Sentence examples for microarray experiments often from inspiring English sources

The sheer volume of data generated in typical microarray experiments often thwarts attempts at discerning central drivers of a given process.

Microarray experiments often measure expressions of genes taken from sample tissues in the presence of external perturbations such as medication, radiation, or disease [ 1, 2].

Microarray experiments often measure expressions of genes taken from sample tissues in the presence of external perturbations such as medication, radiation, or disease.

For microarray data, Due to the high cost of GeneChip, microarray experiments often have low sample sizes that present challenging for statistical analysis.

Analysis of microarray experiments often involves testing for the overrepresentation of pre-defined sets of genes among lists of genes deemed individually significant.

Last but not least, raw intensity data from microarray experiments often need to be preprocessed and normalized, which could dramatically impact the fold change estimation, depending on the procedure used, leaving the definition of fold change obscure.

Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes.

Microarray experiments are often subject to such RNA quality variation (Upton et al., 2009).

However, due to the high cost of chips, microarray experiments are often carried out with a limited sample size, which requires a careful experimental design.

Such microarray experiments are often difficult to interpret, because of the drastic physiological change induced and the lack of distinction between direct and indirect targets.

Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates.