Sentence examples for the datasets were calculated from inspiring English sources

The co-clustering frequencies of sample pairs across the datasets were calculated.

In addition, the average rank of each fingerprint over all of the datasets is calculated per method.

For each cellular function, the number of associated transcriptome networks and the number of associated translatome networks across all the datasets was calculated.

Mean value and standard deviation of the ligand occurrence in the dataset were calculated (μ = 1.80, σ2 = 1.33).

Bootstrap values based on 100 re-samplings in ML of the dataset were calculated (TBR, full heuristic search option) [27].

Mean Cq values, standard deviation (SD), coefficient of variation (CV), and maximum fold change (MFC, the ratio of the maximum and minimum values observed within the dataset) were calculated.

To do this, the TM-score for each protein in the dataset was calculated as described in Materials & Methods section.

At first, the mean of the dataset is calculated and subtracted from each of the data dimensions.

From the FastUnifrac distance matrix the amount of variance that each clinical variable e.g. sample collection method, age etc. contributed to the dataset was calculated using the adonis function of the vegan package in R [26].

The lengths of the branches of the morphological datasets were calculated using a Euclidean distance matrix (SAS Procedure DISTANCE SASS version 9.1.3).

The likelihood values for each of the two datasets were calculated using the default punctuation asymmetry threshold of 100.