chr3.14791_chr3_37192837_37194264_-_2.R 

fitVsDatCorrelation=0.72162135362298
cont.fitVsDatCorrelation=0.266856526631828

fstatistic=14352.1744882719,59,853
cont.fstatistic=7398.85174512769,59,853

residuals=-0.471055627969307,-0.0830714991862146,-0.00484571546423794,0.0742154002839459,0.779494387685762
cont.residuals=-0.420557422304657,-0.119343295348452,-0.0192051861625313,0.0912880794824288,0.974898418872076

predictedValues:
Include	Exclude	Both
chr3.14791_chr3_37192837_37194264_-_2.R.tl.Lung	50.6794343641441	47.4926121972427	51.1068286720079
chr3.14791_chr3_37192837_37194264_-_2.R.tl.cerebhem	54.6759687581876	52.7486323260885	59.6428436960279
chr3.14791_chr3_37192837_37194264_-_2.R.tl.cortex	51.1418660143995	47.416464971054	55.5786313166404
chr3.14791_chr3_37192837_37194264_-_2.R.tl.heart	49.1761464335491	47.7626597170745	48.7163707712572
chr3.14791_chr3_37192837_37194264_-_2.R.tl.kidney	50.3454724104281	47.5515930409014	52.385223999534
chr3.14791_chr3_37192837_37194264_-_2.R.tl.liver	50.6880479662121	47.6477114604118	51.7925295161543
chr3.14791_chr3_37192837_37194264_-_2.R.tl.stomach	49.6055833085472	51.7449232546719	54.9751116357288
chr3.14791_chr3_37192837_37194264_-_2.R.tl.testicle	49.5669809368547	46.0373330334975	51.4512079279025


diffExp=3.18682216690139,1.92733643209904,3.72540104334544,1.41348671647459,2.79387936952668,3.0403365058003,-2.13933994612465,3.52964790335722
diffExpScore=1.17744107359846
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	52.4222922410516	60.928887151115	51.9146673428717
cerebhem	47.9728941417449	51.7910550134009	51.4292029921525
cortex	49.5634207061063	55.4789499644971	50.2287178999063
heart	50.0627877121219	50.5715850273151	50.5040594908293
kidney	50.1181915862113	54.5751615373823	51.4140275419834
liver	48.4359342756384	50.6997638014554	51.3470171341302
stomach	48.4569102988074	51.2712620394752	49.4296770102337
testicle	50.0529410789098	52.0564719987115	49.4322686114240
cont.diffExp=-8.50659491006338,-3.81816087165591,-5.91552925839072,-0.508797315193171,-4.45696995117092,-2.263829525817,-2.81435174066777,-2.00353091980172
cont.diffExpScore=0.968038624164683

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=0.599764837474324
cont.tran.correlation=0.806994434261437

tran.covariance=0.000932437772548298
cont.tran.covariance=0.0014236711852211

tran.mean=49.6425893870791
cont.tran.mean=51.5286567858715

weightedLogRatios:
wLogRatio
Lung	0.252837951989299
cerebhem	0.142952955569456
cortex	0.294729264869190
heart	0.113182441954931
kidney	0.222114000808042
liver	0.240912636139695
stomach	-0.165734079625856
testicle	0.285618839010229

cont.weightedLogRatios:
wLogRatio
Lung	-0.606692808237615
cerebhem	-0.299350716144291
cortex	-0.446450447039032
heart	-0.0396217457137630
kidney	-0.337114301633493
liver	-0.178289918537052
stomach	-0.220678841941888
testicle	-0.154350272611667

varWeightedLogRatios=0.0228743306177465
cont.varWeightedLogRatios=0.0321825189720439

