chr1.1575_chr1_120476930_120484214_-_2.R 

fitVsDatCorrelation=0.66762681949963
cont.fitVsDatCorrelation=0.239886905299935

fstatistic=14631.9718871797,59,853
cont.fstatistic=8599.37288364401,59,853

residuals=-0.34580479842257,-0.0801388090162637,-0.00274310436679111,0.0654212958145018,0.580164990488191
cont.residuals=-0.385427631739589,-0.0997975361715321,-0.0169770770367877,0.0767031844040302,1.01710766161425

predictedValues:
Include	Exclude	Both
chr1.1575_chr1_120476930_120484214_-_2.R.tl.Lung	43.665240840746	41.6628696361140	50.2070619109146
chr1.1575_chr1_120476930_120484214_-_2.R.tl.cerebhem	54.2302825949935	50.427845878461	55.3085547070569
chr1.1575_chr1_120476930_120484214_-_2.R.tl.cortex	46.5354901432034	41.7714934127873	57.1160146246527
chr1.1575_chr1_120476930_120484214_-_2.R.tl.heart	45.8658521731532	44.0761031964262	50.836699062536
chr1.1575_chr1_120476930_120484214_-_2.R.tl.kidney	43.245090660078	41.4642367365394	51.4026911762015
chr1.1575_chr1_120476930_120484214_-_2.R.tl.liver	49.1772664179448	47.3414030693165	51.3010206687907
chr1.1575_chr1_120476930_120484214_-_2.R.tl.stomach	45.5337149149474	44.3600074183843	48.8875586293484
chr1.1575_chr1_120476930_120484214_-_2.R.tl.testicle	47.1759185112023	45.4169710354351	50.2668319053305


diffExp=2.00237120463196,3.80243671653248,4.76399673041612,1.78974897672695,1.7808539235386,1.83586334862831,1.17370749656303,1.75894747576718
diffExpScore=0.949768750075262
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	50.9103112606805	50.0475495196922	50.0216781871239
cerebhem	51.0331810848675	47.7378160689291	48.8132555819931
cortex	49.7998600693645	52.6423565527782	48.018770392602
heart	50.9647215174028	49.3505997919341	49.2869902168229
kidney	50.0509411193677	52.0808472121599	52.3104880934531
liver	49.3799019154476	48.2711317838408	48.3324952791219
stomach	48.9233369308176	49.0878721028887	52.2668704795682
testicle	50.2394840429669	51.6680176728764	47.7050783726499
cont.diffExp=0.862761740988319,3.29536501593843,-2.84249648341374,1.61412172546864,-2.02990609279220,1.10877013160678,-0.164535172071027,-1.42853362990951
cont.diffExpScore=9.42850203405465

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=0.936568537068859
cont.tran.correlation=-0.103661527635512

tran.covariance=0.00463530501813636
cont.tran.covariance=-5.55730335586271e-05

tran.mean=45.7468616649833
cont.tran.mean=50.1367455403759

weightedLogRatios:
wLogRatio
Lung	0.176177391430233
cerebhem	0.287649993429099
cortex	0.408915424356672
heart	0.151483486334154
kidney	0.157522394962387
liver	0.147482647003904
stomach	0.0993768008835114
testicle	0.145716855311613

cont.weightedLogRatios:
wLogRatio
Lung	0.0670263680596836
cerebhem	0.26027333282117
cortex	-0.218469993446137
heart	0.126000682204715
kidney	-0.156356966584314
liver	0.088299990279549
stomach	-0.0130670872555551
testicle	-0.110211250067793

varWeightedLogRatios=0.0102573216013232
cont.varWeightedLogRatios=0.0257378902477905

