JMIR Publications

ABSTRACT

Background:

There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. Recent utilization of non-negative matrix factorization (NMF) in computational biology has served its capability to derive essential details from a high amount of data in particular gene expression microarrays.

Objective:

A common problem in NMF is finding the proper number rank (r) of factors. Thus, various techniques have been suggested to select the optimal value of rank factorization (r).

Methods:

This study focused on the unit invariant knee (UIK) method to calculate factorization rank (basis vector) of the non-negative matrix factorization (NMF) of gene expression data sets is employed. Because the UIK method requires an extremum distance estimator (EDE) that is eventually employed for inflection and identification of a knee point, this study finds the first inflection point of curvature of RSS of the proposed algorithms using the UIK method on gene expression datasets as a target matrix.

Results:

Computation was conducted for the UIK task using the esGolub data set of R studio, and consequently, the distinct results of NMF was subjected to compare on different algorithms. The proposed UIK method is easy to perform, free of a priori rank value input, and does not require initial parameters that significantly influence the model9s functionality.

Conclusions:

This study demonstrates that the UIK method provides a credible prediction for both gene expression data and precisely estimating of simulated mutational processes data with known dimensions.