chr4.17243_chr4_134109650_134132041_+_2.R 

fitVsDatCorrelation=0.942782388453383
cont.fitVsDatCorrelation=0.238344422495357

fstatistic=6739.99425304177,52,692
cont.fstatistic=782.613322331244,52,692

residuals=-0.862269720791243,-0.111750071199574,0.00067275633857515,0.095337788153853,1.08644143092819
cont.residuals=-0.910440347776277,-0.346367521620352,-0.170464983193713,0.240865747352652,2.59048575330206

predictedValues:
Include	Exclude	Both
chr4.17243_chr4_134109650_134132041_+_2.R.tl.Lung	60.7181318594335	44.5441195993114	148.692263722801
chr4.17243_chr4_134109650_134132041_+_2.R.tl.cerebhem	58.1402323188469	52.6997801001793	128.610630584814
chr4.17243_chr4_134109650_134132041_+_2.R.tl.cortex	66.4099134293299	44.0585486654301	143.099973763036
chr4.17243_chr4_134109650_134132041_+_2.R.tl.heart	66.8602192939048	44.4518263162556	125.624794904430
chr4.17243_chr4_134109650_134132041_+_2.R.tl.kidney	57.5912535316436	42.7642301688495	127.934690625057
chr4.17243_chr4_134109650_134132041_+_2.R.tl.liver	66.2291317328947	45.862353601682	98.329598357608
chr4.17243_chr4_134109650_134132041_+_2.R.tl.stomach	76.1015339210719	45.7119217658319	127.757947738477
chr4.17243_chr4_134109650_134132041_+_2.R.tl.testicle	70.4212473947309	49.0748062662648	145.683286960303


diffExp=16.1740122601221,5.44045221866752,22.3513647638998,22.4083929776492,14.8270233627941,20.3667781312126,30.38961215524,21.3464411284661
diffExpScore=0.993519289836959
diffExp1.5=0,0,1,1,0,0,1,0
diffExp1.5Score=0.75
diffExp1.4=0,0,1,1,0,1,1,1
diffExp1.4Score=0.833333333333333
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	72.694242189597	88.3479900993329	60.5310669360255
cerebhem	63.8218085375047	72.6646408776872	57.3720031668512
cortex	62.6337382134425	69.1700170004961	60.508155307938
heart	72.2640167465541	63.8426602242719	61.1597958036707
kidney	61.6736414945059	69.4018135294288	60.1094830917186
liver	62.9132819867396	62.4743169288124	58.5587115129975
stomach	68.8532578234962	75.6514411681261	62.604131405647
testicle	64.1654350174776	65.3864576270189	70.2343928684173
cont.diffExp=-15.6537479097359,-8.84283234018249,-6.53627878705365,8.42135652228226,-7.72817203492288,0.438965057927192,-6.79818334462986,-1.22102260954132
cont.diffExpScore=1.42961663633827

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=-1,0,0,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.0590324086658545
cont.tran.correlation=0.501492944174685

tran.covariance=-0.000272439062402291
cont.tran.covariance=0.00353574961981199

tran.mean=55.7274531228538
cont.tran.mean=68.4974224665308

weightedLogRatios:
wLogRatio
Lung	1.22398242127236
cerebhem	0.394335809012545
cortex	1.63748470392186
heart	1.63218223762463
kidney	1.16225825541569
liver	1.47335114550903
stomach	2.07819372872825
testicle	1.47129323850551

cont.weightedLogRatios:
wLogRatio
Lung	-0.85492893045952
cerebhem	-0.547713442131217
cortex	-0.415609508288884
heart	0.522676315042089
kidney	-0.493580057244988
liver	0.0289750726102969
stomach	-0.402911510369844
testicle	-0.0786233468793975

varWeightedLogRatios=0.239652525627546
cont.varWeightedLogRatios=0.180233490281111

