chr5.18196_chr5_144265060_144267958_+_2.R 

fitVsDatCorrelation=0.907201002729006
cont.fitVsDatCorrelation=0.247787854796642

fstatistic=9286.63449627925,54,738
cont.fstatistic=1740.03480585468,54,738

residuals=-0.54588330462817,-0.0983123165906221,-0.000177537462254562,0.0859776602174288,0.900955041105579
cont.residuals=-0.697675974349343,-0.255839759169901,-0.116651738951642,0.135173594020360,1.41860610835198

predictedValues:
Include	Exclude	Both
chr5.18196_chr5_144265060_144267958_+_2.R.tl.Lung	85.3365916346489	63.566473233592	66.1035564799021
chr5.18196_chr5_144265060_144267958_+_2.R.tl.cerebhem	83.8336071477337	84.766400835246	76.5739401678693
chr5.18196_chr5_144265060_144267958_+_2.R.tl.cortex	78.4190966169646	57.2700854506394	65.0358282564835
chr5.18196_chr5_144265060_144267958_+_2.R.tl.heart	75.2845608506377	61.6679717672381	64.7284646135548
chr5.18196_chr5_144265060_144267958_+_2.R.tl.kidney	85.0307333436233	63.0407461951594	64.1615028235254
chr5.18196_chr5_144265060_144267958_+_2.R.tl.liver	84.8483521981925	65.3542886073578	62.0118387556263
chr5.18196_chr5_144265060_144267958_+_2.R.tl.stomach	77.963815280119	59.2906868327163	65.8946381629009
chr5.18196_chr5_144265060_144267958_+_2.R.tl.testicle	82.0034678452994	65.1724696084283	67.5865199418561


diffExp=21.7701184010569,-0.932793687512316,21.1490111663252,13.6165890833995,21.9899871484639,19.4940635908347,18.6731284474027,16.8309982368711
diffExpScore=1.00647937893736
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=1,0,1,0,1,0,1,0
diffExp1.3Score=0.8
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	71.4701329178012	68.0929448574356	76.098854609919
cerebhem	82.7090367723608	63.5463570714367	63.9399773086769
cortex	71.9573070572237	70.0662607397757	68.3917176452907
heart	73.4727907218457	70.2562292377194	81.7216261019024
kidney	81.6209264203496	73.2406356300974	73.2215544150227
liver	74.8122701125266	62.9248074976111	82.7166293714846
stomach	80.580699224342	72.2033967740894	82.4577237621156
testicle	75.8899387931711	75.5863690724346	75.4072274549026
cont.diffExp=3.3771880603656,19.1626797009241,1.89104631744796,3.21656148412633,8.3802907902522,11.8874626149154,8.3773024502526,0.303569720736505
cont.diffExpScore=0.982637713660751

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=0,1,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,1,0,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.43567909866173
cont.tran.correlation=0.0202137141743286

tran.covariance=0.00264910571122466
cont.tran.covariance=5.47474965819819e-05

tran.mean=73.3030842154748
cont.tran.mean=73.0268814312638

weightedLogRatios:
wLogRatio
Lung	1.26623079712585
cerebhem	-0.0490675111297835
cortex	1.32156127440358
heart	0.842237031463325
kidney	1.28471953436511
liver	1.12518309173823
stomach	1.15522627884146
testicle	0.985954011259877

cont.weightedLogRatios:
wLogRatio
Lung	0.205487318987544
cerebhem	1.12896879998686
cortex	0.113524083736103
heart	0.191354414823549
kidney	0.471032926148788
liver	0.731698966510985
stomach	0.475793090912212
testicle	0.0173444015178355

varWeightedLogRatios=0.202880507317467
cont.varWeightedLogRatios=0.137112412944657

