Sentence examples for microarray data different from inspiring English sources

Among the three data sets used in the study, there is, as expected, the smallest difference between TCGA RNA-Seq RNA-Seq and TCGAray data sets because 496 out of 522 samicroarray the TCGA microarray data are drawn from the sets patient pool as the TCGA RNA-Seq data set, whereas the largest difference separated the TCGA RNA-Seq and EGA microarray data (different patients and different technologies).

Since the normalization procedure greatly changes the microarray data, different normalization procedures may lead to very different clustering results.

In order to correlate TRAC and microarray data different linear models were tested and the best selected based on Akaike Information Content.

Different labs produce microarray data in different ways using different technology platforms, such as Affymetrix GeneChip, spotted cDNA array, and spotted oligo array.

In this paper, we aim to address several issues in comparing microarray data across different platforms and different labs.

In microarray data analysis, different ranking statistics may give very different lists of significant gene [ 37], [ 38].

This study aims to provide a basis for further development of methodologies for comparing microarray data across different experiments and for the integration of microarray data from different labs.

It has been reported that microarray data from different platforms or laboratories often show great differences.

Consequently, it is often difficult to compare microarray data from different laboratories, or for that matter to compare data from the same laboratory run at different times.

This method, termed Extreme Values Analysis, can detect extreme differences in gene expression on a subject-by-subject basis from microarray data across different chips.

This suggests the potential application for the broad integration of microarray data from different platforms.