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bioNMF Results


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Job's parameters:
Job's ID: SC_example
Analysis method: Sample Classification
Input file format: ASCII-text
Data matrix has numeric column-headers: No
Data matrix has numeric row-labels: No
Transpose data matrix: No
Normalization method: Do not normalize
Method to make data positive: Do not do anything
Initial values of output matrices W and H: Selected randomly
NMF algorithm: Divergence NMF
Range of factorization ranks: [2...5]
Number of runs per rank: 40
Number of iterations per run: 2000
Stopping threshold: 40 iteration(s)
Save option: Save only the best factorization

NMF results:
Best factorization rank:2
Best run (0-based indexing):22
Distance between input matrix and W*H: 1.4445185e-12
Cophenetic Correlation Coefficient(s)
Heatmap of input matrix Profile plot of input matrix
   
Matrix W Matrix H
Matrix W (numeric data) Matrix H (numeric data)
Heatmap of matrix W Heatmap of matrix H
Profile plot of matrix W Profile plot of matrix H

Sample Classification results:
Results shown for each factorization rank:
  • A plot of all Cophenetic Correlation Coefficients (CCC), indicating with an arrow the CCC value asociated to the current factorization rank. A higher coefficient indicates a more stable clustering.
  • A blue-red heatmap of the ordered Consensus Matrix showing clusters' stability. Red color indicates a stable clustering.
  • Cluster ID where each column of the input dataset was assigned.
2 factors:

Cluster IDs

3 factors:

Cluster IDs

4 factors:

Cluster IDs

5 factors:

Cluster IDs

Output log (detailed information and timings).

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