chr1.1483_chr1_138994541_138997754_-_2.R 

fitVsDatCorrelation=0.85654725110296
cont.fitVsDatCorrelation=0.258478186247377

fstatistic=11305.0819035491,52,692
cont.fstatistic=3216.89590981734,52,692

residuals=-0.499080707716959,-0.0948132245649386,-0.00533322427951814,0.0935416473991634,0.54177113294279
cont.residuals=-0.667756692948434,-0.222081265833507,0.00519039071785884,0.219734188574817,0.814744675903186

predictedValues:
Include	Exclude	Both
chr1.1483_chr1_138994541_138997754_-_2.R.tl.Lung	82.8364774382695	110.757389451656	89.1714874315924
chr1.1483_chr1_138994541_138997754_-_2.R.tl.cerebhem	62.3574381228412	67.1703353981244	60.1303577258445
chr1.1483_chr1_138994541_138997754_-_2.R.tl.cortex	60.0391628791176	80.2615632925244	65.7374049584162
chr1.1483_chr1_138994541_138997754_-_2.R.tl.heart	71.0981589381473	104.452352957708	80.1469358687927
chr1.1483_chr1_138994541_138997754_-_2.R.tl.kidney	71.5663717389629	101.512697074021	68.655182239523
chr1.1483_chr1_138994541_138997754_-_2.R.tl.liver	68.7117635213502	104.388384350843	68.4708800690907
chr1.1483_chr1_138994541_138997754_-_2.R.tl.stomach	68.3271162226853	103.041050124318	69.2113313881038
chr1.1483_chr1_138994541_138997754_-_2.R.tl.testicle	65.640790448889	101.157050372621	66.866252745472


diffExp=-27.9209120133865,-4.81289727528328,-20.2224004134068,-33.3541940195606,-29.9463253350577,-35.6766208294924,-34.7139339016331,-35.5162599237322
diffExpScore=0.995518981356146
diffExp1.5=0,0,0,0,0,-1,-1,-1
diffExp1.5Score=0.75
diffExp1.4=0,0,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.833333333333333
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	70.2077295383589	82.248482341097	74.5648430914382
cerebhem	71.2204553943936	71.1918001837126	70.3918354417828
cortex	74.9352832699204	66.3836846835028	73.2110973874326
heart	74.9506275111396	75.9928867605969	70.9313873814314
kidney	82.3671291482107	84.3854171297356	74.0754087649192
liver	75.025752419311	71.1116991143718	68.1854390162353
stomach	74.6706670576442	69.6677616092254	71.909804526336
testicle	79.028761588495	73.9848003894532	78.3177655835636
cont.diffExp=-12.0407528027381,0.0286552106809665,8.5515985864176,-1.04225924945727,-2.01828798152488,3.91405330493926,5.0029054484188,5.0439611990418
cont.diffExpScore=4.46007547634826

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=0.754348861881957
cont.tran.correlation=0.283563681251268

tran.covariance=0.0125407308692065
cont.tran.covariance=0.00108570020662442

tran.mean=82.7073813957549
cont.tran.mean=74.835808633698

weightedLogRatios:
wLogRatio
Lung	-1.32517122667027
cerebhem	-0.310038555647199
cortex	-1.23088740979521
heart	-1.71424003161355
kidney	-1.55392935958269
liver	-1.85638901051109
stomach	-1.81981969047508
testicle	-1.90308706934737

cont.weightedLogRatios:
wLogRatio
Lung	-0.685475891774934
cerebhem	0.00171658066971999
cortex	0.51571919064187
heart	-0.0597113972710656
kidney	-0.107079745142859
liver	0.229912090663757
stomach	0.296706445858248
testicle	0.286023794589834

varWeightedLogRatios=0.278474746179291
cont.varWeightedLogRatios=0.135006274233845

