chr14.7518_chr14_26838076_26839936_-_1.R 

fitVsDatCorrelation=0.87937840043679
cont.fitVsDatCorrelation=0.245830159792448

fstatistic=11368.2357583141,48,600
cont.fstatistic=2733.38062980021,48,600

residuals=-0.454530012470063,-0.0795628729548894,2.02941517080849e-05,0.0676015931675278,0.594073717826529
cont.residuals=-0.490976190595488,-0.185116030231477,-0.0705694120474567,0.128849412857014,1.34435654992045

predictedValues:
Include	Exclude	Both
chr14.7518_chr14_26838076_26839936_-_1.R.tl.Lung	45.562440421354	57.0901839187302	61.908090112514
chr14.7518_chr14_26838076_26839936_-_1.R.tl.cerebhem	58.273116991432	76.8586274736813	60.657719960722
chr14.7518_chr14_26838076_26839936_-_1.R.tl.cortex	48.2256217020119	64.471162378797	76.59787671863
chr14.7518_chr14_26838076_26839936_-_1.R.tl.heart	47.567217496446	59.7792670474971	72.2949751893286
chr14.7518_chr14_26838076_26839936_-_1.R.tl.kidney	46.3609090401369	58.9517045761698	69.003617450988
chr14.7518_chr14_26838076_26839936_-_1.R.tl.liver	49.5482575095499	58.5755676462404	66.4991223539472
chr14.7518_chr14_26838076_26839936_-_1.R.tl.stomach	51.4212046750037	61.9859881537825	62.2189153466174
chr14.7518_chr14_26838076_26839936_-_1.R.tl.testicle	48.3761229559313	59.7939664216741	64.2062430403456


diffExp=-11.5277434973762,-18.5855104822493,-16.2455406767851,-12.2120495510511,-12.5907955360330,-9.02731013669049,-10.5647834787788,-11.4178434657428
diffExpScore=0.990307408001537
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,-1,-1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,-1,-1,-1,-1,0,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	67.8694010447077	61.6279517521692	63.4105007719863
cerebhem	56.4675577883624	56.6057901012754	61.8814359422382
cortex	61.0012567424323	72.0730757806186	60.5430663150681
heart	66.7630501381529	64.0053972810324	61.5695725775301
kidney	59.3091030102782	67.1735031593776	61.6995566481603
liver	56.0735437757275	59.8541446606399	60.6723356869294
stomach	71.4129137811373	62.055598957804	61.9577505434324
testicle	60.494263022834	62.7815594169385	61.7723974902209
cont.diffExp=6.24144929253855,-0.138232312913004,-11.0718190381863,2.75765285712050,-7.86440014909936,-3.78060088491236,9.3573148233333,-2.28729639410453
cont.diffExpScore=5.58684134857689

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=0.914508885953076
cont.tran.correlation=0.113927180449382

tran.covariance=0.00662266767654338
cont.tran.covariance=0.00102814903075863

tran.mean=55.8025849005274
cont.tran.mean=62.848006900843

weightedLogRatios:
wLogRatio
Lung	-0.886824620386382
cerebhem	-1.16365671893062
cortex	-1.16742294049304
heart	-0.90866767411368
kidney	-0.950617312366222
liver	-0.667244980133398
stomach	-0.75368683179126
testicle	-0.844405688859297

cont.weightedLogRatios:
wLogRatio
Lung	0.402216215713009
cerebhem	-0.00986531833034457
cortex	-0.699548769055846
heart	0.176324793241238
kidney	-0.51612170285985
liver	-0.264854577447953
stomach	0.589636386580137
testicle	-0.152945975133282

varWeightedLogRatios=0.0314249515759876
cont.varWeightedLogRatios=0.194529316797877

