chr16.9431_chr16_38588390_38591341_+_0.R 

fitVsDatCorrelation=0.773031326587093
cont.fitVsDatCorrelation=0.269242822000623

fstatistic=9918.36766889007,51,669
cont.fstatistic=4295.9045082905,51,669

residuals=-0.558658119044917,-0.0927606365875345,-0.0039377752721975,0.0781915229757704,0.816965937957723
cont.residuals=-0.507163001478218,-0.159582743364537,-0.0340041901768401,0.114996874106985,0.76882367850829

predictedValues:
Include	Exclude	Both
chr16.9431_chr16_38588390_38591341_+_0.R.tl.Lung	72.0929335163598	51.3801933929668	65.3624698452504
chr16.9431_chr16_38588390_38591341_+_0.R.tl.cerebhem	64.0880512184251	56.7026432338251	57.3358747361505
chr16.9431_chr16_38588390_38591341_+_0.R.tl.cortex	66.5334335630759	46.2077459602427	60.0557975774061
chr16.9431_chr16_38588390_38591341_+_0.R.tl.heart	66.2362204085806	49.1902968707137	59.1082375715156
chr16.9431_chr16_38588390_38591341_+_0.R.tl.kidney	64.6409610244925	49.1776107118759	56.0135305150077
chr16.9431_chr16_38588390_38591341_+_0.R.tl.liver	65.5940319636108	50.4356936793593	56.0986317757615
chr16.9431_chr16_38588390_38591341_+_0.R.tl.stomach	62.6621845154356	45.9744172387102	57.404792523317
chr16.9431_chr16_38588390_38591341_+_0.R.tl.testicle	63.2155214164106	49.246775095749	52.8975452969475


diffExp=20.712740123393,7.3854079846,20.3256876028332,17.045923537867,15.4633503126167,15.1583382842515,16.6877672767255,13.9687463206615
diffExpScore=0.992172086437195
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,0,1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,0,1,1,1,1,1,0
diffExp1.3Score=0.857142857142857
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	57.5965104410708	62.7429718581477	58.741105517399
cerebhem	55.0858082456581	58.0230428830799	58.0361121810037
cortex	56.9471118699301	54.3828103405384	60.7608614341792
heart	60.5172263723517	54.5731090492952	57.5898422255114
kidney	59.5393556331997	55.0184316275947	57.9092912522653
liver	60.33128114327	54.1233986378672	54.945064895832
stomach	56.3098585913292	58.3383943127241	61.472130205374
testicle	55.1818430153656	63.7777187839966	54.7993252039694
cont.diffExp=-5.14646141707691,-2.93723463742179,2.56430152939166,5.94411732305647,4.52092400560499,6.20788250540278,-2.02853572139487,-8.59587576863099
cont.diffExpScore=24.8151793394832

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,0,0,0
cont.diffExp1.4Score=0
cont.diffExp1.3=0,0,0,0,0,0,0,0
cont.diffExp1.3Score=0
cont.diffExp1.2=0,0,0,0,0,0,0,0
cont.diffExp1.2Score=0

tran.correlation=0.132984087659287
cont.tran.correlation=-0.644680332273564

tran.covariance=0.000428371194936256
cont.tran.covariance=-0.00162762948088399

tran.mean=57.7111696131146
cont.tran.mean=57.6555545503387

weightedLogRatios:
wLogRatio
Lung	1.39159776089018
cerebhem	0.501874538160289
cortex	1.46385220796031
heart	1.20335305535757
kidney	1.10242762590433
liver	1.06483133985929
stomach	1.23340349880937
testicle	1.00424175055355

cont.weightedLogRatios:
wLogRatio
Lung	-0.350576227344491
cerebhem	-0.209603644860838
cortex	0.185178794361793
heart	0.418844206375077
kidney	0.319600764112464
liver	0.43928346098765
stomach	-0.143282008577541
testicle	-0.591098347297143

varWeightedLogRatios=0.087233614824561
cont.varWeightedLogRatios=0.148716673982641

