Sentence examples for microarray data usually from inspiring English sources

A term enrichment method discovered that biofilm-associated microarray data usually contained high expression levels of both uspE and gadX.

The feature of small replicates and large gene numbers, e.g., about 6,000 in yeast and 23,000 in Arabidopsis, in microarray data usually results in poor estimation of gene-specific variances.

Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected.

However, the DNA microarray data set usually contain multiple missing value and therefore, selection of important genes using the incomplete data set may be erroneous, resulting misclassification in disease prediction.

Published microarray data analyses usually used the combination of SAM and Limma methods with popular Quantile and Lowess normalizations.

For microarray data, which usually have only a few points (due to high costs), the traditional spectral methods do not efficiently detect the exact diurnal or circadian rhythm period [35].

Microarray data are usually peppered with missing values due to various reasons.

Considering microarray data are usually noisy, one needs more quantitative measurements, e.g., real time PCR to confirm these results.

For example, the renormalization for microarray data is usually done by normalizing all the training and testing samples before retraining the classifier in diagnostics [ 42, 43].

The modified GGM is considered appropriate for analysis of microarray data that usually has a high-dimensionality problem (i.e. the number of genes is much higher than the number of measurements).

Moreover, PCC indicates the collaborative degree of two genes' expression levels, rather than the individual strength, and is an appropriate tool for analysis of microarray data that usually contain considerable noise [ 57].