chr12.5462_chr12_81871176_81873004_+_2.R 

fitVsDatCorrelation=0.91707468705673
cont.fitVsDatCorrelation=0.258371166227770

fstatistic=4449.11150693669,61,899
cont.fstatistic=745.659134144918,61,899

residuals=-0.84385847415324,-0.140983757699239,-0.00492670969311029,0.130236749467557,1.2505999313986
cont.residuals=-1.17446905992223,-0.491766457208496,-0.194888854020319,0.422044900970509,2.08633359947327

predictedValues:
Include	Exclude	Both
chr12.5462_chr12_81871176_81873004_+_2.R.tl.Lung	80.564999444346	48.1110457097205	55.9744179969424
chr12.5462_chr12_81871176_81873004_+_2.R.tl.cerebhem	73.0717529954955	59.5953546665144	67.235314591449
chr12.5462_chr12_81871176_81873004_+_2.R.tl.cortex	80.1160663775825	46.3234749092151	57.4695230256426
chr12.5462_chr12_81871176_81873004_+_2.R.tl.heart	88.3248745401327	47.4032598324896	58.242580157817
chr12.5462_chr12_81871176_81873004_+_2.R.tl.kidney	150.190605170112	50.2829503584344	78.314444417026
chr12.5462_chr12_81871176_81873004_+_2.R.tl.liver	266.271793790468	52.7202390201729	117.106703966152
chr12.5462_chr12_81871176_81873004_+_2.R.tl.stomach	100.673754894331	45.685426255051	56.2631190137508
chr12.5462_chr12_81871176_81873004_+_2.R.tl.testicle	155.962521045457	52.6972304064916	92.4286410261544


diffExp=32.4539537346255,13.4763983289812,33.7925914683673,40.921614707643,99.9076548116777,213.551554770295,54.9883286392795,103.265290638965
diffExpScore=0.99831467506474
diffExp1.5=1,0,1,1,1,1,1,1
diffExp1.5Score=0.875
diffExp1.4=1,0,1,1,1,1,1,1
diffExp1.4Score=0.875
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	85.8091963842928	74.4835755018212	85.8745474336532
cerebhem	93.102500843234	115.200249085057	96.7903365436455
cortex	102.102886169114	68.7517401011801	92.5173906255896
heart	104.60914566161	69.0717002042509	89.504897622112
kidney	94.0730496237613	60.5207878295331	110.143308074258
liver	81.5150232262143	70.9693763281453	102.156532722167
stomach	77.4179950956307	84.0437269268688	93.520958500995
testicle	95.4665900079283	61.0854015949374	99.9981025651759
cont.diffExp=11.3256208824716,-22.0977482418232,33.3511460679343,35.5374454573591,33.5522617942282,10.5456468980689,-6.62573183123817,34.3811884129909
cont.diffExpScore=1.43099208716604

cont.diffExp1.5=0,0,0,1,1,0,0,1
cont.diffExp1.5Score=0.75
cont.diffExp1.4=0,0,1,1,1,0,0,1
cont.diffExp1.4Score=0.8
cont.diffExp1.3=0,0,1,1,1,0,0,1
cont.diffExp1.3Score=0.8
cont.diffExp1.2=0,-1,1,1,1,0,0,1
cont.diffExp1.2Score=1.25

tran.correlation=0.20212464398056
cont.tran.correlation=-0.217466583676548

tran.covariance=0.00718937154343118
cont.tran.covariance=-0.00574027748925104

tran.mean=87.3747093385008
cont.tran.mean=83.6389340364737

weightedLogRatios:
wLogRatio
Lung	2.12989593867228
cerebhem	0.854091250821916
cortex	2.25133168091585
heart	2.59502995064603
kidney	4.88554312855409
liver	7.73280801928243
stomach	3.33174358057572
testicle	4.89043000878781

cont.weightedLogRatios:
wLogRatio
Lung	0.620169685053074
cerebhem	-0.988224526295004
cortex	1.75127628908284
heart	1.84409709245176
kidney	1.90704259421028
liver	0.600083606862814
stomach	-0.360520752672821
testicle	1.93582662661934

