API reference

This page gives an overview of all public bioScience objects, functions and methods. All classes and functions exposed in bioscience.* namespace are public.

The following subpackages are public.

bioscience.base: It contains functions for the management of I/O operations, files and objects and methods that form the core of the library and are used by the rest of the library’s subpackages.
bioscience.preprocess: This subpackage includes the functions associated with each of the preprocessing methods implemented in bioScience.
bioscience.stats: This subpackage includes the functions associated with each of the statistical methods implemented in bioScience.
bioscience.dataMining: This subpackage contains all the source code for those data mining techniques that can be used in bioScience.

Within each subpackage there may be various functions and methods that may be private because they are often intermediate operations of the implemented methods.

Contents:

API reference

bioscience.base

bioscience.base.files.load(db, apiKey=None, separator='\t', skipr=0, naFilter=False, index_gene=-1, index_lengths=-1, head=None) → DataFrame

Load any data from a txt or csv file. (Reuse function)

Parameters

db (str) – The path where the file is stored or ID database
apiKey (str) – API Key NCBI
separator (str,optional) – An attribute indicating how the columns of the file are separated.
skipr (int, optional) – Number of rows the user wishes to omit from the file, defaults to 0.
naFilter (boolean, optional) – Boolean to detect NA values in a file. NA values shall be replaced by 0’s, defaults to False.
index_gene (int, optional) – Column position where the gene names are stored in the dataset, defaults to -1 (deactivated).
index_lengths (int, optional) – Column position where the gene lengths are store in the dataset, defaults to -1 (deactivated).
head (int, optional) – Row number(s) containing column labels and marking the start of the data (zero-indexed), defaults to None.

Returns

A dataset object from the reading of a file

Return type

bioscience.base.models.Dataset

bioscience.base.files.loadNetwork(db, index_nodeA, index_nodeB, index_weight, separator='\t', skipr=0, head=None)

Load a network dataset from a file.

Parameters

db (str) – The path where the file is stored
index_nodeA (int) – Column position where the node A gene names are stored in the dataset, defaults to -1 (deactivated).
index_nodeB (int) – Column position where the node B gene names are stored in the dataset, defaults to -1 (deactivated).
separator (str,optional) – An attribute indicating how the columns of the file are separated.
skipr (int, optional) – Number of rows the user wishes to omit from the file, defaults to 0.
head (int, optional) – Row number(s) containing column labels and marking the start of the data (zero-indexed), defaults to None.

Index_weight

Column position where the edge weight is stored in the dataset, defaults to -1 (deactivated).

Returns

A network dataset object from the reading of a file

Return type

bioscience.base.models.NetworkDataset

bioscience.base.files.saveBinaryDatasets(path, datasets)

If the dataset has been binarised, this function allows storing the binary dataset.

Parameters

path (str) – The path where the file will be stored.
datasets (bioscience.base.models.Dataset) – The dataset object which stores the binary dataset.

bioscience.base.files.saveGenes(path, models, data)

Save the gene names from the results of applying a data mining technique.

Parameters

path (str) – The path where the file will be stored.
models (bioscience.dataMining.biclustering.BiclusteringModel) – The results of the data mining technique.
data (bioscience.base.models.Dataset) – The dataset object which stores the original dataset.

bioscience.base.files.saveResults(path, models, data)

Save the results of applying a data mining technique.

Parameters

path (str) – The path where the file will be stored.
models (bioscience.dataMining.biclustering.BiclusteringModel) – The results of the data mining technique.
data (bioscience.base.models.Dataset) – The dataset object which stores the original dataset.

bioscience.base.files.saveResultsIndex(path, models)

Save the results index (rows and columns index of the dataset) of applying a data mining technique.

Parameters

path (str) – The path where the file will be stored.
models (bioscience.dataMining.biclustering.BiclusteringModel) – An attribute indicating how the columns of the file are separated.

class bioscience.base.models.Bicluster(rows, cols=None, data=None, validations=None)

Bases: object

This is a conceptual class representing a bicluster after applying a Biclustering technique.

Parameters

rows (np.array) – Rows of the bicluster.
cols (np.array, optional) – Columns of the bicluster.
data (np.array, optional) – Bicluster values according to the original dataset.
validations (np.array, optional) – A set of instances from bioscience.base.models.Validation.

property cols: Getter and setter methods of the cols property.

property data: Getter and setter methods of the data property.

property rows: Getter and setter methods of the rows property.

sizeBicluster(): Number of total elements in the bicluster.

sort(): Sort the column and row array by theirs indices.

property validations: Getter and setter methods of the validations property.

class bioscience.base.models.BiclusteringModel(results=None)

Bases: object

This is a conceptual class representing a set of biclusters generated after applying a Biclustering technique.

