Sentence examples for microarray analyses methods from inspiring English sources

Through the intersection of two different microarray analyses methods (classical single gene analysis and GSEA) and two different chondrocyte differentiation systems (primary chondrocytes treated with a pharmacological inhibitor of PI3K and microdissected growth plates), we were able to identify a high number of genes grouped in GSEA functional categories regulated by the PI3K signaling pathway.

This method predicts cis-acting motifs based on a MAMA score calculated from similarities in sequence and gene expression profiles in microarray analyses (Methods, Additional file 2).

As is standard in microarray analyses, empirical Bayesian variance methods were incorporated to perform site-specific moderated t-tests [ 40].

Typical microarray analyses rely on normalization methods that assume absence of global transcript shifts, or that use internal control features such as housekeeping genes (e.g., [ 2]).

Traditional methods for microarray analyses tend to miss by focusing on identifying lists of target genes based on differential gene expression, determined through a number of statistical tests over a two or more time-series snapshots.

Specifically, large-scale sequencing efforts are allowing biologists to define the communities associated with particular animal species (e.g., [ 2- 6]), and development of such methods as microarray analyses are enabling biologists to determine host and symbiont responses under natural and experimental conditions [ 6, 7].

We performed microarray analyses using the 2-dye method, which directly compares expression profiles between 2 samples on the same microarray slide.

Unlike gene expression microarray analyses, there is no standard method for normalization and differential expression analyses in proteomics.

The development of microarray analyses represents a genome-wide method for measuring differences across strains, focusing on expression differences.

Next, to build a multivariate statistical model on the qPCR data and validate the results of the microarray analyses we applied RLS classification method.

We identified 45 genes whose expression was altered 2-fold or more by SSeCKS overexpression using at least three independent Affymetrix microarray analyses, as described in Materials and Methods, of RNA from S24 cells grown at 37°C in the presence or absence of tet.