HCS-Tools for KNIME version 3.1.200.v201807180901 by Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)
The node computes the mutual information between two specified groups for a selected subset of parameter.
Typically, in Hight Content Screening, a parameters quality can be judged by the mutual information between
the library measurements and the reference measurments. the lower the mutual information value the more
independent the distributions and thus the Library carries information the reference does not. The mutual
information algorithm is a histogram based approach implemented according:
"Moddemeijer R., A statistic to estimate the variance of the histogram based mutual information
estimator based on dependent pairs of observations , Signal Processing, 1999, vol. 75, nr. 1, pp. 51-63"
The method to calculate mutual information:
- unbiased (default)
- mmse (minimum mean square estimate)
The logarithmic base to use for entropy calculation (default is 2).
Number of bins to discretize the data. the default is round(numberOfTableRows^1/3) and
will be calculated for each parameter if the input is 0.
If true the lower and upper bounds are determined using the combined information
x and y vector. The unchecked box leads to seperate calculation of the lower and upper bound for each vector
Column containing the cathegorical variable to group the measurments.
The Reference is one or a set of negative controls. ideally it contains the most
negative control that is used for plate normalization (like DMSO or MOCK).
The library wells should contain measuerements of everything else than the reference
(positive controls, library reagents, ...)
Numerical columns between which the mutuali information is calculated can be selected
with the column filter.
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