chr4.17144_chr4_10837280_10841644_-_1.R 

fitVsDatCorrelation=0.951308732086103
cont.fitVsDatCorrelation=0.273613726055117

fstatistic=9141.68671455523,42,462
cont.fstatistic=928.98367036767,42,462

residuals=-0.600399468368845,-0.0871975463446145,0.00543100377885477,0.088878609865537,0.468321406135945
cont.residuals=-0.857374094839403,-0.386544414112105,-0.106411978462238,0.359690142095559,1.27135954595687

predictedValues:
Include	Exclude	Both
chr4.17144_chr4_10837280_10841644_-_1.R.tl.Lung	62.4906288842065	139.386030997235	69.043235753594
chr4.17144_chr4_10837280_10841644_-_1.R.tl.cerebhem	62.4549970078715	102.713773815720	54.2954421310474
chr4.17144_chr4_10837280_10841644_-_1.R.tl.cortex	54.8418017975144	108.947438419575	57.1495458823815
chr4.17144_chr4_10837280_10841644_-_1.R.tl.heart	55.2965330612762	150.952119370914	58.5784776789737
chr4.17144_chr4_10837280_10841644_-_1.R.tl.kidney	59.0829437162854	130.680855614610	66.1619851728584
chr4.17144_chr4_10837280_10841644_-_1.R.tl.liver	58.6715132136833	138.614652408255	60.0564804381884
chr4.17144_chr4_10837280_10841644_-_1.R.tl.stomach	65.5348613449743	201.837262228363	115.000927416179
chr4.17144_chr4_10837280_10841644_-_1.R.tl.testicle	64.9821880945756	188.225695314114	119.876196538312


diffExp=-76.8954021130288,-40.2587768078482,-54.1056366220601,-95.6555863096382,-71.5979118983248,-79.943139194572,-136.302400883389,-123.243507219538
diffExpScore=0.998527251071033
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	85.9752514352114	74.5886678178054	83.0983523225468
cerebhem	95.0514183145337	91.8164808351979	81.313645956909
cortex	85.1705628695392	79.92813939495	75.1002855833094
heart	72.303188962459	72.7130473910736	90.5408194441158
kidney	102.825445346749	79.5475618189487	89.9974354907918
liver	86.7150588717795	86.6776690413598	77.1542526844722
stomach	77.0062887707185	71.5098025368914	80.795947924245
testicle	80.3593245112914	64.7428794532539	77.6955075125127
cont.diffExp=11.386583617406,3.23493747933585,5.24242347458917,-0.409858428614598,23.2778835278005,0.0373898304196985,5.49648623382711,15.6164450580375
cont.diffExpScore=0.99722138135741

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=0,0,0,0,1,0,0,1
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.59126736713146
cont.tran.correlation=0.607572580586645

tran.covariance=0.0086441724122326
cont.tran.covariance=0.00774074169410194

tran.mean=102.794580955573
cont.tran.mean=81.6831742107352

weightedLogRatios:
wLogRatio
Lung	-3.63902420509737
cerebhem	-2.18064002032654
cortex	-2.98428905010365
heart	-4.53403572002596
kidney	-3.55300084538604
liver	-3.87041561745820
stomach	-5.33757798404941
testicle	-5.0048350337106

cont.weightedLogRatios:
wLogRatio
Lung	0.62270013034516
cerebhem	0.157102607749432
cortex	0.280341732254988
heart	-0.0242140194832866
kidney	1.15625467146363
liver	0.00192451932211540
stomach	0.318934035067784
testicle	0.924509783623561

varWeightedLogRatios=1.09603616335666
cont.varWeightedLogRatios=0.186840512997622

