chr11.3789_chr11_97665125_97666441_-_0.R 

fitVsDatCorrelation=0.889951650738633
cont.fitVsDatCorrelation=0.255278985765736

fstatistic=12476.8757585304,59,853
cont.fstatistic=2764.67427019306,59,853

residuals=-0.787019000421036,-0.0776225761543317,-0.00598784499882322,0.080713986464056,0.66096859729726
cont.residuals=-0.558065426075879,-0.187380016419753,-0.0615079172579038,0.105220734272680,1.48516804447898

predictedValues:
Include	Exclude	Both
chr11.3789_chr11_97665125_97666441_-_0.R.tl.Lung	47.1647668464342	54.0386543579254	63.9577838260742
chr11.3789_chr11_97665125_97666441_-_0.R.tl.cerebhem	52.7266135020363	61.7111019632622	59.8539447117591
chr11.3789_chr11_97665125_97666441_-_0.R.tl.cortex	45.7171522141727	54.3081907897531	58.5676338785611
chr11.3789_chr11_97665125_97666441_-_0.R.tl.heart	47.2479882452848	56.100619221661	62.3708960476195
chr11.3789_chr11_97665125_97666441_-_0.R.tl.kidney	46.1587001704747	54.9918437963235	64.1547900310523
chr11.3789_chr11_97665125_97666441_-_0.R.tl.liver	50.463807867226	55.2088474609598	67.7924164740312
chr11.3789_chr11_97665125_97666441_-_0.R.tl.stomach	48.2114437459584	62.758179750327	65.2331008976892
chr11.3789_chr11_97665125_97666441_-_0.R.tl.testicle	47.5724533774417	53.641363294544	64.184806116687


diffExp=-6.8738875114912,-8.9844884612259,-8.59103857558044,-8.85263097637618,-8.83314362584878,-4.74503959373378,-14.5467360043686,-6.06890991710225
diffExpScore=0.985400580620655
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,-1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,0,0,0,-1,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	65.5513463544229	54.9720941364263	59.0698772918773
cerebhem	55.0339885454086	59.8626744579367	54.8472832297796
cortex	60.3707071195611	56.2786412928222	57.3738606779945
heart	58.6993292597818	53.0594304870299	50.5318710641151
kidney	54.2087255601508	53.8971698225905	57.1497808887006
liver	55.81594384778	54.5149860317184	56.1129756391276
stomach	54.0700663382024	52.5213709974122	57.3537815907779
testicle	54.835716080385	64.1775397151139	58.2890823271495
cont.diffExp=10.5792522179966,-4.8286859125281,4.09206582673892,5.63989877275195,0.311555737560298,1.30095781606159,1.54869534079015,-9.34182363472888
cont.diffExpScore=3.65397414010723

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=0.581273182358015
cont.tran.correlation=-0.186012791000163

tran.covariance=0.00170502066888734
cont.tran.covariance=-0.000808388784394553

tran.mean=52.3763579127365
cont.tran.mean=56.7418581279214

weightedLogRatios:
wLogRatio
Lung	-0.53355332325808
cerebhem	-0.636264486171151
cortex	-0.673062394698487
heart	-0.676862449002183
kidney	-0.686325833464164
liver	-0.356428854336876
stomach	-1.05673261265409
testicle	-0.470935648718596

cont.weightedLogRatios:
wLogRatio
Lung	0.720723083876091
cerebhem	-0.340614133254613
cortex	0.285346688447755
heart	0.406277049707435
kidney	0.0229977901724160
liver	0.0945778449963071
stomach	0.115537601458780
testicle	-0.642302732954758

varWeightedLogRatios=0.0426418646506307
cont.varWeightedLogRatios=0.180349813589723

