chr11.4691_chr11_54134446_54135804_-_2.R 

fitVsDatCorrelation=0.800302100379227
cont.fitVsDatCorrelation=0.248254704961461

fstatistic=12296.8503517474,60,876
cont.fstatistic=4702.27288890112,60,876

residuals=-0.594309613649659,-0.078456938980633,-0.00784123169131184,0.0656098547851901,0.942081308964037
cont.residuals=-0.48374131912521,-0.151175646915555,-0.042624948216892,0.106738445413402,1.19659975962856

predictedValues:
Include	Exclude	Both
chr11.4691_chr11_54134446_54135804_-_2.R.tl.Lung	56.193830995207	54.830026050033	55.5862689190649
chr11.4691_chr11_54134446_54135804_-_2.R.tl.cerebhem	55.6168695559141	64.1450159317008	52.9588717491649
chr11.4691_chr11_54134446_54135804_-_2.R.tl.cortex	56.8194124795395	56.7875477915066	59.0121108608853
chr11.4691_chr11_54134446_54135804_-_2.R.tl.heart	56.7670067005016	57.5506227346686	56.5484454034836
chr11.4691_chr11_54134446_54135804_-_2.R.tl.kidney	55.0740155790643	58.0876744258063	53.2106224493492
chr11.4691_chr11_54134446_54135804_-_2.R.tl.liver	55.6719984901049	57.2329494819324	53.5192878993268
chr11.4691_chr11_54134446_54135804_-_2.R.tl.stomach	58.288536646992	61.8804173213	55.6739257098203
chr11.4691_chr11_54134446_54135804_-_2.R.tl.testicle	58.0155902389828	59.9451950620708	59.8128121208303


diffExp=1.36380494517400,-8.52814637578674,0.0318646880328828,-0.783616034166975,-3.01365884674201,-1.56095099182755,-3.59188067430808,-1.92960482308804
diffExpScore=1.09422057344446
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	55.0553992893234	54.9283859036265	56.7096769700722
cerebhem	56.8417721317444	55.3553871587206	56.441019556911
cortex	56.3488661982718	56.6837889752162	56.1520874003776
heart	56.1582693652119	50.8472615363447	57.8081401164727
kidney	57.2175241005817	55.8220414570008	59.9179036135459
liver	57.0875055809226	58.7220676322408	54.251520272237
stomach	58.5543324107536	56.1180592945894	55.7126640619431
testicle	54.5988163166207	53.1790282532236	59.3246719732333
cont.diffExp=0.127013385696934,1.48638497302382,-0.334922776944481,5.31100782886718,1.39548264358092,-1.63456205131826,2.43627311616425,1.41978806339706
cont.diffExpScore=1.26225661782482

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=0.209164174509665
cont.tran.correlation=0.479997368828926

tran.covariance=0.000218626269940332
cont.tran.covariance=0.00045944963462844

tran.mean=57.6816693428328
cont.tran.mean=55.8449066002745

weightedLogRatios:
wLogRatio
Lung	0.0986820112763422
cerebhem	-0.583452521847716
cortex	0.00226606748250536
heart	-0.0554667089537934
kidney	-0.214983379419154
liver	-0.111530813153314
stomach	-0.244892346454219
testicle	-0.133397521051996

cont.weightedLogRatios:
wLogRatio
Lung	0.00925529835256001
cerebhem	0.106706022340101
cortex	-0.0239091673623884
heart	0.395254738664566
kidney	0.0996178812374229
liver	-0.114578408577524
stomach	0.172059531185783
testicle	0.105045572484716

varWeightedLogRatios=0.0423713618092696
cont.varWeightedLogRatios=0.023107299013578

