Sentence examples for microarray features was from inspiring English sources

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.

One of these, IPP, represented by two microarray features, was selected for QPCR, another 7-dehydrocholesterol reductase was also on the array but was not significantly changed (p = 0.07).

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

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).

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

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

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

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

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

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.

ESTs for all upregulated microarray features were anchored to the map-based sequence of japonica rice using the TIGR rice genome browser.