chr1.774_chr1_72962564_72976360_+_2.R 

fitVsDatCorrelation=0.881269214781269
cont.fitVsDatCorrelation=0.210925770336619

fstatistic=16230.0110370996,65,991
cont.fstatistic=3782.32131436185,65,991

residuals=-0.536009076577366,-0.0797418688504887,-0.00250508800084419,0.0735757965857885,0.52620154380441
cont.residuals=-0.578874541929239,-0.207833786913982,-0.0449535480674559,0.194689851394328,1.02474841551897

predictedValues:
Include	Exclude	Both
chr1.774_chr1_72962564_72976360_+_2.R.tl.Lung	56.6637148956904	77.3780489103906	56.9248300459745
chr1.774_chr1_72962564_72976360_+_2.R.tl.cerebhem	56.1151791068436	64.601453635665	70.7797396196682
chr1.774_chr1_72962564_72976360_+_2.R.tl.cortex	57.9940101008691	73.3680569511307	65.3179486646267
chr1.774_chr1_72962564_72976360_+_2.R.tl.heart	55.381727430221	78.0674684117994	53.5692929985222
chr1.774_chr1_72962564_72976360_+_2.R.tl.kidney	57.0287663123704	81.0709002656423	62.1568641918921
chr1.774_chr1_72962564_72976360_+_2.R.tl.liver	57.2639615182973	90.9007268113291	55.7157516478889
chr1.774_chr1_72962564_72976360_+_2.R.tl.stomach	57.999497224681	80.2012733634842	58.9501076199126
chr1.774_chr1_72962564_72976360_+_2.R.tl.testicle	56.8552472642233	74.9661380257374	55.327324025156


diffExp=-20.7143340147002,-8.48627452882138,-15.3740468502616,-22.6857409815784,-24.0421339532719,-33.6367652930318,-22.2017761388032,-18.1108907615140
diffExpScore=0.993985033410551
diffExp1.5=0,0,0,0,0,-1,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,-1,-1,-1,0,0
diffExp1.4Score=0.75
diffExp1.3=-1,0,0,-1,-1,-1,-1,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	60.9779986142684	62.7889377594754	58.5102481214105
cerebhem	60.3679332702624	63.627878479718	60.4427435320343
cortex	63.8112577022072	65.8495970600222	62.4218105131503
heart	60.361618226237	57.5244199228732	60.4901000548222
kidney	61.4996800570186	64.5083687230022	63.0205523160049
liver	62.6351662630324	64.9135353970916	59.2764954344902
stomach	61.916163129346	61.9589953827412	60.5986443836252
testicle	60.3895686279598	63.5811273293278	61.2768242055728
cont.diffExp=-1.81093914520704,-3.25994520945567,-2.03833935781505,2.83719830336383,-3.00868866598361,-2.27836913405926,-0.0428322533951544,-3.19155870136805
cont.diffExpScore=1.33888464582437

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=0.283892667733078
cont.tran.correlation=0.596150916440686

tran.covariance=0.000442545068130337
cont.tran.covariance=0.000492037943003538

tran.mean=67.2410106392734
cont.tran.mean=62.2945153715365

weightedLogRatios:
wLogRatio
Lung	-1.30638377938899
cerebhem	-0.577098532986087
cortex	-0.982432003037924
heart	-1.43712274420805
kidney	-1.48426509539874
liver	-1.97718147964985
stomach	-1.36852931770336
testicle	-1.15554761709476

cont.weightedLogRatios:
wLogRatio
Lung	-0.120725644480259
cerebhem	-0.217041132427709
cortex	-0.131172079331539
heart	0.196248052767424
kidney	-0.197877936828879
liver	-0.148461992712518
stomach	-0.00285337799342346
testicle	-0.212519418745386

varWeightedLogRatios=0.165545451437728
cont.varWeightedLogRatios=0.0195345109968046

