CGGA | Analyse

Output - mRNA data (distribution)

Dataset:

mRNAseq_325

Gene:

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Plot (Beta)
Data
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CGGA.ID	PRS.type	Histology	Grade	Gender	OS	Censor	Age	IDH.mutation.status	1p19q.codel.status	Expression
CGGA_1001	Primary	GBM	WHO IV	Male	3817	0	11	Wildtype	Non-codel	6.78
CGGA_1006	Primary	AA	WHO III	Male	254	1	42	Wildtype	Non-codel	2.06
CGGA_1007	Primary	GBM	WHO IV	Female	345	1	57	Wildtype	Non-codel	3.94
CGGA_1011	Primary	GBM	WHO IV	Female	109	1	46	Wildtype	Non-codel	3.16
CGGA_1015	Primary	GBM	WHO IV	Male	164	1	62	Wildtype	Non-codel	3.53
CGGA_1019	Recurrent	rGBM	WHO IV	Male	212	1	60	Wildtype	Non-codel	5
CGGA_1022	Recurrent	rA	WHO II	Female	518	0	62	Wildtype	Non-codel	5.81
CGGA_1023	Primary	GBM	WHO IV	Female	681	1	56	Wildtype	Non-codel	5.9
CGGA_1024	Primary	GBM	WHO IV	Male	3074	1	64	Wildtype	Non-codel	2.8
CGGA_1026	Primary	GBM	WHO IV	Male	1570	1	57	Wildtype	Non-codel	0.91
CGGA_1034	Primary	AA	WHO III	Male	753	1	23	Wildtype	Non-codel	3.96
CGGA_1035	Primary	GBM	WHO IV	Female	567	1	60	Wildtype	Non-codel	17.63
CGGA_1039	Primary	GBM	WHO IV	Male	727	1	40	Wildtype	Non-codel	1.1
CGGA_1045	Primary	GBM	WHO IV	Male	348	1	79	Wildtype	Non-codel	6.27
CGGA_1049	Primary	GBM	WHO IV	Male	809	1	58	Wildtype	Non-codel	15.79
CGGA_1052	Primary	O	WHO II	Male	NA	NA	41	Mutant	Codel	0.56
CGGA_1072	Primary	GBM	WHO IV	Male	1090	1	26	Wildtype	Non-codel	2.4
CGGA_1077	Primary	GBM	WHO IV	Male	591	1	47	Wildtype	Non-codel	4.26
CGGA_1078	Recurrent	rGBM	WHO IV	Female	315	1	61	Wildtype	Non-codel	1.97
CGGA_1079	Primary	AA	WHO III	Male	471	1	38	Wildtype	Non-codel	3.87
CGGA_1081	Recurrent	rAA	WHO III	Female	216	1	30	Wildtype	Non-codel	1.69
CGGA_1083	Primary	GBM	WHO IV	Male	450	1	59	Wildtype	Non-codel	4.48
CGGA_1091	Primary	GBM	WHO IV	Female	1116	1	51	Wildtype	Non-codel	2.73
CGGA_1109	Primary	GBM	WHO IV	Male	1348	1	42	Wildtype	Non-codel	1.87
CGGA_1124	Primary	GBM	WHO IV	Male	484	1	47	Wildtype	Non-codel	5.77
CGGA_1139	Primary	GBM	WHO IV	Female	1196	1	53	Wildtype	Non-codel	2.51
CGGA_1140	Recurrent	rAA	WHO III	Female	921	1	49	Wildtype	Non-codel	0.13
CGGA_1145	Recurrent	rAA	WHO III	Male	239	1	41	Wildtype	Non-codel	2.92
CGGA_1171	Primary	GBM	WHO IV	Male	412	1	40	Wildtype	Non-codel	9.37
CGGA_1177	Recurrent	rGBM	WHO IV	Male	449	1	40	Wildtype	Non-codel	6.75
CGGA_1180	Primary	GBM	WHO IV	Female	1226	0	48	Wildtype	Non-codel	3.39
CGGA_1188	Secondary	sGBM	WHO IV	Female	256	1	30	Mutant	Non-codel	3.37
CGGA_1214	Primary	GBM	WHO IV	Female	604	1	49	Wildtype	Non-codel	4.06
CGGA_1216	Primary	GBM	WHO IV	Male	312	1	44	Wildtype	Non-codel	8.44
CGGA_1218	Secondary	sGBM	WHO IV	Male	286	1	53	Wildtype	Non-codel	4.29
CGGA_1224	Primary	GBM	WHO IV	Male	271	1	57	Wildtype	Non-codel	9.17
CGGA_1234	Primary	GBM	WHO IV	Male	705	1	32	Wildtype	Non-codel	2.72
CGGA_1237	Primary	GBM	WHO IV	Male	296	1	62	Wildtype	Non-codel	1.55
CGGA_1240	Primary	GBM	WHO IV	Male	19	1	33	Wildtype	Non-codel	4.13
CGGA_1251	Primary	GBM	WHO IV	Female	272	1	42	Wildtype	Non-codel	2.08
CGGA_1258	Primary	GBM	WHO IV	Male	114	1	42	Wildtype	Non-codel	6.24
CGGA_1263	Primary	AA	WHO III	Male	772	1	50	Wildtype	Non-codel	3.3
CGGA_1270	Primary	GBM	WHO IV	Female	188	1	60	Wildtype	Non-codel	5.96
CGGA_1271	Recurrent	rGBM	WHO IV	Male	523	1	17	Wildtype	Non-codel	6.3
CGGA_1272	Secondary	sGBM	WHO IV	Male	124	1	32	Mutant	Non-codel	1.45
CGGA_1280	Primary	AA	WHO III	Male	447	1	74	Wildtype	Non-codel	7.55
CGGA_1281	Primary	AA	WHO III	Male	2909	0	45	Wildtype	Codel	0.43
CGGA_1283	Secondary	sGBM	WHO IV	Male	20	1	17	Wildtype	Non-codel	0.72
CGGA_1284	Primary	AA	WHO III	Male	97	1	73	Wildtype	Non-codel	6.52
CGGA_1285	Recurrent	rGBM	WHO IV	Female	79	1	58	Wildtype	Non-codel	17.61
CGGA_1292	Primary	AA	WHO III	Male	2901	0	17	Wildtype	Non-codel	0.55
CGGA_1299	Primary	GBM	WHO IV	Male	550	1	54	Wildtype	Non-codel	2.29
CGGA_1301	Secondary	sGBM	WHO IV	Male	239	1	48	Wildtype	Non-codel	1.39
CGGA_1313	Primary	GBM	WHO IV	Male	379	1	42	Wildtype	Non-codel	4.25
CGGA_1320	Primary	GBM	WHO IV	Male	59	1	19	Wildtype	Non-codel	30.45
CGGA_1332	Primary	GBM	WHO IV	Female	510	0	55	Wildtype	Non-codel	3.04
CGGA_1338	Primary	GBM	WHO IV	Female	1122	1	61	Wildtype	Non-codel	7.21
CGGA_1340	Primary	AA	WHO III	Male	1109	1	35	Wildtype	Non-codel	2.01
CGGA_1342	Primary	GBM	WHO IV	Male	186	1	47	Wildtype	Non-codel	6.13
