I'm working with biological sequence data where each position in the sequence has an associated continuous value. I'm ignoring the sequence content so the data is very similar to a time series with measurements at discrete timepoints -- all values are equally spaced. I would like to be able to detect whether high values tend to cluster together (occur in runs) so I applied the Wald-Wolfowitz runs test for non-random placement of values >1.
There are some issues with that approach:
Wald-Wolfowitz works on binary data so I have to binarise the continuous values I have (everything larger than 1 becomes 1 and the rest is 0). Ideally I would like to be able to detect features such as runs of similar values (let's say 10 values of 0.5 in a row) as well. I would imagine there are some methods that would operate on continuous values (e.g. based on autocorrelation) but couldn't find any.
While I get a measure of clustering (the test p-value), I don't know which parts are actually clustered or how many clusters there are.
I would also like to extend this approach to 3D (mapping of sites on the protein structure) and the test doesn't support multiple dimensions, either.
I was wondering if there are more sophisticated statistical approaches that I could apply?