Sentence examples for differential expression model from inspiring English sources

The method considers these sources of technical variation as a covariate in the differential expression model.

In baySeq, two differential expression models, one for population, the other for sex, were taken into account when estimating the posterior probabilities for all genes to be differentially expressed.

The GOF statistics were reduced to the extent that, in this particular experiment with only one subject per treatment per flow cell, we believe these terms should not be included in the differential expression models in order to avoid over-fitting.

Model 1 represents, genes with differential expression FDR<0.01, Model 2 represents, genes with differential methylation FDR <0.01, Model 3 represents, genes having both differential expression and differential methylation FDR <0.01, Model 4 represents, genes that have FDR<0.01 after significance values of methylation and expression are combined.

With respect to gene expression analyses, this is a case where differential expression (DE) models that look for mean difference will fail: there is no true underlying survival difference between classes that may be attributed to X (Fig. 1D).

BitSeq estimates the variance in the transcript expression based on a hierarchical log-normal model and determines the probability of differential expression by Bayesian model averaging.

This measurement error is propagated downstream into the analysis, for instance when detecting differentially expressed transcripts This has been shown to increase the accuracy in detecting differential expression, making the model more resistant to outliers even at small sample sizes [ 29].

The selection of the miRNAs is based on their potential role in the pathology of the lung (mmu-miR-1, 146b, -203, -21, -223, -29b, -29c) or on their high and significant differential expression in the model (mmu-miR-455, -574-5p, -672, -690) or for their high (mmu-let-7b a, mmu-miR-145) or low (mmu-miR-450a-5p) signal intensity in microarray analysis.

For the calculation of the p-values for differential expression the dispersion model of EdgeR is employed, which is optimized for the use of CPM values.

Moreover, the quality of the samples has a major effect on the ability to detect true differential expression and subsequent model building.

In situations where outliers lead to an increase of variance or a decrease in average differential expression, the Gaussian noise model will overlook differentially expressed genes, which would be captured by the more appropriate t-distributed noise model.