CGGA_1343	Recurrent	rGBM	WHO IV	Male	94	1	34	Wildtype	Non-codel	1.36
CGGA_1346	Secondary	sGBM	WHO IV	Female	284	1	57	Wildtype	Non-codel	3.91
CGGA_1370	Recurrent	rGBM	WHO IV	Male	325	0	43	Wildtype	Non-codel	3.81
CGGA_1393	Secondary	sGBM	WHO IV	Male	275	1	41	Wildtype	Non-codel	3.84
CGGA_1408	Primary	AA	WHO III	Female	1362	1	64	Wildtype	Non-codel	2.57
CGGA_1411	Recurrent	rAA	WHO III	Female	329	1	40	Wildtype	Non-codel	3.99
CGGA_235	Primary	O	WHO II	Male	2340	1	24	Mutant	Codel	0.59
CGGA_269	Primary	A	WHO II	Female	2932	0	45	Wildtype	Non-codel	2.44
CGGA_272	Secondary	sGBM	WHO IV	Male	217	1	51	Wildtype	Non-codel	0.96
CGGA_274	Primary	GBM	WHO IV	Female	610	0	55	Wildtype	Non-codel	3.85
CGGA_276	Primary	O	WHO II	Male	529	0	44	Mutant	Non-codel	1.02
CGGA_309	NA	NA	NA	Male	139	1	44	Wildtype	NA	0.95
CGGA_318	Secondary	sGBM	WHO IV	Female	222	1	40	Wildtype	Non-codel	9.3
CGGA_323	Primary	O	WHO II	Female	4338	0	48	Wildtype	Non-codel	1.43
CGGA_330	Primary	AA	WHO III	Male	1121	1	45	Wildtype	Non-codel	3.04
CGGA_413	Primary	GBM	WHO IV	Male	183	0	54	Wildtype	Non-codel	2.11
CGGA_426	Primary	AA	WHO III	Male	1560	1	50	Wildtype	Non-codel	5.08
CGGA_448	Primary	AA	WHO III	Male	354	1	64	Wildtype	Non-codel	2.11
CGGA_483	Primary	GBM	WHO IV	Female	277	1	59	Wildtype	Non-codel	2.55
CGGA_488	Primary	AA	WHO III	Male	435	1	23	Wildtype	Non-codel	0.37
CGGA_493	Primary	A	WHO II	Female	2382	1	32	Mutant	Non-codel	0.47
CGGA_494	Primary	GBM	WHO IV	Female	4435	0	58	Wildtype	Non-codel	0.47
CGGA_495	NA	NA	NA	Male	295	1	53	Wildtype	NA	6.18
CGGA_499	Primary	GBM	WHO IV	Male	122	1	51	Wildtype	Non-codel	2.31
CGGA_525	Primary	GBM	WHO IV	Male	138	1	61	Wildtype	Non-codel	7.62
CGGA_541	Primary	A	WHO II	Female	4376	0	36	Wildtype	Non-codel	1.47
CGGA_564	Primary	AA	WHO III	Male	679	1	47	Wildtype	Non-codel	3.26
CGGA_565	Primary	AA	WHO III	Male	3605	0	51	Wildtype	Non-codel	0.52
CGGA_578	Primary	AA	WHO III	Female	362	1	61	Wildtype	Non-codel	7.43
CGGA_599	Primary	AO	WHO III	Female	418	1	66	Wildtype	Non-codel	1.84
CGGA_604	Primary	GBM	WHO IV	Male	381	1	46	Wildtype	Non-codel	9.28
CGGA_616	Primary	AA	WHO III	Male	952	1	43	Wildtype	Non-codel	5.14
CGGA_624	Recurrent	rGBM	WHO IV	Female	122	1	50	Wildtype	Non-codel	8.98
CGGA_630	Recurrent	rO	WHO III	Male	4300	0	43	Mutant	Codel	0.51
CGGA_633	Primary	O	WHO II	Female	1812	1	56	Wildtype	Non-codel	3.09
CGGA_638	Primary	O	WHO II	Male	2386	0	42	Mutant	Codel	1.4
CGGA_643	Primary	AA	WHO III	Male	331	1	56	Wildtype	Non-codel	1.42
CGGA_658	Primary	GBM	WHO IV	Male	372	1	55	Wildtype	Non-codel	2.76
CGGA_659	Primary	O	WHO II	Female	730	0	50	Wildtype	Non-codel	1.24
CGGA_661	Primary	AA	WHO III	Male	NA	1	42	Wildtype	Non-codel	5.78
CGGA_669	Recurrent	rGBM	WHO IV	Female	607	1	56	Wildtype	Codel	0.37
CGGA_676	Primary	GBM	WHO IV	Male	376	1	60	Wildtype	Non-codel	3.08
CGGA_678	Recurrent	rAA	WHO III	Male	386	1	56	Wildtype	Non-codel	5.14
CGGA_679	Primary	GBM	WHO IV	Female	263	1	63	Wildtype	Non-codel	2.3
CGGA_680	Primary	GBM	WHO IV	Male	2373	1	44	Wildtype	Non-codel	0.54
CGGA_681	NA	NA	NA	Male	110	1	42	Wildtype	NA	1.28
CGGA_700	Primary	GBM	WHO IV	Female	170	0	24	Mutant	Non-codel	0.8
CGGA_727	Primary	AA	WHO III	Male	1023	1	45	Wildtype	Non-codel	1.22
CGGA_731	Primary	GBM	WHO IV	Male	503	1	50	Wildtype	Non-codel	5.25
CGGA_732	Primary	AA	WHO III	Female	NA	NA	35	Wildtype	Non-codel	0.53
CGGA_766	Primary	O	WHO II	Male	4103	0	32	Mutant	Codel	0.48
CGGA_773	Secondary	sGBM	WHO IV	Female	443	1	51	Mutant	Non-codel	0.72
CGGA_782	Primary	GBM	WHO IV	Male	289	1	60	Wildtype	Non-codel	6.78
CGGA_789	Primary	GBM	WHO IV	Male	345	1	50	Wildtype	Non-codel	9.14
CGGA_791	Primary	AA	WHO III	Male	252	1	55	Wildtype	Non-codel	1.05
CGGA_796	Primary	A	WHO II	Male	559	0	60	Wildtype	Non-codel	3.86
CGGA_802	Primary	GBM	WHO IV	Male	681	1	42	Wildtype	Non-codel	7.75
CGGA_804	Primary	GBM	WHO IV	Female	614	0	53	Wildtype	Non-codel	1.55
CGGA_807	Primary	A	WHO II	Male	4040	0	42	Wildtype	Non-codel	0.35
CGGA_808	Primary	GBM	WHO IV	Male	1167	0	38	Wildtype	Non-codel	1.5
CGGA_837	Primary	GBM	WHO IV	Male	432	1	57	Wildtype	Non-codel	3.39
CGGA_842	Primary	GBM	WHO IV	Female	204	1	63	Wildtype	Non-codel	7.43
CGGA_848	Primary	GBM	WHO IV	Female	239	0	54	Wildtype	Non-codel	3.25
CGGA_850	Primary	GBM	WHO IV	Female	564	0	52	Wildtype	Non-codel	2.06
CGGA_859	Primary	GBM	WHO IV	Male	387	1	56	Wildtype	Non-codel	8.78
