In particular, the assumption of a single consensus sequence as the common ancestor of all sequences in the data sample ignores the possibility of important phylogenetic relationships among these sequences.
We determined the mean thickness by measuring two fields from five sections per sample, and the entire depth of field was sampled, ignoring the upper and lower 0.5 μm.
We then developed a mechanistic and unbiased (only dependent on the phenotype) algorithm, which, when given a gene expression data set for a number of samples labeled "high stage" or "low stage," leads to a selection of genes that are coordinatedly overexpressed only in high-stage samples, ignoring the effect of the rest, thus precisely reflecting the observed phenomenon.
While random sampling ignores the activity of the selected users during the past, the activity-based sampling selects users based on their historical timelines.
The comparisons were carried out on the full sample, ignoring group, and they used the t-test for pairs.
Despite this improvement, the consensus methods neglect much of the available information in a sample by ignoring the context in which an edge occurs (i.e. the remaining topology of the tree as well as all other edge lengths).
The probability of a perfect test run (i.e., finding the test sample and ignoring the controls) by chance was 1/6, and the probability of performing all the runs without making any errors follows the binomial distribution.
What we can do is to calculate confidence intervals for a few of our results, under the assumption that we had been able to use fully random samples, thus ignoring the complexities of the first page search and normalising by journal size.
The mean densities for all the samples were calculated separately and mean intensities in positive samples were calculated ignoring the negative samples.
Genes with a CPM value less than a given cutoff (e.g. 1 or 100) in more samples (ignoring condition labels) than the size of the smallest group are subsequently filtered from the analysis.
