Sentence examples for microarray data genes from inspiring English sources

In the microarray data, genes are often represented by multiple oligonucleotide probes.

To analyze the microarray data, genes were grouped based on their expression vectors using the k-means algorithm.

For the microarray data, genes with fold-change differences in expression were significant after adjustment for multiple testing using the Benjamini-Hochberg method [ 31] (adjusted P ≤ 0.05).

Prior to performing the comparison between RNA-seq and microarray data, genes with more than one predicted isoform were removed from both datasets.

In our microarray data, genes encoding pathogen related proteins (PR) such as PR3 were down-regulated at 12 dai and genes encoding PR3 at 10 wai showed a mixed response; some were up-regulated while others were down-regulated.

For each of the four transcription factors selected from the qRT-PCR results and confirmed by the microarray data, genes showing an HRG-induced expression pattern were correlated with these transcription factors (correlation coefficient ≥0.85) and those that showed higher expression with HRG than with EGF (I > 2.2) were extracted.

To confirm the validity of microarray data gene specific mRNAs were quantified from treated and untreated cultures by quantitative real-time PCR (qRT-PCR).

To corroborate the microarray data gene-specific qPCRs were performed.

For microarray data, gene expression levels are estimated using the Affymetrix GeneChip and associated manufacturer software.

For the quantitative real time verification of microarray data, gene expression was shown as mean values ± standard deviation (SD).

For pathway analysis of the microarray data, gene lists were analyzed by Ingenuity Pathway Analysis (IPA; Ingenuity® Systems, Redwood City, CA, http://www.ingenuity.com), a web-based bioinformatics tool.