Hi Everyone,
We have a question about whether one can to do a particular type of Granger
Causality (GC) network validation in R. We hope you'll agree it's an
interesting problem and that someone's figured out how to solve it.
We have a cellular network with n nodes (proteins). We have two different n x
s x k time series matrices that describe the network activity under two
mutually exclusive conditions, C (cancerous cell) and H (healthy cell), where s
is the length of the time series data, and k is the number of observations.
Using the time series matrices, we calculated two different n x n GC matrices,
one for healthy cells and one for cancerous cells, so that ij th element in
each matrix represents the GC influence of node i on node j. Using the various
standard tests, we know that many of the GC values are extremely significant.
Now we’re given a brand-new observation in the form of a n x s x 1 time series
matrix Y that represents the activity of the same n nodes (we don’t know a
priori whether the new data come from a healthy cell or a cancerous cell).
Given this matrix Y :
(1) How can we go about determining if Y comes from a cancerous cell (condition
C) or a healthy cell (condition H)?
(2) Is there a package in R that we can use for this purpose?
Thank you very much!
Pat
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