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.91710152918435	0.0700670590667568	55.9050369939504	4.4169219475509e-240	***
df.mm.trans1	-0.130829440171278	0.0554565073201886	-2.35913595163702	0.0186370243214844	*  
df.mm.trans2	0.135681646743759	0.0554565073201886	2.44663166326677	0.0147057197091765	*  
df.mm.exp2	0.563796659038741	0.0736020446962185	7.66006788759372	7.488512171955e-14	***
df.mm.exp3	-0.0345259039927928	0.0736020446962185	-0.469088924571231	0.639176474858142	   
df.mm.exp4	-0.0660169414682242	0.0736020446962185	-0.896944395236563	0.370108329404637	   
df.mm.exp5	-0.0590487873271615	0.0736020446962185	-0.802271018025063	0.422713846278218	   
df.mm.exp6	0.0380109932042359	0.0736020446962185	0.516439364709507	0.605737859526765	   
df.mm.exp7	0.198234906887644	0.0736020446962186	2.69333423692004	0.00727190499860007	** 
df.mm.exp8	0.069745648925172	0.0736020446962186	0.947604773930354	0.343712181489794	   
df.mm.trans1:exp2	-0.317739489241738	0.0562144901873241	-5.65227022753264	2.44969268588759e-08	***
df.mm.trans2:exp2	-0.266461123075471	0.0562144901873241	-4.74007897585729	2.67088683662308e-06	***
df.mm.trans1:exp3	0.0913326522534957	0.0562144901873241	1.62471725615846	0.104747972435816	   
df.mm.trans2:exp3	0.156111741386723	0.0562144901873241	2.77707297293829	0.00565645514444347	** 
df.mm.trans1:exp4	0.109077055235982	0.0562144901873241	1.94037257782652	0.0528025467171149	.  
df.mm.trans2:exp4	0.112043645888723	0.0562144901873241	1.99314528185453	0.046697957281278	*  
df.mm.trans1:exp5	0.0764217117490085	0.0562144901873241	1.35946642038997	0.174509525997900	   
df.mm.trans2:exp5	0.0911351379900844	0.0562144901873241	1.62120367340154	0.105499325897941	   
df.mm.trans1:exp6	0.0458523985042395	0.0562144901873241	0.815668671039177	0.415013339741153	   
df.mm.trans2:exp6	-0.0123255094557651	0.0562144901873241	-0.21925858287948	0.826523189583546	   
df.mm.trans1:exp7	-0.0772679793268815	0.0562144901873241	-1.37452068086717	0.169793234872337	   
df.mm.trans2:exp7	-0.115958736432176	0.0562144901873241	-2.06279085776222	0.0395622748675695	*  
df.mm.trans1:exp8	-0.00982298642877123	0.0562144901873241	-0.174741181429165	0.861341954715083	   
df.mm.trans2:exp8	-0.0234730805462516	0.0562144901873241	-0.41756281108362	0.676416169271704	   
df.mm.trans1:probe2	0.223614875782552	0.0411447938059306	5.43482795994278	7.98625317332343e-08	***
df.mm.trans1:probe3	0.185966238241355	0.0411447938059306	4.51979998049108	7.45872479553924e-06	***
df.mm.trans1:probe4	0.118214239669765	0.0411447938059306	2.87312752683489	0.00420808437066884	** 
df.mm.trans1:probe5	0.0906377472670373	0.0411447938059306	2.20289710757945	0.0279813980281124	*  
df.mm.trans1:probe6	0.070610774776604	0.0411447938059306	1.71615332694719	0.0866501608360774	.  
df.mm.trans2:probe2	-0.129047587550348	0.0411447938059306	-3.13642567171517	0.00179393116419443	** 
df.mm.trans2:probe3	-0.14051295520057	0.0411447938059306	-3.41508468515685	0.000680672445630001	***
df.mm.trans2:probe4	0.153472707133844	0.0411447938059306	3.73006382916233	0.000209592244863386	***