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.12463055716629	0.0809679742197616	63.2920683337973	8.94736099698616e-230	***
df.mm.trans1	-0.94673788686547	0.0650498209274938	-14.5540429376543	9.06328561401237e-40	***
df.mm.trans2	-0.199057137064805	0.0650498209274938	-3.06007202213022	0.00234162420226022	** 
df.mm.exp2	-0.0655787897249623	0.087341755332625	-0.750829766074858	0.453137357873114	   
df.mm.exp3	-0.187884164276041	0.087341755332625	-2.15113794725694	0.0319831886043665	*  
df.mm.exp4	0.121774576119004	0.087341755332625	1.39423092260108	0.163918008997497	   
df.mm.exp5	-0.077936618841033	0.087341755332625	-0.892317981751406	0.372687038726590	   
df.mm.exp6	0.070835673466788	0.087341755332625	0.811017287172938	0.417773341297322	   
df.mm.exp7	-0.0924271967300546	0.087341755332625	-1.05822463011034	0.290506019857410	   
df.mm.exp8	-0.212235541299761	0.087341755332625	-2.42994362194235	0.0154811175905081	*  
df.mm.trans1:exp2	0.0650084315864273	0.0690497204220631	0.941472770477076	0.346954647759002	   
df.mm.trans2:exp2	-0.239722270586156	0.0690497204220631	-3.47173412319217	0.000565709586796867	***
df.mm.trans1:exp3	0.0573202663171105	0.0690497204220631	0.830130317208283	0.406893853889173	   
df.mm.trans2:exp3	-0.0584975713836765	0.0690497204220631	-0.847180423412476	0.3973332031814	   
df.mm.trans1:exp4	-0.244080970490021	0.0690497204220631	-3.53485820070071	0.000449108947280008	***
df.mm.trans2:exp4	-0.0420591650976645	0.0690497204220631	-0.609114198299137	0.542748147612773	   
df.mm.trans1:exp5	0.0218622935508575	0.0690497204220631	0.316616684574901	0.75167748412999	   
df.mm.trans2:exp5	0.0134474677043682	0.0690497204220631	0.194750501843761	0.845673884478538	   
df.mm.trans1:exp6	-0.133897966559877	0.0690497204220631	-1.93915291389208	0.0530912198497729	.  
df.mm.trans2:exp6	-0.0763851603183727	0.0690497204220631	-1.10623417229602	0.269200865444281	   
df.mm.trans1:exp7	0.139992824411397	0.0690497204220631	2.02742058267141	0.0431929750541947	*  
df.mm.trans2:exp7	0.462641651651117	0.0690497204220631	6.70012345920074	6.07444182220243e-11	***
df.mm.trans1:exp8	0.251332136680429	0.0690497204220631	3.63987189439976	0.000303582090177251	***
df.mm.trans2:exp8	0.512630005836379	0.0690497204220631	7.42407069431929	5.51225619455283e-13	***
df.mm.trans1:probe2	-0.116983222051253	0.0463199606073266	-2.52554666535595	0.0118852118272757	*  
df.mm.trans1:probe3	-0.285542446290186	0.0463199606073266	-6.16456582748088	1.54205210000352e-09	***
df.mm.trans1:probe4	-0.143251199493041	0.0463199606073266	-3.09264510623056	0.00210393687558878	** 
df.mm.trans1:probe5	0.0714982531750173	0.0463199606073266	1.54357327246319	0.123376405514929	   
df.mm.trans1:probe6	-0.168862325135038	0.0463199606073266	-3.64556279670773	0.00029712649178949	***
df.mm.trans2:probe2	-0.0985509841589073	0.0463199606073266	-2.12761372995035	0.0338980538576051	*  
df.mm.trans2:probe3	0.24944751782552	0.0463199606073266	5.38531368668875	1.15358003927941e-07	***
df.mm.trans2:probe4	-0.047106008347295	0.0463199606073266	-1.01696995700476	0.309699987079203	   
df.mm.trans2:probe5	-0.181460931267888	0.0463199606073266	-3.91755366128671	0.000102965395464119	***
df.mm.trans2:probe6	0.252778383191153	0.0463199606073266	5.45722362188646	7.9029382875282e-08	***
df.mm.trans3:probe2	0.305041183334601	0.0463199606073266	6.58552337556073	1.23545851696911e-10	***
df.mm.trans3:probe3	0.411254052193522	0.0463199606073266	8.87854926475203	1.50577198644791e-17	***
df.mm.trans3:probe4	-0.0478376370429608	0.0463199606073266	-1.03276506317655	0.302254171745645	   
df.mm.trans3:probe5	0.0463363585282916	0.0463199606073266	1.00035401413883	0.317662863928794	   
df.mm.trans3:probe6	0.626707250942611	0.0463199606073266	13.5299607928311	2.26595839073484e-35	***
df.mm.trans3:probe7	0.51848726839252	0.0463199606073266	11.1936033967721	6.72430789993889e-26	***
df.mm.trans3:probe8	0.348206978452531	0.0463199606073266	7.51742820777473	2.92695058615513e-13	***
