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However, there is no consistency in major branching orders between microarray data and MLST trees.
Overall, there is significant agreement between microarray data and our other assays.
A comparison between microarray data and QRT-PCR showed good correlation (Table 1).
As shown in Figure 4A, despite slight differences in absolute fold-changes in gene expression results between microarray data and qRT-PCR results, we observed a strong correlation between both methods.
The discrepancies of target genes expression between microarray data and real-time quantitative PCR results probably best reflects differences in the relative sensitivity and specificity of the two methods.
Thus, we hypothesized that the correlation between microarray data and RNA-seq data would share this dependence, since the log2-fold change in RNA-seq will have lower variance for longer genes that for shorter genes.
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From this transcriptomic analysis, 411 unique transcripts were identified as differentially expressed via pairwise statistical comparison (FDR < 0.05) between microarray data from shGFP- and shMKRN1-transfected ESC populations (Table EV4).
In addition, 4 genes had a fold change between 1 and 2. There was only 44% (8/18) concordance between the microarray data and the Relative quantitation RT-PCR data, when a 2 fold difference was used as the criteria.
Recent studies by the MAQC (microarray quality control) project, show substantial agreement between illumina microarray data and real-time PCR data on the same samples[49] [51].
Finally, the percentage agreement between the microarray data and the data produced by capillary sequencing represents the proportion of identical calls between the two methods.
We found a correlation of 0.93 between the microarray data and the qRT-PCR data.
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