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.38870538661785	0.0654435518923349	51.7805847731616	1.57847417124552e-265	***
df.mm.trans1	0.434430396263975	0.0548827864879669	7.91560385439293	7.63516409058882e-15	***
df.mm.trans2	0.588394162029138	0.0494706752056767	11.8937968722452	2.72347572111535e-30	***
df.mm.exp2	0.31055331494609	0.0630896327654154	4.92241437037385	1.02614722083830e-06	***
df.mm.exp3	0.0618428645231697	0.0630896327654154	0.980238143929582	0.327246510088068	   
df.mm.exp4	0.0643346467544593	0.0630896327654154	1.01973405034189	0.308143733846822	   
df.mm.exp5	-0.00715193796205119	0.0630896327654154	-0.113361540534624	0.909770603276424	   
df.mm.exp6	0.0308057640370926	0.0630896327654154	0.488285676850218	0.625472999412453	   
df.mm.exp7	0.151794793842565	0.0630896327654154	2.40601802846088	0.0163392874228470	*  
df.mm.exp8	-0.00231568361924422	0.0630896327654154	-0.0367046615702229	0.970729079528378	   
df.mm.trans1:exp2	-0.199080135559521	0.0548827864879669	-3.62736931374957	0.000303330359505778	***
df.mm.trans2:exp2	-0.177789077575644	0.0414874869444047	-4.2853662795698	2.03223488206626e-05	***
df.mm.trans1:exp3	-0.09301646379532	0.0548827864879669	-1.69482035712079	0.0904744912958595	.  
df.mm.trans2:exp3	-0.056867417614809	0.0414874869444047	-1.37071251606573	0.170825155561249	   
df.mm.trans1:exp4	-0.0625717192702753	0.0548827864879669	-1.14009734698864	0.254565768089702	   
df.mm.trans2:exp4	-0.0268874083968204	0.0414874869444047	-0.648084769097989	0.51710452962127	   
df.mm.trans1:exp5	-0.0144097481828492	0.0548827864879669	-0.262554966774666	0.79295701047388	   
df.mm.trans2:exp5	0.0246372056561235	0.0414874869444047	0.593846662468097	0.552772120657801	   
df.mm.trans1:exp6	0.0368034906829896	0.0548827864879669	0.670583493260838	0.502667417361424	   
df.mm.trans2:exp6	-0.00938215543703823	0.0414874869444047	-0.226144221500106	0.821143427468483	   
df.mm.trans1:exp7	-0.129845527580641	0.0548827864879669	-2.36586980890829	0.0182104722396411	*  
df.mm.trans2:exp7	-0.00220548166857233	0.0414874869444047	-0.0531601654139183	0.957616741127205	   
df.mm.trans1:exp8	0.0109224180513042	0.0548827864879669	0.199013547785132	0.84229957298298	   
df.mm.trans2:exp8	-0.00506345458680357	0.0414874869444047	-0.122047753665795	0.902889945358063	   
df.mm.trans1:probe2	-0.00960702140143036	0.0411620898659752	-0.233394889149484	0.815510778229246	   
df.mm.trans1:probe3	0.00273851132642529	0.0411620898659752	0.066529938964274	0.946971512479578	   
df.mm.trans1:probe4	0.0826161945479414	0.0411620898659752	2.00709426603318	0.0450547049711957	*  
df.mm.trans1:probe5	0.0811798880413125	0.0411620898659752	1.97220035002198	0.0489094040059444	*  
df.mm.trans1:probe6	0.121354383783649	0.0411620898659752	2.94820754191009	0.00328342293895932	** 
df.mm.trans1:probe7	0.0765326398044719	0.0411620898659752	1.8592991768315	0.0633288876838062	.  
df.mm.trans1:probe8	0.0102858853238392	0.0411620898659752	0.249887344333834	0.802734637102222	   
df.mm.trans1:probe9	0.072530407530631	0.0411620898659752	1.76206814976577	0.0784159270735394	.  
df.mm.trans1:probe10	0.0248879163992075	0.0411620898659752	0.604631992210387	0.545584337735146	   
df.mm.trans1:probe11	-0.0213741891263972	0.0411620898659752	-0.519268802822987	0.603708006809455	   
df.mm.trans1:probe12	0.0311188740916371	0.0411620898659752	0.756008117978483	0.449853052630522	   
df.mm.trans1:probe13	0.112689251718253	0.0411620898659752	2.73769509966991	0.00631597410481084	** 
df.mm.trans1:probe14	0.147320038311481	0.0411620898659752	3.57902231862277	0.000364341365731447	***
df.mm.trans2:probe2	0.203069767725346	0.0411620898659752	4.93341733586769	9.7148803699851e-07	***
df.mm.trans2:probe3	0.0228094877961990	0.0411620898659752	0.554138234245813	0.579629485060235	   
df.mm.trans2:probe4	-0.0726540365290522	0.0411620898659752	-1.76507161724819	0.0779095112423405	.  
