Sentence examples for microarray results reflected from inspiring English sources

Additionally we tested whether the microarray results reflected actual expression patterns in the sample by qRT-PCR of the two major cellulase genes cel7a/cbh1 and cel6a/cbh2.

These results confirmed the microarray results reflecting that RA causes a significant decrease in LAMA1 expression.

We observed that the allergen microarray results reflect the clinical diagnoses of patients, detecting pollen allergen-specific IgE only in patients who were diagnosed as pollen allergic (Fig. 6).

Our microarray results reflect this to some extent, with considerably more genes downregulated in the liver than upregulated.

Therefore it is possible that the microarray results reflect cross-hybridisation of different, but related transcripts, although a previous microarray study has also reported a decrease in PPARγ mRNA following VO feeding in salmon [ 35].

Our analyses provide a better understanding of how preprocessed microarray results reflect RNA levels in diverse biological samples, translating microarray expression value differences into molecular changes between samples, and highlighting tissue-specific noise properties.

Overall validation of the microarray results was reflected in the high correlation of the ratio of mRNA expression between the two strains as measured by microarray and by QPCR (Pearson's correlation coefficient, r>0.8, examining vehicle-treated mice).

Nine target genes were selected on the basis of microarray results and reflected the whole range of fold change (both up-regulated and down-regulated genes in different samples).

Microarray results may reflect expression of microRNA precursors or processed microRNAs, because RNA hybridized to the microarray was not specifically selected to include small RNA species.

Hierarchical cluster (HC) results reflected that the three biological replicates between 4 M and 5 F or between PP and 5 F clustered together indicating the microarray analyses are repeatable (Fig. 1B).

This adjustment is important when attempting integrated analysis of multiple microarray datasets to eliminate results reflecting non-biological technical variation between datasets.