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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 uPA protease
Step1:  TPA protease
Step2:  Collagenase
Step3:  elastases
Step4:  Cathepsin
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Python Code:
paired_bp_tn_split??
cc = codes.ix[matched_rna.columns.get_level_values(0)].dropna().unique()
r = pd.DataFrame({c: ttest_rel(matched_rna.ix['PLAU'].ix[ti(codes==c)])
              for c in cc}).T
fig, ax = subplots(figsize=(7,3))
cc = ['HNSC','LUSC','LUAD','BLCA','THCA','BRCA','COAD','READ']
paired_bp_tn_split(matched_rna.ix['PLAU'], codes[codes.isin(cc)], ax=ax)
fig.savefig('/cellar/users/agross/figures/plau.pdf')
r.sort('p')
ttest_rel(matched_rna.ix['PLAU'])
Explanation: uPA protease
End of explanation
paired_bp_tn_split(matched_rna.ix['PLAT'], codes)
Explanation: TPA protease
End of explanation
paired_bp_tn_split(matched_rna.ix['MMP1'], codes)
Explanation: Collagenase
End of explanation
g = ['CELA1','CELA2A','CELA2B','CELA3A','CELA3B','CTRC','ELANE','MMP12']
paired_bp_tn_split?
fig, axs = subplots(8, 1, figsize=(15,20), sharex=True)
for i,gene in enumerate(g):
    paired_bp_tn_split(matched_rna.ix[gene], codes, ax=axs[i],
                      data_type='')
Explanation: elastases
End of explanation
g = ['CTSA','CTSB','CTSC','CTSD','CTSE','CTSF','CTSG','CTSH',
     'CTSK','CTSL1','CTSL2','CTSO','CTSS','CTSW','CTSZ']
len(g)
fig, axs = subplots(15, 1, figsize=(15,40), sharex=True)
for i,gene in enumerate(g):
    paired_bp_tn_split(matched_rna.ix[gene], codes, ax=axs[i],
                      data_type='')
Explanation: Cathepsin
End of explanation
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