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.06270219654756	0.095430021096184	32.0936971549096	3.99647425825029e-139	***
df.mm.trans1	1.19352610061784	0.083193963627644	14.3463064935790	4.71311494243341e-41	***
df.mm.trans2	0.719066805488817	0.0751416737528465	9.56948081638356	1.8540146684114e-20	***
df.mm.exp2	0.269836011722336	0.0993149597856648	2.71697247126393	0.00675290129830909	** 
df.mm.exp3	0.116978551855319	0.0993149597856648	1.17785429413429	0.239259604883734	   
df.mm.exp4	0.262866872993240	0.0993149597856648	2.64680037690739	0.00831055614154515	** 
df.mm.exp5	0.0567091232115308	0.0993149597856648	0.571002831133565	0.568183105312778	   
df.mm.exp6	0.52959624617536	0.0993149597856648	5.33249217759641	1.31317272796879e-07	***
df.mm.exp7	0.403446425202354	0.0993149597856648	4.06229258988824	5.4156106312119e-05	***
df.mm.exp8	0.265562131840403	0.0993149597856649	2.67393887500456	0.0076735095574961	** 
df.mm.trans1:exp2	-0.313220486883099	0.0922954158980174	-3.39367328090482	0.000728930935259419	***
df.mm.trans2:exp2	-0.101704878534367	0.0748614676541558	-1.35857446723088	0.174724158850972	   
df.mm.trans1:exp3	-0.0273745740180859	0.0922954158980175	-0.296597330991321	0.766862945022256	   
df.mm.trans2:exp3	-0.127939300660642	0.0748614676541558	-1.70901405849661	0.0878967195018101	.  
df.mm.trans1:exp4	-0.166505078245505	0.0922954158980174	-1.80404494226979	0.0716589880552161	.  
df.mm.trans2:exp4	-0.264940974576019	0.0748614676541558	-3.53908336128261	0.000428379222301545	***
df.mm.trans1:exp5	-0.109580781584497	0.0922954158980174	-1.18728303587233	0.235523388809709	   
df.mm.trans2:exp5	-0.0974872634492562	0.0748614676541558	-1.30223553590516	0.193269139372749	   
df.mm.trans1:exp6	-0.442718189776386	0.0922954158980175	-4.79675166386997	1.97595926240238e-06	***
df.mm.trans2:exp6	-0.500431798291351	0.0748614676541558	-6.68477140473976	4.76474616653167e-11	***
df.mm.trans1:exp7	-0.177620370023475	0.0922954158980174	-1.92447661994111	0.0547045835035559	.  
df.mm.trans2:exp7	-0.377567440851926	0.0748614676541558	-5.04354847270972	5.84407302824043e-07	***
df.mm.trans1:exp8	-0.117309470763221	0.0922954158980175	-1.27102163874361	0.204148034800025	   
df.mm.trans2:exp8	-0.168696489071484	0.0748614676541558	-2.25344886171382	0.0245431038827367	*  
df.mm.trans1:probe2	0.250518842345228	0.0565191686370253	4.43245802064248	1.08307761708088e-05	***
df.mm.trans1:probe3	0.711811816618443	0.0565191686370253	12.5941664356354	6.81026874528654e-33	***
df.mm.trans1:probe4	-0.0202590516128363	0.0565191686370253	-0.358445676066876	0.720119136267157	   
df.mm.trans1:probe5	-0.340310524837607	0.0565191686370253	-6.02115234608516	2.8102739586508e-09	***
df.mm.trans1:probe6	-0.363491228967634	0.0565191686370253	-6.4312911483541	2.35954826490022e-10	***
df.mm.trans1:probe7	0.446587629434147	0.0565191686370253	7.90152509677204	1.08614047020618e-14	***
df.mm.trans1:probe8	-0.589243654938649	0.0565191686370253	-10.4255541818540	9.75174729015357e-24	***
df.mm.trans1:probe9	-0.483643784857447	0.0565191686370253	-8.55716381752678	7.43398749093601e-17	***
df.mm.trans1:probe10	-0.404649700454189	0.0565191686370253	-7.15951260806597	2.07313522084056e-12	***
df.mm.trans1:probe11	-0.512857824660731	0.0565191686370253	-9.07405110564139	1.17472794313780e-18	***
