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In addition, it is important that the number of considered cells (i.e., the network's size) in the datasets is large to include a significant number of faulty cells.
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In "small data" (Estrin 2014), even if the datasets are large, the unit of analysis is similarly scoped as the unit of sampling.
When datasets are large in size in terms of volume and contain unstructured data, data processing and result generate take a considerable amount of time and KM-HMR makes an attempt to process faster within a reasonable execution time.
However, these peak sequence datasets are large and different motif finding Web tools accept the datasets with different limited sizes.
This process dramatically reduces computation time when datasets are large; however, is only valid when categorical covariates are used.
Binary intersections of the pairs of bimodal genes from different datasets are large and statistically significant (Table 2).
Since the gene expression datasets are large and reasonably well annotated, we can use the gene expression surrogate to test the prognostic significance of a DNA level change.
Our datasets are large enough to permit numerous samples of reasonable size to arrive at stable conclusions within the resampled data [ 47].
In conclusion, provided datasets are large enough, the use of GAM-type modeling is an efficient method of assessing multiple covariates with possible linear and nonlinear relationships (including exposure variables) in landmark analyses.
We computed the AIC instead of the AICc for given models, in that the sample size n (i.e., the sequence length) for most of our simulated datasets was large enough compared with the number of parameters (K) [ 40, 58].
The vast amount of high-throughput data generated by ENCODE has provided tremendously valuable resources but we realized that the numbers of YY1-binding loci provided in original ENCODE datasets are large.
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