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In addition, to increase our confidence on differential expression analysis and to correct for multiple testing (not available in Progenesis LC-MS), we used linear modelling to identify differentially expressed proteins.
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We used linear modeling to find that mutation (as exemplified by the mutational ENC) greatly contributes to ENC variability, incorporating the contribution of the Position and Strand predictors.
We used linear models to evaluate the relationship between BAF values and in situ arthropod/plant diversities and vegetation structures.
We used linear models to assess this effect.
First, we used linear models to deconvolve time-dependent effects on gene expression from effects due to social defeat.
We used linear models to describe early growth (before 30 June) of nine individual ponds during 2008.
We used linear models to test if UV-chroma changed following sequential removal of the outer layers.
We used linear models to describe early growth rates during 2008 prior to 30 June before any of the ponds dried completely.
Since we detected a considerable amount of structure in the present panel we used linear models to control for population structure and to reduce the false positive associations.
Considering all measurements over time and adjusting for patient and study site characteristics we used linear models to compare HRQL scores (all scores from 0-100) between administration formats.
In addition, despite a quadratic fit between counts and metabolic cost was indicated in some individuals, we used linear models to derive ICPs to avoid overfitting of the models based on only five observations.
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