CGGA_871	Primary	O	WHO II	Male	3964	0	37	Mutant	Codel	1.39
CGGA_876	Primary	GBM	WHO IV	Female	159	1	60	Wildtype	Non-codel	7.15
CGGA_878	Primary	GBM	WHO IV	Male	863	1	42	Wildtype	Non-codel	3.14
CGGA_902	Primary	GBM	WHO IV	Male	193	1	54	Wildtype	Non-codel	2.06
CGGA_D02	Recurrent	rGBM	WHO IV	Male	484	1	44	Wildtype	Non-codel	8.8
CGGA_D03	Primary	GBM	WHO IV	Male	423	1	44	Wildtype	Non-codel	2.35
CGGA_D06	Recurrent	rAA	WHO III	Female	NA	1	49	Wildtype	Codel	1.6
CGGA_D09	Primary	GBM	WHO IV	Male	233	1	57	Wildtype	Non-codel	8.12
CGGA_D11	Primary	AA	WHO III	Female	827	1	53	Wildtype	Non-codel	2.53
CGGA_D16	Primary	AA	WHO III	Female	261	1	59	Wildtype	Non-codel	3.62
CGGA_D18	Primary	AA	WHO III	Male	NA	NA	20	Wildtype	Non-codel	0.64
CGGA_D26	Recurrent	rGBM	WHO IV	Male	263	1	55	Wildtype	Non-codel	7.3
CGGA_D32	Recurrent	rGBM	WHO IV	Male	358	1	61	Wildtype	Non-codel	2.64
CGGA_D35	Primary	GBM	WHO IV	Female	661	1	68	Wildtype	Non-codel	7.44
CGGA_D36	Recurrent	rGBM	WHO IV	Male	34	1	58	Wildtype	Non-codel	5.36
CGGA_D37	Primary	GBM	WHO IV	Male	965	1	55	Wildtype	Non-codel	13.48
CGGA_D44	Primary	AA	WHO III	Female	313	1	18	Wildtype	Non-codel	9.07
CGGA_D51	Recurrent	rGBM	WHO IV	Female	135	1	60	Wildtype	Non-codel	7.08
CGGA_D57	Primary	GBM	WHO IV	Male	766	1	40	Wildtype	Non-codel	5.15
CGGA_D58	Recurrent	rAA	WHO III	Male	181	1	24	Wildtype	Codel	0.54
CGGA_342	Primary	GBM	WHO IV	Female	500	1	49	Wildtype	NA	1.91
CGGA_719	Secondary	sGBM	WHO IV	Male	101	1	51	Mutant	NA	0.3
CGGA_J030	Primary	O	WHO II	Male	3389	0	38	Mutant	Codel	0.25
CGGA_J130	Recurrent	rA	WHO II	Male	3116	0	40	Wildtype	Non-codel	1.09
CGGA_1005	Primary	A	WHO II	Female	3815	0	41	Mutant	Non-codel	0.4
CGGA_1013	Primary	A	WHO II	Female	1725	1	38	Mutant	Non-codel	0.94
CGGA_1020	Primary	A	WHO II	Female	1120	1	38	Mutant	Non-codel	1.12
CGGA_1027	Primary	AO	WHO III	Male	2688	0	37	Mutant	Codel	2.18
CGGA_1031	Primary	A	WHO II	Female	2457	1	31	Mutant	Non-codel	0.6
CGGA_1046	Primary	A	WHO II	Male	3559	0	21	NA	NA	0.43
CGGA_1050	Primary	O	WHO II	Male	NA	0	39	Mutant	Codel	0.38
CGGA_1060	Recurrent	rGBM	WHO IV	Male	227	1	42	Mutant	Non-codel	0.99
CGGA_1061	Recurrent	rAA	WHO III	Female	532	1	43	Mutant	Non-codel	1.16
CGGA_1068	Secondary	sGBM	WHO IV	Male	68	1	29	Mutant	Non-codel	2.12
CGGA_1094	Recurrent	rAA	WHO III	Female	252	1	40	Mutant	Non-codel	1.06
CGGA_1105	Secondary	sGBM	WHO IV	Male	609	1	29	Mutant	Non-codel	2.88
CGGA_1113	Primary	AO	WHO III	Male	1265	1	34	Mutant	Codel	0.52
CGGA_1129	Secondary	sGBM	WHO IV	Male	168	1	34	Mutant	Non-codel	3.12
CGGA_1136	Secondary	sGBM	WHO IV	Female	236	1	38	Mutant	Non-codel	1.44
CGGA_1146	Primary	AA	WHO III	Male	2498	0	35	Mutant	Non-codel	1.48
CGGA_1160	Primary	AA	WHO III	Female	426	1	43	Mutant	Non-codel	1.4
CGGA_1170	Secondary	sGBM	WHO IV	Male	346	1	25	Mutant	Non-codel	0.33
CGGA_1175	Secondary	sGBM	WHO IV	Female	441	1	36	Mutant	Non-codel	0.8
CGGA_1189	Primary	AO	WHO III	Female	3082	0	34	Mutant	Codel	0.9
CGGA_1190	Primary	AA	WHO III	Female	3082	0	45	Mutant	Non-codel	0.95
CGGA_1215	Recurrent	rAA	WHO III	Male	29	1	37	Mutant	Non-codel	2.24
CGGA_1219	Primary	GBM	WHO IV	Female	2120	1	57	Mutant	Non-codel	0.92
CGGA_1227	Recurrent	rGBM	WHO IV	Female	1197	1	46	Wildtype	Non-codel	5.98
CGGA_1231	Recurrent	rAA	WHO III	Female	2279	1	31	Wildtype	Non-codel	10.6
CGGA_1287	Primary	GBM	WHO IV	Male	533	1	29	Mutant	Non-codel	0.7
CGGA_1307	Primary	AA	WHO III	Female	965	0	25	Mutant	Non-codel	3.71
CGGA_1314	Primary	GBM	WHO IV	Female	172	1	27	Mutant	Non-codel	1.9
CGGA_1322	Primary	AA	WHO III	Male	253	1	10	Mutant	Non-codel	1.04
CGGA_1324	Secondary	sGBM	WHO IV	Male	848	1	42	Mutant	Codel	0.65
CGGA_1329	Recurrent	rAA	WHO III	Female	326	1	43	Mutant	Non-codel	0.2
CGGA_1330	Primary	A	WHO II	Male	2846	0	47	Mutant	Non-codel	1.35
CGGA_1374	Recurrent	rAA	WHO III	Male	141	1	22	Mutant	Non-codel	0.56
CGGA_1375	Secondary	sGBM	WHO IV	Female	92	1	50	Mutant	Non-codel	0.35
CGGA_1381	Recurrent	rGBM	WHO IV	Female	46	1	36	Mutant	Non-codel	1.2
CGGA_1384	Primary	GBM	WHO IV	Female	191	1	39	Mutant	Non-codel	0.28
CGGA_1388	Recurrent	rAA	WHO III	Male	485	1	37	Mutant	Non-codel	0.94
CGGA_1394	Recurrent	rGBM	WHO IV	Male	139	1	37	Mutant	Non-codel	0.33
CGGA_1406	Primary	A	WHO II	Male	2154	1	34	Mutant	Non-codel	3.21
CGGA_1409	Primary	GBM	WHO IV	Female	2727	0	24	Mutant	Non-codel	0.67
CGGA_1412	Secondary	sGBM	WHO IV	Female	296	1	48	Mutant	Codel	0.78
