Sentence examples for microarray platforms often from inspiring English sources

Due to background, microarray platforms often cannot reliably measure expression in the low-intensity range.

Computational methods on top of microarray platforms often identify genomic regions with high density of unusual intensity signals [ 18, 19].

In a broader context, gene expression patterns derived via different methodologies (e.g. qPCR vs. microarray platforms) often do not strongly correlate, although there appears to be more concordance between qPCR and next-generation sequencing platforms than with microarrays (cf. [ 53, 54]), which may relate to overall transcript abundance [ 52].

Although good reproducibility exist within RT-qPCR profiling platforms, the correlation with microarray platforms is often not high, as reported by Chen et al. [ 26].

As different experiments use different microarray platforms that often contain different sets of genes and gene identifiers, the problem of gene matching identifying the genes in common across studies must be addressed prior to meta-GSCA.

In the absence of a "gold standard"/ reference method" for the gene expression measurements, studies evaluating and comparing the performance of various microarray platforms have often yielded subjective and conflicting conclusions.

Most of functional analyses performed on microarray datasets are usually applied to data that were derived from a single microarray platform, where often only the expression of a few genes has been validated experimentally by alternative methods, usually RT-qPCR.

Inherent to the microarray platforms, there are often multiple probesets or probes mapping to the same gene.

The observed variability in the proportion of the present spots (additional file 1), even between the two channels of a single array is often associated with two-colour microarray platforms [ 24].

This is mainly because the data often has been generated with different microarray platforms, hybridization protocols, and the authors use different methods and different thresholds to calculate differentially expressed genes (DEGs) [8].

A statistical challenge in performing a meta-analysis of microarray studies is that often samples are hybridized to different microarray platforms, and the technical differences among platforms lead to fundamental differences in the nature of the gene expression measurements produced.