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.80286047270298	0.0628744199143895	60.4834283621383	0	***
df.mm.trans1	-0.0662947086524922	0.055546545137734	-1.19349832627946	0.232959900397861	   
df.mm.trans2	0.585276747659586	0.0490966734229705	11.9209043475796	1.03441102221986e-30	***
df.mm.exp2	-0.408035100447550	0.0644472514440004	-6.33130337299335	3.67907905926416e-10	***
df.mm.exp3	-0.167544047768636	0.0644472514440004	-2.5997081956896	0.00946906415136667	** 
df.mm.exp4	0.0467415647788266	0.0644472514440004	0.7252685526774	0.468458514725053	   
df.mm.exp5	-0.034886926159412	0.0644472514440004	-0.541325275752466	0.588405068698285	   
df.mm.exp6	0.193071012397926	0.0644472514440004	2.99579901503930	0.00280537737443005	** 
df.mm.exp7	0.0241767163252873	0.0644472514440004	0.375139603064299	0.707636916688593	   
df.mm.exp8	0.000172577711056322	0.0644472514440004	0.00267781336192869	0.997863955544872	   
df.mm.trans1:exp2	0.398307391743251	0.0616233589750693	6.46357807117255	1.60168322151443e-10	***
df.mm.trans2:exp2	0.227568878519974	0.0474839871219876	4.79253938670616	1.89873634908164e-06	***
df.mm.trans1:exp3	0.190749721909603	0.0616233589750693	3.09541260136070	0.00202057600049223	** 
df.mm.trans2:exp3	0.114329562701315	0.0474839871219876	2.40774984643979	0.0162330371513063	*  
df.mm.trans1:exp4	-0.0696259121601646	0.0616233589750693	-1.12986233334559	0.258807633693348	   
df.mm.trans2:exp4	-0.0378712668931267	0.0474839871219876	-0.797558696910487	0.425317683159879	   
df.mm.trans1:exp5	0.0413086818872874	0.0616233589750692	0.670341288990747	0.502796430392157	   
df.mm.trans2:exp5	0.081507875328238	0.0474839871219876	1.71653393635295	0.0863768545246667	.  
df.mm.trans1:exp6	-0.182533587328993	0.0616233589750692	-2.9620843518582	0.00312857248038554	** 
df.mm.trans2:exp6	-0.0320061500853078	0.0474839871219876	-0.674040914110333	0.500442489993081	   
df.mm.trans1:exp7	-0.000876431305787781	0.0616233589750692	-0.0142223877497875	0.988655421600448	   
df.mm.trans2:exp7	0.0116595412082731	0.0474839871219876	0.245546802510905	0.806083915249391	   
df.mm.trans1:exp8	0.00320188143896557	0.0616233589750692	0.0519588917614982	0.958571919953582	   
df.mm.trans2:exp8	-0.0318391936816044	0.0474839871219876	-0.670524857144128	0.502679494441872	   
df.mm.trans1:probe2	0.66935223088806	0.0377364464313195	17.7375533254379	2.39102655780768e-61	***
df.mm.trans1:probe3	0.170997421024993	0.0377364464313194	4.5313599237864	6.57467778359564e-06	***
df.mm.trans1:probe4	0.165780940896939	0.0377364464313194	4.39312538870508	1.23759113061800e-05	***
df.mm.trans1:probe5	0.193229913812273	0.0377364464313195	5.12051165612407	3.66004674898243e-07	***
df.mm.trans1:probe6	0.250569137578051	0.0377364464313195	6.63997703212696	5.16148262451276e-11	***
df.mm.trans1:probe7	0.872668562127672	0.0377364464313195	23.1253508121368	5.81613400557894e-95	***
df.mm.trans1:probe8	0.220730723456351	0.0377364464313194	5.84927157510929	6.70107199695971e-09	***
df.mm.trans1:probe9	0.26127793389889	0.0377364464313195	6.92375564229179	7.89560403518082e-12	***
df.mm.trans1:probe10	0.137798902339874	0.0377364464313194	3.65161310540114	0.000274229145910356	***