CGGA_171	Primary	O	WHO II	Female	NA	NA	37	Mutant	Codel	1.36
CGGA_236	Primary	O	WHO II	Male	706	0	47	Mutant	Codel	0.36
CGGA_241	Primary	A	WHO II	Male	4809	0	34	Mutant	Non-codel	1.84
CGGA_245	Recurrent	rAA	WHO III	Female	318	1	39	Mutant	Non-codel	1.55
CGGA_251	Primary	A	WHO II	Male	1299	0	45	Mutant	Non-codel	1.67
CGGA_252	Primary	A	WHO II	Female	165	0	24	Mutant	Non-codel	0.48
CGGA_254	Primary	O	WHO II	Male	4793	0	38	Mutant	Codel	1.15
CGGA_256	Primary	A	WHO II	Male	158	0	39	Mutant	Non-codel	0.91
CGGA_261	Primary	A	WHO II	Male	4130	0	27	Mutant	Non-codel	0.39
CGGA_267	Primary	A	WHO II	Male	1374	1	36	Mutant	Non-codel	0.68
CGGA_273	Primary	A	WHO II	Female	2635	1	35	Mutant	Non-codel	1.67
CGGA_283	Primary	O	WHO II	Female	3470	1	47	Mutant	Codel	1.07
CGGA_300	Primary	A	WHO II	Female	2199	1	35	Mutant	Non-codel	0.73
CGGA_314	Recurrent	rO	WHO III	Male	2568	1	39	Mutant	Codel	0.6
CGGA_333	Primary	A	WHO II	Male	1783	0	40	Mutant	Non-codel	1.76
CGGA_337	Primary	AA	WHO III	Female	1008	1	50	Mutant	Non-codel	2.07
CGGA_374	Secondary	sGBM	WHO IV	Male	493	1	37	Mutant	Non-codel	0.34
CGGA_400	Recurrent	rAA	WHO III	Female	1321	1	38	Mutant	Non-codel	1.4
CGGA_431	Recurrent	rO	WHO III	Male	4515	0	41	Mutant	Codel	0.77
CGGA_446	Primary	O	WHO II	Female	4508	0	35	Mutant	Codel	0.67
CGGA_458	Recurrent	rA	WHO II	Male	419	1	51	Mutant	Non-codel	0.43
CGGA_479	Primary	O	WHO II	Female	4445	0	36	Mutant	Codel	0.77
CGGA_485	Primary	O	WHO II	Male	2437	0	31	Mutant	Codel	0.59
CGGA_490	Primary	AO	WHO III	Female	3804	0	37	Mutant	Codel	0.72
CGGA_491	Primary	GBM	WHO IV	Female	1074	1	36	Mutant	Non-codel	1.31
CGGA_501	Primary	O	WHO II	Male	4427	0	40	Mutant	Codel	0.61
CGGA_502	Primary	A	WHO II	Male	4414	0	39	Mutant	Non-codel	0.46
CGGA_510	Primary	AO	WHO III	Male	4403	0	42	Mutant	Codel	0.4
CGGA_538	Primary	O	WHO II	Female	NA	NA	43	Mutant	Codel	1.22
CGGA_543	Primary	O	WHO II	Male	4371	0	43	Mutant	Codel	0.58
CGGA_545	Recurrent	rGBM	WHO IV	Female	555	1	38	Mutant	Non-codel	1.92
CGGA_551	Primary	O	WHO II	Male	4363	0	44	Mutant	Codel	0.36
CGGA_560	Primary	AA	WHO III	Male	4354	0	43	Mutant	Non-codel	1.39
CGGA_580	Primary	A	WHO II	Male	1394	1	43	Mutant	Non-codel	0.26
CGGA_591	Primary	AA	WHO III	Female	1654	1	50	Mutant	Non-codel	1.21
CGGA_598	Primary	AO	WHO III	Female	3677	1	61	Mutant	Codel	0.23
CGGA_601	Primary	A	WHO II	Male	1133	1	36	Mutant	Non-codel	1.78
CGGA_603	Recurrent	rAA	WHO III	Male	168	0	32	Mutant	Non-codel	1.63
CGGA_605	Recurrent	rA	WHO III	Male	4300	0	43	Mutant	Non-codel	2.9
CGGA_607	Primary	A	WHO II	Male	1389	0	25	Mutant	Non-codel	0.5
CGGA_632	Primary	A	WHO II	Female	576	1	29	Mutant	Non-codel	0.86
CGGA_635	Primary	O	WHO II	Male	4263	0	45	Mutant	Codel	0.49
CGGA_641	Primary	AO	WHO III	Female	3411	1	51	Mutant	Codel	0.57
CGGA_642	Primary	A	WHO II	Male	4249	0	29	Mutant	Non-codel	0.43
CGGA_655	Primary	O	WHO II	Male	4231	0	35	Mutant	Codel	1.07
CGGA_662	Primary	O	WHO II	Female	284	1	59	Mutant	Codel	0.41
CGGA_666	Primary	O	WHO II	Female	2499	1	45	Mutant	Codel	0.58
CGGA_671	Primary	O	WHO II	Female	4215	0	36	Mutant	Codel	0.79
CGGA_675	Primary	O	WHO II	Female	4210	0	23	Mutant	Codel	0.61
CGGA_685	Primary	A	WHO II	Female	3239	1	20	Mutant	Non-codel	0.91
CGGA_689	Primary	O	WHO II	Female	4194	0	36	Mutant	Codel	1.08
CGGA_694	Recurrent	rA	WHO II	Male	1194	1	34	Mutant	Non-codel	1.78
CGGA_698	Primary	A	WHO II	Female	1851	1	61	Mutant	Non-codel	0.89
CGGA_704	Primary	A	WHO II	Male	2572	1	36	Mutant	Non-codel	0.99
CGGA_710	Primary	GBM	WHO IV	Male	660	1	41	Mutant	Non-codel	1.42
CGGA_715	Primary	O	WHO II	Male	3429	0	43	Mutant	Codel	0.54
CGGA_725	Primary	O	WHO II	Male	4147	0	58	Mutant	Codel	0.94
CGGA_747	Primary	GBM	WHO IV	Male	970	1	29	Mutant	Non-codel	0.6
CGGA_752	Primary	O	WHO II	Male	3680	0	39	Mutant	Codel	0.54
CGGA_757	Primary	A	WHO II	Male	1047	1	45	Mutant	Non-codel	0.41
CGGA_760	Primary	O	WHO II	Female	4110	0	34	Mutant	Codel	0.4
CGGA_761	Primary	GBM	WHO IV	Male	1840	1	45	Mutant	Non-codel	1.06
CGGA_762	Primary	O	WHO II	Female	4105	0	40	Mutant	Codel	0.59
CGGA_785	Primary	O	WHO II	Male	3517	1	43	Mutant	Codel	0.28
CGGA_812	Primary	O	WHO II	Male	4035	0	27	Mutant	Codel	0.88
CGGA_815	Primary	A	WHO II	Female	2219	1	53	Mutant	Non-codel	1.67
CGGA_818	Primary	A	WHO II	Male	4027	0	46	Mutant	Non-codel	1.08
CGGA_822	Secondary	sGBM	WHO IV	Male	2237	1	46	Mutant	Non-codel	0.32
CGGA_834	Primary	A	WHO II	Male	4013	0	24	Wildtype	Non-codel	3.57