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.76956268496731	0.0598102008323675	63.0254142689207	0	***
df.mm.trans1	0.199960638294042	0.0516506264320812	3.87140780484016	0.000116472650939776	***
df.mm.trans2	0.0941100993088986	0.0456330949960763	2.06232120168467	0.0394794853498359	*  
df.mm.exp2	0.0264117207319335	0.0586987508975225	0.449953709884622	0.652858140681654	   
df.mm.exp3	-0.0764020492838208	0.0586987508975225	-1.30159582811575	0.193405997565461	   
df.mm.exp4	0.0234614660963881	0.0586987508975225	0.399692765819628	0.689482881029835	   
df.mm.exp5	-0.0300768220982939	0.0586987508975225	-0.512392881252323	0.608508717048113	   
df.mm.exp6	-0.00989741663390111	0.0586987508975225	-0.168613752125326	0.866140435229914	   
df.mm.exp7	-0.00862708595986253	0.0586987508975225	-0.146972223905137	0.88318867332083	   
df.mm.exp8	-0.0600326184951486	0.0586987508975225	-1.02272395199610	0.306728337904115	   
df.mm.trans1:exp2	0.0494923696971314	0.0542564760941334	0.912192852541022	0.361924932564259	   
df.mm.trans2:exp2	0.078551957566098	0.0400710632262685	1.96031627916984	0.0502839890464206	.  
df.mm.trans1:exp3	0.0854853123725425	0.0542564760941334	1.57557804204291	0.115493864512380	   
df.mm.trans2:exp3	0.0747974136725679	0.0400710632262684	1.86661914235245	0.0622977025824068	.  
df.mm.trans1:exp4	-0.0535729832140713	0.0542564760941334	-0.98740255672196	0.323725324684313	   
df.mm.trans2:exp4	-0.0177914755986565	0.0400710632262684	-0.443998091545357	0.65715657260774	   
df.mm.trans1:exp5	0.0234653204223531	0.0542564760941334	0.432488840256441	0.665495477105155	   
df.mm.trans2:exp5	0.0313179467114579	0.0400710632262684	0.781560163118595	0.434689975769972	   
df.mm.trans1:exp6	0.0100673646663606	0.0542564760941334	0.185551392038326	0.85284068677847	   
df.mm.trans2:exp6	0.0131578512131407	0.0400710632262684	0.328362917121579	0.74271786620231	   
df.mm.trans1:exp7	-0.012789714401995	0.0542564760941334	-0.235726964276213	0.813701128951701	   
df.mm.trans2:exp7	0.0943792456795845	0.0400710632262684	2.35529676731199	0.0187334966592373	*  
df.mm.trans1:exp8	0.0378373291643756	0.0542564760941334	0.697379039116527	0.485755642206278	   
df.mm.trans2:exp8	0.0289111072552637	0.0400710632262684	0.721495885747078	0.470802182272745	   
df.mm.trans1:probe2	-0.175350521967663	0.0371468698093708	-4.72046562382031	2.74952994593202e-06	***
df.mm.trans1:probe3	-0.0913963349689448	0.0371468698093708	-2.46040475113972	0.0140752612732485	*  
df.mm.trans1:probe4	0.0626019055201556	0.0371468698093708	1.68525385426589	0.0923053623866234	.  
df.mm.trans1:probe5	0.13042341119134	0.0371468698093708	3.51102022487071	0.000469799909746365	***
df.mm.trans1:probe6	0.28736714644658	0.0371468698093708	7.73597204613152	2.89355058314453e-14	***
df.mm.trans1:probe7	-0.105752700648887	0.0371468698093708	-2.84688053641089	0.00452096337500013	** 
df.mm.trans1:probe8	0.245564282877106	0.0371468698093708	6.61063190888722	6.7400620681233e-11	***
df.mm.trans1:probe9	-0.102260484538887	0.0371468698093708	-2.75286948977569	0.00603304231951644	** 
df.mm.trans1:probe10	-0.0726006170121251	0.0371468698093708	-1.95442085388876	0.0509778379855148	.  
df.mm.trans1:probe11	-0.139651410077135	0.0371468698093708	-3.7594395111565	0.000181901739021903	***
df.mm.trans1:probe12	-0.114361428425561	0.0371468698093708	-3.07862894000052	0.00214611815581934	** 
df.mm.trans1:probe13	-0.181044655775286	0.0371468698093708	-4.87375266622371	1.30553088557136e-06	***
df.mm.trans1:probe14	-0.126982354263077	0.0371468698093708	-3.41838639203575	0.000659782094375821	***
df.mm.trans1:probe15	-0.0156697017630234	0.0371468698093708	-0.421831014118732	0.673254653360461	   
df.mm.trans1:probe16	-0.115477951515225	0.0371468698093708	-3.10868592987327	0.00194157219898115	** 
df.mm.trans1:probe17	-0.154337001443092	0.0371468698093708	-4.15477810741832	3.58408069917299e-05	***
