Sentence examples for microarray data assumed from inspiring English sources
Our analysis of the microarray data assumed that any differences observed were due to the fibrocytes and not contaminating monocyte, or monocyte-derived populations.
The aim of this research is to propose a hierarchical mixed model analysis of microarray gene expression data assuming within-gene heterogeneous residual variances.
Commonly, microarray data normalization methods assume that relatively few transcripts change from sample to sample [36].
We generated two Affymetrix microarray data sets, which are assumed to be from two independent studies.
The significantly up- or down- regulated genes in cancer samples in the two expression microarray data sets described above assuming a paired sample design was analyzed further.
In light of these microarray data, it is reasonable to assume that phloretin may be beneficial for reducing insulin resistance, in a similar way to the thiazolidinedione class of antidiabetic drugs.
Assuming the microarray data to be more reliable due to internal standards and protocols, this length bias stems from the increased representation of long genes within the EST databases, perhaps because longer genes are more likely to survive the enzymatic conditions within the homogenized samples that lead to the cDNA libraries represented in the EST databases.
Assuming the microarray data to be the more accurate measurement of expression levels, due to reliable internal standards, it is shown that the abundance within the EST database method is biased by coding sequence length, and an explicit form of the length bias is presented.
When microarray data are normalized it is generally assumed that the overall distribution of expression intensities within each treatment is similar [30] [32].
These simulations mimic the most commonly assumed models for single channel microarray data such as that provided by Affymetrix.
Thus, miRNA microarray data have been in general analyzed without normalization, assuming that the total amounts of input RNAs are constant among samples [34], [35].
