Generalized variables for alldoms, where base dom is not the first domain (ind).
Left out s at end.
Added missing arguments in predAllFuncs.
Added ability to pick next highest score if function is 0 and fixed check for it. Added check for threshold to see if it is an integer. Instead of < 1 and >=1, it's float vs integer to decide what kind of thresholding as score range is 0-2.
Removed cputimes and other minor changes.
Minor changes.
Tested if merging 0 funcs and no func check help. Both decreased accuracy.
Change simMat threshold to < 1 instead of <= 1 and see if it makes a diff.
Made threshold an input option
Start evaluation of domain to function mapping.
Added binding data and changed .Dom to .IPR
Added booleans to easily change settings.
Remove overlaps and duplicates more accurately, prioritizing smaller chunks but not including PRINTS and ProSitePatterns
Removed comat and protList from various scripts, changed predAllFuncs to go through row by row instead of protList to get exact same order as MF.data, made changes to clustering in mergeClusters, merge 0 clusters, use all windows in genSig to generate domSig instead of just those with base domains (2 rows).
Message switch with previous commit. Added 2 new scripts to evaluate domain-func mapping using bindingData and also to generate protein-protein similarity matrix from T_table generated from predFunc.
Changing mergeClusters to use evalclusters and not checking for function coherence after merging at this point. Use parallel pool to speed up search for kmeans.
More changes to mergeClusters and predFunc. Changed predFunc output to get similarity score rather than just return indices.
Last commit before changing method to clustering with domSig instead of funcs.
Changed to not cluster with funcs, just group all proteins with same domain into clusters individually, and merge with domSig and seq after that. Instead of getting consensus sequence in genSig, append repeat domains in one line separated by dash.
Instead of mergeClusters, changed to genClusterTree to use it to get sig and generate hierarchical tree to build relationship between proteins. varyCutThreshold can cut the tree at diff thresholds to generate different sized clusters to share functions. Also added function probabilities for each cluster to get a confidence score, dependent on how many annotated proteins in that cluster have it. Can scale with cutoff threshold (higher threshold, lower score) to get rarer functions at higher thresholds (bigger clusters) and get consensus score weighted by threshold. Changed topFuncs to use a different method, starting from roots instead of leaves.