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A rigorous algorithm was developed to identify genes differentially expressed between two samples by Audic and Claverie (1997).
To identify differentially expressed genes (DEGs), we developed an algorithm to be used between two samples by referring to "the significance of digital gene expression profiles" [ 39].
Then, we identified differentially expressed genes between two samples by FDR (False Discovery Rate) method according to Audic et al. [ 23].
Fold change in representation of a given unigene was calculated by dividing the highest normalized unigene-count observed between two samples by the lowest normalized unigene-count observed between the same two samples.
In our analysis we use the consecutive sampling method [ 23], which quantifies dispersion between two samples by ranking the probe sets according to the mean signal intensity, grouping them in bins containing k consecutive gene pairs, and calculating standard deviations from the difference of expressions (in this study k = 12).
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To identify genes showing a significant change in expression during different developmental stages, the differentially expressed tags between two samples were identified by an algorithm developed by Audic et al. [ 29].
Differentially expressed genes between two samples were identified by tophat and cufflinks softwares (Trapnell et al. [2009]; Roberts et al. [2011]), The P ≤ 0.05 and the absolute value of log2 Ratio ≥ 1 were chosen as the threshold to judge the significance of gene expression difference.
The differences between two samples were analyzed by Student's t test.
Differences between two samples were analyzed by Student's t test.
Statistical analysis between two samples was performed by using the Student t test.
Differences in expression between two samples were calculated by the 2ΔΔCt method [ 103, 104].
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