chr1.848_chr1_157785119_157789386_-_1.R 

fitVsDatCorrelation=0.843647408478901
cont.fitVsDatCorrelation=0.270030180419227

fstatistic=7039.89645607094,52,692
cont.fstatistic=2179.75203336526,52,692

residuals=-0.602653703300925,-0.107728294967638,0.00333280136399394,0.0900657924157805,1.14583302240427
cont.residuals=-0.746926427076214,-0.238221135881355,-0.0701296406756969,0.165880750013460,1.39240623898893

predictedValues:
Include	Exclude	Both
chr1.848_chr1_157785119_157789386_-_1.R.tl.Lung	63.388591918548	60.2703547267084	98.2578152464772
chr1.848_chr1_157785119_157789386_-_1.R.tl.cerebhem	62.6143642442255	59.172002664852	81.5773034543102
chr1.848_chr1_157785119_157789386_-_1.R.tl.cortex	59.7727124313244	48.7347555355055	68.8743443634127
chr1.848_chr1_157785119_157789386_-_1.R.tl.heart	64.2344997396845	52.9219996695726	77.9617478864944
chr1.848_chr1_157785119_157789386_-_1.R.tl.kidney	60.3762134255961	49.8998580199124	77.882622838013
chr1.848_chr1_157785119_157789386_-_1.R.tl.liver	61.8166528074954	52.3096647562531	77.0012359070022
chr1.848_chr1_157785119_157789386_-_1.R.tl.stomach	70.130994696633	54.3423083212064	90.6470429679789
chr1.848_chr1_157785119_157789386_-_1.R.tl.testicle	87.38820365928	54.5606411672882	80.3542780862442


diffExp=3.11823719183968,3.44236157937350,11.0379568958190,11.3125000701119,10.4763554056837,9.50698805124232,15.7886863754265,32.8275624919918
diffExpScore=0.98984881310114
diffExp1.5=0,0,0,0,0,0,0,1
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,0,1
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,0,1
diffExp1.3Score=0.5
diffExp1.2=0,0,1,1,1,0,1,1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	77.7959304437889	71.5890362589017	69.7469158851875
cerebhem	65.3458598787638	66.3812334034168	71.4017028129922
cortex	68.3318845127952	59.0854303242788	71.0216802907401
heart	68.0599685172967	58.489204891394	62.9765339161034
kidney	63.5936479729917	66.2861182268613	67.2643154149082
liver	59.6069810515789	75.2018099440114	67.50640239807
stomach	69.6661294556958	55.1024721969014	63.1277359785044
testicle	68.9779483721431	68.5017406396214	66.4107013179876
cont.diffExp=6.20689418488718,-1.03537352465295,9.24645418851639,9.57076362590264,-2.69247025386967,-15.5948288924324,14.5636572587944,0.476207732521743
cont.diffExpScore=2.73151273669703

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,-1,1,0
cont.diffExp1.2Score=2

tran.correlation=0.170970539374380
cont.tran.correlation=-0.177161349366912

tran.covariance=0.00197111683332790
cont.tran.covariance=-0.00177099750058539

tran.mean=60.1208636115053
cont.tran.mean=66.3759622556526

weightedLogRatios:
wLogRatio
Lung	0.208032350572517
cerebhem	0.232332636846221
cortex	0.814273449552288
heart	0.787608855925174
kidney	0.763319541960613
liver	0.674759283987841
stomach	1.05157730249707
testicle	1.99480932728483

cont.weightedLogRatios:
wLogRatio
Lung	0.358573524529304
cerebhem	-0.0658296174258998
cortex	0.603621618132368
heart	0.62810408079074
kidney	-0.17305164443819
liver	-0.977014488209962
stomach	0.967734726144712
testicle	0.0293064466454479

varWeightedLogRatios=0.311273335790993
cont.varWeightedLogRatios=0.368364043262113

