Sentence examples for set summaries from inspiring English sources

Data was processed by performing background correction, quantile normalization, and calculation of expression set summaries using the Robust Multichip Average (RMA) protocol (Irizarry et al., 2003) as implemented in the Bioconductor affy package.

Data preprocessing, including background correction, normalization, and expression set summaries, was performed using RMA.

We calculated probe set summaries for the complete dataset using MAS 5.0, dChip [ 146] and RMA [ 147].

We used the rate monotonic algorithm to perform all steps of data preprocessing, including background correction, normalization, and expression set summaries.

Raw microarray data (cel files) were imported into BRB-ArrayTools [ 24] and probe set summaries were computed using the RMA algorithm.

Main effects and interaction effects relating to strain, sex and stress differences were investigated using general linear models implement in R (http://cran.r-project.org) considering strain, sex and stress as fixed effects and untransformed probe set summaries as dependent variables.

The logistic regression compares the proportion of the scenario (dependent variable) with the differences between observed and simulated data set summary statistics (see Cornuet et al., 2010).

Li-Wong model based index was used as probe set summary measure [107].

Tests based on expression indices were carried out on the RMA probe set summary values.

Different gene set summary statistics are developed that are appropriate for detecting different proportions of active genes.

We used RMA [ 49] to obtain the probe set summary values for the Affymetrix U133A and U133Plus_2 Genechips.