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However, many of the duplicated regions with higher dS were not defined within that dataset, and we could not confidently separate the β and γ duplications from one another based on dS due to saturation of substitutions.
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Canonical pathway analysis of entire datasets identified the pathways from the IPA library of canonical pathways that were most significant to the dataset, based upon genes within the dataset that were associated with a canonical pathway in the Ingenuity Pathways Knowledge Base.
While it is likely that the majority of these separations represent cleft related procedures or surgeries, it is possible within this dataset, that a separation would have been recorded for an individual who had been admitted to hospital with a cleft primary diagnostic code and then discharged without having undergone any procedure (due to illness or a hospital initiated postponement).
There were limitations within the dataset that should be taken into account when interpreting the results.
The acquisition of this locus appeared to have occurred in lineages that, within this dataset, show no subsequent evidence of travel outside of their original geographic area.
The first step is to identify any variables within the dataset that are associated with the indicator of missingness (response or not at a particular assessment).
Several positions showed a similar pattern with a variant unique within the dataset that seemed to be associated with a reduction in false deletions.
Also, a global variance σ j ′ 2 is computed as the variance across all values within the dataset that are associated with the j th dimension.
A theme was identified as a consistent meaningful pattern of codes within the dataset that described or interpreted aspects of the phenomenon.
In this approach the median absolute deviation (MAD) represents a measure of variation within the dataset that is less sensitive to outliers than other measures, such as standard deviation.
Thematic analysis involves reviewing coded data to finding patterns or trends within the dataset that are compared to study objectives and existing knowledge in order to refine the interpretation of their meaning [ 39].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com