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.66634558564848	0.0582344055189361	62.958410118161	0	***
df.mm.trans1	0.0846456851561196	0.0490445989627053	1.72589208488556	0.084728959231445	.  
df.mm.trans2	0.0387811927926226	0.0439868417754872	0.881654404527728	0.378212087424789	   
df.mm.exp2	0.310848394159511	0.0560548092191754	5.54543666260797	3.91145773242653e-08	***
df.mm.exp3	-0.0626621670689691	0.0560548092191754	-1.11787316631404	0.263935902388097	   
df.mm.exp4	0.093013115592559	0.0560548092191754	1.65932445205327	0.0974180151284003	.  
df.mm.exp5	-0.0379825270262933	0.0560548092191754	-0.677596223328152	0.498211481226416	   
df.mm.exp6	0.22509904083638	0.0560548092191754	4.01569542331753	6.44927812429412e-05	***
df.mm.exp7	0.131261424138705	0.0560548092191754	2.34166213331439	0.0194273741418572	*  
df.mm.exp8	0.162416948843247	0.0560548092191754	2.89746680268224	0.00385819462916747	** 
df.mm.trans1:exp2	-0.0941613037726306	0.049245739652121	-1.91207004784169	0.0562019441119271	.  
df.mm.trans2:exp2	-0.119915189812729	0.0367375112087628	-3.26410760737994	0.00114181101819308	** 
df.mm.trans1:exp3	0.126325035939384	0.049245739652121	2.56519725019387	0.0104816551778017	*  
df.mm.trans2:exp3	0.06526598242933	0.0367375112087628	1.77654882657817	0.07599885598416	.  
df.mm.trans1:exp4	-0.0438446182698006	0.049245739652121	-0.890323073214563	0.373543471425509	   
df.mm.trans2:exp4	-0.0367056733890139	0.0367375112087628	-0.999133370261039	0.318013428918834	   
df.mm.trans1:exp5	0.0283138617137694	0.049245739652121	0.574950481275794	0.56547639889108	   
df.mm.trans2:exp5	0.0332035015966452	0.0367375112087628	0.903803782677728	0.366354625592652	   
df.mm.trans1:exp6	-0.106219970296751	0.049245739652121	-2.15693725075720	0.0312893030381611	*  
df.mm.trans2:exp6	-0.097324114803077	0.0367375112087628	-2.64917550484103	0.00821807680916566	** 
df.mm.trans1:exp7	-0.0893607669334688	0.049245739652121	-1.81458878604984	0.0699382772103748	.  
df.mm.trans2:exp7	-0.0685334101736188	0.0367375112087628	-1.86548864957602	0.0624560439366521	.  
df.mm.trans1:exp8	-0.0850857689803588	0.049245739652121	-1.72777928773975	0.0843897417830078	.  
df.mm.trans2:exp8	-0.0761414180412595	0.0367375112087628	-2.07257964777695	0.0385112758670333	*  
df.mm.trans1:probe2	-0.0355078462251753	0.0366656880487485	-0.968421652907923	0.333108328064089	   
df.mm.trans1:probe3	0.0479167812389576	0.0366656880487485	1.30685618595921	0.191613762467350	   
df.mm.trans1:probe4	0.0756701889022814	0.0366656880487485	2.06378750622857	0.0393398376465623	*  
df.mm.trans1:probe5	0.057306074020443	0.0366656880487485	1.56293464189878	0.118438869274068	   
df.mm.trans1:probe6	0.0773683605873239	0.0366656880487485	2.11010251558513	0.0351401752362099	*  
df.mm.trans1:probe7	0.120250672107360	0.0366656880487485	3.27965131726104	0.00108147686999455	** 
df.mm.trans1:probe8	0.112242987949994	0.0366656880487485	3.06125410222121	0.00227320561253042	** 
df.mm.trans1:probe9	0.153209873818038	0.0366656880487485	4.17856262820814	3.23588669808673e-05	***
df.mm.trans1:probe10	0.0650048424001539	0.0366656880487485	1.77290665631932	0.0766009899802303	.  
df.mm.trans1:probe11	-0.0356970921118646	0.0366656880487485	-0.973583042118395	0.330539611246472	   
df.mm.trans1:probe12	-0.00650950258173318	0.0366656880487485	-0.177536626970658	0.85912904695406	   
df.mm.trans1:probe13	0.0604460685781395	0.0366656880487485	1.64857314276427	0.0996033427236901	.  
df.mm.trans1:probe14	0.0378485441983647	0.0366656880487485	1.03226057419252	0.302242645323343	   
df.mm.trans1:probe15	0.0884055893045714	0.0366656880487485	2.41112587842434	0.0161137975717041	*  
df.mm.trans2:probe2	0.152300990739254	0.0366656880487485	4.15377424628616	3.59953375539064e-05	***
df.mm.trans2:probe3	0.117490439354949	0.0366656880487485	3.20437023297479	0.00140387008091566	** 
