Sentence examples for microarray dataset contains from inspiring English sources

A typical microarray dataset contains hundreds of sample points for thousands or tens of thousands of genes.

The microarray dataset contains a low number of samples (~100 200) with a very large number of genes (~10,000 40,000) used as the training set.

The microarray dataset contains two files (processed data, in.xlxs format): a file containing all miRNAs from miRBase version 17 and the associated RMA normalised intensity signal for all samples.

The complete microarray dataset containing 34 samples from the inner and outer cortex, inner and outer medulla, papillary tips, and renal pelvis and from glomeruli is available at https://www.med.stanford.edu/jhiggins/Normal_Kidney/index.shtml.shtml

The JH microarray dataset contained 501 significantly differentially expressed EST contigs (Table  1, Additional file 1: Table S1A, B).

We apply our mining approach to an integrated yeast microarray dataset containing 500 yeast experiments (see METHOD) and calculate the ΔAIC value for each gene in the dataset.

Following the general simulation setup of Broët et al. [ 24], we generate a microarray dataset containing 300 miRNAs and five repeat arrays under two conditions.

After excluding probes whose expression values were missing in GSE28521, we identified probe IDs that were mapped to the same Entrez GeneIDs in each set of microarray data and collapsed their expression values by averaging them to make each microarray dataset contain non-redundant sets of genes.

The performance of the SSHN classifier was reproducible in both an independent validation patient dataset of 163 tumors (GSE11969) [ 30] generated in a different array platform (Agilent) (AUC of 1) (Additional file 1, Figure S2A), as well as in our own microarray dataset containing 23 SCC and 10 SCLC samples (AUC of 0.94 with 95% CI of [0.85, 1])(Additional file 1, Figure S2B).

In order to further validate our observations and to determine whether our proliferation meta-gene was simply identifying the Luminal B subtype of tumors, previously described as having high proliferation and poor outcome [ 4], we applied the proliferation meta-gene to a large microarray dataset containing 337 patients with long-term follow-up and containing a Luminal B group.

To verify our histological and immunohistochemical findings, namely that expression of nuclear LEF1 and TCF4 are associated with shorter survival and to prove possible β-catenin independence in brain metastasis samples, we analyzed a microarray dataset containing 19 adenocarcinoma brain metastases of the lung.