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.81144209015805	0.0732402086921503	65.6939975469203	1.30538808334828e-299	***
df.mm.trans1	-0.433147596104581	0.0630514231049716	-6.86975130416092	1.43491935535799e-11	***
df.mm.trans2	-0.115768903341050	0.0568589655752543	-2.03607121884459	0.042123526437788	*  
df.mm.exp2	-0.390049461315077	0.0742698549136458	-5.25178703753482	2.00703857957467e-07	***
df.mm.exp3	-0.339029487589206	0.0742698549136458	-4.56483304004564	5.91554897810257e-06	***
df.mm.exp4	-0.104718491901221	0.0742698549136458	-1.40997302368475	0.158996937505870	   
df.mm.exp5	0.0280632620761556	0.0742698549136458	0.377855350717798	0.705653922041947	   
df.mm.exp6	0.0179809758139465	0.0742698549136458	0.242103284500193	0.808771863489184	   
df.mm.exp7	-0.0113797405493198	0.0742698549136458	-0.153221526587754	0.878268250086887	   
df.mm.exp8	-0.0354721258250447	0.0742698549136458	-0.47761135209283	0.633077669046741	   
df.mm.trans1:exp2	0.106063909452944	0.0677513631333147	1.56548746103073	0.117925810286998	   
df.mm.trans2:exp2	-0.110060954756510	0.0537867357740189	-2.04624714946309	0.0411094630598534	*  
df.mm.trans1:exp3	0.0171580383882413	0.0677513631333147	0.253250083758158	0.800150182520875	   
df.mm.trans2:exp3	0.0169782014812650	0.0537867357740189	0.315657777646105	0.752357452071112	   
df.mm.trans1:exp4	-0.0480885787525425	0.0677513631333147	-0.709780239519586	0.478079518444577	   
df.mm.trans2:exp4	0.046107378159741	0.0537867357740189	0.857225810345841	0.391616871013232	   
df.mm.trans1:exp5	-0.174306480038034	0.0677513631333147	-2.57273760964854	0.0102970548378937	*  
df.mm.trans2:exp5	-0.115221505703183	0.0537867357740189	-2.14219182564411	0.0325264043147269	*  
df.mm.trans1:exp6	-0.204929074097123	0.0677513631333147	-3.02472252394219	0.00258075862784577	** 
df.mm.trans2:exp6	-0.0772046961731793	0.0537867357740189	-1.43538541728112	0.151629126189756	   
df.mm.trans1:exp7	-0.181182068376015	0.0677513631333147	-2.67422026652811	0.00766714051006052	** 
df.mm.trans2:exp7	-0.0608349343332237	0.0537867357740189	-1.13103971560604	0.258430199615884	   
df.mm.trans1:exp8	-0.197199078992085	0.0677513631333147	-2.91062895080141	0.00372291928795699	** 
df.mm.trans2:exp8	-0.0551957394493652	0.0537867357740189	-1.02619611796608	0.30515772354916	   
df.mm.trans1:probe2	-0.177242787172957	0.0443536785706936	-3.99612372377315	7.12954273300952e-05	***
df.mm.trans1:probe3	-0.136425866950535	0.0443536785706936	-3.07586363401834	0.00218164264061308	** 
df.mm.trans1:probe4	0.0115986072289989	0.0443536785706936	0.261502711900488	0.79378267025092	   
df.mm.trans1:probe5	-0.0869154433705664	0.0443536785706936	-1.95959943281898	0.0504436570768077	.  
df.mm.trans1:probe6	0.144366386018224	0.0443536785706936	3.25489092833921	0.00118958516427158	** 
df.mm.trans1:probe7	-0.0263622040250275	0.0443536785706936	-0.594363418651054	0.552463333938678	   
df.mm.trans1:probe8	0.00337332628637537	0.0443536785706936	0.0760551637447333	0.93939719319453	   
df.mm.trans1:probe9	0.30031459207753	0.0443536785706936	6.7709060838972	2.73414004907988e-11	***
df.mm.trans1:probe10	0.158527702086460	0.0443536785706936	3.57417258714604	0.000375746917583245	***
df.mm.trans1:probe11	-0.140852987737169	0.0443536785706936	-3.17567769520332	0.00156122117634261	** 
df.mm.trans1:probe12	0.301687229916688	0.0443536785706936	6.8018536373672	2.23625798308914e-11	***