df.mm.trans1:probe12	-0.448250698307437	0.0565191686370253	-7.93094996117462	8.74363162443273e-15	***
df.mm.trans1:probe13	-0.469937053685299	0.0565191686370253	-8.31464908309792	4.87353624120798e-16	***
df.mm.trans1:probe14	-0.42510473944178	0.0565191686370253	-7.5214259107714	1.68703176179984e-13	***
df.mm.trans1:probe15	-0.116038691380849	0.0565191686370253	-2.05308560226827	0.0404396969999096	*  
df.mm.trans1:probe16	0.103035340701741	0.0565191686370253	1.82301585791980	0.0687323126863028	.  
df.mm.trans1:probe17	-0.455612255619399	0.0565191686370253	-8.0611988216849	3.32177755640384e-15	***
df.mm.trans1:probe18	-0.200085981682341	0.0565191686370253	-3.54014375135848	0.000426692359222344	***
df.mm.trans1:probe19	-0.432116714395706	0.0565191686370253	-7.64548957134923	6.97502449287125e-14	***
df.mm.trans2:probe2	0.0492303428304088	0.0565191686370253	0.87103798618436	0.384035485471329	   
df.mm.trans2:probe3	-0.0319537892140758	0.0565191686370253	-0.565361982220367	0.572010827360839	   
df.mm.trans2:probe4	0.0703853331596994	0.0565191686370253	1.24533560661029	0.213429928196717	   
df.mm.trans2:probe5	0.0340372201425153	0.0565191686370253	0.602224359687729	0.547222073753179	   
df.mm.trans2:probe6	0.0548346647872013	0.0565191686370253	0.970195884149638	0.332287839907277	   
df.mm.trans3:probe2	1.47673139968294	0.0565191686370253	26.1279745490727	3.11185794080044e-105	***
df.mm.trans3:probe3	-0.362797553203139	0.0565191686370253	-6.41901786512608	2.54621572857752e-10	***
df.mm.trans3:probe4	-0.515684080913324	0.0565191686370253	-9.12405637501721	7.7870213218759e-19	***
df.mm.trans3:probe5	-0.353817453449192	0.0565191686370253	-6.26013195136435	6.74738827076948e-10	***
df.mm.trans3:probe6	-0.084929971810193	0.0565191686370253	-1.50267553218318	0.13337892427377	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.54748886075018	0.277976522761708	16.3592551470559	4.23593565564288e-51	***
df.mm.trans1	-0.407194270478133	0.242334314279024	-1.68029967893564	0.093350337557848	.  
df.mm.trans2	-0.0638605333561577	0.218878932901611	-0.291761900104217	0.770556118407819	   
df.mm.exp2	-0.271995474883046	0.289292896117173	-0.940207929519563	0.347438988035383	   
df.mm.exp3	-0.393295478720933	0.289292896117173	-1.35950617522815	0.174429072196126	   
df.mm.exp4	-0.341131002992807	0.289292896117173	-1.17918900730503	0.238728183163945	   
df.mm.exp5	-0.398786878294602	0.289292896117173	-1.37848831978605	0.168498048015760	   
df.mm.exp6	-0.457905916336549	0.289292896117173	-1.58284535321283	0.113913612788009	   
df.mm.exp7	-0.243106135249697	0.289292896117173	-0.840346024781857	0.40100465442098	   
df.mm.exp8	-0.574447175219563	0.289292896117174	-1.98569402473987	0.0474615330758383	*  
df.mm.trans1:exp2	0.141828251620912	0.268845783365362	0.527545010546686	0.597984264742053	   
df.mm.trans2:exp2	0.0765669215786385	0.218062725212711	0.351123382063353	0.725602659617392	   
df.mm.trans1:exp3	0.244337378932477	0.268845783365362	0.908838427271974	0.363751829910766	   
df.mm.trans2:exp3	0.148579517905058	0.218062725212711	0.681361373247655	0.495870807842683	   
df.mm.trans1:exp4	0.335195132771503	0.268845783365362	1.24679334217406	0.212895125627016	   
df.mm.trans2:exp4	0.0162691762390812	0.218062725212711	0.0746077818811597	0.94054834394853	   
df.mm.trans1:exp5	0.234381331815075	0.268845783365362	0.871805869079043	0.383616659153921	   
