Sentence examples for microarray experiments typically from inspiring English sources

Microarray experiments typically collect expression data on thousands of genes and the high dimensionality of the data impose statistical challenges.

The output of microarray experiments typically consists of intensity measurements that are manipulated by scaling, background subtraction and other correction procedures, the details of which are often proprietary.

Using the Affymetrix Latin Square spike-in experiments, we show that the background noise generated by microarray experiments typically is not well modeled by a single overall normal distribution.

A microarray experiment typically provides a list of differentially expressed genes that represents the starting point of a highly difficult process of results interpretation.

This type of analysis applied to the samples of a microarray experiment typically results in clusters of samples with a similar expression pattern, thereby revealing the main factors that lead to changes in gene expression.

Unfortunately, direct comparisons of gene expression profiles obtained in independent, publicly available microarray experiments are typically compromised by substantial, experiment-specific biases.

Even though the fold changes seen by microarray experiments is typically modest, such experiments may underestimate actual changes in malignant epithelial cells due to the dilution of changes in malignant epithelial cells by stromal cells that constitute a significant proportion of pathological specimens.

Large-scale biological research, including genetic linkage/association studies, copy number variation, microarray and RNA-Seq expression experiments, typically compare two or more different phenotypes to infer a unique genetic background, associated with a particular phenotype.

On the one hand, microarray and other gene expression experiments typically generate high-dimensional data in the form of a real vector that comprises expression levels of multiple genes at each sampled time and/or condition point, whereas fitness measurements map the state of the system into a much lower dimensional space e.g. that of a single real variable, such as growth rate.

This compares favourably to Affymetrix microarray experiments done with LNCaP, which typically find between two and eight thousand genes (Gene Expression Omibus (GEO); [ 22, 23]; January 16th , 2006.

Moreover, with calibration it allows for absolute quantification, rather than the use of relative values as typically output in microarray experiments.