CGGA_835	Primary	O	WHO II	Male	517	0	31	Mutant	Codel	0.59
CGGA_838	Primary	O	WHO II	Female	4011	0	36	Mutant	Codel	0.34
CGGA_843	Primary	O	WHO II	Male	4005	0	29	Mutant	Codel	0.84
CGGA_856	Primary	O	WHO II	Male	1297	0	53	Mutant	Codel	0.24
CGGA_858	Primary	O	WHO II	Male	3984	0	42	Mutant	Codel	0.22
CGGA_864	Primary	O	WHO II	Male	3977	0	34	Mutant	Codel	0.45
CGGA_884	Primary	A	WHO II	Female	1131	1	48	Mutant	Non-codel	0.79
CGGA_892	Primary	O	WHO II	Male	448	0	34	Mutant	Codel	1.01
CGGA_893	Primary	A	WHO II	Female	3160	1	36	Mutant	Non-codel	0.63
CGGA_898	Primary	A	WHO II	Male	1663	1	38	Mutant	Non-codel	0.57
CGGA_899	Recurrent	rGBM	WHO IV	Male	231	1	48	Mutant	Non-codel	0.7
CGGA_904	Primary	A	WHO II	Male	774	1	33	Mutant	Non-codel	0.31
CGGA_905	Primary	A	WHO II	Male	3174	1	46	Mutant	Non-codel	0.74
CGGA_908	Primary	A	WHO II	Male	2436	1	35	Mutant	Non-codel	0.45
CGGA_909	Primary	A	WHO II	Female	3920	0	39	Mutant	Non-codel	2.43
CGGA_D04	Primary	A	WHO II	Male	3697	0	37	Mutant	Non-codel	0.18
CGGA_D07	Primary	O	WHO II	Male	3691	0	36	Mutant	Codel	1.35
CGGA_D14	Recurrent	rAA	WHO III	Male	NA	NA	27	Mutant	Non-codel	0.98
CGGA_D15	Primary	O	WHO II	Female	3669	0	38	Mutant	Codel	0.47
CGGA_D20	Primary	A	WHO II	Female	2367	1	40	Mutant	Non-codel	0.3
CGGA_D21	Primary	AA	WHO III	Female	1208	1	37	Mutant	Non-codel	1.1
CGGA_D24	Primary	AA	WHO III	Female	837	1	37	Mutant	Non-codel	1.13
CGGA_D27	Recurrent	rO	WHO III	Male	3650	0	33	Mutant	Codel	1.34
CGGA_D29	Primary	O	WHO II	Male	3643	0	36	Mutant	Codel	0.38
CGGA_D33	Recurrent	rAA	WHO III	Male	116	1	38	Mutant	Non-codel	1.36
CGGA_D34	Secondary	sGBM	WHO IV	Male	147	1	45	Mutant	Non-codel	0.82
CGGA_D38	Recurrent	rGBM	WHO IV	Female	689	1	41	Mutant	Codel	0.43
CGGA_D39	Recurrent	rA	WHO II	Female	NA	NA	27	Mutant	Non-codel	0.99
CGGA_D40	Recurrent	rA	WHO II	Male	3557	0	44	Mutant	Non-codel	1.7
CGGA_D45	Recurrent	rAA	WHO III	Male	856	0	32	Mutant	Non-codel	1.43
CGGA_D46	Recurrent	rAA	WHO III	Female	300	1	36	Mutant	Non-codel	1.15
CGGA_D47	Primary	O	WHO II	Male	1048	1	36	Mutant	Codel	2.06
CGGA_D48	Recurrent	rAA	WHO III	Female	221	1	23	Mutant	Non-codel	0.57
CGGA_D52	Primary	AO	WHO III	Male	3529	0	46	Mutant	Codel	0.34
CGGA_D56	Recurrent	rAA	WHO III	Male	64	1	45	Mutant	Non-codel	2.64
CGGA_D59	Secondary	sGBM	WHO IV	Female	NA	NA	30	Mutant	Non-codel	0.52
CGGA_277	Primary	AO	WHO III	Male	4739	0	40	Mutant	NA	1.06
CGGA_518	Secondary	sGBM	WHO IV	Male	212	1	27	Mutant	NA	0.53
CGGA_J023	Primary	AO	WHO III	Male	1028	1	36	Mutant	Non-codel	0.23
CGGA_J024	Recurrent	rA	WHO II	Female	3410	0	29	Mutant	Non-codel	0.62
CGGA_J042	Recurrent	rA	WHO II	Male	1409	1	38	Mutant	Non-codel	3.53
CGGA_J100	Recurrent	rGBM	WHO IV	Male	NA	NA	55	Mutant	Non-codel	1.43
CGGA_1004	Primary	A	WHO II	Female	899	1	63	Wildtype	Non-codel	17.04
CGGA_1008	Primary	GBM	WHO IV	Female	315	1	54	Wildtype	Non-codel	1.95
CGGA_1053	Primary	GBM	WHO IV	Female	370	1	63	Wildtype	Non-codel	2.81
CGGA_1059	Recurrent	rA	WHO II	Female	21	1	54	Mutant	Non-codel	0.86
CGGA_1070	Primary	GBM	WHO IV	Male	333	1	37	Wildtype	Non-codel	3.38
CGGA_1071	Primary	A	WHO II	Male	3252	1	42	Wildtype	Non-codel	4.32
CGGA_1073	Primary	GBM	WHO IV	Female	270	1	42	Wildtype	Non-codel	2.63
CGGA_1074	Primary	GBM	WHO IV	Male	86	1	50	Wildtype	Non-codel	8.23
CGGA_1095	NA	NA	NA	Male	2778	1	29	Mutant	Non-codel	0.42
CGGA_1099	Secondary	sGBM	WHO IV	Male	122	1	8	Wildtype	Non-codel	2.25
CGGA_1114	Primary	GBM	WHO IV	Male	460	1	53	Wildtype	Non-codel	7.19
CGGA_1116	Recurrent	rGBM	WHO IV	Female	248	1	43	Mutant	Non-codel	0.32
CGGA_1118	Recurrent	rAA	WHO III	Male	398	1	31	Mutant	Non-codel	0.79
CGGA_1119	Recurrent	rGBM	WHO IV	Male	422	1	52	Mutant	Codel	3.61
CGGA_1197	Secondary	sGBM	WHO IV	Male	942	1	43	Mutant	Codel	0.66
CGGA_1246	Primary	AO	WHO III	Male	2972	0	53	Mutant	Codel	0.49
CGGA_1275	Primary	GBM	WHO IV	Male	183	1	70	Wildtype	Non-codel	2.55
CGGA_1450	Secondary	sGBM	WHO IV	Male	957	1	37	Mutant	Codel	0.42
CGGA_1460	Secondary	sGBM	WHO IV	Male	209	1	51	Wildtype	Non-codel	1.58
CGGA_1475	Secondary	sGBM	WHO IV	Male	182	1	38	Mutant	Non-codel	1.38
CGGA_243	Primary	O	WHO II	Female	2977	0	30	Mutant	Codel	0.53
CGGA_247	Primary	AA	WHO III	Male	609	1	60	Mutant	Non-codel	1.28
CGGA_738	Primary	A	WHO II	Male	4132	0	43	Mutant	Non-codel	3.44
CGGA_759	Primary	GBM	WHO IV	Male	1263	1	30	Mutant	Non-codel	1.13
CGGA_D30	Primary	GBM	WHO IV	Male	215	1	70	Wildtype	Non-codel	3.28