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21827725335404	0.0727220765746734	58.005456555171	3.37219455418168e-263	***
df.mm.trans1	0.26526364447797	0.0620891861940914	4.27230023032922	2.21536149735112e-05	***
df.mm.trans2	-0.299006984224708	0.0563109970080747	-5.30992168691014	1.49430459618095e-07	***
df.mm.exp2	0.111891704186549	0.0733909702576645	1.52459769633396	0.127832041349848	   
df.mm.exp3	-0.101682610008947	0.0733909702576645	-1.38549210688937	0.166363773333712	   
df.mm.exp4	-0.0277070104250134	0.0733909702576645	-0.377526149712129	0.705902371510583	   
df.mm.exp5	0.00143290915243304	0.0733909702576645	0.0195243249599006	0.984428653681165	   
df.mm.exp6	0.0398118772935101	0.0733909702576645	0.542462882746157	0.587680322930986	   
df.mm.exp7	-0.121545098052571	0.0733909702576645	-1.65613150535883	0.0981641143054906	.  
df.mm.exp8	0.0377762656469064	0.0733909702576645	0.514726341868485	0.606914270640882	   
df.mm.trans1:exp2	-0.229589796090872	0.0659070368958294	-3.4835399511854	0.00052708074407983	***
df.mm.trans2:exp2	-0.0133236323072247	0.0527256295166635	-0.252697453389607	0.80057963818961	   
df.mm.trans1:exp3	0.0214311616702957	0.0659070368958294	0.325172586717396	0.745152119766896	   
df.mm.trans2:exp3	-0.00442270012817779	0.0527256295166635	-0.083881409643104	0.933175813565814	   
df.mm.trans1:exp4	-0.0570215701809444	0.0659070368958294	-0.865181820737457	0.387249134644507	   
df.mm.trans2:exp4	-0.0158493592500087	0.0527256295166635	-0.300600664141898	0.763812416032542	   
df.mm.trans1:exp5	-0.110540657721585	0.0659070368958294	-1.67722086939369	0.0939664189276251	.  
df.mm.trans2:exp5	-0.0452472117086129	0.0527256295166635	-0.858163517882947	0.391109520777175	   
df.mm.trans1:exp6	-0.134283191652346	0.0659070368958294	-2.03746364541603	0.0419965988534575	*  
df.mm.trans2:exp6	-0.0583655006016	0.0527256295166635	-1.10696640583787	0.268706315722926	   
df.mm.trans1:exp7	-0.0186527840392147	0.0659070368958294	-0.283016577860975	0.777251677706416	   
df.mm.trans2:exp7	0.0103774372016904	0.0527256295166635	0.196819597922690	0.844028490990803	   
df.mm.trans1:exp8	-0.169182432620895	0.0659070368958294	-2.56698587266636	0.0104749762681066	*  
df.mm.trans2:exp8	-0.0801851361104129	0.0527256295166635	-1.52079997613060	0.128782515076917	   
df.mm.trans1:probe2	-0.430167278539107	0.0442117844083984	-9.7296972808316	5.14749941603571e-21	***
df.mm.trans1:probe3	0.0113453002887889	0.0442117844083984	0.256612585096968	0.79755675267018	   
df.mm.trans1:probe4	-0.409573355835313	0.0442117844083984	-9.26389561778265	2.64698100203937e-19	***
df.mm.trans1:probe5	0.117162068234211	0.0442117844083984	2.65001898932528	0.00823895470738492	** 
df.mm.trans1:probe6	-0.517176246540098	0.0442117844083984	-11.6977012681229	6.86225453361219e-29	***
df.mm.trans1:probe7	-0.576973646384079	0.0442117844083984	-13.0502230141717	7.69460293291988e-35	***
df.mm.trans1:probe8	-0.503979193107359	0.0442117844083984	-11.3992049823627	1.24132135864226e-27	***
df.mm.trans1:probe9	-0.148853025272220	0.0442117844083984	-3.36681785781856	0.000803940120733505	***
df.mm.trans1:probe10	-0.337506720110324	0.0442117844083984	-7.63386333816944	7.88304370834516e-14	***
df.mm.trans1:probe11	-0.196715813374685	0.0442117844083984	-4.44939773426827	1.00845754416913e-05	***
