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Pairwise correlation coefficients of transcriptome data sets.
Correlation coefficients of transcriptome profiles among RNA-seq samples in grape cv.
We first calculated correlation coefficients of transcriptome profiles among the six samples and between the technical replicates (Table 2).
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Microsoft Excel Built-In commands were used to calculate the Pearson correlation coefficients of the transcriptome at the first steady state and the rest of the sampling times.
To further investigate the robustness of our RNA-Seq dataset, the correlation coefficients of the transcriptome profiles among the eight samples were calculated and were found to reach 0.99 between each set of biological replicates (Additional file 1: Table S3).
The Spearman rank correlation coefficient has been proposed for the comparison of transcriptome and proteome [ 16].
Linear regression [ Q-PCR value) = a (RNA-Seq value) + b] analysis showed an overall correlation coefficient of 0.746**, which indicated that the results of transcriptome analysis were consistent with those of real-time PCR.
Data were log transformed, centered, and a compositional dissimilarity matrix of transcriptome libraries was constructed based on Spearman correlation coefficients.
We clustered all transcriptome profiles and obtained an overview of transcriptome relationships (Fig. 4c).
High-throughput transcriptome sequencing provides large amounts of transcriptome data for gene discovery and the development of molecular markers.
Detailed steps of transcriptome analysis.
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