Package 'BASiNETEntropy'

Performs the classification methodology using complex network and entropy theories

Description

Given three or two distinct data sets, one of mRNA, one of lncRNA and one of sncRNA. The classification of the data is done from the structure of the networks formed by the sequences, that is filtered by an entropy methodology. After this is done, the classification starts.

Usage

classify(
  mRNA,
  lncRNA,
  sncRNA = NULL,
  trainingResult,
  save_dataframe = NULL,
  save_model = NULL,
  predict_with_model = NULL
)

Arguments

mRNA	Directory where the file .FASTA lies with the mRNA sequences
lncRNA	Directory where the file .FASTA lies with the lncRNA sequences
sncRNA	Directory where the file .FASTA lies with the sncRNA sequences (optional)
trainingResult	The result of the training, (three or two matrices)
save_dataframe	save when set, this parameter saves a .csv file with the features in the current directory. No file is created by default.
save_model	save when set, this parameter saves a .rds file with the model in the current directory. No file is created by default.
predict_with_model	predict the input sequences with the previously generated model.

Value

Results

Author(s)

Murilo Montanini Breve

Examples

library(BASiNETEntropy)
arqSeqMRNA <- system.file("extdata", "mRNA.fasta",package = "BASiNETEntropy")
arqSeqLNCRNA <- system.file("extdata", "ncRNA.fasta", package = "BASiNETEntropy")
load(system.file("extdata", "trainingResult.RData", package = "BASiNETEntropy"))
r_classify <- classify(mRNA=arqSeqMRNA, lncRNA=arqSeqLNCRNA, trainingResult = trainingResult)

---

Creates an untargeted graph from a biological sequence

Description

A function that from a biological sequence generates a graph not addressed having as words vertices, this being able to have its size parameter set by the' word 'parameter. The connections between words depend of the' step 'parameter that indicates the next connection to be formed

Usage

createedges(sequence, word = 3, step = 1)

Arguments

sequence	It is a vector that represents the sequence
word	This integer parameter decides the size of the word that will be formed
step	It is the integer parameter that decides the step that will be taken to make a new connection

Value

Returns the array used to creates the edge list

Author(s)

Murilo Montanini Breve

---

Creates a feature matrix using complex network topological measures

Description

A function that from the complex network topological measures create the feature matrix.

Usage

creatingDataframe(measures, tamM, tamLNC, tamSNC)

Arguments

measures	The complex network topological measures
tamM	mRNA sequence size
tamLNC	lncRNA sequence size
tamSNC	snRNA sequence size

Value

Returns the feature matrix in scale 0-1

Author(s)

Murilo Montanini Breve

---

Creates an entropy curve

Description

A function that from the entropy measures and threshold creates an entropy curve.

Usage

curveofentropy(H, threshold)

Arguments

H	The 'training' return for the entropy measures
threshold	The 'training' return for the threshold

Value

Returns a entropy curve

Author(s)

Murilo Montanini Breve

---

Calculates the entropy

Description

A function that calculates the entropy

Usage

entropy(x)

Arguments

x	The probabilities P0 and P1

Value

Returns the entropy

Author(s)

Murilo Montanini Breve

---

Filters the edges

Description

A function that filters the edges after the maximum entropy is obtained

Usage

filtering(edgestoselect, edgestofilter)

Arguments

edgestoselect	The selected edges
edgestofilter	The edges used to filter

Value

Returns the filtered edges

Author(s)

Murilo Montanini Breve

---

Compares the matrices

Description

A function that compares the matrices 'trainingResult' and the adjacency matrix to produce a filtered adjacency matrix.

Usage

matrixmultiplication(data, histodata)

Arguments

data	Adjacency matrix
histodata	'trainingResult' data

Value

Returns the filtered adjacency matrix

Author(s)

Murilo Montanini Breve

---

Calculates the maximum entropy

Description

A function that calculates the maximum entropy

Usage

maxentropy(histogram)

Arguments

histogram

The histogram (used in 'training' function)

Value

Returns the maximum entropy

Author(s)

Murilo Montanini Breve

---

Rescales the results between values from 0 to 1

Description

Given the results the data is rescaled for values between 0 and 1, so that the length of the sequences does not influence the results. The rescaling of the sequences are made separately

Usage

preprocessing(datah, tamM, tamLNC, tamSNC)

Arguments

datah	Array with results numerics
tamM	Integer number of mRNA sequences
tamLNC	Integer number of lncRNA sequences
tamSNC	Integer number of sncRNA sequences

Value

Returns the array with the rescaled values

Author(s)

Murilo Montanini Breve

---

Selects the edges of the adjacency matrix

Description

A function that selects the edges of the adjacency matrix

Usage

selectingEdges(MAX, data)

Arguments

MAX	The maximum entropy
data	The adjacency matrix

Value

Returns the selected edges of the adjacency matrix

Author(s)

Murilo Montanini Breve

---

Trains the algorithm to select the edges that maximize the entropy

Description

A function that trains the algorithm to select the edges that maximize the entropy

Usage

training(mRNA, lncRNA, sncRNA = NULL)

Arguments

mRNA	Directory where the file .FASTA lies with the mRNA sequences
lncRNA	Directory where the file .FASTA lies with the lncRNA sequences
sncRNA	Directory where the file .FASTA lies with the sncRNA sequences (optional)

Value

Returns the edge lists and the 'curveofentropy' function inputs

Author(s)

Murilo Montanini Breve

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