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It is very close to the background very close to the expected background signature (Chi-square p = 0.027).
In order to understand which model (replicative or transcriptional strand) is appropriate to explain the strand-bias, Chi-square tests were used between the number of observed mutations for each class and expected ones under the background signature.
An underlying feature distribution, corresponding to a user stroke, is then represented with the Gaussian mixture model, in the color space, i.e. each foreground and background signature is not represented with a single Gaussian model.
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Reflectance of canopy and shadowed ground are nonlinear mixtures of leaves and background signatures, which are moreover influenced by parameters of canopy, such as leaf area index (LAI), coefficient of attenuation, and leaves transmittance.
The mine (background) model captures the characteristics of the mine (background) signatures.
Similarly, background signatures may exhibit large variations due to different soil conditions and data preprocessing techniques.
In our experiments, data sets are comprised of a variety of mine and background signatures.
Note that high and low uptake regions in PET image were used as foreground and background signatures.
It agrees very well with the background mutational signature.
Again, it agrees very well with the background mutational signature (Chi-square p = 0.71).
In addition, ten mutations within mitochondrial protein-coding, tRNA and rRNA genes showed significantly higher recurrent rate than expected from background mutational signature (Supplementary file 5).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com