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.99233396124321	0.0669399118381954	59.6405619848041	0	***
df.mm.trans1	-0.0505577714736589	0.057884945111453	-0.873418319328349	0.382674469150267	   
df.mm.trans2	0.0700782249519847	0.0510237488531066	1.37344327939788	0.169965979160544	   
df.mm.exp2	0.195008613257544	0.0655751950716844	2.97381674647506	0.0030218515628788	** 
df.mm.exp3	-0.0136563204142847	0.0655751950716844	-0.208254362024483	0.835078773244064	   
df.mm.exp4	0.0414137955892473	0.0655751950716844	0.631546662483813	0.527847910491939	   
df.mm.exp5	0.0812647312830697	0.0655751950716844	1.23926022933266	0.215581057336049	   
df.mm.exp6	0.0714562246829489	0.0655751950716844	1.08968375320631	0.276152138606037	   
df.mm.exp7	0.155988561787247	0.0655751950716844	2.37877388876581	0.0175837587538925	*  
df.mm.exp8	0.0478138755435314	0.0655751950716844	0.729145761461525	0.466107402532822	   
df.mm.trans1:exp2	-0.205329030847319	0.0608053252709258	-3.37682645282218	0.000765562146233833	***
df.mm.trans2:exp2	-0.0381001837074747	0.0446015859625533	-0.854233832390241	0.393208981082226	   
df.mm.trans1:exp3	0.0247273746515912	0.0608053252709258	0.406664622570725	0.684353660926106	   
df.mm.trans2:exp3	0.0487354285631085	0.0446015859625533	1.09268375801762	0.274833076031446	   
df.mm.trans1:exp4	-0.0312654886862555	0.0608053252709258	-0.514189975087677	0.607248861497931	   
df.mm.trans2:exp4	0.00701319575757796	0.0446015859625533	0.157240950208948	0.875091190135666	   
df.mm.trans1:exp5	-0.101393695049037	0.0608053252709258	-1.66751340605883	0.0957697739830741	.  
df.mm.trans2:exp5	-0.0235491985864125	0.0446015859625533	-0.527990161744113	0.597639879893345	   
df.mm.trans1:exp6	-0.0807859063677216	0.0608053252709258	-1.32859919764872	0.184326225340815	   
df.mm.trans2:exp6	-0.0285644167718818	0.0446015859625533	-0.640435001478737	0.522057314706693	   
df.mm.trans1:exp7	-0.119390096851742	0.0608053252709258	-1.96348093394426	0.0499062903647961	*  
df.mm.trans2:exp7	-0.0350227565847443	0.0446015859625533	-0.785235677810847	0.4325277166346	   
df.mm.trans1:exp8	-0.0159090859988254	0.0608053252709258	-0.261639682510380	0.793660751513585	   
df.mm.trans2:exp8	0.0413788891068383	0.0446015859625533	0.927744792339431	0.353795511179849	   
df.mm.trans1:probe2	0.203738621155701	0.0416305603341562	4.89396778521237	1.17612149710643e-06	***
df.mm.trans1:probe3	0.124150188228992	0.0416305603341562	2.98218873905312	0.00294124323844622	** 
df.mm.trans1:probe4	0.0195052198459319	0.0416305603341562	0.46853128301347	0.639521268293099	   
df.mm.trans1:probe5	0.371908022334768	0.0416305603341562	8.93353390753264	2.39276336110930e-18	***
df.mm.trans1:probe6	0.0124285841001768	0.0416305603341562	0.298544722925088	0.765358249237391	   
df.mm.trans1:probe7	0.410473790606417	0.0416305603341562	9.85991510351206	8.07648548805081e-22	***
df.mm.trans1:probe8	0.49436455635241	0.0416305603341562	11.8750396916182	2.90867367448745e-30	***
df.mm.trans1:probe9	0.424105414656827	0.0416305603341562	10.1873578268622	4.13079781903489e-23	***
df.mm.trans1:probe10	0.257400005861421	0.0416305603341562	6.18295799516863	9.63686627935707e-10	***
df.mm.trans1:probe11	-0.0151936775899661	0.0416305603341562	-0.364964522889216	0.715225981620648	   
df.mm.trans1:probe12	-0.0962838736346053	0.0416305603341562	-2.31281714350619	0.0209635961023836	*  
df.mm.trans1:probe13	-0.144588213938080	0.0416305603341562	-3.47312677940228	0.000539629442899533	***
df.mm.trans1:probe14	-0.154038422367611	0.0416305603341562	-3.70012849049328	0.000228934450240239	***
df.mm.trans1:probe15	-0.0361360323342544	0.0416305603341562	-0.86801695783581	0.385622775279175	   
df.mm.trans1:probe16	-0.0912144544405933	0.0416305603341562	-2.19104556144433	0.0287112567084889	*  
df.mm.trans1:probe17	0.176055871230627	0.0416305603341562	4.22900556267988	2.59404860508545e-05	***