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.51814260807278	0.0851659611127928	53.0510376333217	4.87350423252237e-254	***
df.mm.trans1	-0.0100531146790822	0.0750236285639832	-0.133999312903247	0.893439650439974	   
df.mm.trans2	-0.332729259263904	0.0676961110556183	-4.91504244594698	1.09432334370038e-06	***
df.mm.exp2	0.123009433290660	0.090151069768111	1.36448112714656	0.172832098453324	   
df.mm.exp3	-0.172559395811626	0.090151069768111	-1.91411367891127	0.0559920986892011	.  
df.mm.exp4	-0.134628243130984	0.090151069768111	-1.49336267974721	0.135769665131164	   
df.mm.exp5	0.0179236865844987	0.090151069768111	0.198818346034079	0.842459603644903	   
df.mm.exp6	0.0858963506621337	0.090151069768111	0.952804563307774	0.341001056995763	   
df.mm.exp7	-0.156826989925180	0.090151069768111	-1.73960209599924	0.082345819995755	.  
df.mm.exp8	-0.0370768516532442	0.090151069768111	-0.41127467204343	0.680990541071464	   
df.mm.trans1:exp2	-0.140778804549594	0.0850342950287306	-1.65555326238700	0.098237448874557	.  
df.mm.trans2:exp2	0.164803633488951	0.0695735141144159	2.3687697191481	0.0181039392337237	*  
df.mm.trans1:exp3	0.0880235345043508	0.0850342950287306	1.03515333989198	0.300936375526654	   
df.mm.trans2:exp3	0.0682516333443932	0.0695735141144159	0.981000229946	0.326914107019064	   
df.mm.trans1:exp4	0.00929998342911664	0.0850342950287306	0.109367443170717	0.912940786024845	   
df.mm.trans2:exp4	0.104306762172072	0.0695735141144159	1.49923089985848	0.134241372685301	   
df.mm.trans1:exp5	-0.0215142649969347	0.0850342950287306	-0.253006919027972	0.800333323709088	   
df.mm.trans2:exp5	-0.0262285851910102	0.0695735141144159	-0.376990950146294	0.706288703840919	   
df.mm.trans1:exp6	-0.0916341176460208	0.0850342950287306	-1.07761365711400	0.281558312813378	   
df.mm.trans2:exp6	-0.0581594684751409	0.0695735141144159	-0.8359426602988	0.403457726421771	   
df.mm.trans1:exp7	0.0664684640106085	0.0850342950287306	0.781666549809707	0.434661258018181	   
df.mm.trans2:exp7	0.0871930509318664	0.0695735141144159	1.25325063771358	0.210511410634495	   
df.mm.trans1:exp8	-0.00276494948143276	0.0850342950287306	-0.0325156982897143	0.974069588914942	   
df.mm.trans2:exp8	0.0620278052074833	0.0695735141144159	0.891543369585682	0.372928311542967	   
df.mm.trans1:probe2	-0.0413508556735382	0.0496493316730396	-0.83285825367902	0.405194118833309	   
df.mm.trans1:probe3	-0.0573455033919229	0.0496493316730396	-1.15501058039543	0.248459926108963	   
df.mm.trans1:probe4	-0.45358842079772	0.0496493316730396	-9.1358414204803	6.15139203950495e-19	***
df.mm.trans1:probe5	-0.278061149607675	0.0496493316730396	-5.60050136100153	3.01684780949484e-08	***
df.mm.trans1:probe6	-0.483254503711699	0.0496493316730396	-9.7333536510445	3.80600792541095e-21	***
df.mm.trans1:probe7	-0.36318700271519	0.0496493316730396	-7.31504313304596	6.72109550067324e-13	***
df.mm.trans1:probe8	-0.445612038423915	0.0496493316730396	-8.9751870449827	2.31397698454039e-18	***
df.mm.trans1:probe9	-0.245783160104767	0.0496493316730396	-4.95038204589229	9.18455487808685e-07	***
df.mm.trans1:probe10	-0.32026805467283	0.0496493316730396	-6.45060152635933	2.01481990158539e-10	***
df.mm.trans1:probe11	0.59359039412605	0.0496493316730396	11.9556572893081	3.07395408933016e-30	***
df.mm.trans1:probe12	0.440425041771305	0.0496493316730396	8.8707144070272	5.4231886349941e-18	***
df.mm.trans1:probe13	0.76309557329672	0.0496493316730396	15.3697048395738	1.86499124290358e-46	***