df.mm.trans1:probe11	0.128707500674856	0.0377364464313195	3.41069477511891	0.000674125097236175	***
df.mm.trans1:probe12	0.291802087028406	0.0377364464313195	7.73263289534926	2.58147710523959e-14	***
df.mm.trans1:probe13	0.226528882006465	0.0377364464313194	6.00292034436121	2.71598891758496e-09	***
df.mm.trans1:probe14	0.319099351010922	0.0377364464313194	8.45599893969044	9.8094230279377e-17	***
df.mm.trans1:probe15	0.243488350855555	0.0377364464313195	6.45233915436911	1.71995766550288e-10	***
df.mm.trans1:probe16	0.646407500155505	0.0377364464313194	17.1295275863341	8.1839907897613e-58	***
df.mm.trans1:probe17	0.288743405324724	0.0377364464313195	7.6515791133179	4.69417485623446e-14	***
df.mm.trans1:probe18	0.577855861710558	0.0377364464313194	15.3129379249384	1.14486569253805e-47	***
df.mm.trans1:probe19	0.455817749938586	0.0377364464313194	12.0789791579389	1.95203325594097e-31	***
df.mm.trans1:probe20	0.711036803034305	0.0377364464313195	18.8421770006457	6.31288472849051e-68	***
df.mm.trans1:probe21	0.296282524769266	0.0377364464313195	7.85136261593951	1.06474686943756e-14	***
df.mm.trans1:probe22	0.585132410316556	0.0377364464313195	15.505763410487	1.03138991042589e-48	***
df.mm.trans1:probe23	0.679217705967039	0.0377364464313194	17.9989842764665	6.9094552054294e-63	***
df.mm.trans1:probe24	0.180073003187817	0.0377364464313194	4.77185904389676	2.09964427077374e-06	***
df.mm.trans1:probe25	0.806086493677287	0.0377364464313194	21.3609539293629	1.42124450208880e-83	***
df.mm.trans1:probe26	0.157203610944500	0.0377364464313194	4.16582974315324	3.37274635486336e-05	***
df.mm.trans1:probe27	0.238489727052938	0.0377364464313194	6.3198777205212	3.95028265215554e-10	***
df.mm.trans1:probe28	0.243699611822955	0.0377364464313194	6.45793748138128	1.66001214408311e-10	***
df.mm.trans1:probe29	0.870340897677456	0.0377364464313195	23.0636686806608	1.47089526322047e-94	***
df.mm.trans1:probe30	0.174542247077960	0.0377364464313194	4.62529632713637	4.23580739334309e-06	***
df.mm.trans1:probe31	0.191438060293925	0.0377364464313194	5.07302828956996	4.67288522486993e-07	***
df.mm.trans1:probe32	0.167195579606711	0.0377364464313195	4.43061272107345	1.04428938003289e-05	***
df.mm.trans2:probe2	-0.149391711871484	0.0377364464313195	-3.95881769480807	8.07021701656935e-05	***
df.mm.trans2:probe3	-0.121134495356621	0.0377364464313194	-3.21001331105956	0.00136994866529033	** 
df.mm.trans2:probe4	-0.09972343061906	0.0377364464313194	-2.64262907745056	0.00835624438914738	** 
df.mm.trans2:probe5	0.0623974835514674	0.0377364464313195	1.65350713838493	0.0985444030964851	.  
df.mm.trans2:probe6	-0.165356875298594	0.0377364464313195	-4.38188782824435	1.30190662634792e-05	***
df.mm.trans3:probe2	-0.425150532804129	0.0377364464313194	-11.2663107687658	8.66905514521531e-28	***
df.mm.trans3:probe3	-0.311141502509479	0.0377364464313194	-8.24511929271767	5.2010438136536e-16	***
df.mm.trans3:probe4	-0.0641362818094193	0.0377364464313195	-1.69958456279522	0.0895229122906848	.  
df.mm.trans3:probe5	-0.333906192478371	0.0377364464313194	-8.84837402711149	4.00896657013343e-18	***