Parameters

results (set(bioscience.base.models.Bicluster), optional) – Data structure (set) that stores all biclusters after running a Biclustering algorithm.
executionTime (float, optional) – Time taken to execute the Biclustering method.

property executionTime: Getter and setter methods of the executionTime property.

property results: Getter and setter methods of the results property.

class bioscience.base.models.CorrelationModel(name, results, rows, executionTime=None)

Bases: object

It is a conceptual class that represents the results generated by a correlation method.

Parameters

results – NumPy vector that stores the results of the row pairs of a dataset.
executionTime (float, optional) – Time taken to execute the Correlation method.

property executionTime: Getter and setter methods of the executionTime property.

property geneInteractionsIndex: Getter and setter methods of the results property.

property name: Getter and setter methods of the name property.

property results: Getter and setter methods of the results property.

class bioscience.base.models.Dataset(data, geneNames=None, columnsNames=None, lengths=None, annotations=None, cut=None)

Bases: object

This is a concept class representing a dataset.

Parameters

original (np.array) – Here the original dataset is stored in a NumPy array.
data (np.array) – This attribute is used to store the dataset once it has undergone the transformations desired by the user.
geneNames (np.array, optional) – Array with the name of the genes involved in the dataset. If the dataset does not have the name of the genes, it shall be replaced by a set of sequential numbers.
columnsNames (np.array, optional) – Array with the name of the columns involved in the dataset. If the dataset does not have the name of the columns, it shall be replaced by a set of sequential numbers.
lengths (np.array, optional) – Array with gene length value (RNA-Seq)
annotations (np.array, optional) – Array that stores data from an annotation file for subsequent validation phases.
cut (float, optional) – Cut-off parameter used in level binarisation.

property annotations: Getter and setter methods of the annotations property.

property columnsNames: Getter and setter methods of the columnsNames property.

property cut: Getter and setter methods of the cut property.

property data: Getter and setter methods of the data property.

property geneNames: Getter and setter methods of the geneNames property.

property lengths: Getter and setter methods of the lengths property.

property original: Getter and setter methods of the original property.

class bioscience.base.models.Edge(nodeA, nodeB, weight, weightRelatedValues, info)

Bases: object

This is a conceptual class represented a edge of a genetic network.

Parameters

nodeA (bioscience.base.models.Node) – Beginning node of the edge
nodeB (bioscience.base.models.Node) – End node of the edge
weight (float) – Weight of the edge.
weightRelatedValues – Values related to the weight such as Spearman or Kendall
weightRelatedValues – np.array
info (np.array) – Aditional information of the edge.

property info: Getter and setter methods of the extraCharacteristics property.

property nodeA: Getter and setter methods of the nodeA property.

property nodeB: Getter and setter methods of the nodeB property.

property weight: Getter and setter methods of the weight property.

property weightRelatedValues: Getter and setter methods of the weightRelatedValues

class bioscience.base.models.NCBIClient(idDB, apiKey=None)

Bases: object

property apiKey: Getter and setter methods of the apiKey property.

property baseUrl: Getter and setter methods of the baseUrl property.

getIdsByGeo()

getSummaryById(id)

property idDB: Getter and setter methods of the idDB property.

class bioscience.base.models.NCBIDataset(accessionNumber, title=None, summary=None, gpl=None, gse=None, taxonomy=None, gdstype=None, suppfile=None, nSamples=None, link=None, bioProject=None, samples=None)

Bases: object

This is a concept class representing a information of NCBI dataset.