df.mm.trans2:probe4	0.0930712367459102	0.0366656880487485	2.53837420484700	0.0113136716120474	*  
df.mm.trans2:probe5	0.0574146700888901	0.0366656880487485	1.56589643190536	0.117743747656355	   
df.mm.trans2:probe6	0.142844021140107	0.0366656880487485	3.89585000969271	0.000105513579007151	***
df.mm.trans3:probe2	0.225353671385507	0.0366656880487485	6.14617325838508	1.21738674672281e-09	***
df.mm.trans3:probe3	0.00591566835878691	0.0366656880487485	0.161340715900975	0.871863261458203	   
df.mm.trans3:probe4	0.141435091459174	0.0366656880487485	3.85742362917425	0.0001232175338338	***
df.mm.trans3:probe5	0.473086444598773	0.0366656880487485	12.9027019476570	6.36173043497756e-35	***
df.mm.trans3:probe6	0.236766520112654	0.0366656880487485	6.45744107673266	1.78615267821208e-10	***
df.mm.trans3:probe7	0.0134628805667042	0.0366656880487485	0.36717926986137	0.713576374249821	   
df.mm.trans3:probe8	0.0613874466209391	0.0366656880487485	1.67424777463121	0.094448494035283	.  
df.mm.trans3:probe9	0.111702306176952	0.0366656880487485	3.04650784211222	0.00238644571314512	** 
df.mm.trans3:probe10	0.130322700656559	0.0366656880487485	3.55435033656779	0.00039973616004906	***
df.mm.trans3:probe11	0.067356187126098	0.0366656880487485	1.83703595133808	0.0665523370118425	.  
df.mm.trans3:probe12	0.0566612400146381	0.0366656880487485	1.54534779053661	0.122633061167932	   
df.mm.trans3:probe13	0.0681678857218397	0.0366656880487485	1.85917377661665	0.063346675545577	.  
df.mm.trans3:probe14	0.104365029197617	0.0366656880487485	2.84639494720132	0.00452779800275144	** 
df.mm.trans3:probe15	0.0808426260857857	0.0366656880487485	2.20485773997483	0.0277302869974082	*  
df.mm.trans3:probe16	0.18044879481065	0.0366656880487485	4.92146211931811	1.03101469005900e-06	***
df.mm.trans3:probe17	0.191240395395231	0.0366656880487485	5.21578635428767	2.29883363481746e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95506390365965	0.0759359391013953	52.0842166497547	3.56569488772312e-267	***
df.mm.trans1	-0.0195139315775683	0.0639527036791563	-0.305130673997265	0.760341067676282	   
df.mm.trans2	-0.0399987543563763	0.0573575381865963	-0.69735828316501	0.485768621708603	   
df.mm.exp2	-0.0203845531117646	0.0730938101158005	-0.278882070581215	0.780402924876337	   
df.mm.exp3	0.0693586926723423	0.0730938101158004	0.948899675122412	0.342940393491656	   
df.mm.exp4	0.00184087141488132	0.0730938101158004	0.0251850520853255	0.97991325032431	   
df.mm.exp5	-0.021940864218441	0.0730938101158004	-0.300174039137934	0.76411754342696	   
df.mm.exp6	-0.0323094711609062	0.0730938101158005	-0.442027459092901	0.65858137918173	   
df.mm.exp7	-0.103078748502105	0.0730938101158004	-1.41022541223122	0.158837668101035	   
df.mm.exp8	0.0660198210720723	0.0730938101158004	0.903220409053502	0.366663919895983	   
df.mm.trans1:exp2	0.0227951019532272	0.0642149851776309	0.354981035815419	0.722691548146736	   
df.mm.trans2:exp2	-0.0268651172977984	0.047904625951377	-0.560804238928123	0.575078270887911	   
df.mm.trans1:exp3	-0.09141200033326	0.0642149851776309	-1.42353066157996	0.154948038500342	   
df.mm.trans2:exp3	-0.0188111832344036	0.047904625951377	-0.392679889693677	0.694654031337451	   
df.mm.trans1:exp4	-0.000772694837911501	0.0642149851776309	-0.0120329364831913	0.990402151093195	   
df.mm.trans2:exp4	-0.015864495687771	0.047904625951377	-0.331168344866598	0.7405986668912	   
df.mm.trans1:exp5	0.00491669155119712	0.0642149851776309	0.0765661089478822	0.938986675250155	   
df.mm.trans2:exp5	0.061764585583924	0.047904625951377	1.28932403410507	0.197634960057334	   
df.mm.trans1:exp6	0.00178748691907439	0.0642149851776309	0.0278359780685125	0.977799481636563	   
df.mm.trans2:exp6	-0.0038303764177008	0.047904625951377	-0.079958382758037	0.936289110409159	   