df.mm.trans1:probe13	0.141379801143651	0.0443536785706936	3.18755525358085	0.0014993926462786	** 
df.mm.trans1:probe14	0.330808710185846	0.0443536785706936	7.45842782033475	2.62989135425804e-13	***
df.mm.trans1:probe15	0.0619483797033072	0.0443536785706936	1.39669091041840	0.162954308940078	   
df.mm.trans1:probe16	-0.0304273379606108	0.0443536785706936	-0.68601610827192	0.492932722838988	   
df.mm.trans1:probe17	0.108572369969625	0.0443536785706936	2.44787745838434	0.0146174218589773	*  
df.mm.trans2:probe2	0.0200182888987839	0.0443536785706936	0.451333227454348	0.65189080494397	   
df.mm.trans2:probe3	0.0611596491582339	0.0443536785706936	1.37890815664261	0.168368605026414	   
df.mm.trans2:probe4	0.134764632372402	0.0443536785706936	3.03840936569908	0.00246785280655218	** 
df.mm.trans2:probe5	-0.0291987236814018	0.0443536785706936	-0.658315716358523	0.510554232821985	   
df.mm.trans2:probe6	-0.0233786616383101	0.0443536785706936	-0.52709633995854	0.598295618526346	   
df.mm.trans3:probe2	0.195513109552209	0.0443536785706936	4.40804722071899	1.20883736958495e-05	***
df.mm.trans3:probe3	0.544459030226746	0.0443536785706936	12.2753973914239	1.77483892141391e-31	***
df.mm.trans3:probe4	-0.154648515144267	0.0443536785706936	-3.48671226666755	0.000519895940326002	***
df.mm.trans3:probe5	0.0809326805219508	0.0443536785706936	1.82471179685706	0.06847554109462	.  
df.mm.trans3:probe6	0.0740478090965967	0.0443536785706936	1.66948518099970	0.0954735367541707	.  
df.mm.trans3:probe7	0.565819855957225	0.0443536785706936	12.7569995137018	1.26249585461441e-33	***
df.mm.trans3:probe8	0.518162030969228	0.0443536785706936	11.6825040823469	6.62493298524805e-29	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31271078022457	0.137096681957489	31.4574409726549	1.49868562279780e-135	***
df.mm.trans1	-0.0745861937348855	0.118024525800073	-0.631955038406429	0.527624975223698	   
df.mm.trans2	0.0975938705397887	0.106433005299969	0.91695118694367	0.359487668795072	   
df.mm.exp2	-0.0724537909080979	0.139024053316444	-0.521160109921267	0.602421972396745	   
df.mm.exp3	-0.130805074295894	0.139024053316444	-0.940880884821837	0.347094224733692	   
df.mm.exp4	0.0362220765140779	0.139024053316444	0.260545392325959	0.794520615978241	   
df.mm.exp5	0.191963205214191	0.139024053316444	1.38079131369626	0.167788915162835	   
df.mm.exp6	0.0103180239200611	0.139024053316444	0.0742175449062427	0.940858733752695	   
df.mm.exp7	-0.0681218264885348	0.139024053316444	-0.490000290334488	0.624289001330964	   
df.mm.exp8	-0.0366371657365123	0.139024053316444	-0.263531129056635	0.792219685435987	   
df.mm.trans1:exp2	0.086775450575416	0.126822236713123	0.68422898715866	0.494059653133147	   
df.mm.trans2:exp2	-0.0719135009604427	0.100682168164446	-0.714262537959901	0.475305707053726	   
df.mm.trans1:exp3	0.195971513239738	0.126822236713123	1.54524567866622	0.122744019760763	   
df.mm.trans2:exp3	-0.0834885497071235	0.100682168164446	-0.829228762443418	0.407260910627433	   
df.mm.trans1:exp4	0.0291491080418503	0.126822236713123	0.229842248467725	0.818282275514978	   
df.mm.trans2:exp4	-0.115327273558629	0.100682168164446	-1.14545878044922	0.252415141663971	   
df.mm.trans1:exp5	-0.0322351783464205	0.126822236713123	-0.254176074968128	0.799435039959142	   
df.mm.trans2:exp5	-0.166313539059258	0.100682168164446	-1.65186688061401	0.0990153454516419	.  
