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This uncertainty is mostly a function of between-template (attributable to different normalization strategies and templates across laboratories) and between-subject (due to small sample sizes) variance.
To investigate power, we generated 50 causal candidate SNPs with normally distributed effect sizes (variance equal to 0.5 divided by the number of causal candidate SNPs) and measured mean Wald statistic on these SNPs.
Frankham (1995) reviewed the factors that reduce Ne relative to N and found that fluctuating population sizes, variance in family sizes and unequal sex ratios are the most important factors driving Ne/ N downwards.
To simulate causal SNPs in the GRM, we selected a fraction P = 0.05 or 0.005 of the SNPs at random and sampled Gaussian effect sizes (variance equal to 0.5 divided by the number of casual SNPs in the GRM) for these SNPs.
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This allowed us to include the data from all experiments in an overall quantification, while at the same time accounting for differences in sample sizes, variances, and response levels across different experiments.
Figure 5 Size variance of biggest CCs in SF during off-peak hours.
Figure 7 Size variance of biggest CCs in SZ during off-peak hours.
The size variance of the biggest connected components in Shenzhen, China during on-peak hours.
Figure 6 Size variance of biggest CCs in SF during on-peak hours.
The size variance of the biggest connected components in Shenzhen, China during off-peak hours.
The size variance of the biggest connected components in San Francisco, USA during off-peak hours.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com