chr15.8812_chr15_9291183_9331284_+_2.R 

fitVsDatCorrelation=0.841203674899487
cont.fitVsDatCorrelation=0.249415821304067

fstatistic=7114.40160529645,69,1083
cont.fstatistic=2207.26270766514,69,1083

residuals=-0.669115523337822,-0.105920502525190,-0.00561753896380229,0.0923800495744801,1.17510421339376
cont.residuals=-0.703074263077218,-0.218226208243054,-0.0855540902567488,0.121880851527069,1.65362390397236

predictedValues:
Include	Exclude	Both
chr15.8812_chr15_9291183_9331284_+_2.R.tl.Lung	47.3655440430797	44.3674844317675	55.0619189344384
chr15.8812_chr15_9291183_9331284_+_2.R.tl.cerebhem	51.1585271310136	44.1523957447697	56.3778940516236
chr15.8812_chr15_9291183_9331284_+_2.R.tl.cortex	48.9406474935167	46.0671597227609	55.6856571331874
chr15.8812_chr15_9291183_9331284_+_2.R.tl.heart	49.4831058019641	48.9523543363345	60.9993974003924
chr15.8812_chr15_9291183_9331284_+_2.R.tl.kidney	73.9575771861098	68.0538154971103	113.119767955701
chr15.8812_chr15_9291183_9331284_+_2.R.tl.liver	56.1062749861608	53.3427224808113	78.5332153649013
chr15.8812_chr15_9291183_9331284_+_2.R.tl.stomach	49.1745902539977	44.7722366366784	55.5157195222146
chr15.8812_chr15_9291183_9331284_+_2.R.tl.testicle	47.7822536344777	44.435473540029	57.1169819900803


diffExp=2.99805961131228,7.00613138624391,2.87348777075582,0.530751465629663,5.90376168899954,2.76355250534947,4.40235361731926,3.34678009444863
diffExpScore=0.967558671425843
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	56.9867599062806	48.8534396927658	63.2315887763181
cerebhem	61.2135959754535	50.2318165279096	56.403860401894
cortex	59.7512463313197	48.4832591215091	53.241457980476
heart	60.8335025303183	50.6600528707792	63.0898318978692
kidney	59.6401494698342	49.9428667248756	59.4347569487799
liver	55.4910319268051	50.4075866235978	60.2879160677183
stomach	60.4837580624306	44.3475014300344	55.5982237026387
testicle	61.5024314472567	52.8939829219756	54.1033519398093
cont.diffExp=8.13332021351474,10.9817794475439,11.2679872098105,10.1734496595391,9.69728274495863,5.08344530320732,16.1362566323962,8.60844852528107
cont.diffExpScore=0.98766680183951

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=0,0,0,0,0,0,1,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,1,1,1,0,0,1,0
cont.diffExp1.2Score=0.8

tran.correlation=0.97532752660821
cont.tran.correlation=0.0627936791441894

tran.covariance=0.0215113442924464
cont.tran.covariance=7.8062695511838e-05

tran.mean=51.1320101825364
cont.tran.mean=54.4826863476966

weightedLogRatios:
wLogRatio
Lung	0.250122916896474
cerebhem	0.568698237262443
cortex	0.233582173928631
heart	0.0420164604040812
kidney	0.354559214171777
liver	0.202141055961178
stomach	0.360944577176723
testicle	0.278145200082956

cont.weightedLogRatios:
wLogRatio
Lung	0.610713444412088
cerebhem	0.79394924464718
cortex	0.83289854431535
heart	0.735056928756157
kidney	0.709727028679287
liver	0.381261807752880
stomach	1.22489402037009
testicle	0.609735343288876

varWeightedLogRatios=0.0229845028540535
cont.varWeightedLogRatios=0.0585602194428434

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.72544848582305	0.0882987030487699	42.1914292870800	7.25171740264646e-231	***
df.mm.trans1	0.107407950380907	0.075305811478019	1.42629032571089	0.154072748084530	   
df.mm.trans2	0.0590105247345181	0.0655949794732701	0.899619531988188	0.368522741436771	   
df.mm.exp2	0.0485556807574232	0.0822382699365881	0.590426826766459	0.555027708405614	   
df.mm.exp3	0.0590424391367053	0.082238269936588	0.717943594657712	0.472946904214005	   
df.mm.exp4	0.03967123035106	0.082238269936588	0.482393785541081	0.629623715916012	   
df.mm.exp5	0.153399730087119	0.0822382699365881	1.86530833157606	0.0624084455203589	.  
df.mm.exp6	-0.00148000669226969	0.0822382699365881	-0.0179965689138510	0.985644905410346	   
df.mm.exp7	0.038355482762662	0.0822382699365881	0.466394572651358	0.641026892332806	   
df.mm.exp8	-0.0263526317677903	0.0822382699365881	-0.320442438637269	0.748694774777087	   
df.mm.trans1:exp2	0.0284784606296401	0.0747685455575659	0.380888252102135	0.703360870695225	   
df.mm.trans2:exp2	-0.0534153599587911	0.0496273617924184	-1.07632882405108	0.282019928122629	   
df.mm.trans1:exp3	-0.0263291963008596	0.0747685455575658	-0.352142683858792	0.724799769100824	   
df.mm.trans2:exp3	-0.0214489819633228	0.0496273617924184	-0.432200729368603	0.66568156774878	   
df.mm.trans1:exp4	0.00406503859351926	0.0747685455575658	0.0543682983693925	0.95665176309044	   
df.mm.trans2:exp4	0.0586693656936617	0.0496273617924184	1.18219795642300	0.23738665654741	   
df.mm.trans1:exp5	0.292196872014731	0.0747685455575659	3.90801867062885	9.88296970821548e-05	***
df.mm.trans2:exp5	0.274392197929138	0.0496273617924184	5.52905066920276	4.03406552911518e-08	***
df.mm.trans1:exp6	0.170832621106533	0.0747685455575659	2.28481936932966	0.0225161209269843	*  
df.mm.trans2:exp6	0.185710695745024	0.0496273617924184	3.74210292543488	0.000192065469687094	***
df.mm.trans1:exp7	-0.00087349637197392	0.0747685455575659	-0.0116826717098756	0.990680940305803	   
df.mm.trans2:exp7	-0.0292741219113427	0.0496273617924184	-0.58987866479364	0.555395043945398	   
df.mm.trans1:exp8	0.0351118940080440	0.0747685455575659	0.469607824335844	0.63872976746508	   
df.mm.trans2:exp8	0.0278838672828858	0.0496273617924184	0.561864791433375	0.574324401099051	   
df.mm.trans1:probe2	-0.0368563191059009	0.0567907871293016	-0.648984121702454	0.516486235066223	   
df.mm.trans1:probe3	-0.00627490790362344	0.0567907871293016	-0.110491652269878	0.912039941345865	   
df.mm.trans1:probe4	-0.10505004805018	0.0567907871293016	-1.84977270716466	0.0646185984872618	.  
df.mm.trans1:probe5	-0.0277849670079688	0.0567907871293017	-0.489251310158949	0.624762857884462	   
df.mm.trans1:probe6	-0.0814171677406657	0.0567907871293016	-1.43363337358390	0.151965549189572	   
df.mm.trans1:probe7	-0.0789846892384208	0.0567907871293017	-1.39080110051280	0.164571438379370	   
df.mm.trans1:probe8	0.163456467494861	0.0567907871293017	2.87822155242719	0.00407782921294087	** 
df.mm.trans1:probe9	0.0784002136334844	0.0567907871293017	1.38050936774273	0.167714718654751	   
df.mm.trans1:probe10	-0.101186559676828	0.0567907871293016	-1.78174251127116	0.0750713748167696	.  
df.mm.trans1:probe11	-0.0515455646049028	0.0567907871293017	-0.90763955230174	0.3642706026799	   
df.mm.trans1:probe12	-0.106517659681200	0.0567907871293017	-1.87561513170578	0.0609769982159385	.  
df.mm.trans1:probe13	-0.143899123807575	0.0567907871293017	-2.53384626418267	0.0114218919840463	*  
df.mm.trans1:probe14	-0.093209971395979	0.0567907871293016	-1.64128683731321	0.101028233795131	   
df.mm.trans1:probe15	-0.0648150966691915	0.0567907871293017	-1.14129597326447	0.253999071035398	   
df.mm.trans1:probe16	-0.0206206535719543	0.0567907871293017	-0.363098569579693	0.716602109152618	   
df.mm.trans1:probe17	0.351553460796021	0.0567907871293017	6.19032555396004	8.50629117620092e-10	***
df.mm.trans1:probe18	0.384485196031217	0.0567907871293016	6.77020368032263	2.10388593145337e-11	***
df.mm.trans1:probe19	0.199648978314597	0.0567907871293016	3.51551701264563	0.000457052243317852	***
df.mm.trans1:probe20	0.245231080298702	0.0567907871293016	4.31814899378588	1.71769866135595e-05	***
df.mm.trans1:probe21	0.193562181445403	0.0567907871293016	3.40833771162037	0.000677647162586965	***
df.mm.trans1:probe22	0.353446738563562	0.0567907871293016	6.22366331635502	6.93130799100141e-10	***
df.mm.trans2:probe2	0.0476393698119168	0.0567907871293016	0.838857360850646	0.401734471003622	   
df.mm.trans2:probe3	-0.0187817882933086	0.0567907871293016	-0.330718928944338	0.740920774025915	   
df.mm.trans2:probe4	-0.00669499450217893	0.0567907871293016	-0.117888742885985	0.906177672894025	   
df.mm.trans2:probe5	0.0431223691905114	0.0567907871293016	0.759319801155954	0.44782651249534	   
df.mm.trans2:probe6	0.143959351175131	0.0567907871293016	2.53490677717433	0.0113875803917165	*  
df.mm.trans3:probe2	0.242645638457932	0.0567907871293016	4.27262326731754	2.10173002784858e-05	***
df.mm.trans3:probe3	0.90644399011092	0.0567907871293017	15.9611098195755	1.15079825995039e-51	***
df.mm.trans3:probe4	-0.207986122044675	0.0567907871293017	-3.66232152357971	0.000262019924389694	***
df.mm.trans3:probe5	0.169235306186317	0.0567907871293017	2.97997817499872	0.00294702283340831	** 
df.mm.trans3:probe6	-0.0274247393135855	0.0567907871293017	-0.482908244450718	0.62925848767639	   
df.mm.trans3:probe7	0.299967927060731	0.0567907871293017	5.28198220563068	1.54409399245570e-07	***
df.mm.trans3:probe8	0.147986789827522	0.0567907871293017	2.60582388989582	0.00929110271744256	** 
df.mm.trans3:probe9	0.186469257765556	0.0567907871293017	3.28344203683955	0.00105827043780666	** 
df.mm.trans3:probe10	0.068919946749541	0.0567907871293016	1.21357618433116	0.225174204032156	   
df.mm.trans3:probe11	0.437652872278058	0.0567907871293017	7.706406169043	2.91506826096825e-14	***
df.mm.trans3:probe12	0.251446439192428	0.0567907871293017	4.42759207791808	1.04940849348004e-05	***
df.mm.trans3:probe13	0.461835318743694	0.0567907871293017	8.13222253271793	1.14541170216325e-15	***
df.mm.trans3:probe14	-0.00959591628214707	0.0567907871293017	-0.168969594668569	0.865852110009934	   
df.mm.trans3:probe15	0.119783059911646	0.0567907871293017	2.10919879731413	0.0351564824797937	*  
df.mm.trans3:probe16	-0.208002681774238	0.0567907871293016	-3.66261311541	0.000261725419867185	***
df.mm.trans3:probe17	-0.210184611331682	0.0567907871293017	-3.70103359992409	0.000225526961558212	***
df.mm.trans3:probe18	-0.06841909938751	0.0567907871293016	-1.20475701862932	0.228560318570548	   
df.mm.trans3:probe19	-0.156017899715861	0.0567907871293017	-2.74723960702708	0.00610963462122167	** 
