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Comparing with traditional linear model, slice pattern model (SPM) can handle the multiple transcriptional time lags.
The iterative procedure is summarized in Additional file 1: The procedure of Slice Pattern Model.
(6) where O i (j) is the j-th slice pattern of gene i in gene expression data, and S i (j) is the j-th slice pattern of gene i.
In this study, we test the performances of linear model and slice pattern model in an artificial gene network.
In this paper, a novel Slice Pattern Model (SPM) is proposed to identify gene regulatory networks from gene expression arrays mixed with noise data.
Yadong Wang, Jack Y . Yang, Yunlong Liuet al [ 2]. developed a method for reconstructing Gene Regulatory Network using Slice Pattern Model.
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Our approach reconstructs a model that has the optimal pattern matching to the expected slice patterns.
Thus, each gene can be represented as a set of frameworks combines with a series slice patterns.
For further analysis, the rank patterns of gene expression levels in each slice are extracted, and those slice patterns indicate the feature of a gene.
A quantitative calibration study of the printing method was described in detail by Markiewicz et al. [17] for generating single-slice patterns with applications to brain imaging studies.
(C ) Representative images of the VNO slice response pattern to E1050 (magenta) and urine (green).
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