df.mm.trans1:probe18	0.148495514597032	0.0416305603341562	3.56698332679413	0.000380691880100973	***
df.mm.trans1:probe19	0.271200471982199	0.0416305603341562	6.51445644270346	1.22878727386760e-10	***
df.mm.trans1:probe20	0.455848757889128	0.0416305603341562	10.9498588111753	3.05592196594320e-26	***
df.mm.trans1:probe21	0.070630159360905	0.0416305603341562	1.69659401156212	0.0901287023640384	.  
df.mm.trans1:probe22	-0.003046995853212	0.0416305603341562	-0.0731913245643264	0.941670578093299	   
df.mm.trans1:probe23	-0.0277873609396449	0.0416305603341562	-0.667475064390293	0.504644525019345	   
df.mm.trans2:probe2	-0.118290633370394	0.0416305603341562	-2.84143745414211	0.00459528637477746	** 
df.mm.trans2:probe3	-0.241919475898254	0.0416305603341562	-5.81110304440867	8.68458777810542e-09	***
df.mm.trans2:probe4	-0.224093510289539	0.0416305603341562	-5.38290881724403	9.41555577391e-08	***
df.mm.trans2:probe5	-0.265808062274820	0.0416305603341562	-6.38492636518119	2.77843623116375e-10	***
df.mm.trans2:probe6	-0.0806758649490996	0.0416305603341562	-1.93790004990416	0.0529564721752505	.  
df.mm.trans3:probe2	-0.150281202078967	0.0416305603341562	-3.60987699595452	0.000323726679897957	***
df.mm.trans3:probe3	-0.164790955489764	0.0416305603341562	-3.95841310246692	8.15636229120753e-05	***
df.mm.trans3:probe4	0.0380824701327418	0.0416305603341562	0.91477198065712	0.360563071861704	   
df.mm.trans3:probe5	0.483802202335571	0.0416305603341562	11.6213233368043	3.89008908261897e-29	***
df.mm.trans3:probe6	-0.0437552224286873	0.0416305603341562	-1.05103611571588	0.293531853105703	   
df.mm.trans3:probe7	-0.0122348220768770	0.0416305603341562	-0.293890401154144	0.768911270606051	   
df.mm.trans3:probe8	-0.00751000774068325	0.0416305603341562	-0.180396508728267	0.85688301862613	   
df.mm.trans3:probe9	-0.124335357600627	0.0416305603341562	-2.98663665832561	0.00289922132856814	** 
df.mm.trans3:probe10	0.0418408364229718	0.0416305603341562	1.00505100308830	0.315149825826480	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.8504892861982	0.108146547283240	35.6043663244616	2.05988899877132e-172	***
df.mm.trans1	0.142049870204739	0.0935175559928297	1.51896474086245	0.129132185739613	   
df.mm.trans2	0.124318173327589	0.0824327686784034	1.50811595098296	0.131885403056252	   
df.mm.exp2	0.0444239378495654	0.105941742970464	0.419324211627804	0.675082010179093	   
df.mm.exp3	0.0645611902103205	0.105941742970464	0.609402756648243	0.542415529588702	   
df.mm.exp4	-0.0765547224184	0.105941742970464	-0.722611505832435	0.470111465177824	   
df.mm.exp5	-0.000371670304357929	0.105941742970464	-0.00350825174229528	0.997201624586737	   
df.mm.exp6	0.147344521627211	0.105941742970464	1.39080703692302	0.164637128247081	   
df.mm.exp7	0.100779938014725	0.105941742970464	0.95127694890598	0.341726190431152	   
df.mm.exp8	-0.0857742913667645	0.105941742970464	-0.809636399796421	0.418369043349812	   
df.mm.trans1:exp2	-0.0124923956527328	0.0982356534974227	-0.127167634234352	0.89883690553492	   
df.mm.trans2:exp2	-0.0366802167743146	0.0720572733478643	-0.509042530616402	0.61085060268565	   
df.mm.trans1:exp3	-0.0413390087245668	0.0982356534974227	-0.420814716987162	0.673993600726827	   
df.mm.trans2:exp3	-0.0331031914000641	0.0720572733478643	-0.459401110561801	0.646060130462661	   
df.mm.trans1:exp4	0.0963887274220347	0.0982356534974228	0.981199024899484	0.326765510035645	   
df.mm.trans2:exp4	-0.000649272613809593	0.0720572733478643	-0.00901050766485652	0.992812803874389	   
df.mm.trans1:exp5	0.0388919490720196	0.0982356534974228	0.395904620037367	0.6922717969156	   
df.mm.trans2:exp5	0.0165102075689914	0.0720572733478643	0.229126177024303	0.818824330299265	   
df.mm.trans1:exp6	-0.111099183447829	0.0982356534974227	-1.13094563422173	0.258387500746705	   
df.mm.trans2:exp6	-0.0805591885375595	0.0720572733478643	-1.11798830006586	0.263878491839179	   
