Statistical analysis of the gene expression data using a linear model revealed that the expression of 221 genes changed significantly upon exposure of S. cerevisiae to E. arvense extracts.
Many of these methods can determine mixture component types by using a linear model where mixture expression is treated as a combination of expression signatures.
We then searched for nearby genes (±500 kb) covered by the expression data using the bioconductor R package GenomicRanges (43) and tested for association at each pair using a linear model with the expression level as an outcome and the SNP allelic dosage as a predictor, adjusting for age, gender and MetSyn case control status.
In training and testing, we computed each pathway feature (denoted P j for pathway j) using a linear combination of the expression levels g ij of the leading edge genes in pathway j, weighted by their SVM global weights w ij, inherited from weights in training data differentiating the two classes without any gene feature selection.
Importantly, combined analysis of Cbr2 and Sftpc expression using a linear regression model revealed a significant correlation of the expression of both proteins (r: 0.3697; P-value: 0.0162) (Fig. 5B), suggesting that similar regulatory pathways are involved in their expression.
We estimated heritability of HSP70 expression using a linear mixed modelling approach in different ontogenetic stages.
The normalized data were statistically analyzed for differential gene expression using a linear model with coefficients for each experimental group [ 24].
For the pre-processing of the microRNA signal, AgiMicroRNA incorporates the robust multiarray average algorithm, a method that produces a summary measure of the microRNA expression using a linear model that takes into account the probe affinity effect.
First, we regressed out the Houseman-estimated white blood cell proportions, the erythrocytes and platelet cell counts, fasting state, RNA quality score, plate number, age and sex on the mRNA expression levels using a linear mixed model.
QTL mapping was performed using R/qtl [60], and testing for linkage of both clinical traits and gene expression (using the mean log ratio of the expression) was conducted using a linear model [33], [59].
