Sentence examples for microarray datasets often from inspiring English sources

However, the metadata associated with many publicly available expression microarray datasets often lacks sample sex information, therefore limiting the reuse of these data in new analyses or larger meta-analyses where the effect of sex is to be considered.

Method (2) is difficult to use on a wide variety of public microarray datasets because it is often difficult to interpret the meaning of the controls.

Microarray analyses based upon individual datasets often identify an exceptionally large number of genes, which limits the utility of microarray data as a tool for selecting candidates in follow-up studies.

The correlation structure of expression patterns among genes may be as informative as differential expression analyses but is often an underexploited aspect of microarray datasets.

However annotating microarray datasets with clinically useful information is not always possible, as this often requires access to detailed patient records.

Most of functional analyses performed on microarray datasets are usually applied to data that were derived from a single microarray platform, where often only the expression of a few genes has been validated experimentally by alternative methods, usually RT-qPCR.

It is often difficult to determine how many clusters the genes should be grouped into for microarray datasets, which usually have complex expression patterns.

The microarray datasets analyzed in this article are available in Gene Expression Omnibus, accession number GSE80748.

Expression of the 10 selected reference genes in the microarray datasets.

Our proposed method is tested on both binary-class and multi-class microarray datasets.

To validate the proposed method, we conducted extensive experiments on six benchmarked microarray datasets.