data.file<-"data.txt"
gene<-"ADAMTSL4"

## load R package
library(ggplot2)
library(ggpubr)
library(gridExtra)
library(plotly)
library(base64enc)

## import data
dat<-read.table(data.file, sep='\t', head=T)
rownames(dat)<-dat$CGGA.ID
#head(dat)
mat<-dat[!is.na(dat$PRS.type)&(dat$PRS.type=="Primary"|dat$PRS.type=="Recurrent")&
           !is.na(dat$Histology)&
           !is.na(dat$Grade)&
           !is.na(dat$Gender)&
           !is.na(dat$Age)&
           !is.na(dat$IDH.mutation.status)&
           !is.na(dat$X1p19q.codel.status),]
#head(mat)

mat$Expression<-log1p(mat$Expression)

### 1.Histology
types<-c('O','OA','A','rO','rA','rOA','AO','AA','rAO','rAA','AOA','rAOA','GBM','rGBM','sGBM')
ptext<-paste0("Anova, p=",compare_means(Expression~Histology,mat,method = "anova")[[3]])
dat_text <- data.frame(Histology=types[5],
                       Expression=max(mat$Expression)+0.5,
                       label = ptext,
                       IDH.mutation.status=NA,
                       Grade = NA,
                       fill_color=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       Age=NA,
                       Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$Histology
set.seed(1)

set.seed(1)
plot1<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=factor(Histology,levels=types)
                      ))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab('Histology')+ylab(paste0("Gene expression of ", gene))+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