varWeightedLogRatios=4.71398600199434
cont.varWeightedLogRatios=1.28915940789418

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.5656770204893	0.124666188302106	28.6017970794819	1.83656096893126e-128	***
df.mm.trans1	0.618974864853542	0.107235411238808	5.7721125671362	1.07724678120614e-08	***
df.mm.trans2	0.316139231010767	0.0943267799546354	3.35153210109375	0.000837165069601624	***
df.mm.exp2	-0.0668604504384543	0.120400396458114	-0.55531752722853	0.578815642938543	   
df.mm.exp3	-0.0698108414462275	0.120400396458114	-0.579822355240451	0.562179698672227	   
df.mm.exp4	0.037414735381592	0.120400396458114	0.310752592867153	0.756060745614165	   
df.mm.exp5	0.33115785631124	0.120400396458114	2.75047147727996	0.0060705011733619	** 
df.mm.exp6	0.548750141812548	0.120400396458114	4.55771042251886	5.88584300984626e-06	***
df.mm.exp7	0.165943913462113	0.120400396458114	1.37826716808066	0.168463724460632	   
df.mm.exp8	0.250060398918666	0.120400396458114	2.07690677335651	0.0380936769563475	*  
df.mm.trans1:exp2	-0.0307619785425473	0.110752256493762	-0.277754869439432	0.781264399134441	   
df.mm.trans2:exp2	0.280926288352034	0.0794913868812061	3.53404688701503	0.000430107544237766	***
df.mm.trans1:exp3	0.0642229492747327	0.110752256493762	0.579879374993593	0.562141260183626	   
df.mm.trans2:exp3	0.0319479002430390	0.0794913868812061	0.401903923135506	0.687850262743704	   
df.mm.trans1:exp4	0.0545427322996527	0.110752256493762	0.492475133477076	0.62250374320321	   
df.mm.trans2:exp4	-0.0522355274910638	0.0794913868812061	-0.657121853580513	0.511270757209165	   
df.mm.trans1:exp5	0.291683027133117	0.110752256493762	2.6336531314786	0.00859228766201708	** 
df.mm.trans2:exp5	-0.287003587025104	0.0794913868812062	-3.61049917840795	0.000322510680549504	***
df.mm.trans1:exp6	0.6467031213447	0.110752256493762	5.83918686461368	7.32373872664181e-09	***
df.mm.trans2:exp6	-0.45726250914511	0.0794913868812061	-5.7523528911183	1.20603645320644e-08	***
df.mm.trans1:exp7	0.0568769204947988	0.110752256493762	0.513550895443854	0.60769220558406	   
df.mm.trans2:exp7	-0.217676358031682	0.0794913868812061	-2.7383640740472	0.0062966388887179	** 
df.mm.trans1:exp8	0.410491024629109	0.110752256493762	3.70638971723551	0.000223092077821870	***
df.mm.trans2:exp8	-0.159009289991967	0.0794913868812061	-2.00033357361841	0.0457646275632565	*  
df.mm.trans1:probe2	-0.0312274674459322	0.0783136715984509	-0.398748606833931	0.690173139536388	   
df.mm.trans1:probe3	-0.174306794264190	0.0783136715984509	-2.22575178390229	0.0262781091608621	*  
df.mm.trans1:probe4	-0.112231805210338	0.0783136715984509	-1.43310616038794	0.152175159004328	   
df.mm.trans1:probe5	0.0731793545715813	0.0783136715984509	0.934439071466404	0.350328366520013	   
df.mm.trans1:probe6	-0.328003006436532	0.0783136715984509	-4.18832369548896	3.08704546602383e-05	***
df.mm.trans1:probe7	-0.211581482579998	0.0783136715984509	-2.70171833680421	0.00702811577028792	** 
df.mm.trans1:probe8	0.277962711893947	0.0783136715984509	3.54935104204009	0.00040619200805133	***
df.mm.trans1:probe9	-0.420303712572256	0.0783136715984509	-5.36692641263636	1.01962495608645e-07	***
df.mm.trans1:probe10	0.105236212573324	0.0783136715984509	1.34377830109814	0.179359135802009	   
df.mm.trans1:probe11	0.660553062093218	0.0783136715984509	8.43470940144612	1.3135297857878e-16	***
df.mm.trans1:probe12	0.67307475726966	0.0783136715984509	8.59460096215146	3.66414095519698e-17	***
df.mm.trans1:probe13	0.704004847405194	0.0783136715984509	8.98955230977984	1.43836730644495e-18	***
df.mm.trans1:probe14	0.717976755409472	0.0783136715984509	9.16796187376911	3.20543271066434e-19	***
df.mm.trans1:probe15	0.5357141268085	0.0783136715984509	6.84062074825639	1.45660011528693e-11	***
df.mm.trans1:probe16	0.711735953151502	0.0783136715984509	9.08827205549614	6.28619023930852e-19	***
df.mm.trans1:probe17	0.749168538978186	0.0783136715984509	9.56625482737557	1.03109763889794e-20	***
