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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)

Release files for Sparse-Optimization-Toolbox 0.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for Sparse-Optimization-Toolbox 0.0.2
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Built distribution (wheel)

Table of built distributions (wheels) for Sparse-Optimization-Toolbox 0.0.2
File Interpreter ABI Platform
Sparse_Optimization_Toolbox-0.0.2-py3-none-any.whl Python 3 none any Details

Total release size: 5.6 kB

Release files / Sparse-Optimization-Toolbox-0.0.2.tar.gz

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0.1.0

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0.0.2 This release

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0.0.1

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