Parameters

accessionNumber (str) – Accession number of the dataset.
data (np.array) – This attribute is used to store the dataset once it has undergone the transformations desired by the user.
geneNames (np.array, optional) – Array with the name of the genes involved in the dataset. If the dataset does not have the name of the genes, it shall be replaced by a set of sequential numbers.
columnsNames (np.array, optional) – Array with the name of the columns involved in the dataset. If the dataset does not have the name of the columns, it shall be replaced by a set of sequential numbers.
lengths (np.array, optional) – Array with gene length value (RNA-Seq)
annotations (np.array, optional) – Array that stores data from an annotation file for subsequent validation phases.
cut (float, optional) – Cut-off parameter used in level binarisation.

property accessionNumber: Getter and setter methods of the accessionNumber property.

property bioProject: Getter and setter methods of the bioProject property.

fullInfo()

property gdstype: Getter and setter methods of the gdstype property.

property gpl: Getter and setter methods of the gpl property.

property gse: Getter and setter methods of the gse property.

property link: Getter and setter methods of the link property.

property nSamples: Getter and setter methods of the nSamples property.

property samples: Getter and setter methods of the samples property.

property summary: Getter and setter methods of the summary property.

property suppfile: Getter and setter methods of the suppfile property.

property taxonomy: Getter and setter methods of the taxonomy property.

property title: Getter and setter methods of the title property.

class bioscience.base.models.Network(node, edge, validations=None, directed=None)

Bases: object

This is a conceptual class representing a genetic network after applying a Genetic Network technique.

Parameters

node (np.array) – List of the nodes of the network.
edge (np.array) – List of the edges of the network.
validations (np.array, optional) – A set of instances from bioscience.base.models.Validation.
directed (boolean, optional) – Represents if a network is a directed graph or not.

property directed

property edges: Getter and setter methods of the edges property

property nodes: Getter and setter methods of the edges property

shared_edges_count(gene)

Function to count the number of edges shared by a given gene (node).

Parameters: gene (str or int) – The gene (node) to check for shared edges.
Returns: Number of edges shared by the given gene.
Return type: int

property sizeEdges

property sizeNodes

property validations: Getter and setter methods of the validation property

class bioscience.base.models.NetworkDataset(data, geneNamesNodeA=None, geneNamesNodeB=None, columnsNames=None, extraInfo=None, importantColumnsName=None)

Bases: Dataset

This is a concept class representing a network dataset.

Parameters

data (np.array) – This attribute is used to store the dataset once it has undergone the transformations desired by the user.
geneNamesNodeA – Array with the name of the genes involved in the dataset. If the dataset does not have the name of the genes, it shall be replaced by a set of sequential numbers.
geneNamesNodeB – Array with the name of the genes involved in the dataset. If the dataset does not have the name of the genes, it shall be replaced by a set of sequential numbers.
columnsNames (np.array, optional) – Array with the name of the columns involved in the dataset. If the dataset does not have the name of the columns, it shall be replaced by a set of sequential numbers.
extraInfo (np.array, optional) – Array that stores extra information about the network dataset.
importantColumnsName (np.array, optional) – Name of the column that contains the most relevant information of the dataset.

property extraInfo: Getter and setter methods of the extraInfo property.

property geneNamesNodeA: Getter and setter methods of the geneNamesNodeA property.

property geneNamesNodeB: Getter and setter methods of the geneNamesNodeB property.

property importantColumnsName: Getter and setter methods of the importantColumnsName property.

class bioscience.base.models.NetworkModel(results=None)

Bases: object

This is a conceptual class representing a set of networks generated after applying a Network technique.

Parameters

results (set(bioscience.base.models.Network), optional) – Data structure (set) that stores all networks after running a Network algorithm.
executionTime (float, optional) – Time taken to execute the Network method.

property executionTime: Getter and setter methods of the executionTime property.

property results: Getter and setter methods of the results property.

sort()

class bioscience.base.models.Node(info, id=None, name=None)

Bases: object

This is a conceptual class represented a node of a genetic network.

Parameters

info (np.array) – Aditional information of the node.
id (integer, optional.) – Id of the gene.
name (string, optional.) – Name of the gene.

property extraCharacteristics: Getter and setter methods of the extraCharacteristics property.

property id: Getter and setter methods of the id property.

property info: Getter and setter methods of the extraCharacteristics property.

property name: Getter and setter methods of the name property.

class bioscience.base.models.Validation(measure, value)

Bases: object

This is a conceptual class representing a validation model after applying a data mining technique.

Parameters

measure (str) – Name of the validation measure used.
value (float) – Value of the validation measure used.

property measure: Getter and setter methods of the measure property.

property value: Getter and setter methods of the value property.

bioscience.preprocess

bioscience.preprocess.Standard.discretize(dataset, n_bins=2, strategy='kmeans')

Discretizes data into N bins.

