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DATA
DATA/CYTOKINES
DATA/DATABASES
DATA/FUSION
DATA/PANEL_1
DATA/PANEL_2
DATA/PANEL_3
DATA/PANEL_4
DATA/PANEL_5
DATA/PANEL_6
DATA/PANEL_7
DATA/PANEL_8
DATA/PANEL_9
DATA/PATIENT
DATA/PATIENT_SAVE
DATA/PATIENT_VIRTUAL
DATA/PATTERN
DATA/REJECTED
DATA/RULES
DATA/VECTOR
IMAGES
PARAMETERS
REPORT
REPORT/IMAGES
REPORT/template
RESULTATS

main.py
analysis.py
cytokines.py
exemple.py
fcsAnalysis.py
machineLearning.py
patternMining.py
preprocessing.py
procedure.py
README.md
reorder.py
report.py
test.py
trashlib.py

1. program_size
   print the number of lines in the program

2. build_cytokines_data
   run a few functions in order to prepare the cytokines data for analysis

   1. Create the patient index file
   2. Create the matrix from cytokines data
   3. format the matrix (drop the OMICID)
   4. split matrix into quantitative and others values

3. describe_autoantibodies run a procedure to plot the actual number of patient postive and negative
   for the autoantibodies.
   diagnostic could be pick among the list:

   1. Control
   2. RA
   3. MCTD
   4. PAPs
   5. SjS
   6. SLE
   7. SSc
   8. UCTD
      Could be a combinaison (list) of the terms, could be set to "all" (i.e a list of all terms)
      Could be set to "overview" and display % values instead of raw count.

Display is a boolean, 1 to see the negative count AND the positive count, 0 for only the positive count

3. process_associationRules
   Filter, format (clp format) and translate association rules.

4. describe_discrete_variable <variable_name>
   plot the proportion of NA data for this variable, and enumerate the possible values.
   variable_name can be in the form pX or can be the real name of the variable (e.g \Clinical\Symptom\ABNORMINFLAM)