df.mm.trans1:probe12	-0.353464470500878	0.0442117844083984	-7.99480218296131	5.7080757521383e-15	***
df.mm.trans1:probe13	-0.344791599136223	0.0442117844083984	-7.79863567485248	2.40670791137714e-14	***
df.mm.trans1:probe14	-0.310662132900344	0.0442117844083984	-7.02668162023634	5.22191966107169e-12	***
df.mm.trans1:probe15	-0.315926111172189	0.0442117844083984	-7.14574440728017	2.34679489281391e-12	***
df.mm.trans2:probe2	0.115628075401954	0.0442117844083984	2.61532251975764	0.00911526336341898	** 
df.mm.trans2:probe3	0.125762876247823	0.0442117844083984	2.84455553944874	0.00458326089541485	** 
df.mm.trans2:probe4	0.00134506321154809	0.0442117844083984	0.0304231830844762	0.975738629550837	   
df.mm.trans2:probe5	-0.0671519789033941	0.0442117844083984	-1.51887058624664	0.129267496469937	   
df.mm.trans2:probe6	0.124153261826885	0.0442117844083984	2.80814863023944	0.00512800575913286	** 
df.mm.trans3:probe2	0.0423430877644251	0.0442117844083984	0.95773306440854	0.338543351685213	   
df.mm.trans3:probe3	-0.0912224238745313	0.0442117844083984	-2.06330563435035	0.0394688465326944	*  
df.mm.trans3:probe4	-0.105283281451356	0.0442117844083984	-2.38133979119279	0.0175285522716319	*  
df.mm.trans3:probe5	-0.0846893587475619	0.0442117844083984	-1.91553812814383	0.0558499094260431	.  
df.mm.trans3:probe6	-0.179095196019607	0.0442117844083984	-4.05084749272384	5.7016274967826e-05	***
df.mm.trans3:probe7	0.289301674327800	0.0442117844083984	6.5435421392502	1.19531194225299e-10	***
df.mm.trans3:probe8	0.0578885803502747	0.0442117844083984	1.30934729563366	0.190866242392467	   
df.mm.trans3:probe9	0.0294858386951608	0.0442117844083984	0.666922611012275	0.505051515387847	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11499628979966	0.110403797163049	37.2722351543983	2.07099780399161e-165	***
df.mm.trans1	-0.0502543679313358	0.0942613610813552	-0.533138577194554	0.594114713902274	   
df.mm.trans2	0.0172718778574727	0.085489141462356	0.202035925990416	0.839950058440483	   
df.mm.exp2	-0.110701905098878	0.111419285251109	-0.993561436419063	0.320795646736756	   
df.mm.exp3	-0.188143569176742	0.111419285251109	-1.68860865291602	0.0917604611975504	.  
df.mm.exp4	-0.0702456974000093	0.111419285251109	-0.630462646046366	0.528607229380172	   
df.mm.exp5	-0.0839408662893142	0.111419285251109	-0.753378251351494	0.451487692121119	   
df.mm.exp6	-0.0345853452772476	0.111419285251109	-0.310407172324804	0.756348018164146	   
df.mm.exp7	-0.140822642170614	0.111419285251109	-1.26389827266651	0.206706750769321	   
df.mm.exp8	0.0429911755930784	0.111419285251109	0.385850398305713	0.699730134407291	   
df.mm.trans1:exp2	0.0661320412365287	0.100057471895664	0.660940557297793	0.508877970155494	   
df.mm.trans2:exp2	0.0324955583550967	0.0800459775165315	0.405961165861027	0.684900945495906	   
df.mm.trans1:exp3	0.176804561103555	0.100057471895664	1.76703006536003	0.0776790796819905	.  
df.mm.trans2:exp3	0.045145117238683	0.0800459775165316	0.563989829836975	0.572950066024858	   
df.mm.trans1:exp4	0.119711772088891	0.100057471895664	1.19643010982450	0.231952592049771	   
df.mm.trans2:exp4	-0.069259618904401	0.0800459775165316	-0.86524796189911	0.387212865117743	   
df.mm.trans1:exp5	0.117116416153489	0.100057471895664	1.17049145790544	0.242220025762732	   
