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Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
In order to quantitatively catch the relative changes of differential gene expressions between PG/control and LPS/control, we parallelly included FC ratio from both qPCR and microarray in Table 2 in which PG/control divided by LPS/control.
In total, 889 specimen/gene combinations were assayed by qRT-PCR and microarray in this study.
We therefore assessed the performance of RNA-seq and microarray in their ability to detect known HrpX target genes.
We further illustrated the quantitative relationship of log2FC between RNA-seq and microarray in the form of a scatter plot as shown in Figure 2B.
We compared the performance of RNA-seq and microarray in their ability to detect known HrpX target genes by profiling the transcriptome from the wild-type and the hrpX mutant strains of γ-Proteobacterium Xanthomonas citri subsp. citri.
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This paper describes the development and use of microdevices and microarrays in chemical sensor research.
The genes, pairs and microarrays columns show the total number of the genes, pairs and microarrays in each dataset, respectively.
The agreement between qRT-PCR and microarrays in expression profiles across samples is expressed as Pearson correlation coefficient.
Because there were large differences in the number of the gene-pairs and microarrays in the datasets, we sought some measure of the reliability of each dataset.
A number of publications have produced fundamental comparisons between RNA-seq and microarrays in an effort to illuminate the differences and similarities [ 5– 11].
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