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Firstly, CytoBank, which is a complete web-based flow cytometry data storage and analysis platform, has been made available to the public in a limited form. [78] Using the CytoBank code base, FlowRepository was developed in 2012 with the support of ISAC to be a public repository of flow cytometry data. [79]
where n i is the number of points in cluster C i, c i is the centroid of C i, and c is the overall centroid of the data. BCSS measures how well the clusters are separated from each other (the higher the better). WCSS (Within-Cluster Sum of Squares) is the sum of squared Euclidean distances between the data points and their respective cluster ...
Flow chart for Hi-C data analysis. [29] Paired-end reads are first iteratively mapped to a reference genome. Mapped reads are then assigned to a restriction fragment/genomic loci, with fragment-level filtering. Data is then binned, filtered at the bin level, and then balanced to correct for potential biases. [29] [30]
In this case, the data are said to be "overdispersed". This situation is the rule rather than the exception in (U-Th)/He geochronology, indicating an incomplete understanding of the isotope system. Several reasons have been proposed to explain the overdispersion of (U-Th)/He data, including unevenly distributed U-Th distributions and radiation ...
This reduces the chi-squared value obtained and thus increases its p-value. The effect of Yates's correction is to prevent overestimation of statistical significance for small data. This formula is chiefly used when at least one cell of the table has an expected count smaller than 5. = =
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
where and are the same as for the chi-square test, denotes the natural logarithm, and the sum is taken over all non-empty bins. Furthermore, the total observed count should be equal to the total expected count: ∑ i O i = ∑ i E i = N {\displaystyle \sum _{i}O_{i}=\sum _{i}E_{i}=N} where N {\textstyle N} is the total number of observations.
Local regression or local polynomial regression, [1] also known as moving regression, [2] is a generalization of the moving average and polynomial regression. [3] Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced / ˈ l oʊ ɛ s / LOH-ess.