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Global differences between different samples were measured by Principal Component Analysis (PCA) and Linear Discrimination Analysis (LDA).
Differences in gene expression between different samples were tested with edgeR [ 48] and DESeq [ 49] packages using read counts from reference-guided mapping.
Differences in protein levels between different samples were normalised using β-actin levels (mouse monoclonal clone C4, Abcam, Millipore, CA; diluted 1 10,000).
Differences in protein levels between different samples were normalised using GAPDH levels (rabbit polyclonal D16H11, Cell Signaling, MA; diluted 1 5000).
Expression signals for each transcript and comparisons between different samples were calculated with the Affymetrix GeneChip software MAS5.0 and Microsoft Excel Microsoft Corp.., Redmond, WA) [40].
Gene expression values were determined as ΔCt (Ct GAPDH) − Ct gene)) and relative quantities between different samples were determined as ΔΔCt ΔCtt(patient) − ΔCt(calibrator sample)) [ 19].
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The statistical significance of differences between different samples was assessed by one-way ANOVA with pairwise comparisons or by student t-test.
Statistical significance of the difference in OoDAD1 expressions between different samples was determined using Student's t-test analysis [ 36].
A comparison between different samples is then based on differences in those calls (see Figure 1).
Furthermore, the only (data) interaction between different samples is in step 3 when ensemble averages are computed.
Shared OTUs (at genus level) between different samples was viewed using online tool Venny (http://www.bioinfogp.cnb.csic.es/tools/venny/index.html).html
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