### 2.Grade
ptext<-paste0("Anova, p=",compare_means(Expression~Grade,mat,method = "anova")[[3]])
dat_text <- data.frame(Grade="WHO III",
                       Expression=max(mat$Expression)+0.5,
                       label = ptext,
                       Histology = NA,
                       IDH.mutation.status=NA,
                       fill_color=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       Age=NA,
                       Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$Grade

set.seed(1)
plot2<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=Grade))+
  guides(fill=FALSE)+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab('Grade')+ylab(paste0("Gene expression of ", gene))+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

### 3.IDH.mutation.status
ptext<-paste0("Anova, p=",compare_means(Expression~IDH.mutation.status,mat,method = "anova")[[3]])
dat_text <- data.frame(IDH.mutation.status=1.5,
                       Expression=max(mat$Expression)+0.5,
                       label=ptext,
                       Histology = NA,
                       Grade=NA,
                       IDH.mutation.status=NA,
                       fill_color=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       Age=NA,
                       Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$IDH.mutation.status

set.seed(1)
plot3<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=IDH.mutation.status))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab('IDH mutation status')+ylab(paste0("Gene expression of ", gene))+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

### 4.IDH.mutation.status.grade
ptext1<-paste0("T-test, p=",compare_means(Expression~IDH.mutation.status,mat[mat$Grade=="WHO II",],method = "t.test")[[5]])
ptext2<-paste0("T-test, p=",compare_means(Expression~IDH.mutation.status,mat[mat$Grade=="WHO III",],method = "t.test")[[5]])
ptext3<-paste0("T-test, p=",compare_means(Expression~IDH.mutation.status,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]])
dat_text <- data.frame(IDH.mutation.status=rep(1.5,3),
                       Expression=rep(max(mat$Expression)+0.5,3),
                       label = c(ptext1, ptext2, ptext3),
                       Grade = c("WHO II", "WHO III", "WHO IV"),
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       Age=NA,
                       Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$IDH.mutation.status

set.seed(1)
plot4<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=IDH.mutation.status
                      ))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab('IDH mutation status')+ylab(paste0("Gene expression of ", gene))+
  facet_grid(.~Grade)+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

### 5.x1p19q.codel.status
ptext<-paste0("T-test, p=",compare_means(Expression~X1p19q.codel.status,mat,method = "t.test")[[5]])
dat_text <- data.frame(X1p19q.codel.status=1.5,
                       Expression=max(mat$Expression)+0.5,
                       label = ptext,
                       Grade = NA,
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       IDH.mutation.status=NA,
                       Age=NA,
                       Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$X1p19q.codel.status

set.seed(1)
plot5<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=X1p19q.codel.status))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  theme(text = element_text(size=10))+
  xlab('1p/19q co-deletion status')+ylab(paste0("Gene expression of ", gene))+
  geom_text(data=dat_text,aes(label = label))

###6. x1p19q.codel.status.grade###
ptext1<-paste0("T-test, p=",compare_means(Expression~X1p19q.codel.status,mat[mat$Grade=="WHO II",],method = "t.test")[[5]])
ptext2<-paste0("T-test, p=",compare_means(Expression~X1p19q.codel.status,mat[mat$Grade=="WHO III",],method = "t.test")[[5]])
ptext3<-paste0("T-test, p=",compare_means(Expression~X1p19q.codel.status,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]])
dat_text <- data.frame(X1p19q.codel.status=rep(1.5,3),
                       Expression=rep(max(mat$Expression)+0.5,3),
                       label = c(ptext1, ptext2, ptext3),
                       Grade = c("WHO II", "WHO III", "WHO IV"),
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       #X1p19q.codel.status=NA,
                       IDH.mutation.status=NA,
                       Age=NA,
                       Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$X1p19q.codel.status

set.seed(1)
plot6<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=X1p19q.codel.status
                      ))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab('1p/19q co-deletion status')+ylab(paste0("Gene expression of ", gene))+
  facet_grid(.~Grade)+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

###7. IDH.mutation.status.X1p19q.codel.status###
mat$IDH.mutation.status.X1p19q.codel.status<-paste0('IDH ',mat$IDH.mutation.status,', ','1p/19q ',mat$X1p19q.codel.status)
mat$IDH.mutation.status.X1p19q.codel.status[mat$Grade=='WHO IV'&mat$IDH.mutation.status=='Mutant']<-'IDH Mutant (GBM)'
mat$IDH.mutation.status.X1p19q.codel.status[mat$Grade=='WHO IV'&mat$IDH.mutation.status=='Wildtype']<-'IDH Wildtype (GBM)'
mat$IDH.mutation.status.X1p19q.codel.status[grepl('IDH Wildtype,',mat$IDH.mutation.status.X1p19q.codel.status)]<-'IDH Wildtype'
mat$IDH.mutation.status.X1p19q.codel.status[mat$Grade=='WHO II'|mat$Grade=='WHO III']<-paste0(mat$IDH.mutation.status.X1p19q.codel.status[mat$Grade=='WHO II'|mat$Grade=='WHO III'],' (LGG)')
ptext<-paste0("Anova, p=",compare_means(Expression~IDH.mutation.status.X1p19q.codel.status,mat,method = "anova")[[3]])
dat_text <- data.frame(
  IDH.mutation.status.X1p19q.codel.status = c("IDH Wildtype (LGG)"),
  Expression = c(max(mat$Expression)+0.5),
  label = ptext,
  Grade = NA,
  fill_color=NA,
  Histology=NA,
  CGGA.ID=NA,
  X1p19q.codel.status=NA,
  IDH.mutation.status=NA,
  Age=NA,
  Gender=NA,
  PRS.type=NA
)
mat$fill_color<-mat$IDH.mutation.status.X1p19q.codel.status
types1<-c('IDH Mutant, 1p/19q Codel (LGG)','IDH Mutant, 1p/19q Non-codel (LGG)',
        'IDH Wildtype (LGG)','IDH Mutant (GBM)','IDH Wildtype (GBM)')

set.seed(1)
plot7<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=factor(IDH.mutation.status.X1p19q.codel.status,levels=types1)
                      ))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab("IDH mutation status & 1p/19q co-deletion status")+
  ylab(paste0("Gene expression of ", gene))+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10),
        axis.text.x = element_text(angle=45,hjust = 1,vjust = 1))