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.61284619876071	0.0898905673856617	40.1916052355120	3.95681161014311e-183	***
df.mm.trans1	0.577110011074672	0.0769551055682626	7.49930763934499	1.97228788197421e-13	***
df.mm.trans2	0.475767263375577	0.069479033092388	6.84763794485938	1.65867340034527e-11	***
df.mm.exp2	0.155362667197478	0.0904110060260956	1.71840436276791	0.086170174729774	.  
df.mm.exp3	0.0841284682459113	0.0904110060260956	0.930511360769827	0.352430878546126	   
df.mm.exp4	0.114611807895384	0.0904110060260957	1.26767539631517	0.205340277902553	   
df.mm.exp5	-0.00511895858306746	0.0904110060260956	-0.0566187548183012	0.954865237987624	   
df.mm.exp6	0.0770029990934766	0.0904110060260957	0.851699394554364	0.394675597104471	   
df.mm.exp7	0.0781651439497215	0.0904110060260956	0.864553414295162	0.387583538330949	   
df.mm.exp8	0.422698267055411	0.0904110060260957	4.67529657764682	3.52949121903018e-06	***
df.mm.trans1:exp2	-0.167651861702259	0.0816870584541917	-2.05236747258165	0.0405095929475741	*  
df.mm.trans2:exp2	-0.173754518235917	0.0645792900186397	-2.69056098612675	0.00730532889190775	** 
df.mm.trans1:exp3	-0.142863132348739	0.0816870584541917	-1.74890778358549	0.0807503190201565	.  
df.mm.trans2:exp3	-0.296576379773181	0.0645792900186397	-4.5924379114044	5.20451932632006e-06	***
df.mm.trans1:exp4	-0.101355269472224	0.0816870584541917	-1.24077511652672	0.215109318307554	   
df.mm.trans2:exp4	-0.244633035815613	0.0645792900186397	-3.78810351964235	0.000165006772931986	***
df.mm.trans1:exp5	-0.043569738558754	0.0816870584541917	-0.533373821793166	0.593946096580309	   
df.mm.trans2:exp5	-0.183703237059962	0.0645792900186397	-2.84461530944269	0.00457786231229319	** 
df.mm.trans1:exp6	-0.102114115083096	0.0816870584541917	-1.25006478401177	0.211698456131051	   
df.mm.trans2:exp6	-0.218662203624502	0.0645792900186398	-3.38594932773942	0.000749411776762695	***
df.mm.trans1:exp7	0.0229157950503055	0.0816870584541917	0.28053152462524	0.779153589002395	   
df.mm.trans2:exp7	-0.181702414730648	0.0645792900186398	-2.81363289497612	0.00503754806729907	** 
df.mm.trans1:exp8	-0.101621870135703	0.0816870584541917	-1.24403879952037	0.213906506675655	   
df.mm.trans2:exp8	-0.522225855093515	0.0645792900186397	-8.08658402628434	2.74671049924118e-15	***
df.mm.trans1:probe2	0.344779430662833	0.0547973446756714	6.29190032297141	5.56197758438064e-10	***
df.mm.trans1:probe3	0.223230929962701	0.0547973446756714	4.07375450916346	5.16168290247292e-05	***
df.mm.trans1:probe4	0.178261536243926	0.0547973446756714	3.25310537032407	0.00119697116323484	** 
df.mm.trans1:probe5	0.0666727603329439	0.0547973446756715	1.21671516617382	0.224127418552943	   
df.mm.trans1:probe6	-0.226586455512646	0.0547973446756714	-4.13498969436824	3.98578071588900e-05	***
df.mm.trans1:probe7	-0.262194128369030	0.0547973446756714	-4.78479623275317	2.09326651148134e-06	***
df.mm.trans1:probe8	0.248110057833910	0.0547973446756714	4.52777519243672	7.01776489571728e-06	***
df.mm.trans1:probe9	-0.440984694141357	0.0547973446756714	-8.0475558943852	3.67836017246642e-15	***
df.mm.trans1:probe10	-0.165208554646685	0.0547973446756714	-3.01490073332027	0.00266467688652547	** 
df.mm.trans1:probe11	-0.359147182906628	0.0547973446756714	-6.55409828765077	1.09404689573855e-10	***
df.mm.trans1:probe12	-0.100302915517803	0.0547973446756714	-1.83043386703252	0.0676150226121619	.  