df.mm.trans3:probe20	-0.072927679120539	0.0567907871293017	-1.28414629919632	0.199365411125343	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81753625983397	0.158138053098385	24.1405290190269	2.28716774898783e-103	***
df.mm.trans1	0.26111336713188	0.134868508856244	1.93605882756663	0.0531204555817526	.  
df.mm.trans2	0.0403492906816797	0.117476950269610	0.343465595498333	0.731314883376778	   
df.mm.exp2	0.213640590427900	0.147284155360339	1.45053342571179	0.147199379593488	   
df.mm.exp3	0.211731487905032	0.147284155360339	1.43757138971955	0.150844564896154	   
df.mm.exp4	0.103878990799492	0.147284155360339	0.705296442413284	0.480777436645299	   
df.mm.exp5	0.129489744431668	0.147284155360339	0.879183128116286	0.379497017032132	   
df.mm.exp6	0.0523916768362795	0.147284155360339	0.355718350749273	0.722120774891595	   
df.mm.exp7	0.091440234705492	0.147284155360339	0.62084230636879	0.5348340176056	   
df.mm.exp8	0.311630406426271	0.147284155360339	2.11584474693731	0.0345857608257243	*  
df.mm.trans1:exp2	-0.142090227627926	0.133906295553863	-1.06111685817469	0.288873371299717	   
df.mm.trans2:exp2	-0.185816758489541	0.0888798374527376	-2.09065142123319	0.0367919961450991	*  
df.mm.trans1:exp3	-0.164360396716248	0.133906295553863	-1.22742844939755	0.219928329055826	   
df.mm.trans2:exp3	-0.219337711672355	0.0888798374527375	-2.46780054912893	0.013748306003364	*  
df.mm.trans1:exp4	-0.0385572836354955	0.133906295553863	-0.28794227691846	0.773446016897369	   
df.mm.trans2:exp4	-0.0675660919595103	0.0888798374527375	-0.760195944276327	0.447302926283526	   
df.mm.trans1:exp5	-0.083979706844033	0.133906295553863	-0.627152789916833	0.530691275609418	   
df.mm.trans2:exp5	-0.107434847218527	0.0888798374527375	-1.20876511813668	0.227016936131820	   
df.mm.trans1:exp6	-0.0789892145517464	0.133906295553863	-0.589884248720581	0.555391301432324	   
df.mm.trans2:exp6	-0.0210747742137533	0.0888798374527375	-0.237115355042812	0.812612144589078	   
df.mm.trans1:exp7	-0.0318843259958893	0.133906295553863	-0.238109237986231	0.8118414115405	   
df.mm.trans2:exp7	-0.188208654450969	0.0888798374527375	-2.11756299116828	0.0344395022964933	*  
df.mm.trans1:exp8	-0.235372655174437	0.133906295553863	-1.75774151768510	0.0790739855893582	.  
df.mm.trans2:exp8	-0.232165607790040	0.0888798374527375	-2.61212907723302	0.00912254906449374	** 
df.mm.trans1:probe2	-0.0283933841916099	0.101709132755803	-0.279162582771997	0.780173356806566	   
df.mm.trans1:probe3	-0.105440932372659	0.101709132755803	-1.03669089997863	0.300111346039022	   
df.mm.trans1:probe4	-0.094920367024228	0.101709132755803	-0.933253135213783	0.350897300462101	   
df.mm.trans1:probe5	-0.0625355680304796	0.101709132755803	-0.614847126664853	0.538784841040264	   
df.mm.trans1:probe6	-0.142152456812266	0.101709132755803	-1.39763709472939	0.162508290970182	   
df.mm.trans1:probe7	-0.0904637910749362	0.101709132755803	-0.889436264215662	0.373966181739719	   
df.mm.trans1:probe8	-0.0955847129960847	0.101709132755803	-0.939784957419482	0.34753743039854	   
df.mm.trans1:probe9	-0.088175745031878	0.101709132755803	-0.866940289851671	0.386166760261149	   
df.mm.trans1:probe10	-0.107884315150008	0.101709132755803	-1.06071413870996	0.289056322605381	   
df.mm.trans1:probe11	-0.100284515467108	0.101709132755803	-0.985993221551546	0.324356664139399	   
df.mm.trans1:probe12	-0.0538377286035435	0.101709132755803	-0.529330328012965	0.596684799719091	   
