Sentence examples for microarray studies due from inspiring English sources

With a limited number of biological replicates in most microarray studies due to cost considerations, it is not clear how well these transcriptomics-based gene classifiers will perform in validations.

However, power and sample size have been viewed as complicated and difficult issues for microarray studies due to the large number of genes being investigated and little knowledge of the degree of natural expression variation within a population.

The benefit of excluding poor quality arrays in differential expression analyses was further demonstrated through statistical power calculations for these two clinical trials.Statistical power is a major limiting factor in clinical microarray studies due to limited sample size and lack of technical replicates.

Of the total of 14,811 clones on the chips, 9376 clones that were filtered out for further analyses in the regular cDNA microarray studies due to the low hybridization intensity (intensities <500 unit after the noise subtracted) were selected for examination of their hybridization intensity in the SSH/microarray assays.

Six patients were excluded for the microarray study due to unavailability of convalescent phase sample, no bacteria growth from blood cultures, or insufficient or low quality RNA.

However, strong down-regulation of major intrinsic proteins in short fiber mutants was not noticed before in our microarray studies, probably due to limitations of microarray techniques.

However, no consistent conclusion could be drawn from most of the previous microarray-based studies due to the limitations of inter-platform differences and the relatively small sample sizes investigated [10], [12], [17], [18].

These genes and their downstream effectors were not identified in the current microarray study, likely due to many differences in experimental design between the two studies (e.g., pathogen exposure levels, tissue types, water temperatures, age of fish at exposure, etc).

In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures.

We first compared our proteomic dataset to data from previous microarray studies measuring transcriptional changes due to rapamycin treatment in yeast [6], [7].

The comparison of our new data sets to previous microarray studies proved surprisingly complicated, due to the use of different gene identifiers and WormBase versions.