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Noise correction in gene expression data: a new approach based on subspace method
Author(s) -
Alharbi Nader,
Ghodsi Zara,
Hassani Hossein
Publication year - 2016
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.3823
Subject(s) - singular spectrum analysis , noise (video) , subspace topology , eigenvalues and eigenvectors , mathematics , signal (programming language) , spectrum (functional analysis) , algorithm , pattern recognition (psychology) , expression (computer science) , computer science , artificial intelligence , mathematical analysis , singular value decomposition , physics , image (mathematics) , quantum mechanics , programming language
We present a new approach for removing the nonspecific noise from Drosophila segmentation genes. The algorithm used for filtering here is an enhanced version of singular spectrum analysis method, which decomposes a gene profile into the sum of a signal and noise. Because the main issue in extracting signal using singular spectrum analysis procedure lies in identifying the number of eigenvalues needed for signal reconstruction, this paper seeks to explore the applicability of the new proposed method for eigenvalues identification in four different gene expression profiles. Our findings indicate that when extracting signal from different genes, for optimised signal and noise separation, different number of eigenvalues need to be chosen for each gene. Copyright © 2016 John Wiley & Sons, Ltd.