df.mm.trans1:probe14	0.404487415050223	0.0496493316730396	8.1468853944285	1.59382405051966e-15	***
df.mm.trans1:probe15	0.470602187081364	0.0496493316730396	9.47852007717776	3.43102233823193e-20	***
df.mm.trans1:probe16	0.611012412376392	0.0496493316730396	12.3065586542060	8.6312694315993e-32	***
df.mm.trans1:probe17	-0.416788528750443	0.0496493316730396	-8.39464529946061	2.37931803613857e-16	***
df.mm.trans1:probe18	-0.41502020688035	0.0496493316730396	-8.35902907240366	3.13625554844262e-16	***
df.mm.trans1:probe19	-0.209156960543304	0.0496493316730396	-4.21268431004641	2.83578793140674e-05	***
df.mm.trans1:probe20	-0.391988416580149	0.0496493316730396	-7.89513984118753	1.05003277070012e-14	***
df.mm.trans1:probe21	-0.420742342986769	0.0496493316730396	-8.47428009217772	1.27868080109091e-16	***
df.mm.trans1:probe22	-0.401192066363756	0.0496493316730396	-8.08051292625173	2.63224505274998e-15	***
df.mm.trans2:probe2	-0.116845727522842	0.0496493316730396	-2.35341994716701	0.0188630123904776	*  
df.mm.trans2:probe3	-0.059355132087079	0.0496493316730396	-1.1954870304792	0.232280796805167	   
df.mm.trans2:probe4	0.0275946182221996	0.0496493316730396	0.555790325717192	0.578522648827524	   
df.mm.trans2:probe5	-0.112682092259295	0.0496493316730396	-2.26955909500154	0.0235220719794382	*  
df.mm.trans2:probe6	-0.105310512930133	0.0496493316730396	-2.12108621368047	0.0342473529238222	*  
df.mm.trans3:probe2	-0.0856529622518326	0.0496493316730396	-1.72515841332751	0.0849175720135243	.  
df.mm.trans3:probe3	-0.0357056608225513	0.0496493316730396	-0.719156927583378	0.472271884447198	   
df.mm.trans3:probe4	-0.00358001445187216	0.0496493316730396	-0.0721059948087108	0.942537091868233	   
df.mm.trans3:probe5	0.204250216613905	0.0496493316730396	4.11385631450134	4.32905954595562e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15073195695673	0.196126701625693	21.1635229805598	4.65600806976447e-78	***
df.mm.trans1	0.193800596605623	0.172770161012542	1.12172492906083	0.262344251470906	   
df.mm.trans2	0.0180898201843602	0.155895792177359	0.116037898981775	0.907654072666418	   
df.mm.exp2	0.251033715812902	0.207606791852331	1.20917872470887	0.226981420391769	   
df.mm.exp3	0.142142596279838	0.207606791852331	0.684672187319105	0.493765763951723	   
df.mm.exp4	-0.0123745834923103	0.207606791852331	-0.0596058702217807	0.952485679359805	   
df.mm.exp5	0.244226206565989	0.207606791852331	1.17638832712036	0.239819044365740	   
df.mm.exp6	-0.116618305649259	0.207606791852331	-0.561726832772448	0.574472693855713	   
df.mm.exp7	0.0983404904826738	0.207606791852331	0.473686287453555	0.63586372674108	   
df.mm.exp8	0.173536919880667	0.207606791852331	0.835892305508491	0.403486038166777	   
df.mm.trans1:exp2	-0.104984489020182	0.195823490877576	-0.536117952701677	0.592038579973427	   
df.mm.trans2:exp2	-0.320137651682072	0.160219219808926	-1.99812264760659	0.0460698698628087	*  
df.mm.trans1:exp3	-0.135349251671897	0.195823490877576	-0.691179853169475	0.489669874009366	   
df.mm.trans2:exp3	-0.113574828004575	0.160219219808926	-0.708871433402447	0.478628123803051	   
df.mm.trans1:exp4	0.0400100859109802	0.195823490877576	0.204317090516957	0.838162046587854	   
df.mm.trans2:exp4	0.0436499521533788	0.160219219808926	0.272438925900619	0.785360752171098	   
df.mm.trans1:exp5	-0.111420167244856	0.195823490877576	-0.568982642202579	0.569541032317373	   
df.mm.trans2:exp5	-0.171349416113209	0.160219219808926	-1.06946854639261	0.285208258092871	   
