Sentence examples for expression measurement technologies from inspiring English sources

The goal of the Microarray Quality Control (MAQC) project was to identify quality metrics for evaluating gene expression measurement technologies[ 1].

Previous studies have compared different gene expression measurement technologies to determine how well they correlate with each other and/or to use one technology as a benchmark for another [ 9- 24].

Another important goal attained by this project is to generate a thoroughly characterized reference data set against which new modifications in the existing microarray platforms and other expression measurement technologies can be compared and validated, and laboratory performance can be assessed.

Reports from the Phase 1 study of the MAQC project, which profiled two standardized reference RNA samples, contain important findings on the performance of different expression measurement technologies and give insights into the level of cross-platform comparability among different technologies [ 8- 13].

Another important factor that needed to be taken into account is the coverage of KEGG pathway genes with the corresponding gene expression measurement technology.

We focus on the matched samples between the TCGA RNA-Seq RNA-Seq and TCGAray data sets to examicroarrayffect of gene expression measurement technology on the reprodatability of our approach.

The reproducibility (See definition in Methods) of our method was evaluated using the gene expression data generated either from different expression measurement technology (Microarray vs. RNA-Seq), different sample sets (TCGA vs. EGA), or both.

This high level of consistency lends support to the premise that the URA has predictive power across mammalian cell types and species, and is independent of expression measurement technology.

The proximity, with respect to the target transcript, of probes has been reported to strongly influence the correlation of expression measurements between technologies [ 36].

Beyond gene expression measurements, recent technology like ChIP-chip or ChIP-seq also enables us to measure temporal variation in chromatin state, histone marks or transcription factor and polymerase occupancy, like in [ 79] or [ 80].

This uniform variance assumption is often inaccurate for expression compendia, because multiple measurement technologies applied in multiple labs will almost certainly have different errors associated with them.