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An integrative rank product was calculated (Figure 1A).
Then, for each gene a combined probability was calculated as a rank product (RP).
These 3 rank lists were combined by using a rank product (x*y*z).
Differentially expressed genes were then identified with the rank product method [38] as implemented in the "RankProd" Bioconductor package [39].
Furthermore, specific pregnancy signatures were defined for both healthy controls and MS patients by mean of rank product analysis.
The Rank Product package [19] was used to identify the differentially expressed genes between controls and treatment in each experiment.
Rank Product was reported to perform well in small sample sizes and highly noise data set [15].
Rank Product [14] was used to detect differentially expressed genes and to calculate False Discovery Rate (FDR).
These two sources of information (gene set demonstrating upregulation with 5-aza) and COPA score were combined by using a rank product.
This approach is equivalent to the Rank Product (RP) method, which was shown to be robust for comparing gene expression profiles.
Feature selection of transcripts differentially expressed between MS and healthy specimens was addressed using the so-called rank product non-parametric method [22].
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