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This approach corrected for any potentially confounding interaction between social networks and spatiotemporal differences in local population density (Farine et al., 2015).
First, we computed animal-to-animal relationships that could be ascertained from genotype data using the basic CE approach corrected for heterozygosity (CEh), as described in [ 8].
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The proposed GDSI approach corrects for q-space sampling density non-uniformity using a fast geometrical approach.
Again, ratios of 13C enrichment to 3PGA were used because this approach corrects for the differences in 3PGA labelling among the different experiments (Figure 4).
To establish significance, we used a robust statistical approach correcting for multiple comparisons in the fixation map space, by applying a one-tailed Pixel test [70] (Zcrit>4.64; p<.05) for the group fixation maps and a two-tailed Pixel test (Zcrit |4.25|; p<.05) on the differential fixation maps.
To establish significance, we used a robust statistical approach correcting for multiple comparisons in the fixation map space, by applying a two-tailed Pixel test [20] on the differential fixation maps with the following threshold (Zcrit>|4.25|; p<.05 – areas delimited with white borders in supporting Figure S1c).
To validate our approach correcting for pairwise dependencies unrelated to disease SNP interactions, extensive tests were performed on simulated data.
The approach corrects for spurious associations due to shared evolutionary history and accounts for potential uncertainties in the phylogenetic relationships of species.
The second approach corrects for strut 'blooming': the intense signal generated by the reflection of light against the metallic struts has an axial thickness itself.
This second approach corrects for any over- or under-representation of tracts of lysine (that can be encoded by a poly-A), phenylalanine (poly-T), proline (poly-C), or glycine (poly-G).
While our approach corrects for bias and heterogeneity induced by the use of imperfect reference tests, other types of bias as publication and spectrum bias, can result in incorrect meta-analysis results.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com