Parameters

dataset (bioscience.base.models.Dataset) – The dataset object to be discretized.
n_bins (int, optional) – Number of bins to produce, defaults to 2.
strategy (str, optional) – Strategy used to define the widths of the bins. Options (kmeans, quantile, uniforme). Defaults to ‘kmeans’.

bioscience.preprocess.Standard.normalDistributionQuantile(dataset, quantiles=1000)

Use Normal Distribution Quantile to preprocess a dataset.

Parameters

dataset (bioscience.base.models.Dataset) – The dataset object to be normal distribution quantile.
quantiles (int, optional) – Number of quantiles to be computed., defaults to 1000.

bioscience.preprocess.Standard.outliers(dataset, view=True, mode=1, replace=3)

Detects and modifies outliers in a dataset.

Parameters

dataset (bioscience.base.models.Dataset) – The dataset object to be checked.
view (boolean, optional) – Graphical visualisation through BoxPlot to identify outliers in the columns of the dataset.
mode (int, optional) – Type of outlier to be detected. If mild outliers are to be detected the value is 1. For extreme outliers the value is 2. Defaults to 1.
replace – Treatment of outliers. If the value is 1, rows containing outliers are deleted. If the value is 2, it shall be replaced by the maximum value when they are outliers above the maximum threshold and the minimum value when they are outliers below the minimum threshold. If the value is 3, outliers shall be replaced by the median. Defaults to 3.

bioscience.preprocess.Standard.scale(dataset)

Scale a dataset

Parameters: dataset (bioscience.base.models.Dataset) – The dataset object to be scaled.

bioscience.preprocess.Standard.standardize(dataset)

Standardize a dataset

Parameters: dataset (bioscience.base.models.Dataset) – The dataset object to be standarized.

bioscience.preprocess.RnaSeq.cpm(dataset)

Apply CPM (counts per million) method preprocessing to a dataset

Parameters: dataset (bioscience.base.models.Dataset) – The dataset object to be preprocess.

bioscience.preprocess.RnaSeq.deseq2Norm(dataset)

Apply DESeq2 normalization method preprocessing to a dataset

Parameters: dataset (bioscience.base.models.Dataset) – The dataset object to be preprocess.

bioscience.preprocess.RnaSeq.fpkm(dataset)

Apply FPKM (transcript per million mapped reads) method preprocessing to a dataset

Parameters: dataset (bioscience.base.models.Dataset) – The dataset object to be preprocess.

bioscience.preprocess.RnaSeq.tpm(dataset)

Apply TPM (transcripts-per-million) method preprocessing to a dataset

Parameters: dataset (bioscience.base.models.Dataset) – The dataset object to be preprocess.

bioscience.preprocess.Binarization.binarize(dataset, threshold=0.0, soc=None)

Applying binarisation to a dataset

Parameters

dataset (bioscience.base.models.Dataset) – The dataset object to be binarized.
threshold (float, optional) – Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices, defaults to 0.0
soc (int, optional) – Threshold representing the number of ones each row of the dataset should have as a minimum. If a row does not exceed this threshold, it shall be removed from the dataset, defaults to None.

bioscience.preprocess.Binarization.binarizeLevels(dataset, inactiveLevel=None, activeLevel=None, cut=0.5, step=0.1, soc=None)

Generate multiple binary datasets by applying fuzzy logic.

Parameters

dataset (bioscience.base.models.Dataset) – The dataset object to be binarized.
inactiveLevel (float, optional) – If an element in the dataset is below this threshold it is considered an inactive gene (value equal to 0 in a binarised dataset).
activeLevel (float, optional) – If an item in the dataset is above this threshold it is considered an active gene (value equal to 1 in a binarised dataset).
cut (float, optional) – Value used in fuzzy logic to determine up to which value an active gene is to be considered. This value will assist in the creation of multiple binarised datasets.
step (float, optional) – Value used in fuzzy logic to determine how much the value in fuzzy logic should be lowered for each binary dataset generated. This value will assist in the creation of multiple binarised datasets.
soc (int, optional) – Threshold representing the number of ones each row of the dataset should have as a minimum. If a row does not exceed this threshold, it shall be removed from the dataset, defaults to None.

bioscience.stats

Correlation methods

bioscience.stats.correlation.continuous.DistCorr.distcorr(dataset, deviceCount=0, mode=1, debug=False)

Application of the Distance Correlation (distcorr) method

Parameters: dataset (bioscience.base.models.Dataset) – The dataset object to be binarized.