df.mm.trans2:exp5	-0.0474374535954032	0.0800459775165316	-0.592627575640589	0.553630675560929	   
df.mm.trans1:exp6	0.0809740898501772	0.100057471895664	0.809275792362748	0.418644251130095	   
df.mm.trans2:exp6	-0.113194624683710	0.0800459775165316	-1.41412008692545	0.157791838802560	   
df.mm.trans1:exp7	0.118230286934387	0.100057471895664	1.18162376776492	0.237774873543745	   
df.mm.trans2:exp7	0.0680365138307391	0.0800459775165316	0.849967930202212	0.395646991803513	   
df.mm.trans1:exp8	-0.0858191905470601	0.100057471895664	-0.85769896961367	0.391365867242785	   
df.mm.trans2:exp8	-0.0266338510783357	0.0800459775165316	-0.332731911142383	0.739440895710304	   
df.mm.trans1:probe2	0.0031821434544633	0.0671205926446441	0.0474093468052419	0.96220112865073	   
df.mm.trans1:probe3	-0.0975216611879611	0.0671205926446441	-1.45293206370017	0.146711492005538	   
df.mm.trans1:probe4	-0.000221863888990194	0.0671205926446441	-0.00330545187770921	0.997363621166604	   
df.mm.trans1:probe5	-0.0473286815353798	0.0671205926446441	-0.705129076942922	0.480975334886254	   
df.mm.trans1:probe6	-0.0548193404734013	0.0671205926446441	-0.81672908884507	0.41437399630678	   
df.mm.trans1:probe7	0.0109961062007351	0.0671205926446441	0.163826119041465	0.869917509323432	   
df.mm.trans1:probe8	-0.00699887759128674	0.0671205926446441	-0.10427317929597	0.916983813857906	   
df.mm.trans1:probe9	0.0374905133646595	0.0671205926446441	0.558554564068662	0.576652650105366	   
df.mm.trans1:probe10	-0.0630776735342204	0.0671205926446441	-0.939766337704612	0.347676608979645	   
df.mm.trans1:probe11	-0.0378194720960934	0.0671205926446441	-0.563455574600192	0.573313507135404	   
df.mm.trans1:probe12	-0.0787092940646891	0.0671205926446441	-1.17265493291156	0.241351591030851	   
df.mm.trans1:probe13	0.0849985630242823	0.0671205926446441	1.26635596730037	0.205826416375661	   
df.mm.trans1:probe14	-0.0679850480636567	0.0671205926446441	-1.01287913865107	0.311484073797535	   
df.mm.trans1:probe15	0.080935875329989	0.0671205926446441	1.20582778162414	0.228310192087226	   
df.mm.trans2:probe2	0.0141060002015658	0.0671205926446441	0.210159053217052	0.83360752447561	   
df.mm.trans2:probe3	0.0374195217358587	0.0671205926446442	0.557496891214422	0.577374464812937	   
df.mm.trans2:probe4	-0.0378228116127986	0.0671205926446441	-0.563505328581402	0.573279656060751	   
df.mm.trans2:probe5	0.0582533748610382	0.0671205926446441	0.867891247168339	0.385765078886717	   
df.mm.trans2:probe6	0.0297199421360010	0.0671205926446442	0.442784262846828	0.658064916747163	   
df.mm.trans3:probe2	0.00397373733348691	0.0671205926446441	0.0592029536229667	0.95280813679494	   
df.mm.trans3:probe3	0.0132970508113942	0.0671205926446441	0.198106874320861	0.84302162843211	   
df.mm.trans3:probe4	-0.0768047159309823	0.0671205926446441	-1.14427946632725	0.252916997735939	   
df.mm.trans3:probe5	0.0549316309655885	0.0671205926446441	0.818402055184651	0.413419055554343	   
df.mm.trans3:probe6	0.00677785994744981	0.0671205926446441	0.100980335250223	0.919596325817247	   
df.mm.trans3:probe7	-0.0131539536854763	0.0671205926446441	-0.195974933581369	0.844689296812217	   
df.mm.trans3:probe8	0.00209530159104170	0.0671205926446441	0.0312169709545748	0.975105816182694	   
df.mm.trans3:probe9	-0.0709833935360344	0.0671205926446442	-1.05755016067634	0.290642287981867