chr9.24020_chr9_108425025_108426552_+_0.R 

fitVsDatCorrelation=0.956172103948332
cont.fitVsDatCorrelation=0.268473065293354

fstatistic=8151.41282833163,42,462
cont.fstatistic=743.162040653075,42,462

residuals=-0.725632705062318,-0.112483451270634,0.00892419574810996,0.102728347809546,0.650773245187318
cont.residuals=-1.17172792426375,-0.527216363763016,-0.0454061658469633,0.496889302237295,1.27450568987821

predictedValues:
Include	Exclude	Both
chr9.24020_chr9_108425025_108426552_+_0.R.tl.Lung	61.0299941049422	269.880316193335	90.1784944480897
chr9.24020_chr9_108425025_108426552_+_0.R.tl.cerebhem	73.5953786350534	195.240439351652	96.978328456608
chr9.24020_chr9_108425025_108426552_+_0.R.tl.cortex	89.7234497031108	179.951474329646	140.104263873266
chr9.24020_chr9_108425025_108426552_+_0.R.tl.heart	69.0517950811825	178.124485375329	98.8740435440113
chr9.24020_chr9_108425025_108426552_+_0.R.tl.kidney	73.909227719756	246.891689927965	101.930162584726
chr9.24020_chr9_108425025_108426552_+_0.R.tl.liver	64.1883550800968	219.658808412467	92.273316171376
chr9.24020_chr9_108425025_108426552_+_0.R.tl.stomach	67.2701894310541	188.632958177224	100.361543554453
chr9.24020_chr9_108425025_108426552_+_0.R.tl.testicle	72.0227386525911	209.367365273536	105.794259080420


diffExp=-208.850322088393,-121.645060716599,-90.2280246265355,-109.072690294147,-172.982462208209,-155.47045333237,-121.36276874617,-137.344626620945
diffExpScore=0.9991055107406
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	135.476217608052	129.594576702885	93.2526466025858
cerebhem	146.426933041864	107.157223565484	116.931780431078
cortex	107.205661521674	121.751625702053	123.72472134057
heart	114.674020810535	137.136399114538	132.968820998304
kidney	120.655535520169	111.668160442301	91.9311280144715
liver	125.003237757807	115.370864169514	103.287441862399
stomach	98.02201484594	112.128202105495	122.126247955884
testicle	111.587333764284	108.080725151078	135.755551622315
cont.diffExp=5.88164090516651,39.2697094763799,-14.5459641803790,-22.462378304003,8.98737507786852,9.632373588293,-14.1061872595553,3.50660861320672
cont.diffExpScore=6.89803706385525

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

tran.correlation=-0.506642196812047
cont.tran.correlation=-0.0525954086268991

tran.covariance=-0.00921155892284767
cont.tran.covariance=-0.000427467810487289

tran.mean=141.158666590559
cont.tran.mean=118.871170738980

weightedLogRatios:
wLogRatio
Lung	-7.21701920072508
cerebhem	-4.66985604358659
cortex	-3.37170026830395
heart	-4.46205688885783
kidney	-5.91705752484768
liver	-5.87685856025136
stomach	-4.87111811397361
testicle	-5.13336428527695

cont.weightedLogRatios:
wLogRatio
Lung	0.216892740267487
cerebhem	1.50819668065172
cortex	-0.602881530026566
heart	-0.86427722623689
kidney	0.368016118534367
liver	0.383958861759972
stomach	-0.625521176688786
testicle	0.150030032798579