df.mm.trans1:probe13	-0.102008620678684	0.0547973446756714	-1.86156138189618	0.0630887080287844	.  
df.mm.trans1:probe14	-0.247335060678354	0.0547973446756714	-4.51363222328114	7.4882565054695e-06	***
df.mm.trans1:probe15	-0.129600397113031	0.0547973446756714	-2.36508535003103	0.0183008257711460	*  
df.mm.trans1:probe16	-0.00382197210256647	0.0547973446756715	-0.0697473960679581	0.944414866969097	   
df.mm.trans2:probe2	-0.0471958233271729	0.0547973446756714	-0.86127938509631	0.389382516353749	   
df.mm.trans2:probe3	0.0258447916450555	0.0547973446756714	0.47164313887877	0.637330198159715	   
df.mm.trans2:probe4	0.129060749246755	0.0547973446756714	2.35523728404406	0.0187890899695957	*  
df.mm.trans2:probe5	-0.000613988209852017	0.0547973446756714	-0.0112047073354744	0.991063353479582	   
df.mm.trans2:probe6	0.0463076351626837	0.0547973446756714	0.84507078649092	0.398363397991026	   
df.mm.trans3:probe2	0.37605291387908	0.0547973446756714	6.86261197700037	1.50371011489620e-11	***
df.mm.trans3:probe3	0.291195157379641	0.0547973446756714	5.31403773491463	1.44764809310178e-07	***
df.mm.trans3:probe4	-0.588658169801201	0.0547973446756714	-10.7424579290345	5.30180763401528e-25	***
df.mm.trans3:probe5	-0.119906105671683	0.0547973446756714	-2.18817364931404	0.0289906175524357	*  
df.mm.trans3:probe6	-0.274108652552704	0.0547973446756714	-5.00222509274981	7.19277610660607e-07	***
df.mm.trans3:probe7	-0.146731381215166	0.0547973446756714	-2.67770969713267	0.00758855394988503	** 
df.mm.trans3:probe8	0.173501274274243	0.0547973446756714	3.16623506670156	0.00161204423764152	** 
df.mm.trans3:probe9	-0.414503166477305	0.0547973446756714	-7.5642929220498	1.24499056878386e-13	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.37919434533527	0.161206340438127	27.1651495433336	3.63894434891959e-111	***
df.mm.trans1	-0.0179758027189552	0.138008372930441	-0.130251537187641	0.896405273989252	   
df.mm.trans2	-0.105488987673545	0.124601067584205	-0.846613835007921	0.397503076491966	   
df.mm.exp2	-0.273371102129676	0.162139675392917	-1.68602226115976	0.092242285602814	.  
df.mm.exp3	-0.339782186677648	0.162139675392917	-2.09561407998533	0.0364792110346239	*  
df.mm.exp4	-0.233688623781657	0.162139675392917	-1.44127970661933	0.149958024346432	   
df.mm.exp5	-0.242293632877774	0.162139675392917	-1.49435128873064	0.135539662206746	   
df.mm.exp6	-0.184432301939708	0.162139675392917	-1.13749026259469	0.255727090070305	   
df.mm.exp7	-0.272409250413406	0.162139675392917	-1.68009001962826	0.0933911358266283	.  
df.mm.exp8	-0.115370043711082	0.162139675392917	-0.711547272014097	0.476984955956258	   
df.mm.trans1:exp2	0.0989760652561258	0.146494478091993	0.675630006982057	0.499501293372268	   
df.mm.trans2:exp2	0.197843551832099	0.115814053852084	1.70828621615113	0.088031703312262	.  
df.mm.trans1:exp3	0.210069552775610	0.146494478091993	1.43397591166333	0.152030836012147	   
df.mm.trans2:exp3	0.147824617515175	0.115814053852084	1.27639619371217	0.202243695283991	   
df.mm.trans1:exp4	0.0999887083857432	0.146494478091993	0.682542507322045	0.495124386200243	   
df.mm.trans2:exp4	0.0315888918542286	0.115814053852084	0.272755255545873	0.785122728361314	   
df.mm.trans1:exp5	0.0407181017017634	0.146494478091993	0.277949737301320	0.78113396600873	   
df.mm.trans2:exp5	0.165332192603752	0.115814053852084	1.42756588777137	0.153867945721879	   