df.mm.trans2:probe5	0.0116104798184880	0.0411447938059306	0.282185879293784	0.77789829379676	   
df.mm.trans2:probe6	-0.0666933165890208	0.0411447938059306	-1.62094181109756	0.105555494407767	   
df.mm.trans3:probe2	0.272015934412613	0.0411447938059306	6.61118720622692	8.43524018064185e-11	***
df.mm.trans3:probe3	0.00664957606735833	0.0411447938059306	0.161614033083327	0.871664211145285	   
df.mm.trans3:probe4	0.473649319402347	0.0411447938059306	11.5117679684197	7.58805836315601e-28	***
df.mm.trans3:probe5	0.0112434369384429	0.0411447938059306	0.273265118096721	0.784743459569627	   
df.mm.trans3:probe6	1.74263428968642e-05	0.0411447938059306	0.000423537008814766	0.999662207167911	   
df.mm.trans3:probe7	0.398974328660188	0.0411447938059306	9.69683626419534	9.42785510807409e-21	***
df.mm.trans3:probe8	0.0711797383537095	0.0411447938059306	1.72998165185724	0.0841477901648796	.  
df.mm.trans3:probe9	-0.0373216086698319	0.0411447938059306	-0.907079735187598	0.364728779816968	   
df.mm.trans3:probe10	0.55573701981989	0.0411447938059306	13.5068612189712	1.72725514283335e-36	***
df.mm.trans3:probe11	-0.049031550968732	0.0411447938059306	-1.19168299153475	0.233856717215212	   
df.mm.trans3:probe12	0.00364366454258328	0.0411447938059306	0.088557122433752	0.92946343503019	   
df.mm.trans3:probe13	0.978333431456748	0.0411447938059306	23.7778183084668	1.49132286634819e-88	***
df.mm.trans3:probe14	0.394463629549929	0.0411447938059306	9.58720637683865	2.37722254596825e-20	***
df.mm.trans3:probe15	-0.0240973677055479	0.0411447938059306	-0.585672340933558	0.55831594492347	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.19346985492700	0.142645551484600	29.3978312767764	2.44927576765787e-118	***
df.mm.trans1	0.0287083913383438	0.112900757866277	0.254279881560642	0.79936646083491	   
df.mm.trans2	-0.0756202029650663	0.112900757866277	-0.669793581497768	0.503247062369956	   
df.mm.exp2	-0.24451401774382	0.149842228230006	-1.63180980843727	0.103244262547267	   
df.mm.exp3	0.096148719863925	0.149842228230006	0.641666378027547	0.521334874383281	   
df.mm.exp4	0.0508780609137747	0.149842228230006	0.339544209364515	0.734318615800052	   
df.mm.exp5	-0.0213064121698004	0.149842228230006	-0.142192307345398	0.886975840578313	   
df.mm.exp6	-0.175984388600328	0.149842228230006	-1.17446457303207	0.240674912435560	   
df.mm.exp7	0.080985408522794	0.149842228230006	0.540471197468328	0.589072683237483	   
df.mm.exp8	-0.0703180375920238	0.149842228230006	-0.469280512060235	0.639039622020564	   
df.mm.trans1:exp2	0.0605950069058285	0.114443892194142	0.529473489096609	0.596672873497414	   
df.mm.trans2:exp2	0.159509766498844	0.114443892194142	1.39378138440322	0.163899627884241	   
df.mm.trans1:exp3	-0.202839539207152	0.114443892194142	-1.77239287582998	0.0768367389240474	.  
df.mm.trans2:exp3	0.0604162960137515	0.114443892194142	0.527911930077156	0.597755638848358	   
df.mm.trans1:exp4	-0.0673135606941381	0.114443892194142	-0.588179582182925	0.556633082356917	   
df.mm.trans2:exp4	-0.0130261784248325	0.114443892194142	-0.113821525772078	0.90941734929143	   
df.mm.trans1:exp5	-0.113516071485039	0.114443892194142	-0.991892789634168	0.321649662911100	   
df.mm.trans2:exp5	0.107469753888298	0.114443892194142	0.939060633362479	0.348077484934661	   
df.mm.trans1:exp6	-0.0149367861945275	0.114443892194142	-0.130516237329545	0.896201754487426	   
df.mm.trans2:exp6	0.146779539179853	0.114443892194142	1.28254585164630	0.200146490252267	   
df.mm.trans1:exp7	-0.0300919758886427	0.114443892194142	-0.262940863961485	0.792686422744705	   
df.mm.trans2:exp7	-0.0740701977715223	0.114443892194142	-0.647218443478574	0.517738112019339	   
df.mm.trans1:exp8	-0.044718713575584	0.114443892194142	-0.390747926501166	0.696122269925655	   
df.mm.trans2:exp8	0.0888638982568116	0.114443892194142	0.776484411296178	0.437769008315982	   
df.mm.trans1:probe2	-0.0197332574302642	0.083764352055583	-0.235580613303974	0.813838504387186	   
df.mm.trans1:probe3	-0.0142046873352823	0.083764352055583	-0.169579146578447	0.865398281620098	   
df.mm.trans1:probe4	-0.0605468933078501	0.083764352055583	-0.722824110997402	0.470069606859896	   
df.mm.trans1:probe5	-0.0117121187127589	0.083764352055583	-0.139822232552903	0.888847368812253	   
df.mm.trans1:probe6	0.0097447836386692	0.083764352055583	0.116335689341964	0.907425428517013	   
df.mm.trans2:probe2	0.0611709773087354	0.083764352055583	0.730274583490416	0.465507394359683	   
df.mm.trans2:probe3	-0.0211268898437906	0.083764352055583	-0.252218149192768	0.800958823933543	   
df.mm.trans2:probe4	0.0357952959630811	0.083764352055583	0.427333287784864	0.669289903495475	   
df.mm.trans2:probe5	0.0300789115593866	0.083764352055583	0.359089646385939	0.719654356057078	   
df.mm.trans2:probe6	-0.0373348597353839	0.083764352055583	-0.445712989107942	0.655965361684899	   
df.mm.trans3:probe2	-0.0147381449048305	0.083764352055583	-0.175947697835122	0.860394398427214	   
df.mm.trans3:probe3	0.0434287573421837	0.083764352055583	0.5184634785137	0.604326077677587	   
df.mm.trans3:probe4	-0.009954343532579	0.083764352055583	-0.118837468306012	0.905443898972668	   
df.mm.trans3:probe5	-0.0931574582438016	0.083764352055583	-1.11213727507837	0.266524535718759	   
df.mm.trans3:probe6	-0.0643260245224367	0.083764352055583	-0.767940334329242	0.442824749467437	   
df.mm.trans3:probe7	-0.0344227256901011	0.083764352055583	-0.410947197051789	0.681257979986399	   
df.mm.trans3:probe8	-0.00300359706151812	0.083764352055583	-0.035857700654391	0.971407750272427	   
df.mm.trans3:probe9	-0.0384411373832807	0.083764352055583	-0.458920011197276	0.646457806838699	   
df.mm.trans3:probe10	0.0543102977642111	0.083764352055583	0.648370057565452	0.516993683520865	   
df.mm.trans3:probe11	-0.068202357057124	0.083764352055583	-0.814216971580791	0.415843689283067	   
df.mm.trans3:probe12	0.0580196923649983	0.083764352055583	0.692653747581054	0.488794863984362	   
df.mm.trans3:probe13	0.0595823680987382	0.083764352055583	0.711309365339584	0.477168984985987	   
df.mm.trans3:probe14	0.0325402199411749	0.083764352055583	0.388473367758905	0.697803562289928	   
df.mm.trans3:probe15	0.124435929083582	0.083764352055583	1.48554756325233	0.137924043626003