df.mm.trans2:exp5	0.157416427807105	0.218062725212711	0.721885997038477	0.470608422100032	   
df.mm.trans1:exp6	0.313401036303123	0.268845783365362	1.16572792171046	0.244126101027035	   
df.mm.trans2:exp6	0.111378010278589	0.218062725212711	0.510761342498789	0.609681079982737	   
df.mm.trans1:exp7	0.188821495192622	0.268845783365362	0.702341293320615	0.482702520023049	   
df.mm.trans2:exp7	0.087959176566739	0.218062725212711	0.403366400566345	0.686803301740902	   
df.mm.trans1:exp8	0.449649663752127	0.268845783365362	1.67251893678039	0.0948740484427388	.  
df.mm.trans2:exp8	0.273478893110722	0.218062725212711	1.25412948427548	0.210218415888659	   
df.mm.trans1:probe2	0.367316427806979	0.164633747185991	2.23111260045617	0.0259933005874546	*  
df.mm.trans1:probe3	0.173275900038752	0.164633747185991	1.05249320385691	0.292940698607018	   
df.mm.trans1:probe4	0.196834551098376	0.164633747185991	1.19559054241781	0.232265922600557	   
df.mm.trans1:probe5	0.122616878635913	0.164633747185991	0.744785809299411	0.456654072491724	   
df.mm.trans1:probe6	0.288871895653650	0.164633747185991	1.75463354622734	0.0797645612360095	.  
df.mm.trans1:probe7	0.129318077891804	0.164633747185991	0.785489488650893	0.432435483307338	   
df.mm.trans1:probe8	0.076099773986953	0.164633747185991	0.46223678491009	0.644056820261972	   
df.mm.trans1:probe9	0.243781857947424	0.164633747185991	1.48075265317273	0.139127643271321	   
df.mm.trans1:probe10	0.175975374625557	0.164633747185991	1.06889005221241	0.285491927006955	   
df.mm.trans1:probe11	0.0916766293729142	0.164633747185991	0.55685198776011	0.577808603634143	   
df.mm.trans1:probe12	0.315620390132515	0.164633747185991	1.91710627697705	0.0556358957093831	.  
df.mm.trans1:probe13	0.0738343805300721	0.164633747185991	0.448476583884467	0.653949671916998	   
df.mm.trans1:probe14	0.14256462424055	0.164633747185991	0.865950187475789	0.3868176022682	   
df.mm.trans1:probe15	0.212803061076067	0.164633747185991	1.29258469003720	0.196586157798926	   
df.mm.trans1:probe16	0.311358262804275	0.164633747185991	1.89121773710542	0.0590126813489075	.  
df.mm.trans1:probe17	0.52382897752234	0.164633747185991	3.18178372585153	0.00152914830207286	** 
df.mm.trans1:probe18	0.150986529670644	0.164633747185991	0.917105588929293	0.359406819701725	   
df.mm.trans1:probe19	0.0524261954172986	0.164633747185991	0.318441366447618	0.750246246963425	   
df.mm.trans2:probe2	0.00664703902430296	0.164633747185991	0.0403747052953466	0.967806041549456	   
df.mm.trans2:probe3	-0.00347077845103851	0.164633747185991	-0.0210818165191702	0.983186467135731	   
df.mm.trans2:probe4	-0.0445274609381051	0.164633747185991	-0.270463751807832	0.786884144717948	   
df.mm.trans2:probe5	0.0746526860247412	0.164633747185991	0.45344704412519	0.650369024111266	   
df.mm.trans2:probe6	-0.0614400239065651	0.164633747185991	-0.373192161125719	0.70911971872538	   
df.mm.trans3:probe2	-0.0485685380563412	0.164633747185991	-0.295009613074482	0.768075021144896	   
df.mm.trans3:probe3	-0.0576363954309977	0.164633747185991	-0.350088584000245	0.72637874557597	   
df.mm.trans3:probe4	0.130644709184107	0.164633747185991	0.793547564926133	0.42773087949436	   
df.mm.trans3:probe5	0.0892959735306298	0.164633747185991	0.542391672770165	0.58772333787773	   
df.mm.trans3:probe6	0.0466003271613216	0.164633747185991	0.283054525319624	0.777219691635689