Sparse-Optimization-Toolbox 0.0.2

Package for Sparse optimization

An sparse opyimization toolbox contains test data generation and network reasoning

Test Data Generation

Import

from sparsetools import matCreater

Data generation

matCreater.matCreater(tfLen=10, sampleNums=200, geneNums=2000, normalLoc=0, normalScale=0.1)

Parameter	Type	Explanation
tfLen	int	The numbers of transcribe factors
sampleNums	int	The numbers of transcribe samples
geneNums	int	The numbers of target genes
normalLoc	float: recommond use 0	Mean value of Gaussian noise
normalScale	float	Variance of Gaussian noise

Return:

Parameter	Type	Shapes	Explanation
W_d	np.array	(tfLen, sampleNums)	Over complete dictionary
zNetwork	np.array	(geneNums, tfLen)	Sparse matrix
xTargetGene	np.array	(geneNums,sampleNums)	Target

Network reasoning

from sparsetools import Optimization
Optimization.networkreasoning(expre, HGS, tf_names, gene_names)

Parameter	Type	Explanation
expre	np.array	The expresion matrix of genes
HGS	np.array	The matrix of network, first row is the names of TF and second row is the names of target genes.
tf_names	np.array	Names of tf
gene_names	np.array	Name of all genes(including TF)