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Change decision can be made by performing a null hypothesis testing with a threshold.
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The statistical significance of shifts in OSSC values for each algorithm relative to ODE15s (control) were determined by performing a Welch t-test with the null hypothesis that the means of the OSSC values were equal at a 1% significance level [89].
We were able to confirm that the cells used in our experiments were truly null for Fmn1-IV expression by performing a western blot on wild type and Fmn1-IV null MEFs with our Fmn1 antibody [32; Fig. 7B].
To assess the overall significance of each model, we compared it to the null model (i.e. one that only included the intercept and the random variables) by performing a likelihood-ratio test that compared the log-likelihoods of both.
By performing an original?
Except for the COUNT function, all aggregate functions perform a Null-elimination step, so that Null values are not included in the final result of the calculation.
A straightforward way to handle this requirement would be to increase the size of the sample from the null distribution by performing more randomizations.
We built the expected null distribution by performing the same sample procedure but permuting DN/DS and PN/PS ratios among genes for each drawing of 100 genes sets.
The threshold for significant CISs is based on the alpha-level defined by the user and on a null-distribution of insertion densities obtained by performing random permutations.
For each trait, the null distribution was calculated by performing G′ test among biological replicates.
The matrix is generated by performing inner joins of the 2 F dataset to the 1 F and 1R matches, and an outer join with the 2R matches to account for short reference alleles with null 2R match conditions.
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