- freely available
Data 2018, 3(4), 48; https://doi.org/10.3390/data3040048
2. Materials and Methods
2.1. The Evaluation of the Stability of the Objective Clustering Inductive Model Based on the SOTA Algorithm to the Level of the Noise Component
- Generation of random values vector. The length of this vector is equal to the length of the studied gene expression profiles and its amplitude corresponds to the minimum value of the studied data gene expression (“white noise”).
- Setup of the vector of coefficients to change the amplitude of the noise component. In the case of the studied gene expression profiles the values of coefficients were changed within the range from 0.2 to 4 with step 0.2. These parameters were determined empirically during the simulation process.
- Formation of gene expression profiles with the noise by adding of the appropriate noise components to the studied gene expression profiles.
- Division of the obtained data into two equal power subsets by the use of the algorithm presented in .
- Gene expression profiles clustering with the use of the method described in detail in  using SOTA clustering algorithm. The value of the sister cell weigh coefficient () was changed within the small range from to with the step . This range was determined empirically during the previous simulation process. The value of the variation coefficient was taken as zero.
- Calculation of the complex balance criterion (general Harrington desirability index) for each value of the sister cell weigh coefficient. Creation of the plots of complex balance criterion versus the weigh coefficient value for both the data without noise and the data with different levels of noise component. Determination of the SOTA clustering algorithm optimal parameters, which correspond to the maximum value of the complex balance criterion. Data clustering with the use of SOTA algorithm with its optimal parameters.
- Calculation of the external clustering quality criteria, which allows us to compare the clustering results for both the data without noise and the data with noise component. The following criteria were used as the external clustering quality criteria in this case:
- Jaccard index:
- Kulczynski index:
- Analysis of the obtained results.
2.2. Evaluation of the Stability of the Model of Gene Regulatory Networks Reconstruction to the Level of the Noise Component
3. Results and Discussion
3.1. Results of the Simulation Concerning the Use of the Objective Clustering Inductive Technology Based on the SOTA Clustering Algorithm
3.2. Results of the Simulations Concerning the Influence of the Level of noise components to the Quality of the Reconstructed Gene Networks
- k = 0.025: = 0.33; = 0.29;
- k = 0.05: = 0.35; = 0.11;
- k = 0.075: = 0.26; = 0.07;
- k = 0.1: = 0.48; = 0.33;
Conflicts of Interest
|SOTA||Self-Organizing Tree Algorithm|
|DBSCAN||Density-Based Spatial Clustering of Applications with Noise|
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|Noise Coef.||Full Data||BC2||BC8||BC12||BC15||BC16||BC18||BC20||BC23||BC25||BC27|
|Noise Coef.||Full Data||BC7||BC8||BC11||BC12||BC14||BC15||BC17||BC25||BC26||BC28|
|Noise Coef.||Full Data||BC2||BC4||BC7||BC10||BC12||BC14||BC15||BC19||BC28||BC33|
|Noise Coef.||Full Data||BC8||BC10||BC11||BC12||BC14||BC20||BC23||BC33||BC38||BC40|
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