Sentence examples for microarray features were from inspiring English sources

Microarray features were printed at a spacing of 0.55mm (center to center) with 1.2mm spacing between each sub array.

Microarray features were extracted using Agilent Feature Extraction software version 10.5.1.1.

Signal intensities of microarray features were calculated by subtracting the median local background from the median signal intensity.

Individual microarray features were spotted in duplicate, hence each slide consists of 11,710 spots (technical replicate) representing 5,855 unique features.

In general, about 45% of the salmonid microarray features were not bound by targets from the various AS tissues and organs.

A total of 1820 microarray features were defined as significantly different (p<0.05) with a log2 transformed fold change greater than one between the conditions.

Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression.

A precisely linear relationship between the microarray and qPCR measurements is not necessarily expected because the microarray features are capable of measuring methylation of a larger local region (1 to 4 Kb) than the qPCR amplicons were designed to interrogate (400 600 bp).

The selected threshold resulted in 1410 microarray features being called significant with a median FDR = 0.3% and 90th percentile FDR = 0.5%.

The difficult task of extracting relevant information from microarray data can be made easier if the number of microarray features is reduced on the basis of a biologically meaningful criterion.

Overall the strength of our approach is that not only microarray features are grouped in biologically meaningful groups, but also that the ranking criterion is based on a multivariate approach.