varWeightedLogRatios=1.33153601901359
cont.varWeightedLogRatios=0.5847267311762

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.83240605096814	0.0925616935546146	63.0110127309503	5.65138944000688e-229	***
df.mm.trans1	-1.75093819533715	0.0743642365823656	-23.5454336090415	3.99788727591533e-81	***
df.mm.trans2	-0.248232610249389	0.0743642365823656	-3.33806439301569	0.000911837479184788	***
df.mm.exp2	-0.209226540198073	0.0998481297022175	-2.09544776474092	0.0366749361637094	*  
df.mm.exp3	-0.460520560762753	0.0998481297022175	-4.6122101849698	5.16564879462158e-06	***
df.mm.exp4	-0.384060273849701	0.0998481297022175	-3.84644434497777	0.00013667264510789	***
df.mm.exp5	-0.0200535317256175	0.0998481297022175	-0.200840334069594	0.840911809265645	   
df.mm.exp6	-0.178410779396997	0.0998481297022175	-1.78682144501936	0.0746218786837408	.  
df.mm.exp7	-0.367811866314386	0.0998481297022175	-3.68371312924269	0.000257069468573199	***
df.mm.exp8	-0.247976994066775	0.0998481297022175	-2.48354170284742	0.0133619305631379	*  
df.mm.trans1:exp2	0.396443323713298	0.0789368774917293	5.0222828202805	7.30703396600532e-07	***
df.mm.trans2:exp2	-0.114520225835415	0.0789368774917293	-1.45078231460845	0.147519088501375	   
df.mm.trans1:exp3	0.845887269367696	0.0789368774917293	10.7159960749185	4.42575000795352e-24	***
df.mm.trans2:exp3	0.0552292008846409	0.0789368774917293	0.69966285264359	0.484490006062461	   
df.mm.trans1:exp4	0.507551700234507	0.0789368774917293	6.42984263328235	3.1911610490184e-10	***
df.mm.trans2:exp4	-0.0314356516181952	0.0789368774917293	-0.398237840374278	0.690638841989396	   
df.mm.trans1:exp5	0.211525769576542	0.0789368774917293	2.67968250452654	0.00763236288178109	** 
df.mm.trans2:exp5	-0.0689753174982708	0.0789368774917293	-0.873803470443808	0.38267925663355	   
df.mm.trans1:exp6	0.228867138631822	0.0789368774917293	2.89936903896157	0.0039172525099734	** 
df.mm.trans2:exp6	-0.0274923363137772	0.0789368774917293	-0.34828254153653	0.727786647332453	   
df.mm.trans1:exp7	0.465163604365507	0.0789368774917293	5.89285539466956	7.32476255730639e-09	***
df.mm.trans2:exp7	0.00963638561577549	0.0789368774917293	0.122077106695602	0.902891033775193	   
df.mm.trans1:exp8	0.413593427879601	0.0789368774917293	5.23954634413979	2.45188798668715e-07	***
df.mm.trans2:exp8	-0.00591115574961498	0.0789368774917293	-0.0748845905417824	0.94033895038948	   
df.mm.trans1:probe2	-0.0769963902835407	0.0529524672009239	-1.45406615316684	0.146606950603563	   
df.mm.trans1:probe3	0.171333683547117	0.0529524672009239	3.23561285439269	0.00130096454805237	** 
df.mm.trans1:probe4	-0.0366204244819456	0.0529524672009239	-0.691571638069144	0.489553751931571	   
df.mm.trans1:probe5	0.296405781504678	0.0529524672009239	5.59758208016994	3.73281826272805e-08	***
df.mm.trans1:probe6	0.0943412647029956	0.0529524672009239	1.78162170130856	0.0754677241560205	.  
df.mm.trans2:probe2	0.0223270932332083	0.0529524672009239	0.421644059539094	0.673480955349256	   
df.mm.trans2:probe3	0.0427619433511463	0.0529524672009239	0.807553370249765	0.419763311317187	   
df.mm.trans2:probe4	0.0225365379680343	0.0529524672009239	0.425599394311906	0.670597965809422	   
df.mm.trans2:probe5	-0.00385887407198253	0.0529524672009239	-0.072874301726874	0.941937682329896	   
df.mm.trans2:probe6	0.123310499223584	0.0529524672009239	2.32870167797266	0.0203053924371856	*  
df.mm.trans3:probe2	1.49951463098476	0.0529524672009239	28.318125863616	5.04673561133736e-103	***
df.mm.trans3:probe3	0.344246099030499	0.0529524672009239	6.5010398424739	2.07224303337009e-10	***
df.mm.trans3:probe4	-0.039517867141414	0.052952467200924	-0.746289440895485	0.455872159072766	   
df.mm.trans3:probe5	0.232717140872236	0.0529524672009239	4.39483093373551	1.37657555726610e-05	***
df.mm.trans3:probe6	0.52426047959206	0.0529524672009239	9.9005864562041	4.37029511441329e-21	***
df.mm.trans3:probe7	1.05930534501959	0.0529524672009239	20.0048345434055	1.38863771445850e-64	***
df.mm.trans3:probe8	0.85997453891756	0.0529524672009239	16.2404999120146	3.00271138159457e-47	***
