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The expected dataset is composed of all possible response patterns and their associated frequencies.
There was no cytoplasmic or extracellular stromal background staining present and the antibody titrated successfully losing the intensity of staining, as expected (Dataset e).
Hardouin et al. [ 16] proposed to obtain a numerical estimation for the standard error of Γ from an expected dataset of the patients' responses.
Moreover, the odds ratio of having an HT in the first 10% bases of coding genes in the observed dataset as compared to the expected dataset generally increases as the number of bases in the HT increases.
More specifically, Γ is set at the assumed value for the group effect, γ, and its standard error is obtained as follows: an expected dataset of the patient's responses is created conditionally on the planning values that are assumed for the sample size in each group, the group effect γ, the items difficulties δj, and the variance of the latent trait σ.
Table 1 illustrates the calculation of the IDI for the expected dataset (i.e., the dataset obtained by multiplying the probabilities in (1) by the sample size of 1000) for the case when sensitivity of T2 was higher than that of T1.
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To corroborate this analysis, we numerically estimated the likelihood surfaces of expected datasets of increasing length, each composed of character state patterns at their expected frequencies given the Felsenstein-zone star tree in Fig. 1a.
As expected, datasets for the heat-treated ovules did not show significant correlations.
Generally, we expect datasets with shorter reads to be more susceptible because a greater fraction of the genome is unmappable with shorter reads.
Even for the case of tumor and normal samples where a large difference is expected in Dataset 3, the differential CpGs accounted for < 10% of total (after normalization and batch correction).
Because we indiscriminately used all RNA isolated from six tissue types and blood, which would contain disease and commensal organisms, we expected our dataset to contain off-target species sequences that did not originate from the garter snake genome.
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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