Sentence examples for microarray gene level from inspiring English sources

This suggests that the microarray gene level estimates are a generally accurate reflection of transcript level in the cell population.

Technically, the microarray gene level expression estimates made by MMBGX appear robust and, if anything, highlight inadequacies in the qPCR assays used to validate them (Figures 2 and S4), with the failure to detect hTERT mRNA in wtBAC-LCLs the only exception.

Comparison of microarray gene level signals with real-time PCR data suggests that the Globin PNAs_Affymetrix method produces the most accurate microarray results, although, all methods produce data that correlate well with the RT-PCR measurements.

Then the following steps were followed for each group comparison separately: 2. Edge weights were calculated for G PX, based on the gene-level statistics described in the section 'Microarray gene-level statistics'.

The small number of genes tested in the present study would suggest that simultaneous thresholds for microarray gene expression level and variance might perform better than expression level thresholds alone to identify present versus absent transcripts, but nCounter measurement of a larger gene set would be necessary to develop such a method.

By quality in this context, we understand the agreement between microarray gene expression-level estimations and true gene expression profile.

While our microarray and qPCR data shows decreased Ldlr expression following TSA treatment, microarray gene expression levels of Pcsk9 are also down regulated.

A subset of the total SDEG was randomly selected for qRT-PCR to confirm and validate the microarray gene expression levels (Additional file 2: Table S3).

For comparison of the four microarray datasets downloaded from GEO with the SOLiD gene expressions profiles, the microarray gene expression levels were averaged across the probes and assigned HGNC gene names using GEOquery R package.

Our analysis of Gossypium gene expression values resulted in branching patterns similar to that created by previous microarray gene expression levels and to the accepted genetic relationships between species [ 1, 44].

In Figure 1(b), we used the GC Robust Multiarray Average (GCRMA) method and R language software [ 8] to remove the chip background associated with the microarray gene expression levels.