df.mm.trans2:probe5	-0.0745822207304056	0.0411620898659752	-1.81191530782930	0.0703508229070432	.  
df.mm.trans2:probe6	0.223758520515052	0.0411620898659752	5.43603401196623	7.11500778318597e-08	***
df.mm.trans3:probe2	0.614707915366776	0.0411620898659752	14.9338363860601	5.68371063109122e-45	***
df.mm.trans3:probe3	0.225767289059782	0.0411620898659752	5.4848354346168	5.45542893535247e-08	***
df.mm.trans3:probe4	-0.460033056250161	0.0411620898659752	-11.1761345876276	3.69691850870120e-27	***
df.mm.trans3:probe5	-0.407307008394086	0.0411620898659752	-9.89519749167955	6.28068833050966e-22	***
df.mm.trans3:probe6	-0.078479168179519	0.0411620898659752	-1.90658852441771	0.0569096578588772	.  
df.mm.trans3:probe7	-0.140306791246017	0.0411620898659752	-3.40864109919732	0.000683473396748705	***
df.mm.trans3:probe8	-0.320641881621810	0.0411620898659752	-7.78973766069283	1.94720291287268e-14	***
df.mm.trans3:probe9	0.321463221789709	0.0411620898659752	7.8096914621294	1.68002825030594e-14	***
df.mm.trans3:probe10	-0.226196219882550	0.0411620898659752	-5.49525596535674	5.15326272850839e-08	***
df.mm.trans3:probe11	-0.556699438326192	0.0411620898659752	-13.5245669046159	6.67971835535854e-38	***
df.mm.trans3:probe12	-0.566306459727623	0.0411620898659752	-13.7579617937654	4.82682405135626e-39	***
df.mm.trans3:probe13	-0.553960926999767	0.0411620898659752	-13.4580369656516	1.40527313756261e-37	***
df.mm.trans3:probe14	-0.474083243778251	0.0411620898659752	-11.5174726385827	1.24521936129115e-28	***
df.mm.trans3:probe15	-0.47551955028488	0.0411620898659752	-11.5523665545939	8.76824652885888e-29	***
df.mm.trans3:probe16	-0.435345054542543	0.0411620898659752	-10.5763593627058	1.19206409980010e-24	***
df.mm.trans3:probe17	-0.48016679852172	0.0411620898659752	-11.6652677277844	2.80400314045084e-29	***
df.mm.trans3:probe18	-0.546413553002354	0.0411620898659752	-13.2746795602821	1.07812412724518e-36	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.012624531449	0.138744789582326	28.9209024967965	1.01928118647722e-128	***
df.mm.trans1	0.140892668124246	0.116355247274660	1.21088366381676	0.226275378011598	   
df.mm.trans2	-0.00304054338914669	0.10488120255452	-0.0289903558987717	0.976879063388137	   
df.mm.exp2	-0.0154868291949759	0.133754320628325	-0.115785636846906	0.907849685799129	   
df.mm.exp3	-0.0297080373488408	0.133754320628325	-0.222108992137855	0.824282203704482	   
df.mm.exp4	0.0102983455683258	0.133754320628325	0.076994489000043	0.93864598446442	   
df.mm.exp5	-0.176694026475864	0.133754320628325	-1.32103415908978	0.186844237506237	   
df.mm.exp6	-0.117770205557067	0.133754320628325	-0.88049645801219	0.378838427671113	   
df.mm.exp7	-0.208676219038777	0.133754320628325	-1.56014563162146	0.119096383459930	   
df.mm.exp8	-0.0103580954475273	0.133754320628325	-0.0774412026383081	0.938290724956194	   
df.mm.trans1:exp2	-0.159395952235940	0.116355247274660	-1.36990772628999	0.171076180975453	   
df.mm.trans2:exp2	0.100714332036050	0.0879562994360507	1.14504967446106	0.252509750774586	   
df.mm.trans1:exp3	-0.0526217061654012	0.116355247274660	-0.452250391778086	0.651203593850736	   
df.mm.trans2:exp3	0.0531974502507102	0.0879562994360507	0.604816830537395	0.545461559941757	   
df.mm.trans1:exp4	-0.120703794728327	0.116355247274660	-1.03737302404078	0.299856020995919	   
df.mm.trans2:exp4	-0.0457114074273958	0.0879562994360507	-0.519705896228963	0.603403409045596	   
df.mm.trans1:exp5	-0.0132978391903239	0.116355247274660	-0.114286544885543	0.909037542606018	   
df.mm.trans2:exp5	0.156946317948235	0.0879562994360507	1.78436699764005	0.0747193938817399	.  
df.mm.trans1:exp6	-0.0430039831794669	0.116355247274660	-0.369592125724717	0.711778152516604	   
df.mm.trans2:exp6	0.109420165352841	0.0879562994360507	1.24402875125955	0.213830723813615	   
