chr13.6190_chr13_57669094_57671338_+_2.R 

fitVsDatCorrelation=0.927404700391384
cont.fitVsDatCorrelation=0.252419301706158

fstatistic=10831.1807969666,52,692
cont.fstatistic=1607.31776200648,52,692

residuals=-0.668307406982637,-0.0943159013290568,-0.000280754991608616,0.0902982162618411,0.592678099484798
cont.residuals=-0.875285489162818,-0.317535623542317,-0.049391796171292,0.270871341673335,1.50917812995239

predictedValues:
Include	Exclude	Both
chr13.6190_chr13_57669094_57671338_+_2.R.tl.Lung	73.6389254432828	82.5540909108796	158.957123834260
chr13.6190_chr13_57669094_57671338_+_2.R.tl.cerebhem	65.4653455065974	62.1195405795781	134.618756955801
chr13.6190_chr13_57669094_57671338_+_2.R.tl.cortex	65.80773482396	92.367805648036	151.675904151707
chr13.6190_chr13_57669094_57671338_+_2.R.tl.heart	67.7635065403885	75.9584606014376	149.962455966267
chr13.6190_chr13_57669094_57671338_+_2.R.tl.kidney	75.3467770850822	89.7809075242211	140.428758458131
chr13.6190_chr13_57669094_57671338_+_2.R.tl.liver	73.789824184587	89.9866638288717	171.747378914292
chr13.6190_chr13_57669094_57671338_+_2.R.tl.stomach	69.9748788207421	67.7615249203614	187.39110210529
chr13.6190_chr13_57669094_57671338_+_2.R.tl.testicle	70.4464842237434	83.1967013995569	171.076613863900


diffExp=-8.91516546759678,3.34580492701932,-26.5600708240760,-8.19495406104907,-14.4341304391389,-16.1968396442847,2.21335390038068,-12.7502171758134
diffExpScore=1.12265784341703
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,-1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,-1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,-1,0,0,-1,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	81.4412715164346	78.487491023738	93.6817616151919
cerebhem	85.3545259442297	76.9034840854594	93.3835062935702
cortex	77.6393217670885	84.6397667837615	71.1361690549867
heart	82.5419724197333	81.3592224355966	89.0341069297207
kidney	81.5546224286342	77.0572509832627	97.3973172512946
liver	71.7507149602167	82.8326362586226	71.2895142577312
stomach	79.0898225405746	72.3492742606839	86.7337012374416
testicle	73.6813584836034	84.4686716253355	72.8491508083397
cont.diffExp=2.95378049269669,8.4510418587703,-7.00044501667303,1.18274998413665,4.49737144537147,-11.0819212984060,6.74054827989069,-10.7873131417321
cont.diffExpScore=8.71832192910284

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=0.477559629499758
cont.tran.correlation=-0.556102646288051

tran.covariance=0.00377223865243667
cont.tran.covariance=-0.00175919867729400

tran.mean=75.3724482525829
cont.tran.mean=79.446962969811

weightedLogRatios:
wLogRatio
Lung	-0.497839367804129
cerebhem	0.217987899134184
cortex	-1.47695053667124
heart	-0.487829746846669
kidney	-0.772899807382352
liver	-0.873226365381703
stomach	0.136025984062049
testicle	-0.721650324333147

cont.weightedLogRatios:
wLogRatio
Lung	0.161862033319789
cerebhem	0.458199659700156
cortex	-0.379441828411355
heart	0.063591897575592
kidney	0.248050501929959
liver	-0.624049113600945
stomach	0.385358788347052
testicle	-0.596812629286624

varWeightedLogRatios=0.30130777047653
cont.varWeightedLogRatios=0.189970167628882