df.mm.trans1:exp6	0.0560549839161669	0.126822236713123	0.441996493430135	0.658629838179077	   
df.mm.trans2:exp6	-0.155811093836165	0.100682168164446	-1.54755401752648	0.122186893882386	   
df.mm.trans1:exp7	0.129750752550742	0.126822236713123	1.02309150125024	0.306622100695257	   
df.mm.trans2:exp7	-0.097885431154708	0.100682168164446	-0.972222121744837	0.331279762823112	   
df.mm.trans1:exp8	0.154990610331884	0.126822236713123	1.22210910601174	0.222082484643244	   
df.mm.trans2:exp8	-0.069248099994609	0.100682168164446	-0.687789121520555	0.491816054234277	   
df.mm.trans1:probe2	0.00588672899782126	0.083024642791642	0.0709033944607814	0.943495127371534	   
df.mm.trans1:probe3	-0.0307047414923084	0.083024642791642	-0.369826842487776	0.711624664607474	   
df.mm.trans1:probe4	0.0609241238060122	0.083024642791642	0.733807719702051	0.463314474161207	   
df.mm.trans1:probe5	0.00297718691185933	0.083024642791642	0.0358590752306017	0.971405069354772	   
df.mm.trans1:probe6	0.0118557331967458	0.083024642791642	0.142797762183679	0.886491472042882	   
df.mm.trans1:probe7	-0.00534143753797779	0.083024642791642	-0.0643355678311392	0.948721612886141	   
df.mm.trans1:probe8	0.107117736626553	0.083024642791642	1.29019208062569	0.197414929751808	   
df.mm.trans1:probe9	0.00783525932972108	0.083024642791642	0.0943726954584361	0.924840423430115	   
df.mm.trans1:probe10	-0.0341984074684584	0.083024642791642	-0.411906710087058	0.680535358708549	   
df.mm.trans1:probe11	-0.0153376094446350	0.083024642791642	-0.184735627024932	0.85349052696101	   
df.mm.trans1:probe12	-0.00398629321448394	0.083024642791642	-0.0480133738664544	0.96171943751337	   
df.mm.trans1:probe13	0.116207281877977	0.083024642791642	1.39967216925714	0.162059634057064	   
df.mm.trans1:probe14	-0.0537382808528697	0.083024642791642	-0.647256995585405	0.517680278972231	   
df.mm.trans1:probe15	0.0144012743366728	0.083024642791642	0.173457829536395	0.86234229826087	   
df.mm.trans1:probe16	0.109544506556023	0.083024642791642	1.31942159427213	0.187464579077481	   
df.mm.trans1:probe17	0.0399025849897078	0.083024642791642	0.480611341982488	0.630944656406865	   
df.mm.trans2:probe2	-0.0756453153973524	0.083024642791642	-0.911118829950178	0.362550032358382	   
df.mm.trans2:probe3	-0.00606363886696658	0.083024642791642	-0.0730342060270448	0.941800002133607	   
df.mm.trans2:probe4	0.0489269467138847	0.083024642791642	0.589306320012378	0.555848140110615	   
df.mm.trans2:probe5	-0.0544811538847189	0.083024642791642	-0.65620461651903	0.511910608535841	   
df.mm.trans2:probe6	0.0794271784678786	0.083024642791642	0.95666992108847	0.339067975907567	   
df.mm.trans3:probe2	-0.0284725571545614	0.083024642791642	-0.342941037711127	0.731746967945023	   
df.mm.trans3:probe3	0.0260608296179534	0.083024642791642	0.313892703921117	0.753697130962788	   
df.mm.trans3:probe4	-0.0654341333097504	0.083024642791642	-0.788129055537925	0.430891108820346	   
df.mm.trans3:probe5	-0.0579961445579726	0.083024642791642	-0.698541331921407	0.485073400782421	   
df.mm.trans3:probe6	-0.0946540005320247	0.083024642791642	-1.14007115657899	0.254651097854252	   
df.mm.trans3:probe7	0.0365344089479361	0.083024642791642	0.440042952543892	0.660043403013582	   
df.mm.trans3:probe8	-0.00843306810080867	0.083024642791642	-0.101573072972710	0.91912497679608