df.mm.trans1:probe13	0.0215769772395514	0.101709132755803	0.212143950645576	0.832034650970439	   
df.mm.trans1:probe14	0.0302666961160796	0.101709132755803	0.297580908380646	0.76608011977227	   
df.mm.trans1:probe15	0.0110417469380527	0.101709132755803	0.108562000666776	0.913570011452839	   
df.mm.trans1:probe16	0.00408363197563063	0.101709132755803	0.0401501012247854	0.967980859074638	   
df.mm.trans1:probe17	-0.141885868741218	0.101709132755803	-1.39501601180571	0.163297028915014	   
df.mm.trans1:probe18	0.0842724092790857	0.101709132755803	0.828562853656596	0.407534374882904	   
df.mm.trans1:probe19	-0.192728929206574	0.101709132755803	-1.89490288614793	0.0583714101559609	.  
df.mm.trans1:probe20	-0.0422762090052829	0.101709132755803	-0.415657943980167	0.67774266270594	   
df.mm.trans1:probe21	-0.175252924198825	0.101709132755803	-1.72307952541092	0.0851595901851167	.  
df.mm.trans1:probe22	-0.134312088918924	0.101709132755803	-1.32055092084404	0.186930137861345	   
df.mm.trans2:probe2	0.327092916431734	0.101709132755803	3.21596406899922	0.00133854197796228	** 
df.mm.trans2:probe3	0.157698042462051	0.101709132755803	1.55048065192605	0.121318352415354	   
df.mm.trans2:probe4	0.158981798495303	0.101709132755803	1.56310248831841	0.118320632902344	   
df.mm.trans2:probe5	0.0979067029594965	0.101709132755803	0.96261466700895	0.335955811110142	   
df.mm.trans2:probe6	0.0627407499306217	0.101709132755803	0.61686446664783	0.537453779933647	   
df.mm.trans3:probe2	0.0705315865874909	0.101709132755803	0.693463651458249	0.488167324939253	   
df.mm.trans3:probe3	0.00402506791827823	0.101709132755803	0.0395743018273704	0.968439809361178	   
df.mm.trans3:probe4	-0.0446777595968061	0.101709132755803	-0.439269890385109	0.660553617766265	   
df.mm.trans3:probe5	-0.0262907489689666	0.101709132755803	-0.258489559950225	0.796078221134874	   
df.mm.trans3:probe6	0.0441130199059938	0.101709132755803	0.43371739302808	0.664580053865985	   
df.mm.trans3:probe7	-0.0218898660025660	0.101709132755803	-0.215220260063785	0.829636117615384	   
df.mm.trans3:probe8	-0.0625483561498566	0.101709132755803	-0.614972858927341	0.538701833299948	   
df.mm.trans3:probe9	0.157263325047429	0.101709132755803	1.54620652822798	0.122346843958880	   
df.mm.trans3:probe10	0.0160799717459844	0.101709132755803	0.158097619262878	0.874409349277911	   
df.mm.trans3:probe11	0.108897953103423	0.101709132755803	1.07068018527775	0.284551805895648	   
df.mm.trans3:probe12	-0.00714019959542632	0.101709132755803	-0.0702021480467194	0.944045724997123	   
df.mm.trans3:probe13	0.0266237991535816	0.101709132755803	0.261764095634397	0.793553096787306	   
df.mm.trans3:probe14	0.0497068981158637	0.101709132755803	0.488716172963609	0.625141598363871	   
df.mm.trans3:probe15	0.113574310287769	0.101709132755803	1.11665793631781	0.264388287627095	   
df.mm.trans3:probe16	0.0297687993226333	0.101709132755803	0.292685607634727	0.769818537025186	   
df.mm.trans3:probe17	0.115990405597794	0.101709132755803	1.14041288579541	0.254366458794691	   
df.mm.trans3:probe18	-0.0244673582963222	0.101709132755803	-0.240562058031374	0.809940089398731	   
df.mm.trans3:probe19	0.0171824786855998	0.101709132755803	0.168937422039118	0.865877410107273	   
df.mm.trans3:probe20	-0.0106417059466808	0.101709132755803	-0.104628814132462	0.916689723119759