df.mm.trans1:exp7	0.0161231999775055	0.116355247274660	0.138568739744467	0.889823652621891	   
df.mm.trans2:exp7	0.163070695290279	0.0879562994360507	1.85399677266823	0.0640846447215955	.  
df.mm.trans1:exp8	-0.168133918954986	0.116355247274660	-1.44500504182764	0.148823715198906	   
df.mm.trans2:exp8	0.165185719123565	0.0879562994360507	1.87804307573973	0.0607162099075475	.  
df.mm.trans1:probe2	0.000288252360496266	0.0872664354559953	0.00330312976564305	0.997365260865898	   
df.mm.trans1:probe3	0.104231533656157	0.0872664354559953	1.19440576564763	0.232651300586399	   
df.mm.trans1:probe4	-0.00779828770023693	0.0872664354559953	-0.0893618223259419	0.928815340214675	   
df.mm.trans1:probe5	-0.0460029199121725	0.0872664354559953	-0.52715479521757	0.598223166485962	   
df.mm.trans1:probe6	0.0497127634592734	0.0872664354559953	0.569666484021126	0.569053914621994	   
df.mm.trans1:probe7	0.121979467788037	0.0872664354559953	1.39778217307324	0.162541874014024	   
df.mm.trans1:probe8	-0.0264606592284962	0.0872664354559953	-0.303216913699187	0.76179849518998	   
df.mm.trans1:probe9	-0.0174314809305603	0.0872664354559953	-0.199750119727879	0.841723621711238	   
df.mm.trans1:probe10	0.0784610706992411	0.0872664354559953	0.899097921087949	0.36885423570418	   
df.mm.trans1:probe11	0.0790955694978193	0.0872664354559953	0.906368744002426	0.364996663727101	   
df.mm.trans1:probe12	0.0630233799002157	0.0872664354559953	0.722194960420902	0.470372555475311	   
df.mm.trans1:probe13	0.135267861002146	0.0872664354559953	1.55005598997287	0.121498996496180	   
df.mm.trans1:probe14	0.169230644032071	0.0872664354559953	1.93924093665321	0.0528014539569636	.  
df.mm.trans2:probe2	-0.00389964448713546	0.0872664354559953	-0.0446866480423837	0.964367535087017	   
df.mm.trans2:probe3	-0.0169335021157944	0.0872664354559953	-0.194043701078328	0.846187863547851	   
df.mm.trans2:probe4	-0.0284230878275845	0.0872664354559953	-0.325704695958586	0.74472767094428	   
df.mm.trans2:probe5	-0.0293895966519697	0.0872664354559953	-0.336780074703401	0.736365534224987	   
df.mm.trans2:probe6	0.0124463688695956	0.0872664354559953	0.142624925660814	0.886620092315585	   
df.mm.trans3:probe2	-0.132706444750477	0.0872664354559953	-1.52070431268383	0.128704633945462	   
df.mm.trans3:probe3	-0.128167171857722	0.0872664354559953	-1.46868806074188	0.142286085547019	   
df.mm.trans3:probe4	-0.0421980610282105	0.0872664354559953	-0.48355431051712	0.628826308713297	   
df.mm.trans3:probe5	-0.148607643797843	0.0872664354559953	-1.70291868828284	0.0889475801419932	.  
df.mm.trans3:probe6	-0.0505678019749934	0.0872664354559953	-0.579464506723121	0.562428789921489	   
df.mm.trans3:probe7	-0.0112490700059899	0.0872664354559953	-0.128904887053194	0.897463296419455	   
df.mm.trans3:probe8	-0.0764346774779176	0.0872664354559953	-0.875877158022116	0.381343392918301	   
df.mm.trans3:probe9	-0.0382228310839724	0.0872664354559953	-0.438001516668416	0.661496074170689	   
df.mm.trans3:probe10	-0.0477014218459603	0.0872664354559953	-0.546618199731718	0.584784014948077	   
df.mm.trans3:probe11	-0.0281951727243731	0.0872664354559953	-0.323092980445966	0.746704011362249	   
df.mm.trans3:probe12	-0.094554615651221	0.0872664354559953	-1.08351641908075	0.278885436368067	   
df.mm.trans3:probe13	-0.102892348926348	0.0872664354559953	-1.17905983427308	0.238703116836400	   
df.mm.trans3:probe14	-0.0596221735923846	0.0872664354559953	-0.683219994959568	0.494653364818848	   
df.mm.trans3:probe15	-0.0445963478404237	0.0872664354559953	-0.511036661545683	0.60945762577061	   
df.mm.trans3:probe16	-0.0632563667785234	0.0872664354559953	-0.724864794213129	0.46873376668539	   
df.mm.trans3:probe17	-0.108136986516253	0.0872664354559953	-1.239158972762	0.215627474296038	   
df.mm.trans3:probe18	-0.114490803768423	0.0872664354559953	-1.3119683778783	0.189883763586368