df.mm.trans1:probe18	-0.0992094044048333	0.0371468698093708	-2.67073389800953	0.00771268508996355	** 
df.mm.trans1:probe19	-0.201021502808454	0.0371468698093708	-5.4115327574046	8.12355532663134e-08	***
df.mm.trans1:probe20	-0.150214966870941	0.0371468698093708	-4.04381224156462	5.73510423263148e-05	***
df.mm.trans1:probe21	-0.100053386084595	0.0371468698093708	-2.69345402716422	0.00721042281261123	** 
df.mm.trans1:probe22	-0.188672445041321	0.0371468698093708	-5.079094039674	4.65932591567334e-07	***
df.mm.trans2:probe2	-0.00339311678108359	0.0371468698093708	-0.0913432759878903	0.927241278130875	   
df.mm.trans2:probe3	0.0178619091403081	0.0371468698093708	0.480845606425826	0.630749540171483	   
df.mm.trans2:probe4	0.0280935773656673	0.0371468698093708	0.756283840599141	0.449687846612335	   
df.mm.trans2:probe5	-0.00943241029350506	0.0371468698093708	-0.253922075854843	0.799616952272676	   
df.mm.trans2:probe6	-0.0827078477785678	0.0371468698093708	-2.22650921068196	0.0262405415478801	*  
df.mm.trans3:probe2	-0.341789309069567	0.0371468698093708	-9.20102584211134	2.66792649261696e-19	***
df.mm.trans3:probe3	-0.154683641965403	0.0371468698093708	-4.16410972873901	3.44342170234914e-05	***
df.mm.trans3:probe4	-0.130187591381999	0.0371468698093708	-3.50467191583278	0.000480980495682616	***
df.mm.trans3:probe5	-0.0558505791708716	0.0371468698093708	-1.50350701034795	0.133078482106521	   
df.mm.trans3:probe6	-0.340862496258638	0.0371468698093708	-9.17607588493634	3.2947090301687e-19	***
df.mm.trans3:probe7	-0.089195087443925	0.0371468698093708	-2.40114679653100	0.0165569141283821	*  
df.mm.trans3:probe8	0.0400916037872080	0.0371468698093708	1.07927273530580	0.280771243123609	   
df.mm.trans3:probe9	-0.0941544734642218	0.0371468698093708	-2.53465430458612	0.0114335805834580	*  
df.mm.trans3:probe10	-0.130521432889186	0.0371468698093708	-3.51365898550785	0.000465224400238311	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.18173094759855	0.083261970336911	50.2237808050615	5.21099727306647e-257	***
df.mm.trans1	-0.24039064171856	0.071903000926616	-3.34326298792316	0.000864065056028412	***
df.mm.trans2	-0.0443404186478692	0.0635259763228979	-0.697988778991607	0.48537443207713	   
df.mm.exp2	-0.241790532495059	0.0817147173563467	-2.95895941780773	0.00317214558133243	** 
df.mm.exp3	-0.116768155077444	0.0817147173563467	-1.42897337046684	0.153377974770418	   
df.mm.exp4	-0.204823856311835	0.0817147173563467	-2.50657241361585	0.0123758449893835	*  
df.mm.exp5	-0.145386101570164	0.0817147173563467	-1.77919114541086	0.0755644458571113	.  
df.mm.exp6	-0.251881578982797	0.0817147173563467	-3.08245059313337	0.00211905525077510	** 
df.mm.exp7	-0.202183603380546	0.0817147173563467	-2.47426179667061	0.0135447226019684	*  
df.mm.exp8	-0.154630992575850	0.0817147173563467	-1.89232732582951	0.0587857741733201	.  
df.mm.trans1:exp2	0.153094753209277	0.0755306125086662	2.02692323184472	0.0429808613828037	*  
df.mm.trans2:exp2	0.079300583999933	0.0557830542496438	1.42158913789557	0.155511060831402	   
df.mm.trans1:exp3	0.060689305660715	0.0755306125086662	0.80350607051878	0.421906134619915	   
df.mm.trans2:exp3	0.0230644242559055	0.0557830542496438	0.413466501003803	0.679368784812966	   
df.mm.trans1:exp4	0.158769902875290	0.0755306125086662	2.10206031173219	0.0358406419193175	*  
df.mm.trans2:exp4	0.0185063144358454	0.0557830542496438	0.331755130384665	0.740155662085766	   
df.mm.trans1:exp5	0.100438223960107	0.0755306125086662	1.32976842930517	0.183950055197420	   
df.mm.trans2:exp5	0.0352575642070435	0.0557830542496438	0.632047934293032	0.527524916057869	   
df.mm.trans1:exp6	0.172791635634705	0.0755306125086662	2.28770335491293	0.02239876233994	*  
df.mm.trans2:exp6	0.068095431110534	0.0557830542496438	1.22071894460617	0.22252978093294	   