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.98250395449403	0.0851677269006994	35.0191799526533	2.30547161145268e-155	***
df.mm.trans1	1.22703921585466	0.0764930736017881	16.0411806988231	1.80116760907910e-49	***
df.mm.trans2	1.25520494661091	0.0703457698888957	17.8433607108627	7.16662191498967e-59	***
df.mm.exp2	-0.235858217558028	0.0963562835675655	-2.44777204791883	0.0146216610464825	*  
df.mm.exp3	0.0467768555268729	0.0963562835675656	0.485457240513772	0.62750569554059	   
df.mm.exp4	-0.108167536661437	0.0963562835675656	-1.12257896067141	0.262005689109544	   
df.mm.exp5	0.230780204538782	0.0963562835675656	2.39507166522210	0.0168820019346947	*  
df.mm.exp6	0.0108646624588893	0.0963562835675656	0.112755100722321	0.910257416369515	   
df.mm.exp7	-0.413060015148353	0.0963562835675656	-4.28679894922176	2.06997293527393e-05	***
df.mm.exp8	-0.110043443216997	0.0963562835675656	-1.14204740098589	0.253829324491144	   
df.mm.trans1:exp2	0.118205379720399	0.0922541178902438	1.28130193452209	0.200516824198708	   
df.mm.trans2:exp2	-0.0485349057837744	0.0802969029729713	-0.604443060526402	0.545747235702307	   
df.mm.trans1:exp3	-0.159213237821476	0.0922541178902439	-1.72581171943885	0.0848276827188298	.  
df.mm.trans2:exp3	0.0655479127821852	0.0802969029729713	0.816319314385628	0.41459844301927	   
df.mm.trans1:exp4	0.025017570848232	0.0922541178902438	0.271181074843681	0.78633264010955	   
df.mm.trans2:exp4	0.0249004305872757	0.0802969029729713	0.310104495507846	0.756574860300997	   
df.mm.trans1:exp5	-0.207852817207982	0.092254117890244	-2.2530464976671	0.0245685930701126	*  
df.mm.trans2:exp5	-0.146861588826800	0.0802969029729713	-1.82898198298177	0.0678325168512914	.  
df.mm.trans1:exp6	-0.00881758827043853	0.0922541178902439	-0.0955793461808279	0.92388234043523	   
df.mm.trans2:exp6	0.0753430913101	0.0802969029729713	0.938306317187117	0.348414387111829	   
df.mm.trans1:exp7	0.362022554564169	0.0922541178902438	3.92418856570579	9.57133582936018e-05	***
df.mm.trans2:exp7	0.21560084406443	0.0802969029729713	2.68504557563077	0.00742569585916174	** 
df.mm.trans1:exp8	0.0657230115228844	0.0922541178902439	0.71241276840429	0.47644933890796	   
df.mm.trans2:exp8	0.117797417715892	0.0802969029729713	1.46702317716467	0.142824056703541	   
df.mm.trans1:probe2	-0.173489648810261	0.0461270589451220	-3.76112530861038	0.000183461460983562	***
df.mm.trans1:probe3	0.154774572201268	0.0461270589451219	3.35539650133353	0.000835788722314003	***
df.mm.trans1:probe4	0.241249853490809	0.0461270589451219	5.23011566329922	2.24706076302517e-07	***
df.mm.trans1:probe5	0.0596226028039332	0.0461270589451219	1.29257325672697	0.196590112082043	   
df.mm.trans1:probe6	-0.156678770268091	0.0461270589451219	-3.39667808551363	0.000721105120437796	***
df.mm.trans1:probe7	0.177656193282914	0.0461270589451220	3.85145286401793	0.000128316332050404	***
df.mm.trans1:probe8	0.558587317651089	0.0461270589451219	12.1097535898755	9.46109530506538e-31	***
df.mm.trans1:probe9	0.611448777051676	0.0461270589451219	13.2557503347249	6.67311317739373e-36	***
df.mm.trans1:probe10	-0.098488531725046	0.0461270589451219	-2.13515741036556	0.0330987201215417	*  
df.mm.trans1:probe11	0.191992941647596	0.0461270589451219	4.16226280275127	3.54849117046962e-05	***
df.mm.trans1:probe12	0.21328875243517	0.0461270589451219	4.62393998908369	4.49325673604691e-06	***
df.mm.trans1:probe13	0.569121126946613	0.0461270589451219	12.3381186653089	9.38290898857626e-32	***
df.mm.trans1:probe14	0.612895157269103	0.0461270589451219	13.2871067717167	4.78035157845281e-36	***
df.mm.trans1:probe15	0.51466750183731	0.0461270589451219	11.1576049634905	1.06668386493074e-26	***
df.mm.trans1:probe16	0.0637209722214267	0.0461270589451220	1.38142282813297	0.167594853581975	   
df.mm.trans1:probe17	-0.148372712207286	0.0461270589451219	-3.21660898397636	0.00135755860590226	** 
df.mm.trans1:probe18	-0.262708496316914	0.0461270589451219	-5.69532292595247	1.82100430249002e-08	***
df.mm.trans1:probe19	-0.305010076598131	0.0461270589451220	-6.61238942116396	7.56381647366055e-11	***
df.mm.trans1:probe20	-0.129889517032984	0.0461270589451219	-2.8159071920782	0.00500243321297811	** 
df.mm.trans1:probe21	-0.283056295020936	0.0461270589451219	-6.13644792219882	1.42028261707397e-09	***