df.mm.trans1:probe18	1.11526364445566	0.0783136715984509	14.2409827261594	1.19988007399426e-41	***
df.mm.trans1:probe19	0.653985083298526	0.0783136715984509	8.35084181280376	2.54583107647325e-16	***
df.mm.trans1:probe20	0.571734279376478	0.0783136715984509	7.30056793030998	6.30526142481258e-13	***
df.mm.trans1:probe21	0.426159528347448	0.0783136715984509	5.44170027594361	6.80766345383466e-08	***
df.mm.trans1:probe22	0.251927685591085	0.0783136715984509	3.21690556002572	0.00134213621273818	** 
df.mm.trans2:probe2	-0.0722162449429352	0.0783136715984509	-0.92214096809584	0.356702453759719	   
df.mm.trans2:probe3	0.0198447095966592	0.0783136715984509	0.253400321956706	0.800016814921561	   
df.mm.trans2:probe4	-0.0954051770593406	0.0783136715984509	-1.21824421090261	0.223450796859392	   
df.mm.trans2:probe5	0.0233924021834504	0.0783136715984509	0.298701385160354	0.765236929624628	   
df.mm.trans2:probe6	-0.0250959736833988	0.0783136715984509	-0.320454566503753	0.748698235567075	   
df.mm.trans3:probe2	-0.539609712713809	0.0783136715984509	-6.89036411778302	1.04602812013291e-11	***
df.mm.trans3:probe3	-0.629632210368931	0.0783136715984509	-8.03987602059237	2.82185057600826e-15	***
df.mm.trans3:probe4	-0.391404137181985	0.0783136715984509	-4.99790303778488	6.96554463334183e-07	***
df.mm.trans3:probe5	-0.550957194951447	0.0783136715984509	-7.03526196264237	3.94067026706072e-12	***
df.mm.trans3:probe6	-0.217913585726653	0.0783136715984509	-2.78257399096282	0.0055059464539624	** 
df.mm.trans3:probe7	-0.058541102762348	0.0783136715984509	-0.74752085513899	0.454944695591114	   
df.mm.trans3:probe8	-0.639586336468318	0.0783136715984509	-8.16698187447732	1.06539100840841e-15	***
df.mm.trans3:probe9	-0.770812452199652	0.0783136715984509	-9.8426294728199	8.87189414073943e-22	***
df.mm.trans3:probe10	-0.606079398693198	0.0783136715984509	-7.73912634055567	2.68693391720743e-14	***
df.mm.trans3:probe11	-0.680401225971557	0.0783136715984509	-8.68815383168698	1.72020880756469e-17	***
df.mm.trans3:probe12	-0.625818860678353	0.0783136715984509	-7.99118273865648	4.08432348377265e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.26365802613565	0.302053093178391	14.1155913394920	5.16706290461583e-41	***
df.mm.trans1	0.176697845134337	0.259820149345110	0.68007752893574	0.496630459345071	   
df.mm.trans2	-0.0188651546630514	0.228543890231172	-0.0825449966917484	0.934231712894298	   
df.mm.exp2	0.398008453813243	0.291717527144957	1.36436249720254	0.172794936988156	   
df.mm.exp3	0.0192690637473469	0.291717527144957	0.0660538430307341	0.947349644394737	   
df.mm.exp4	0.0812653928163725	0.291717527144957	0.278575626263248	0.780634582900359	   
df.mm.exp5	-0.364541150192556	0.291717527144957	-1.24963746182934	0.211757241099838	   
df.mm.exp6	-0.273287811733383	0.291717527144957	-0.936823420958125	0.349100988401444	   
df.mm.exp7	-0.0674464381292122	0.291717527144957	-0.23120461354966	0.81720845530072	   
df.mm.exp8	-0.243919299098499	0.291717527144957	-0.836148933133159	0.403293306357934	   
df.mm.trans1:exp2	-0.316433592703543	0.268341096379396	-1.17922150938875	0.23862196499886	   
df.mm.trans2:exp2	0.0380848186257296	0.192599289474730	0.197741220798878	0.843292238465942	   
df.mm.trans1:exp3	0.154585744369342	0.268341096379396	0.576079275426301	0.564705783560134	   
df.mm.trans2:exp3	-0.0993456549755104	0.192599289474730	-0.515815272457404	0.60611026400667	   
df.mm.trans1:exp4	0.116839404899447	0.268341096379396	0.435413756878493	0.663366641806354	   
df.mm.trans2:exp4	-0.156698932366710	0.192599289474730	-0.813600781155888	0.416089120070668	   
df.mm.trans1:exp5	0.456486569599113	0.268341096379396	1.70114296974365	0.08926194549552	.  
df.mm.trans2:exp5	0.156949418683216	0.192599289474730	0.814901337960586	0.415344607693864	   
df.mm.trans1:exp6	0.221948964340501	0.268341096379396	0.827115068601705	0.408391326754524	   
df.mm.trans2:exp6	0.22495763839071	0.192599289474730	1.16800866194382	0.243112932433337	   