df.mm.trans3:probe6	-0.546330802454737	0.0377364464313195	-14.4775370794137	3.11557257613330e-43	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09885917632038	0.130042268765781	31.5194376045749	1.33258119992011e-151	***
df.mm.trans1	-0.0281703500694840	0.114886129552324	-0.245202359756179	0.806350515533853	   
df.mm.trans2	0.0479163700000852	0.101545951588406	0.471868836231934	0.637124289781153	   
df.mm.exp2	-0.029276863842912	0.13329533385609	-0.219639075097109	0.826197488804946	   
df.mm.exp3	0.0282980873188809	0.13329533385609	0.212296158464425	0.83191964887812	   
df.mm.exp4	-0.131006828944178	0.13329533385609	-0.98283132015422	0.325930345504642	   
df.mm.exp5	-0.0387240727581858	0.13329533385609	-0.290513340849527	0.771484328270138	   
df.mm.exp6	0.0470800732481851	0.13329533385609	0.353201210321543	0.724012717038885	   
df.mm.exp7	-0.033108566900968	0.13329533385609	-0.248385040519971	0.803887967818114	   
df.mm.exp8	-0.0433587362039665	0.13329533385609	-0.325283225973821	0.745035386290423	   
df.mm.trans1:exp2	0.0192218010230656	0.127454717212463	0.150812786246456	0.880154091097707	   
df.mm.trans2:exp2	0.0425496713907242	0.0982104554411194	0.433249914172674	0.6649275026913	   
df.mm.trans1:exp3	0.0171184198199366	0.127454717212463	0.134309817591143	0.893184861575771	   
df.mm.trans2:exp3	0.0192963141625212	0.0982104554411194	0.196479224903809	0.844275376000056	   
df.mm.trans1:exp4	0.120847151405684	0.127454717212463	0.948157542135033	0.343280406556992	   
df.mm.trans2:exp4	0.0434374732701791	0.0982104554411194	0.442289704034835	0.658376088509768	   
df.mm.trans1:exp5	0.0472429245736487	0.127454717212463	0.370664386590701	0.710966733054306	   
df.mm.trans2:exp5	0.0657401281558226	0.0982104554411194	0.669380137385027	0.503408934872832	   
df.mm.trans1:exp6	-0.0202663121392763	0.127454717212463	-0.159007940879058	0.873695015883053	   
df.mm.trans2:exp6	-0.0138028211779303	0.0982104554411194	-0.140543296698238	0.888259306667278	   
df.mm.trans1:exp7	0.0483767086386134	0.127454717212463	0.379559969977189	0.704353399278223	   
df.mm.trans2:exp7	0.0198024607223535	0.0982104554411194	0.201632918139004	0.84024508875952	   
df.mm.trans1:exp8	0.0336620007358589	0.127454717212463	0.264109492940505	0.791750461524628	   
df.mm.trans2:exp8	0.0558965147780408	0.0982104554411194	0.569150346844208	0.569383082779354	   
df.mm.trans1:probe2	0.106612196478022	0.0780497556203144	1.3659517013308	0.172264024534376	   
df.mm.trans1:probe3	0.0511379970208864	0.0780497556203144	0.655197400868947	0.512492722302246	   
df.mm.trans1:probe4	-0.00672407386276122	0.0780497556203144	-0.0861511200044183	0.931363689271308	   
df.mm.trans1:probe5	0.0641452691257732	0.0780497556203144	0.821850992561952	0.411359388445226	   
df.mm.trans1:probe6	0.0465085633400572	0.0780497556203144	0.595883522894109	0.551389108552851	   
df.mm.trans1:probe7	0.036420084894126	0.0780497556203144	0.466626507727934	0.640869679634162	   
df.mm.trans1:probe8	0.0603368767008156	0.0780497556203144	0.773056574249048	0.439673244003339	   
df.mm.trans1:probe9	0.0486242885901465	0.0780497556203144	0.622990913984243	0.533433829922746	   
df.mm.trans1:probe10	-0.0215256651442055	0.0780497556203144	-0.275794138919801	0.782763683837744	   
