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We therefore used only the first principal component in the models for differential expression analysis.
Mallick et al 4 constructed binary classification models for differential classification of different cancers, including leukemia.
RUVg normalization constructs the factors that capture technical variation from negative control genes, which are treated as additional covariates in the models for differential expression analysis.
The third analysis offered by Pathway Processor 2.0 is the application of the recently developed GSVA, coupled with linear models for differential expression analysis.
82 Since RNA-seq read counts are integer numbers that range from zero to millions and are highly skewed, many kinds of transformation algorithms have been applied to the counts so that the numbers can be fit to statistic distribution models for differential expression detection.
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We propose an empirical model for differential spectra D(E) of galactic and anomalous cosmic rays in energy interval 1 MeV–100 GeV during solar cycle.
The two sequences of gag are also tested as DNA vaccines in a murine model for differential immune responses between the two constructs.
In addition, the best choice of statistical model for differential gene expression is not straightforward.
We applied the edgeR software [ 53] which uses a negative binomial model for differential expression analysis.
For instance, Li et al developed PoissonSeq, a Poisson log-linear model for differential gene expression assay.
Hence, we used the plate effects as the covariance in the ANOVA model for differential analysis between tumor and normal.
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