df.mm.trans1:exp7	-0.0354604982384806	0.268341096379397	-0.132147102016549	0.894897509999275	   
df.mm.trans2:exp7	0.188205022164915	0.192599289474730	0.977184405395266	0.328740741563442	   
df.mm.trans1:exp8	0.350569457810738	0.268341096379397	1.30643223323155	0.191739737619274	   
df.mm.trans2:exp8	0.0456135722783202	0.192599289474730	0.236831467046014	0.81284149199586	   
df.mm.trans1:probe2	0.0486472827397725	0.189745808920904	0.256381329402913	0.79771505986259	   
df.mm.trans1:probe3	-0.0835815975713812	0.189745808920904	-0.440492456970274	0.659686368063855	   
df.mm.trans1:probe4	0.0289940122489139	0.189745808920904	0.152804493621253	0.878586742472737	   
df.mm.trans1:probe5	0.00454230786527322	0.189745808920904	0.0239389101193096	0.980906649753972	   
df.mm.trans1:probe6	0.0109638558381107	0.189745808920904	0.0577818076745033	0.953935269587	   
df.mm.trans1:probe7	0.0735007625756249	0.189745808920904	0.3873643533611	0.698578176293763	   
df.mm.trans1:probe8	-0.0120666306340075	0.189745808920904	-0.0635936609226371	0.949307916082748	   
df.mm.trans1:probe9	0.132186137911275	0.189745808920904	0.696648525008407	0.486202864810799	   
df.mm.trans1:probe10	0.246018720525518	0.189745808920904	1.29656998446838	0.195111888293533	   
df.mm.trans1:probe11	-0.166944780698924	0.189745808920904	-0.87983382425335	0.379184461582202	   
df.mm.trans1:probe12	-0.0688359485742647	0.189745808920904	-0.362779810345951	0.716854648436332	   
df.mm.trans1:probe13	0.107967648147148	0.189745808920904	0.56901203120726	0.569490099757722	   
df.mm.trans1:probe14	0.0588441108621812	0.189745808920904	0.310120740989386	0.756541015912029	   
df.mm.trans1:probe15	0.130344018549421	0.189745808920904	0.686940171646978	0.492297586261257	   
df.mm.trans1:probe16	-0.201617964075135	0.189745808920904	-1.06256873457047	0.288262915345088	   
df.mm.trans1:probe17	-0.132014974836102	0.189745808920904	-0.695746459892206	0.486767436619925	   
df.mm.trans1:probe18	0.320401449282573	0.189745808920904	1.68858248361171	0.0916462645466476	.  
df.mm.trans1:probe19	0.0294427982707874	0.189745808920904	0.155169689587509	0.876722374384826	   
df.mm.trans1:probe20	-0.128994580140789	0.189745808920904	-0.67982834969789	0.496788166107828	   
df.mm.trans1:probe21	-0.0801641459608375	0.189745808920904	-0.422481773994039	0.672774425573673	   
df.mm.trans1:probe22	0.0825581748473869	0.189745808920904	0.435098805696422	0.663595140116685	   
df.mm.trans2:probe2	0.150163695200606	0.189745808920904	0.791394002611155	0.428922852585601	   
df.mm.trans2:probe3	0.116183746038576	0.189745808920904	0.612312581233389	0.540485804428162	   
df.mm.trans2:probe4	0.296355216436597	0.189745808920904	1.56185381970747	0.118674360719547	   
df.mm.trans2:probe5	0.297017301683155	0.189745808920904	1.56534314708878	0.117854383360562	   
df.mm.trans2:probe6	0.324423838443052	0.189745808920904	1.70978131368524	0.0876513897852678	.  
df.mm.trans3:probe2	-0.123159569246364	0.189745808920904	-0.649076624916146	0.516454629461085	   
df.mm.trans3:probe3	-0.0570655570747937	0.189745808920904	-0.300747391467189	0.763676665558279	   
df.mm.trans3:probe4	-0.106907113028759	0.189745808920904	-0.563422789872123	0.573287521913775	   
df.mm.trans3:probe5	0.168900718554906	0.189745808920904	0.890142024824974	0.373627771509127	   
df.mm.trans3:probe6	0.0122824927025118	0.189745808920904	0.0647312990593207	0.948402332669288	   
df.mm.trans3:probe7	0.0429513974542522	0.189745808920904	0.226362825606106	0.820970694654025	   
df.mm.trans3:probe8	0.0769755438006235	0.189745808920904	0.405677175366286	0.685076345001952	   
df.mm.trans3:probe9	-0.0415169186461069	0.189745808920904	-0.218802823009457	0.826853309712008	   
df.mm.trans3:probe10	-0.00505892592314908	0.189745808920904	-0.0266615950672086	0.978735561914286	   
df.mm.trans3:probe11	0.111135627757918	0.189745808920904	0.585707944696926	0.558218795341167	   
df.mm.trans3:probe12	0.298223469996948	0.189745808920904	1.57169990574739	0.116372023335734