df.mm.trans1:probe11	0.0504368326179219	0.0780497556203144	0.64621384419549	0.51829049041844	   
df.mm.trans1:probe12	-0.0103503417055014	0.0780497556203144	-0.132612096261420	0.894527086188867	   
df.mm.trans1:probe13	0.0415547398563871	0.0780497556203144	0.532413452497363	0.594559018178836	   
df.mm.trans1:probe14	0.183886953009211	0.0780497556203144	2.35602214956006	0.0186657591666318	*  
df.mm.trans1:probe15	0.0887598610253591	0.0780497556203144	1.13722151107232	0.25572058705559	   
df.mm.trans1:probe16	-0.0201999428968157	0.0780497556203144	-0.258808534841308	0.79583671751198	   
df.mm.trans1:probe17	0.0518028417416754	0.0780497556203144	0.663715617428435	0.507026714307098	   
df.mm.trans1:probe18	0.186555342421171	0.0780497556203144	2.39021046175595	0.0170246687583859	*  
df.mm.trans1:probe19	0.0976635715123522	0.0780497556203144	1.25129887641740	0.211120757195903	   
df.mm.trans1:probe20	0.105884856220373	0.0780497556203144	1.35663277070933	0.175206879549083	   
df.mm.trans1:probe21	0.0375709049347853	0.0780497556203144	0.481371205280322	0.630358968832399	   
df.mm.trans1:probe22	-0.000416575651429007	0.0780497556203144	-0.00533730885020969	0.995742538076705	   
df.mm.trans1:probe23	-0.0840799118217451	0.0780497556203144	-1.07726041104812	0.281626104107268	   
df.mm.trans1:probe24	0.0498874377600155	0.0780497556203144	0.639174810523443	0.522856925061505	   
df.mm.trans1:probe25	0.0940817298734627	0.0780497556203144	1.20540710378567	0.228333923328019	   
df.mm.trans1:probe26	0.084518256834445	0.0780497556203144	1.08287663635486	0.279126587941588	   
df.mm.trans1:probe27	0.0849046210537073	0.0780497556203144	1.08782686606656	0.276936039093256	   
df.mm.trans1:probe28	-0.00659254534183716	0.0780497556203144	-0.0844659318846258	0.93270304812213	   
df.mm.trans1:probe29	0.0292747167654052	0.0780497556203144	0.375077622379969	0.707682995809602	   
df.mm.trans1:probe30	-0.0249646994788799	0.0780497556203144	-0.319856215826283	0.749144750261896	   
df.mm.trans1:probe31	0.0544063155180481	0.0780497556203144	0.697072208434789	0.485921091324269	   
df.mm.trans1:probe32	0.0332026861008962	0.0780497556203144	0.425404100717702	0.670634477611848	   
df.mm.trans2:probe2	-0.0212630904900117	0.0780497556203144	-0.272429943194819	0.785348203499862	   
df.mm.trans2:probe3	-0.0101253197787514	0.0780497556203144	-0.129729038845524	0.896807132842648	   
df.mm.trans2:probe4	-0.0357158400595434	0.0780497556203144	-0.457603483517474	0.647337647025865	   
df.mm.trans2:probe5	0.018881312683501	0.0780497556203144	0.241913796314138	0.808897009425678	   
df.mm.trans2:probe6	-0.0357367266283661	0.0780497556203144	-0.457871089337078	0.647145431915875	   
df.mm.trans3:probe2	-0.0725969940456525	0.0780497556203144	-0.93013736518039	0.352526482430326	   
df.mm.trans3:probe3	-0.00137316264244824	0.0780497556203144	-0.0175934265461151	0.985966742075789	   
df.mm.trans3:probe4	-0.114152080374822	0.0780497556203144	-1.46255525680482	0.143906166649346	   
df.mm.trans3:probe5	-0.0782159046663493	0.0780497556203144	-1.00212875805586	0.316526035754964	   
df.mm.trans3:probe6	-0.0300815130293292	0.0780497556203144	-0.385414570362854	0.700013000467014