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In total, 100 rest and 4 × 100 smoking datasets for different DA level were generated.
Biases in the estimated parameters were calculated based on the analysis of phantom smoking datasets.
To create sensitivity maps, we started with 100 simulated smoking datasets.
For example, the smoking datasets showed the largest fluctuations, the kidney datasets showed the smallest fluctuations, and the lung cancer subtype datasets showed medium fluctuations.
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For example, the smoking dataset of Vanni showed the largest fluctuations whereas the kidney dataset showed the smallest fluctuations.
Additional file 4 Result of the SAMR differential gene expression analysis and KEGG pathway enrichment analysis in the two human COPD cohorts (GSE27536 and GSE19407) and the public available mouse smoking dataset (GSE18033; presented in Additional file 13 ), respectively.
For example, while it should be relatively straightforward to compare the methods in the breast cancer ER subtype dataset which showed relatively small fluctuations, it would be difficult to do so in the smoking dataset which showed a high extent of fluctuation.
In the present dataset, smoking was defined as daily smoking of cigarettes, cigars or a pipe.
The prevalence of smoking in the dataset was 24%, with 297 current smokers.
In the dataset, smoking history is classified at document level using five classes: "current," "past," "never," "ever," and "unknown".
Age of initiation (age of regular smoking in this dataset) was dichotomized as ≤ 15 years and > 15 years of age, similar to the strategy by Messer [ 22].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com