Description

The priors matrix consists of a prior bias to be used in computation to mutual information between features.

Usage

## S4 method for signature 'mRMRe.Data'
priors(object)
## S4 replacement method for signature 'mRMRe.Data'
priors(object) <- value

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data = data.frame(cgps.ge))
priors(feature_data)

Accessor function for the 'sampleCount' information in a mRMRe.Data, mRMRe.Filter and mRMRe.Network object.

Description

The feature count is simply the total number of samples considered in the mRMRe procedure.

Usage

## S4 method for signature 'mRMRe.Data'
sampleCount(object)
## S4 method for signature 'mRMRe.Filter'
sampleCount(object)
## S4 method for signature 'mRMRe.Network'
sampleCount(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data = data.frame(cgps.ge))
sampleCount(feature_data)
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
sampleCount(filter)

Accessor function for the 'sampleNames' information in a mRMRe.Data, mRMRe.Filter and mRMRe.Network object.

Description

sampleNames are the names of the samples given as input to the mRMRe procedure.

Usage

## S4 method for signature 'mRMRe.Data'
sampleNames(object)
## S4 method for signature 'mRMRe.Filter'
sampleNames(object)
## S4 method for signature 'mRMRe.Network'
sampleNames(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data = data.frame(cgps.ge))
sampleNames(feature_data)
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
sampleNames(filter)

Accessor function for the 'sampleStrata' information in a mRMRe.Data object

Description

The sampleStrata vector consists of a sampling stratification that will be used in computing mutual information between features. If known batch effects or sample stratification is present between samples, identify such subsets using this.

Usage

## S4 method for signature 'mRMRe.Data'
sampleStrata(object)
## S4 replacement method for signature 'mRMRe.Data'
sampleStrata(object) <- value

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data = data.frame(cgps.ge))

# No stratification (default)
sampleStrata(feature_data)

# Random stratification 
sampleStrata(feature_data) <- as.factor(sample(c(0,1), 
  sampleCount(feature_data), replace=TRUE))
# Show result
sampleStrata(feature_data)

Accessor function for the 'sampleWeights' information in a mRMRe.Data object

Description

TODO

Usage

## S4 method for signature 'mRMRe.Data'
sampleWeights(object)
## S4 replacement method for signature 'mRMRe.Data'
sampleWeights(object) <- value

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data = data.frame(cgps.ge))

# Uniform weight (default)
sampleWeights(feature_data)

# Random weighting  
sampleWeights(feature_data) <- runif(sampleCount(feature_data))
# Show result
sampleWeights(feature_data)

mRMR Scores as per the MI gain for each feature

Description

The scores method returns the scores of individual features in respect to previously selected features as per standard mRMR procedure. For each target, the score of a feature is defined as the mutual information between the target and this feature minus the average mutual information of previously selected features and this feature.

Usage

## S4 method for signature 'mRMRe.Data'
scores(object, solutions)
## S4 method for signature 'mRMRe.Filter'
scores(object)
## S4 method for signature 'mRMRe.Network'
scores(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))

# Create an mRMR filter and obtain the indices of selected features
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
scores(filter)

openMP Thread Count

Description

This methods allows you to set the number of cores currently accessible to openMP

Usage

set.thread.count(thread_count)

Arguments

Value

No return, sets the number of cores available to openMP for C level parallelization.

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

# Access to number of available threads
threads <- get.thread.count()
# Force a single threaded openMP job
set.thread.count(1)

# Revert back to all accessible threads
set.thread.count(threads)

openMP Thread Count

Description

This methods allows you to set the number of cores currently accessible to openMP

Arguments

Value

No return, sets the number of cores available to openMP for C level parallelization.

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Basic result of the mRMR procedure

Description

The 'solutions' method allows one to access the set of selected features resulting of the mRMR algorithm. More generally, the set of feature are identified by their indices in the inputed feature set (1 being the first feature (column)). At the network level, 'solutions' consists of the topology of the network, identifying which features is connected to others.

Usage

## S4 method for signature 'mRMRe.Filter'
solutions(object, mi_threshold, causality_threshold, with_fixed_features)
## S4 method for signature 'mRMRe.Network'
solutions(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))

# Create an mRMR filter and obtain the indices of selected features
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
solutions(filter)

# Build an mRMR-based network and obtain feature connections (topology)
network <- new("mRMRe.Network", data = feature_data, target_indices = c(1, 2),
			levels = c(2, 1), layers = 1)
solutions(network)

Returns a mRMRe.Data object using a subset of the current mRMRe.Data object.

Description

This method is used to extract a subset of the current mRMRe.Data object.

Usage

## S4 method for signature 'mRMRe.Data'
subsetData(object, row_indices, column_indices)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data = data.frame(cgps.ge))
# Subset the same dimensions, equivalent to making a copy
feature_data_copy <- subsetData(feature_data, 
    row_indices=sampleCount(feature_data),
		column_indices=featureCount(feature_data))

# Use only half of the samples
feature_data_samples <- subsetData(feature_data, row_indices=sampleCount(feature_data)/2)

# Use only half of the features
feature_data_features <- subsetData(feature_data,
    column_indices=featureCount(feature_data))

mRMR Target(s)

Description

The 'target' method allows you to access the target of a mRMR procedure. In a mRMRe.Network setting, the target consists of the seed or the starting set of features given in the network building.

Usage

## S4 method for signature 'mRMRe.Filter'
target(object)
## S4 method for signature 'mRMRe.Network'
target(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))

# Create an mRMR filter and obtain the targets of that filter
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
target(filter)

# Build an mRMR-based network and obtain targets (seeds) of the network
network <- new("mRMRe.Network", data = feature_data, target_indices = c(1, 2),
				levels = c(2, 1), layers = 1)
target(network)

mRMRe Network display

Description

The 'visualize' methods allows the visual display of an inferred mRMRe.Network topology.

Usage

## S4 method for signature 'mRMRe.Network'
visualize(object)

Arguments

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))

# Build an mRMR-based network and display it
network <- new("mRMRe.Network", data = feature_data, target_indices = c(1),
				levels = c(3, 1), layers = 2)
visualize(network)