STY composition

from katlas.core import *
import pandas as pd

ref = pd.read_parquet('out/CDDM_pssms_LO_eval.parquet')

ref2 =preprocess_ref(ref)

info = Data.get_kinase_info().sort_values('kinase').drop_duplicates('uniprot')

info['kinase_id'] = info['uniprot']+'_' + info['gene']

# some are not in the kinome info
ref2[~ref2.index.isin(info.kinase_id)]

	-20A	-20C	-20D	-20E	-20F	-20G	-20H	-20I	-20K	-20L	...	20Q	20R	20S	20T	20V	20W	20Y	20s	20t	20y
index
P07948-2_LYN	-0.201025	-0.563368	-0.074525	-0.276853	0.451878	0.212021	-0.306423	0.321053	0.383523	-0.119475	...	-0.457078	-0.198480	-0.486640	0.043253	-0.611027	0.248941	-0.522681	-0.603284	-0.501659	0.703503
P05771-2_PRKCB	-0.378789	-0.105544	-0.414208	-0.766561	0.469130	-0.148789	1.248263	1.264304	-1.293812	-0.955833	...	-1.575331	-0.418613	-0.064964	0.312925	0.395611	0.787799	-1.955805	0.418784	-0.654678	0.035912
Q13976-2_PRKG1	0.377940	-20.551687	0.272132	0.693775	-1.166458	-0.992964	-0.676831	-0.271748	-0.022510	-0.591422	...	0.315217	0.056898	0.258544	-0.059560	-0.228413	0.678348	-1.480295	0.379722	-1.179166	1.511422
Q9NRA0_SPHK2	1.165531	-20.551687	-0.220384	0.123256	-1.658974	-0.807408	1.830652	0.498770	-0.836954	-0.083938	...	-1.138814	0.240296	-0.365413	0.749442	0.317555	0.224317	-0.934327	0.118335	-0.633198	0.057392
P48736_PIK3CG	0.428566	-20.551687	0.000830	0.666398	-1.115832	0.057662	-0.626205	-0.543050	0.513543	-22.986938	...	1.268002	-22.498627	0.555977	-21.767089	0.694625	-19.707253	0.665134	-1.282202	-0.033737	-19.874178

5 rows × 943 columns

hue = hue.merge(info,left_on='index',right_on='uniprot')

hue.set_index('kinase_id')['group']

kinase_id
P12931_SRC         TK
P29320_EPHA3       TK
P07332_FES         TK
Q16288_NTRK3       TK
Q9UM73_ALK         TK
                ...  
Q15746_MYLK      CAMK
Q01973_ROR1        TK
O14976_GAK      Other
P15056_BRAF       TKL
Q6P0Q8_MAST2      AGC
Name: group, Length: 330, dtype: object

ref2

	-20A	-20C	-20D	-20E	-20F	-20G	-20H	-20I	-20K	-20L	...	20Q	20R	20S	20T	20V	20W	20Y	20s	20t	20y
index
P12931_SRC	-0.303165	-0.374748	0.053554	-0.168660	0.079632	0.214991	-0.291338	0.351244	0.244339	0.005575	...	-0.228598	0.025968	-0.328084	0.112171	-0.368079	-0.025931	-0.102114	-0.546950	0.073484	1.274269
P29320_EPHA3	-0.184360	-0.772408	0.041784	-0.335815	0.038333	0.361651	-0.112498	0.240746	0.271984	-0.137271	...	-0.387529	-0.047099	-0.814426	-0.006232	-0.171474	-0.194322	-0.031039	-0.527715	-0.113239	0.754229
P07332_FES	-0.047304	-0.668876	0.095709	-0.338939	0.048756	0.238490	-0.102075	0.353049	0.371674	0.065796	...	-0.164643	-0.071350	-0.644998	0.017345	-0.382362	-0.170745	-0.106998	-0.402258	-0.220906	1.121760
Q16288_NTRK3	0.004691	0.150181	-0.006480	-0.157199	0.057431	0.143462	-0.424928	0.496946	0.519908	-0.051274	...	-0.577591	-0.125417	-0.978218	-0.241875	-0.606648	-0.214459	-0.247573	-0.779873	-0.071975	0.803039
Q9UM73_ALK	-0.027114	0.139216	-0.115000	-0.300995	0.428488	0.150419	-0.326997	0.286672	0.400256	-0.126111	...	-0.431957	0.125002	-0.813833	-0.317107	-0.515009	-0.224103	-0.198324	-0.467588	-0.148733	0.849979
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Q15746_MYLK	0.247994	-20.551687	-0.957350	1.123256	2.247916	-0.222446	-21.072348	0.498770	-0.251992	-0.498976	...	0.147490	-1.280754	-0.079108	-21.767089	-22.421369	-19.707253	0.351976	0.404639	0.653105	-19.874178
Q01973_ROR1	-1.458959	-20.551687	-22.445312	0.416303	-0.196003	0.655563	-21.072348	-0.623221	-22.739954	-0.620966	...	-22.070383	-1.093127	2.108518	-21.767089	-1.015868	-19.707253	-20.865897	1.914194	-20.564768	1.531322
O14976_GAK	1.165531	-20.551687	-0.039812	-0.544169	-0.156474	-0.304908	-21.072348	-0.583692	-22.739954	-0.581438	...	-22.070383	-1.171130	-0.969484	0.560409	-0.093871	-19.707253	-20.865897	0.514264	0.762730	-19.874178
P15056_BRAF	-0.497433	-20.551687	0.882185	0.377829	-21.561975	-0.382911	0.255150	-21.989192	-1.412456	0.340559	...	-0.664883	0.491835	0.693481	1.223374	0.569094	1.698247	-20.865897	0.592266	-20.564768	-19.874178
Q6P0Q8_MAST2	0.050054	-20.551687	-0.155289	0.662282	-0.271951	0.579615	3.025030	-21.989192	-1.449931	-0.696915	...	-22.070383	2.037269	-0.931010	1.183845	1.751958	3.243681	-20.865897	-0.447262	-20.564768	-19.874178

335 rows × 943 columns

pssms=pd.read_parquet('out/CDDM_pssms.parquet')

pssms =preprocess_ref(pssms)

pssms = pssms[['0s','0t','0y']]

pssms_info = pssms.merge(hue,left_on='index',right_on='kinase_id')

pssms_info.columns

Index(['0s', '0t', '0y', 'index', 'kinase', 'ID_coral', 'uniprot', 'gene',
       'modi_group', 'group', 'family', 'subfamily_coral', 'subfamily',
       'in_pspa_st', 'in_pspa_tyr', 'in_pspa', 'in_cddm', 'kd_ID',
       'active_D1_D2', 'active_kd_ID', 'pspa_ID', 'pseudo',
       'pspa_category_small', 'pspa_category_big', 'cddm_big', 'cddm_small',
       'length', 'human_uniprot_sequence', 'kinasecom_domain', 'nucleus',
       'cytosol', 'cytoskeleton', 'plasma membrane', 'mitochondrion',
       'Golgi apparatus', 'endoplasmic reticulum', 'vesicle', 'centrosome',
       'aggresome', 'main_location', 'kinase_id'],
      dtype='object')

plot_bar(pssms_info,'0s','subfamily',figsize=(30,4))

plot_bar(pssms_info,'0y','subfamily',figsize=(30,4))

plot_bar(pssms_info,'0t','subfamily',figsize=(30,4))