Kstar

- Fix aggregation so that it does not throw error from non-numeric columns
- Throw error if binarizing data does not output any evidence
- Fixed issue where evidence columns were incorrectly removed if no quantification was greater than 1
- Various updates for pandas v2
- Minor fixes to plotting code
- Remove setuptools as requirement, as it's no longer used

Updates/changes:
- Renamed modules for which their name no longer reflected their true use: normalize -> random_experiments, validate -> analysis
- Completely removed normalization functions from the first iterations of KSTAR that are no longer in use
- Added catch to the pruning procedure to ensure that the code is not stopped if a kinase does not have any remaining edges, and instead keeps the kinase with fewer edges and records the error in the log.

New features:
- New functions in pruning module intended to guide users to best parameter values to use for their purposes + whether their parameter values are actually feasible.
- In addition to binarizing experiments by a threshold, you can now instead provide the desired number of phosphorylation sites to use for each sample and KSTAR will grab that number of sites with the greatest abundance (or least if greater = False)
- New function in KinaseActivity class, called test_threshold, intended to make it easier to check how a threshold value impacts the number of sites used across all samples
- Can add the number of phosphorylation sites used for each sample to a dotplot using evidence_size() function in DotPlot class
- Added new submodule in analysis module, called coverage, which is for exploring the coverage (number of sites with connections in network) of the phosphoproteome and phosphoproteomic experiments by KSTAR networks (or other kinase-substrate networks)
- Added new submodule in analysis module, called interactions, which is intended to contain functions for determining what active kinases are interacting with in the sample. Currently, contains two functions for outputting the phosphorylation sites that contributed most to a kinases activity prediction, based on the number of different networks they are predicted to interact.

Updated previous release to fix bugs and expand the number of parameters that can be inputted into the pruning.py script via the command line

In this release, two major updates were made:
1. Redundant steps were removed during the random experiment generation and activity calculation steps to reduce memory burden
2. Additional parameters were added to the pruning class to allow for user to input different site accession and number columns (if different from those used in NetworKIN). Goal is to make it so that it can be used for any kinase-substrate network.

Small changes to errors in pruning.py and other fixes to previous release. Functionally identical release to v0.3.1.

The primary change of this release was to remove the normalization pipeline, which generated normalized p-values based on the random experiments, and instead focus on Mann Whitney generated p-values (as this works better). Other changes include:
- Added PROCESSES parameter to the pruning functions, as was done with activity calculation
- Updated plotting functions to fix various visualization errors