###8. Gender###
ptext<-paste0("T-test, p=",compare_means(Expression~Gender,mat,method = "t.test")[[5]])
dat_text <- data.frame(Gender = 1.5,
                       Expression = c(max(mat$Expression)+0.5),
                       label = ptext,
                       Grade = NA,
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       IDH.mutation.status=NA,
                       Age=NA,
                       #Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$Gender

set.seed(1)
plot8<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=Gender))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab("Gender")+
  ylab(paste0("Gene expression of ", gene))+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

###9. Gender.grade###
ptext1<-paste0("T-test, p=",compare_means(Expression~Gender,mat[mat$Grade=="WHO II",],method = "t.test")[[5]])
ptext2<-paste0("T-test, p=",compare_means(Expression~Gender,mat[mat$Grade=="WHO III",],method = "t.test")[[5]])
ptext3<-paste0("T-test, p=",compare_means(Expression~Gender,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]])
dat_text <- data.frame(Gender=rep(1.5,3),
                       Expression=rep(max(mat$Expression)+0.5,3),
                       label = c(ptext1, ptext2, ptext3),
                       Grade = c("WHO II", "WHO III", "WHO IV"),
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       IDH.mutation.status=NA,
                       Age=NA,
                       #Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$Gender

set.seed(1)
plot9<-ggplot(mat,aes(fill=fill_color,
                      y=Expression,
                      x=Gender))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab("Gender")+
  ylab(paste0("Gene expression of ", gene))+
  facet_grid(.~Grade)+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

###10.Age.status###
med.Age<-median(mat$Age)
mat$Age.status<-ifelse(mat$Age>=med.Age,paste0('>=',med.Age),paste0('<',med.Age))
ptext<-paste0("T-test, p=",compare_means(Expression~Age.status,mat,method = "t.test")[[5]])
dat_text <- data.frame(Age.status = 1.5,
                       Expression = c(max(mat$Expression)+0.5),
                       label = ptext,
                       Grade = NA,
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       IDH.mutation.status=NA,
                       Age=NA,
                       Gender=NA,
                       PRS.type=NA
                       )
mat$fill_color<-mat$Age.status

set.seed(1)
plot10<-ggplot(mat,aes(fill=fill_color,
                       y=Expression,
                       x=Age.status))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab("Age status")+
  ylab(paste0("Gene expression of ", gene))+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

###11. Age.status.grade###
ptext1<-paste0("T-test, p=",compare_means(Expression~Age.status,mat[mat$Grade=="WHO II",],method = "t.test")[[5]])
ptext2<-paste0("T-test, p=",compare_means(Expression~Age.status,mat[mat$Grade=="WHO III",],method = "t.test")[[5]])
ptext3<-paste0("T-test, p=",compare_means(Expression~Age.status,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]])
dat_text <- data.frame(Age.status=rep(1.5,3),
                       Expression=rep(max(mat$Expression)+0.5,3),
                       label = c(ptext1, ptext2, ptext3),
                       Grade = c("WHO II", "WHO III", "WHO IV"),
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       IDH.mutation.status=NA,
                       Age=NA,
                       Gender=NA,
                       PRS.type=NA)
fill_color<-mat$Age.status

set.seed(1)
plot11<-ggplot(mat,aes(fill=fill_color,
                       y=Expression,
                       x=Age.status))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab("Age status")+
  ylab(paste0("Gene expression of ", gene))+
  facet_grid(.~Grade)+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

###12. PRS.type###
ptext<-paste0("T-test, p=",compare_means(Expression~PRS.type,mat,method = "t.test")[[5]])
dat_text <- data.frame(PRS.type = 1.5,
                       Expression = c(max(mat$Expression)+0.5),
                       label = ptext,
                       Grade = NA,
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       IDH.mutation.status=NA,
                       Age=NA,
                       Gender=NA#,
                       #PRS.type=NA
                       )
mat$fill_color<-mat$PRS.type

set.seed(1)
plot12<-ggplot(mat,aes(fill=fill_color,
                       y=Expression,
                       x=PRS.type
                       ))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab("Progression status")+
  ylab(paste0("Gene expression of ", gene))+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

###13. PRS.type.grade###
ptext1<-paste0("T-test, p=",compare_means(Expression~PRS.type,mat[mat$Grade=="WHO II",],method = "t.test")[[5]])
ptext2<-paste0("T-test, p=",compare_means(Expression~PRS.type,mat[mat$Grade=="WHO III",],method = "t.test")[[5]])
ptext3<-paste0("T-test, p=",compare_means(Expression~PRS.type,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]])
dat_text <- data.frame(PRS.type = rep(1.5,3),
                       Expression = rep(max(mat$Expression)+0.5,3),
                       label = c(ptext1,ptext2,ptext3),
                       Grade = c("WHO II", "WHO III", "WHO IV"),
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       X1p19q.codel.status=NA,
                       IDH.mutation.status=NA,
                       Age=NA,
                       Gender=NA#,
                       #PRS.type=NA
                       )
mat$fill_color<-mat$PRS.type

set.seed(1)
plot13<-ggplot(mat,aes(fill=fill_color,
                       y=Expression,
                       x=PRS.type))+
  geom_boxplot(outlier.shape = NA, width = 0.5)+
  guides(fill=FALSE)+
  geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+
  xlab("Progression status")+
  ylab(paste0("Gene expression of ", gene))+
  facet_grid(~Grade)+
  geom_text(data=dat_text,aes(label = label))+
  theme(text = element_text(size=10))

## output pdf 
grid.arrange(arrangeGrob(plot1,ncol=1),
             arrangeGrob(plot2,ncol=1),
             arrangeGrob(plot3,ncol=1),
             arrangeGrob(plot4,ncol=1),
             arrangeGrob(plot5,ncol=1),
             arrangeGrob(plot6,ncol=1),
             arrangeGrob(plot7,ncol=1),
             arrangeGrob(plot8,ncol=1),
             arrangeGrob(plot9,ncol=1),
             arrangeGrob(plot10,ncol=1),
             arrangeGrob(plot11,ncol=1),
             arrangeGrob(plot12,ncol=1),
             arrangeGrob(plot13,ncol=1),
             ncol=1,
             heights=rep(1,13))

Output - mRNA data (distribution)

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