df.mm.trans1:exp7	0.0632677871547409	0.0642149851776309	0.985249579669452	0.324780867139311	   
df.mm.trans2:exp7	0.0837172048604697	0.047904625951377	1.74758080661025	0.0808964648470462	.  
df.mm.trans1:exp8	-0.0792840506457271	0.0642149851776309	-1.23466587162502	0.21729488541375	   
df.mm.trans2:exp8	-0.0341543885726241	0.047904625951377	-0.712966397176145	0.476061542164779	   
df.mm.trans1:probe2	-0.000202640553204364	0.0478109706790974	-0.00423836936013007	0.996619271640744	   
df.mm.trans1:probe3	-0.0206342876636485	0.0478109706790974	-0.431580605257815	0.666155313884647	   
df.mm.trans1:probe4	-0.0223117304333096	0.0478109706790974	-0.466665497821907	0.640858380309964	   
df.mm.trans1:probe5	-0.00653669046643712	0.0478109706790974	-0.136719467803964	0.89128480217395	   
df.mm.trans1:probe6	-0.0146765836904336	0.0478109706790974	-0.306971046225801	0.758940332267506	   
df.mm.trans1:probe7	-0.0121274352650785	0.0478109706790974	-0.253653818209981	0.79982413947506	   
df.mm.trans1:probe8	0.0264259431381818	0.0478109706790974	0.552717143426143	0.58060191927536	   
df.mm.trans1:probe9	-0.0280070479795636	0.0478109706790974	-0.585787060621383	0.55817357141506	   
df.mm.trans1:probe10	-0.0642837880256364	0.0478109706790974	-1.34454053353367	0.179131081735647	   
df.mm.trans1:probe11	-0.0188744899914838	0.0478109706790974	-0.39477320212065	0.693108961671268	   
df.mm.trans1:probe12	-0.0114571133356312	0.0478109706790974	-0.239633564700667	0.810671911342253	   
df.mm.trans1:probe13	0.0163862228220700	0.0478109706790974	0.342729348292315	0.731886519926656	   
df.mm.trans1:probe14	-0.0124185287991445	0.0478109706790974	-0.259742243730973	0.795125282214916	   
df.mm.trans1:probe15	-0.00678551764876708	0.0478109706790974	-0.141923862920726	0.887173657869979	   
df.mm.trans2:probe2	-0.0420882875688263	0.0478109706790974	-0.880306067227096	0.378941472377396	   
df.mm.trans2:probe3	0.0296362787240233	0.0478109706790974	0.619863564848727	0.53551315881927	   
df.mm.trans2:probe4	-0.03047900392436	0.0478109706790974	-0.637489753741502	0.523976951811675	   
df.mm.trans2:probe5	0.008578718674058	0.0478109706790974	0.179429920627162	0.857642751290762	   
df.mm.trans2:probe6	-0.0137546297868235	0.0478109706790974	-0.28768773341046	0.773655659523648	   
df.mm.trans3:probe2	0.0155063602328893	0.0478109706790974	0.324326404853113	0.745770444137336	   
df.mm.trans3:probe3	0.0163601761105035	0.0478109706790974	0.342184563043312	0.732296293251332	   
df.mm.trans3:probe4	0.037738603083564	0.0478109706790974	0.789329364108957	0.430138870155083	   
df.mm.trans3:probe5	-0.0201763599329912	0.0478109706790974	-0.422002725450042	0.673129363698414	   
df.mm.trans3:probe6	0.0205132732760750	0.0478109706790974	0.429049504427721	0.667995534862081	   
df.mm.trans3:probe7	0.00395808551713632	0.0478109706790974	0.0827861359206156	0.934041015606517	   
df.mm.trans3:probe8	-0.0463764527762997	0.0478109706790974	-0.96999605148312	0.332323417808282	   
df.mm.trans3:probe9	0.0412901544914849	0.0478109706790974	0.863612553876397	0.388043528703495	   
df.mm.trans3:probe10	0.0968482516100216	0.0478109706790974	2.02564913940061	0.0431116407067348	*  
df.mm.trans3:probe11	-0.00498520642780152	0.0478109706790974	-0.104269090482637	0.91698031721671	   
df.mm.trans3:probe12	0.0213297261437275	0.0478109706790974	0.446126189047501	0.655619316543651	   
df.mm.trans3:probe13	0.0364872394491508	0.0478109706790974	0.763156215631965	0.445581241762264	   
df.mm.trans3:probe14	0.0102375004482833	0.0478109706790974	0.214124505377572	0.830501139854874	   
df.mm.trans3:probe15	0.0456790485974152	0.0478109706790974	0.955409353723615	0.339641404802262	   
df.mm.trans3:probe16	0.056922294700391	0.0478109706790974	1.19056973518584	0.234153754265972	   
df.mm.trans3:probe17	-0.0439433173072237	0.0478109706790974	-0.919105315852443	0.358300312913283