df.mm.trans3:probe9	0.0948809674558344	0.0463199606073266	2.04838186846011	0.0410871986103985	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.39564611706859	0.252654720999789	17.3978388358406	1.64839779113811e-52	***
df.mm.trans1	0.0574321960671756	0.202983272286327	0.282940537021997	0.777349146950903	   
df.mm.trans2	-0.0913569745427052	0.202983272286327	-0.450071444379109	0.65287003286978	   
df.mm.exp2	0.32987275593204	0.272543645037995	1.21034836782216	0.226764107512793	   
df.mm.exp3	0.160936283319045	0.272543645037995	0.590497288229228	0.55514603313853	   
df.mm.exp4	-0.284434916230900	0.272543645037995	-1.04363070432718	0.297202048175593	   
df.mm.exp5	0.163583577934909	0.272543645037995	0.600210575124964	0.548660194926408	   
df.mm.exp6	0.232993946748425	0.272543645037995	0.854886734621694	0.39305694770341	   
df.mm.exp7	-0.124228381785310	0.272543645037995	-0.455810964764898	0.648740084102322	   
df.mm.exp8	-0.141888775057868	0.272543645037995	-0.520609368962124	0.602888325974259	   
df.mm.trans1:exp2	-0.229514246165276	0.215464670031128	-1.06520593901598	0.28733912191016	   
df.mm.trans2:exp2	-0.122069534367255	0.215464670031128	-0.566540836368302	0.571301292640123	   
df.mm.trans1:exp3	-0.170339896240321	0.215464670031128	-0.790569963120691	0.429600599876613	   
df.mm.trans2:exp3	-0.0917968996266664	0.215464670031128	-0.426041539030063	0.67027599373585	   
df.mm.trans1:exp4	0.111243670958045	0.215464670031128	0.516296574013612	0.605894309021338	   
df.mm.trans2:exp4	0.258967163852563	0.215464670031128	1.2019008212119	0.230017660162929	   
df.mm.trans1:exp5	0.0153897864562818	0.215464670031128	0.0714260321845731	0.943089600373927	   
df.mm.trans2:exp5	-0.099217063031644	0.215464670031128	-0.460479497716773	0.645388745392937	   
df.mm.trans1:exp6	-0.224425869464418	0.215464670031128	-1.04159011049002	0.298146503894491	   
df.mm.trans2:exp6	-0.082786251724607	0.215464670031128	-0.384221931663539	0.700990743727605	   
df.mm.trans1:exp7	0.0140559918215311	0.215464670031128	0.0652357150687416	0.948014543122442	   
df.mm.trans2:exp7	0.0820743307629057	0.215464670031128	0.380917812423951	0.703439329056046	   
df.mm.trans1:exp8	0.0743374283569859	0.215464670031128	0.345009826187497	0.730243996377534	   
df.mm.trans2:exp8	0.000323910013173644	0.215464670031128	0.00150330916491715	0.998801182164156	   
df.mm.trans1:probe2	0.10076349979393	0.144538094681749	0.697141469975759	0.486064908099331	   
df.mm.trans1:probe3	0.0706694223690365	0.144538094681749	0.488932848635094	0.625121257869421	   
df.mm.trans1:probe4	-0.100098188828627	0.144538094681749	-0.692538455339599	0.488947186324997	   
df.mm.trans1:probe5	-0.0122346386707913	0.144538094681749	-0.0846464642953135	0.932579125253147	   
df.mm.trans1:probe6	-0.0443825792583399	0.144538094681749	-0.307064925382223	0.758932310639042	   
df.mm.trans2:probe2	-0.120949678708893	0.144538094681749	-0.836801391184836	0.40313683878686	   
df.mm.trans2:probe3	0.114357726856942	0.144538094681749	0.791194370651838	0.429236561787785	   
df.mm.trans2:probe4	0.273214207245754	0.144538094681749	1.8902574290005	0.0593491580551257	.  
df.mm.trans2:probe5	-0.0906235217717626	0.144538094681749	-0.626987106556938	0.530977396318021	   
df.mm.trans2:probe6	-0.060507025360889	0.144538094681749	-0.418623377415596	0.675685937852981	   
df.mm.trans3:probe2	0.117597964112107	0.144538094681749	0.813612247837084	0.416286233865363	   
df.mm.trans3:probe3	0.000170792787690849	0.144538094681749	0.00118164548984065	0.999057693567938	   
df.mm.trans3:probe4	0.0751364356368004	0.144538094681749	0.519838287630949	0.603425268873037	   
df.mm.trans3:probe5	0.108252075577752	0.144538094681749	0.748951865015976	0.454267356925307	   
df.mm.trans3:probe6	0.119274491939092	0.144538094681749	0.825211458624225	0.409677431525183	   
df.mm.trans3:probe7	0.0520978434229981	0.144538094681749	0.360443684675031	0.718680096295494	   
df.mm.trans3:probe8	0.168071419218014	0.144538094681749	1.16281745368293	0.24550384455125	   
df.mm.trans3:probe9	-0.115869203870191	0.144538094681749	-0.801651662320012	0.423166601396791