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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.
The number of cycles necessary to reach the same amplification level (at the threshold) between different samples is converted in the corresponding ratio in the amount of DNA or cDNA.
The switching behavior for single tests is usually much better defined as suggested by Figure 3 and is well represented with the result shown in Figure 2. The reason for the large variety between different samples is most probably due to the manual fabrication method of the double mushroom.
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Global differences between different samples were measured by Principal Component Analysis (PCA) and Linear Discrimination Analysis (LDA).
The statistical significance of differences between different samples was assessed by one-way ANOVA with pairwise comparisons or by student t-test.
Differences in protein levels between different samples were normalised using GAPDH levels (rabbit polyclonal D16H11, Cell Signaling, MA; diluted 1 5000).
Statistical significance of the difference in OoDAD1 expressions between different samples was determined using Student's t-test analysis [ 36].
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).
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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