df.mm.trans1:exp7	0.123526633666139	0.0755306125086662	1.63545123709895	0.102323410735229	   
df.mm.trans2:exp7	0.0296066047174856	0.0557830542496438	0.530745494590316	0.595733305184323	   
df.mm.trans1:exp8	0.108380334118770	0.0755306125086662	1.43491930647769	0.151676628741633	   
df.mm.trans2:exp8	-0.00274727784901329	0.0557830542496438	-0.0492493264481092	0.960732131143872	   
df.mm.trans1:probe2	0.0644947150476601	0.051712275316473	1.24718385824178	0.212672408415414	   
df.mm.trans1:probe3	0.0102765372104043	0.0517122753164729	0.198725295831852	0.842524990260178	   
df.mm.trans1:probe4	0.0797151547975803	0.051712275316473	1.54151319603968	0.123562807903037	   
df.mm.trans1:probe5	0.0344596778506812	0.0517122753164729	0.666373267078118	0.505352728206036	   
df.mm.trans1:probe6	0.0267586449488272	0.0517122753164729	0.517452476903549	0.604974492638419	   
df.mm.trans1:probe7	0.0427924354363587	0.051712275316473	0.827510202838188	0.408179381924442	   
df.mm.trans1:probe8	0.0219358447800382	0.0517122753164729	0.424190284527096	0.671534001345174	   
df.mm.trans1:probe9	0.0739169061412194	0.051712275316473	1.42938800679059	0.153258862842915	   
df.mm.trans1:probe10	0.0601036710296121	0.051712275316473	1.16227086628435	0.245450538531103	   
df.mm.trans1:probe11	0.0190548071567805	0.0517122753164729	0.368477446412238	0.712608687313228	   
df.mm.trans1:probe12	0.00747214598622059	0.051712275316473	0.144494628025782	0.885144029894715	   
df.mm.trans1:probe13	-0.0280403116185942	0.0517122753164729	-0.542237050042583	0.587796837880454	   
df.mm.trans1:probe14	0.0684209325449555	0.0517122753164729	1.32310814262624	0.186153971696972	   
df.mm.trans1:probe15	0.0254006965459465	0.0517122753164729	0.491192785281584	0.623416452648894	   
df.mm.trans1:probe16	0.0240970633251193	0.0517122753164729	0.465983428067091	0.641346326256953	   
df.mm.trans1:probe17	-0.0168372100423453	0.051712275316473	-0.325594067159173	0.744811352064786	   
df.mm.trans1:probe18	-0.0355694559465078	0.0517122753164729	-0.687833898795343	0.491744377187755	   
df.mm.trans1:probe19	0.0740727678583996	0.051712275316473	1.43240202456928	0.152395147784031	   
df.mm.trans1:probe20	0.0315484732400012	0.051712275316473	0.610077066749207	0.541973245951746	   
df.mm.trans1:probe21	-0.00301350602020831	0.051712275316473	-0.0582744812864259	0.953543642306974	   
df.mm.trans1:probe22	-0.00532816860063611	0.051712275316473	-0.103034890033911	0.917959497718523	   
df.mm.trans2:probe2	-0.0691684774146836	0.051712275316473	-1.33756399213496	0.181395127611632	   
df.mm.trans2:probe3	-0.0991306506050067	0.0517122753164729	-1.91696555601816	0.0555761103480155	.  
df.mm.trans2:probe4	-0.0996741304601075	0.051712275316473	-1.92747524354931	0.0542522018114261	.  
df.mm.trans2:probe5	-0.107694840040253	0.0517122753164729	-2.08257786726988	0.03758715474258	*  
df.mm.trans2:probe6	-0.0672619693286883	0.051712275316473	-1.30069638044455	0.193713675450290	   
df.mm.trans3:probe2	0.0596160307536469	0.0517122753164729	1.15284099159830	0.249298612003982	   
df.mm.trans3:probe3	0.0816071365456503	0.051712275316473	1.57809990077258	0.114913410857021	   
df.mm.trans3:probe4	0.0680315580839208	0.0517122753164729	1.31557850950429	0.188669034470099	   
df.mm.trans3:probe5	0.0228999382170595	0.0517122753164729	0.442833700062791	0.657998300403016	   
df.mm.trans3:probe6	0.0101002722228713	0.0517122753164729	0.195316724337865	0.845191518748252	   
df.mm.trans3:probe7	0.0129673548949066	0.0517122753164729	0.250759704838897	0.802060285402555	   
df.mm.trans3:probe8	0.074672817671139	0.051712275316473	1.4440056488358	0.149104564497985	   
df.mm.trans3:probe9	0.119560997295917	0.0517122753164729	2.31204286727316	0.0210126698002650	*  
df.mm.trans3:probe10	0.0765586020762112	0.0517122753164729	1.48047251078553	0.139116438860240