df.mm.trans3:probe9	1.53650122529081	0.0529524672009239	29.0166125680353	3.71377382833106e-106	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.11676225260754	0.304514681189176	16.8030067799221	8.53763479212059e-50	***
df.mm.trans1	-0.166054807689951	0.244647660658825	-0.678750850274934	0.497635597097415	   
df.mm.trans2	-0.270574428328159	0.244647660658825	-1.10597594761182	0.269312502118302	   
df.mm.exp2	-0.338661608739391	0.328486009881249	-1.03097726707393	0.303090891273388	   
df.mm.exp3	-0.579221539783428	0.328486009881249	-1.76330657123821	0.0785097949045445	.  
df.mm.exp4	-0.46493973182581	0.328486009881249	-1.41540192836185	0.157624404114601	   
df.mm.exp5	-0.250463001971678	0.328486009881249	-0.762476922722592	0.446164514688	   
df.mm.exp6	-0.298918920600408	0.328486009881249	-0.909989806593193	0.363302384297121	   
df.mm.exp7	-0.738114961737341	0.328486009881249	-2.24702099795415	0.0251099782396851	*  
df.mm.exp8	-0.75106450991819	0.328486009881249	-2.28644291484349	0.0226800367501256	*  
df.mm.trans1:exp2	0.416392053104276	0.259690992681331	1.60341353700794	0.109526778429432	   
df.mm.trans2:exp2	0.148547807222576	0.259690992681331	0.572017557054281	0.567588293371734	   
df.mm.trans1:exp3	0.345174490620451	0.259690992681331	1.32917390417163	0.184446604074374	   
df.mm.trans2:exp3	0.51679371783168	0.259690992681331	1.99003327953635	0.0471762051001138	*  
df.mm.trans1:exp4	0.298237124342816	0.259690992681331	1.14843076097285	0.25138504311556	   
df.mm.trans2:exp4	0.521504839719557	0.259690992681331	2.00817453980585	0.0452063196720759	*  
df.mm.trans1:exp5	0.134606564696577	0.259690992681331	0.518333590652312	0.604473686102742	   
df.mm.trans2:exp5	0.101583685520158	0.259690992681331	0.391171385927936	0.695850892307552	   
df.mm.trans1:exp6	0.218462450522391	0.259690992681331	0.841239999380604	0.40064869520265	   
df.mm.trans2:exp6	0.182659829315223	0.259690992681331	0.703373757515598	0.482177162716821	   
df.mm.trans1:exp7	0.414510947357622	0.259690992681331	1.59616990592458	0.111134625403877	   
df.mm.trans2:exp7	0.593346903448758	0.259690992681331	2.28481895857227	0.0227759332753245	*  
df.mm.trans1:exp8	0.557075947581039	0.259690992681331	2.14514928619273	0.0324616199236434	*  
df.mm.trans2:exp8	0.569531976325303	0.259690992681331	2.19311409473559	0.0287968993322405	*  
df.mm.trans1:probe2	-0.125759633137703	0.174206013833957	-0.72190178955343	0.47072006774305	   
df.mm.trans1:probe3	-0.106555595096039	0.174206013833957	-0.611664274676542	0.541060770750784	   
df.mm.trans1:probe4	-0.146181588614009	0.174206013833957	-0.83913055236624	0.401830028410893	   
df.mm.trans1:probe5	-0.280367690416517	0.174206013833957	-1.60940305243278	0.108211285203283	   
df.mm.trans1:probe6	0.0301944701101786	0.174206013833957	0.173326221326425	0.862470926487086	   
df.mm.trans2:probe2	0.089092647829227	0.174206013833957	0.511421195333388	0.609300487980477	   
df.mm.trans2:probe3	0.154008352477400	0.174206013833957	0.884058759442092	0.377124317999871	   
df.mm.trans2:probe4	-0.00572028847837506	0.174206013833957	-0.0328363433183616	0.9738192771177	   
df.mm.trans2:probe5	0.0455217538636439	0.174206013833957	0.261309887424625	0.793969989457631	   
df.mm.trans2:probe6	-0.00955578066915874	0.174206013833957	-0.0548533340431448	0.956279022247435	   
df.mm.trans3:probe2	-0.0876045696207639	0.174206013833957	-0.50287913541414	0.615288888261273	   
df.mm.trans3:probe3	-0.248961890816182	0.174206013833957	-1.42912340014552	0.153644549713810	   
df.mm.trans3:probe4	-0.256293463985214	0.174206013833957	-1.47120904924383	0.141915367991459	   
df.mm.trans3:probe5	0.0122837192365659	0.174206013833957	0.0705126015240442	0.943816182264518	   
df.mm.trans3:probe6	-0.330987200679784	0.174206013833957	-1.89997574363456	0.0580586597872082	.  
df.mm.trans3:probe7	-0.237889579954132	0.174206013833957	-1.36556468240456	0.172740107748291	   
df.mm.trans3:probe8	-0.0291309746564656	0.174206013833957	-0.167221406513736	0.867269008593984	   
df.mm.trans3:probe9	-0.124801229563908	0.174206013833957	-0.71640023680733	0.474106217196187