df.mm.trans1:exp6	-0.0818841210062607	0.146494478091993	-0.558957047888458	0.576371869323963	   
df.mm.trans2:exp6	0.233665663644154	0.115814053852084	2.01759333925561	0.0440191624917769	*  
df.mm.trans1:exp7	0.162034380622328	0.146494478091993	1.10607841833176	0.269076952525751	   
df.mm.trans2:exp7	0.0106618956570216	0.115814053852084	0.0920604650506303	0.926676642935508	   
df.mm.trans1:exp8	-0.00493221347635368	0.146494478091993	-0.0336682552174864	0.973151401996586	   
df.mm.trans2:exp8	0.0712872620876893	0.115814053852084	0.615532050874728	0.538405790381501	   
df.mm.trans1:probe2	-0.046844573429178	0.0982714834026148	-0.476685319150596	0.633736703712805	   
df.mm.trans1:probe3	0.0843128002149894	0.0982714834026149	0.857957947674025	0.391212736526148	   
df.mm.trans1:probe4	0.042174953413184	0.0982714834026148	0.429167770271612	0.667934713652902	   
df.mm.trans1:probe5	0.0734113696443228	0.0982714834026149	0.747026167739413	0.455301504216071	   
df.mm.trans1:probe6	-0.069507503021842	0.0982714834026149	-0.707300842677547	0.47961766187294	   
df.mm.trans1:probe7	-0.0573087704815668	0.0982714834026148	-0.583167858032373	0.559970303012307	   
df.mm.trans1:probe8	-0.0100330437703552	0.0982714834026149	-0.102095169656188	0.918710711075854	   
df.mm.trans1:probe9	0.0402736904012716	0.0982714834026148	0.409820723233328	0.682064296314417	   
df.mm.trans1:probe10	-0.0899650172690297	0.0982714834026149	-0.915474297873841	0.360261586732794	   
df.mm.trans1:probe11	-0.0265600787331224	0.0982714834026149	-0.270272492217368	0.787031210303417	   
df.mm.trans1:probe12	0.0549897902449575	0.0982714834026149	0.559570165636619	0.575953725326058	   
df.mm.trans1:probe13	-0.120106268286384	0.0982714834026149	-1.22218841242390	0.222052518465503	   
df.mm.trans1:probe14	-0.0830935609137952	0.0982714834026149	-0.845551100245061	0.398095480268686	   
df.mm.trans1:probe15	0.0833368232168339	0.0982714834026149	0.848026511164036	0.396716428877298	   
df.mm.trans1:probe16	-0.0461867085228397	0.0982714834026149	-0.469990956925056	0.638509552783513	   
df.mm.trans2:probe2	-0.044886940719867	0.0982714834026149	-0.456764660160534	0.647983548145898	   
df.mm.trans2:probe3	-0.0448563911462422	0.0982714834026149	-0.456453790999237	0.648206920154054	   
df.mm.trans2:probe4	0.0363722492771871	0.0982714834026149	0.370120079781143	0.71140627138216	   
df.mm.trans2:probe5	0.0621057378084892	0.0982714834026149	0.631981279391541	0.527607838099457	   
df.mm.trans2:probe6	-0.050185959653637	0.0982714834026149	-0.510686904440293	0.609733183439823	   
df.mm.trans3:probe2	-0.052974979032356	0.0982714834026149	-0.53906766437339	0.590013548582358	   
df.mm.trans3:probe3	-0.0134239640797595	0.0982714834026149	-0.136600808443707	0.891386068023529	   
df.mm.trans3:probe4	0.0412759953839309	0.0982714834026149	0.420020070469727	0.674601170254613	   
df.mm.trans3:probe5	-0.0084623114516686	0.0982714834026149	-0.0861115672488509	0.931402637361005	   
df.mm.trans3:probe6	-0.0748038943019896	0.0982714834026149	-0.761196348237877	0.4467991106449	   
df.mm.trans3:probe7	0.0222193673869953	0.0982714834026148	0.22610188243484	0.821188945507024	   
df.mm.trans3:probe8	-0.0417249838742657	0.0982714834026149	-0.424588928848462	0.671268339139178	   
df.mm.trans3:probe9	0.0301876027008364	0.0982714834026149	0.307185784274354	0.758794384295008