df.mm.trans1:exp7	-0.0391647937412394	0.0982356534974227	-0.398682070581094	0.690224638999789	   
df.mm.trans2:exp7	-0.0793525270466922	0.0720572733478643	-1.10124243341278	0.271093651208614	   
df.mm.trans1:exp8	0.0774465566072734	0.0982356534974228	0.78837523699382	0.430690539567663	   
df.mm.trans2:exp8	0.0534081419109578	0.0720572733478643	0.741190159293486	0.458776789813464	   
df.mm.trans1:probe2	0.0479818494398477	0.0672573542147497	0.713406734476112	0.475784116595973	   
df.mm.trans1:probe3	-0.0759597956551547	0.0672573542147497	-1.12939018404766	0.259042438666579	   
df.mm.trans1:probe4	0.0506899716858552	0.0672573542147497	0.753671807011682	0.451248834138468	   
df.mm.trans1:probe5	-0.0190420284962461	0.0672573542147497	-0.283121878916702	0.777150301039278	   
df.mm.trans1:probe6	0.054447463380046	0.0672573542147497	0.80953918000101	0.418424907898385	   
df.mm.trans1:probe7	0.0799133634815645	0.0672573542147496	1.18817286844803	0.235087340812963	   
df.mm.trans1:probe8	0.0682273127206635	0.0672573542147497	1.01442159771580	0.310661718752923	   
df.mm.trans1:probe9	0.0776831534729479	0.0672573542147496	1.15501352052757	0.248399849394338	   
df.mm.trans1:probe10	0.125800827276572	0.0672573542147497	1.87043972730322	0.0617560479007584	.  
df.mm.trans1:probe11	0.00906861503943038	0.0672573542147497	0.134834549252037	0.892773662472442	   
df.mm.trans1:probe12	0.0378149841548691	0.0672573542147496	0.562243112242084	0.574094228111236	   
df.mm.trans1:probe13	-0.0200166161743764	0.0672573542147496	-0.29761230438034	0.766069646608305	   
df.mm.trans1:probe14	0.0519970573518901	0.0672573542147496	0.773105899852466	0.439668264144644	   
df.mm.trans1:probe15	0.008191743907537	0.0672573542147497	0.121796999052046	0.903087724067087	   
df.mm.trans1:probe16	0.0309029657641325	0.0672573542147496	0.459473408148955	0.64600824363451	   
df.mm.trans1:probe17	0.103270559476344	0.0672573542147496	1.53545379062349	0.125033480853598	   
df.mm.trans1:probe18	-0.0222950436621419	0.0672573542147496	-0.331488562439652	0.740354764413254	   
df.mm.trans1:probe19	-0.004818459803317	0.0672573542147496	-0.0716421253790608	0.942903067739056	   
df.mm.trans1:probe20	0.0113620032304327	0.0672573542147497	0.168933246974811	0.865888146512939	   
df.mm.trans1:probe21	-0.0312113209292952	0.0672573542147496	-0.464058113699192	0.642721405900282	   
df.mm.trans1:probe22	-0.0102270903396464	0.0672573542147496	-0.152059064158125	0.879175363947447	   
df.mm.trans1:probe23	-0.0523557195638629	0.0672573542147497	-0.778438583782132	0.436520708719493	   
df.mm.trans2:probe2	0.0467676084683589	0.0672573542147496	0.695353080928995	0.487018467134148	   
df.mm.trans2:probe3	0.147125679482698	0.0672573542147496	2.18750322846379	0.0289694758950226	*  
df.mm.trans2:probe4	0.139542681809860	0.0672573542147497	2.07475722824758	0.0383005088406191	*  
df.mm.trans2:probe5	0.065885369148208	0.0672573542147496	0.979600965833998	0.327553583581342	   
df.mm.trans2:probe6	0.100243505756816	0.0672573542147496	1.49044676120835	0.136466771812332	   
df.mm.trans3:probe2	-0.109914789297239	0.0672573542147496	-1.63424194395585	0.102567352474668	   
df.mm.trans3:probe3	-0.100166643880031	0.0672573542147496	-1.48930395864523	0.136767268391763	   
df.mm.trans3:probe4	-0.180620767384796	0.0672573542147496	-2.68551698908765	0.00737873006759529	** 
df.mm.trans3:probe5	0.008807797019581	0.0672573542147496	0.130956638458571	0.895839677204642	   
df.mm.trans3:probe6	-0.0828086259945581	0.0672573542147496	-1.23122039160437	0.218570891055676	   
df.mm.trans3:probe7	-0.0819233329622107	0.0672573542147496	-1.21805762237737	0.223530043019703	   
df.mm.trans3:probe8	-0.099104432312482	0.0672573542147496	-1.47351071818921	0.140972607732902	   
df.mm.trans3:probe9	-0.0567687754277648	0.0672573542147496	-0.844053056956488	0.398870192244224	   
df.mm.trans3:probe10	-0.0866250686910711	0.0672573542147497	-1.28796426357304	0.198098519891060