Sentence examples for summarization of the expression from inspiring English sources

Summarization of the expression levels and normalization for microarray data were conducted using GeneSpring® 7.3 (Silicon Genetics, Redwood City, CA).

Nimblescan software was used to perform Robust Multi-array Analysis (RMA) for quantile normalization and probe-level summarization of the expression data, with normalization occurring first between arrays of the same sample type and then across all samples.

A contextual gene set interaction network is built from S U, which is a set of all samples after the expression summarization of the original gene expression matrix D, by computing the likelihood of dependency d i j = Pr G i ↔ G j | S U ) (= d j i ) between each pair of contextual gene sets G i and G j. G i ↔ G j is a connection between two contextual gene sets G i and G j in any direction.

To this end we used the Statminer implementation of Limma to carry out normalization and summarization of the TaqMan array expression data.

For the single-color mRNA data, data preprocessing steps included RMA background correction [ 43], quantile normalization [ 44] and summarization of mRNA expression using the median polish algorithm.

The mRNA expression profiles were obtained using Affymetrix Human Gene 1.0 ST arrays and the preprocessing steps included RMA background correction [ 43], quantile normalization [ 44] and summarization of mRNA expression using the median polish algorithm.

GCRMA [ 31] was used for quantile normalization and summarization of the data to generate expression scores in the log2 scale.

Summarization of gene expression data was performed by implementing the robust multichip averaging algorithm, with subsequent baseline normalization of the log-summarized values for each probe set to that of the median log summarized value for the same probe set in the control group.

After normalization and summarization of gene expression data by GC Robust Multi-array Averaging (116) using the affy package (117), MAS5.0 presence/absence calls were used to filter out all probe sets lacking a present call in at least four of the eight samples.

Figure  1 shows the general process of data transformation, from the retrieval from public repositories through data curation to the summarization of expression vectors into representative datasets.

Data analysis was conducted using either Genespring GX v 7.3.1 (Agilent Technologies, UK) or the R statistical package [ 28] and Bioconductor [ 29]. Background correction, scaling and summarization of the raw data to generate expression values were done with the MAS5 algorithm.