df.mm.trans1:exp6	0.162320575113588	0.195823490877576	0.828912682468043	0.407421823911193	   
df.mm.trans2:exp6	0.0376851793858658	0.160219219808926	0.235210104198537	0.81411091316707	   
df.mm.trans1:exp7	0.0216390256353465	0.195823490877576	0.110502705974508	0.91204074684968	   
df.mm.trans2:exp7	-0.0397270070026862	0.160219219808926	-0.247954065998223	0.804238920213633	   
df.mm.trans1:exp8	-0.113532443986222	0.195823490877576	-0.57976927833035	0.562247163241566	   
df.mm.trans2:exp8	-0.0691345642711156	0.160219219808926	-0.431499818520925	0.666231005352511	   
df.mm.trans1:probe2	-0.158543499277053	0.114336285667662	-1.38664203014156	0.165969655997034	   
df.mm.trans1:probe3	-0.194873900655921	0.114336285667662	-1.70439243778091	0.0887287035100336	.  
df.mm.trans1:probe4	-0.120367418086434	0.114336285667662	-1.05274906722353	0.292800573966997	   
df.mm.trans1:probe5	-0.186591738807329	0.114336285667662	-1.63195557488800	0.103115440243117	   
df.mm.trans1:probe6	-0.103108814110843	0.114336285667662	-0.901803075976653	0.367455698385087	   
df.mm.trans1:probe7	-0.086068375336663	0.114336285667662	-0.752765185908131	0.451831074743872	   
df.mm.trans1:probe8	-0.0917916920959808	0.114336285667662	-0.802822057406945	0.422336056599985	   
df.mm.trans1:probe9	-0.121834094198431	0.114336285667662	-1.06557680693391	0.286963473981015	   
df.mm.trans1:probe10	-0.117671757671051	0.114336285667662	-1.02917247122303	0.303735813094075	   
df.mm.trans1:probe11	-0.233611450543832	0.114336285667662	-2.04319607882718	0.0413877163576276	*  
df.mm.trans1:probe12	-0.124851572363460	0.114336285667662	-1.09196806275798	0.275203485640572	   
df.mm.trans1:probe13	0.0518177537201756	0.114336285667662	0.45320480211149	0.650534491706857	   
df.mm.trans1:probe14	-0.0374749075159522	0.114336285667662	-0.327760406918232	0.743185783949595	   
df.mm.trans1:probe15	0.0166758702900001	0.114336285667662	0.145849326769905	0.884080161899549	   
df.mm.trans1:probe16	-0.120185885016238	0.114336285667662	-1.05116135542114	0.293528558212401	   
df.mm.trans1:probe17	0.0512581730100077	0.114336285667662	0.448310636563778	0.654060611200578	   
df.mm.trans1:probe18	-0.191967646493205	0.114336285667662	-1.67897396152252	0.093580418523975	.  
df.mm.trans1:probe19	-0.140141741433155	0.114336285667662	-1.22569786673411	0.220703384053500	   
df.mm.trans1:probe20	-0.0117158396581078	0.114336285667662	-0.102468254847476	0.918412844396121	   
df.mm.trans1:probe21	-0.00691269660273858	0.114336285667662	-0.0604593420397747	0.951806165254202	   
df.mm.trans1:probe22	-0.103867406318177	0.114336285667662	-0.908437821918449	0.363943504487637	   
df.mm.trans2:probe2	0.0645401752937484	0.114336285667662	0.564476753087339	0.572601233493695	   
df.mm.trans2:probe3	0.0914946686789073	0.114336285667662	0.800224252035369	0.423838393419197	   
df.mm.trans2:probe4	0.175871570913639	0.114336285667662	1.53819559457126	0.124429416183163	   
df.mm.trans2:probe5	0.164853377853915	0.114336285667662	1.44182904745646	0.149774810295016	   
df.mm.trans2:probe6	0.0758103471991923	0.114336285667662	0.663047139904018	0.507507400253971	   
df.mm.trans3:probe2	-0.152201100378849	0.114336285667662	-1.33117058587374	0.183543954930137	   
df.mm.trans3:probe3	-3.06412343077707e-05	0.114336285667662	-0.000267992213747739	0.999786245593719	   
df.mm.trans3:probe4	-0.0485545843428321	0.114336285667662	-0.424664699043698	0.671204895587414	   
df.mm.trans3:probe5	0.0478405254042079	0.114336285667662	0.418419446852284	0.675762201049396