df.mm.trans1:probe22	-0.170566873739740	0.0461270589451219	-3.69776173986437	0.000234737175795122	***
df.mm.trans2:probe2	0.278431184761642	0.0461270589451219	6.03617900488509	2.57272265103562e-09	***
df.mm.trans2:probe3	0.643137279810916	0.0461270589451220	13.9427332788779	4.03501625371903e-39	***
df.mm.trans2:probe4	0.0471452266439726	0.0461270589451219	1.02207311114420	0.307103466783206	   
df.mm.trans2:probe5	0.504502917902323	0.0461270589451220	10.9372444166132	8.59010253957889e-26	***
df.mm.trans2:probe6	0.108486814058103	0.0461270589451220	2.35191266339289	0.0189564826984418	*  
df.mm.trans3:probe2	-0.482418992210683	0.0461270589451219	-10.4584814909753	7.22647502847746e-24	***
df.mm.trans3:probe3	-0.705921845362196	0.0461270589451220	-15.3038555135727	9.41917489706134e-46	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14316629129007	0.220312056466111	18.8058990404249	4.822239927525e-64	***
df.mm.trans1	0.242813277847921	0.197872444925909	1.22712021847629	0.220194718421320	   
df.mm.trans2	0.203536550538324	0.181970586651728	1.11851346024328	0.263735885565083	   
df.mm.exp2	0.0297320166176352	0.249254638566945	0.119283704361834	0.905085212940248	   
df.mm.exp3	0.302964417121221	0.249254638566945	1.21548156079691	0.224596988726474	   
df.mm.exp4	0.100243690968278	0.249254638566945	0.402173823302207	0.687680295400718	   
df.mm.exp5	-0.0558948961649437	0.249254638566945	-0.224248168404422	0.822630397377007	   
df.mm.exp6	0.200352805178150	0.249254638566945	0.80380772983825	0.421784051664521	   
df.mm.exp7	-0.0336708003090103	0.249254638566945	-0.135085952673121	0.89258319870314	   
df.mm.exp8	0.224821765195123	0.249254638566945	0.901976254033642	0.367383311130207	   
df.mm.trans1:exp2	0.0171992936656234	0.238643147698002	0.0720711817269055	0.942566084922094	   
df.mm.trans2:exp2	-0.0501200965040938	0.207712198805788	-0.241295873772712	0.80939728730561	   
df.mm.trans1:exp3	-0.350772560073668	0.238643147698002	-1.46986227535669	0.1420535501138	   
df.mm.trans2:exp3	-0.227499466480091	0.207712198805788	-1.09526290602125	0.273782522600141	   
df.mm.trans1:exp4	-0.0868189360007325	0.238643147698002	-0.363802341857308	0.716116734138626	   
df.mm.trans2:exp4	-0.0643087584329694	0.207712198805788	-0.309605111316059	0.75695447320243	   
df.mm.trans1:exp5	0.0572857401638771	0.238643147698002	0.24004770602662	0.810364363390004	   
df.mm.trans2:exp5	0.0375042989335436	0.207712198805788	0.180558961626565	0.856766634954174	   
df.mm.trans1:exp6	-0.327037151588897	0.238643147698002	-1.37040243871890	0.171005690460498	   
df.mm.trans2:exp6	-0.146469925808570	0.207712198805788	-0.70515803429302	0.480949172556048	   
df.mm.trans1:exp7	0.00437283546901268	0.238643147698002	0.0183237420022066	0.985385869070969	   
df.mm.trans2:exp7	-0.0477630396896856	0.207712198805788	-0.229948168496085	0.818200000403122	   
df.mm.trans1:exp8	-0.324954101404429	0.238643147698002	-1.36167371466141	0.173744021383053	   
df.mm.trans2:exp8	-0.151380311647518	0.207712198805788	-0.728798368694077	0.466371578560963	   
df.mm.trans1:probe2	0.100326897120004	0.119321573849001	0.840811044337764	0.400744230526003	   
df.mm.trans1:probe3	-0.0737217404171182	0.119321573849001	-0.617840831620371	0.536883534683582	   
df.mm.trans1:probe4	0.161503705348516	0.119321573849001	1.35351638550206	0.176332653163124	   
df.mm.trans1:probe5	0.0750941509330208	0.119321573849001	0.6293426118235	0.529332487645335	   
df.mm.trans1:probe6	-0.0252305314809455	0.119321573849001	-0.211449871696079	0.832598501285218	   
df.mm.trans1:probe7	0.0833367254872465	0.119321573849001	0.698421272859738	0.485148411175352	   
df.mm.trans1:probe8	0.140323388600232	0.119321573849001	1.17601020564654	0.239995211481495	   
df.mm.trans1:probe9	-0.100488563148803	0.119321573849001	-0.842165921109699	0.399986042542277	   
df.mm.trans1:probe10	0.04941049588953	0.119321573849001	0.414095241084047	0.678932674059643	   
df.mm.trans1:probe11	0.00814903226595507	0.119321573849001	0.0682947098591535	0.945570763548336	   
df.mm.trans1:probe12	-0.00971521617380906	0.119321573849001	-0.0814204494662757	0.935131129602314	   
df.mm.trans1:probe13	-0.125703248507685	0.119321573849001	-1.05348299098669	0.29248738304667	   
df.mm.trans1:probe14	-0.0517041025698008	0.119321573849001	-0.433317302998629	0.664919304279665	   
df.mm.trans1:probe15	0.0393563425467404	0.119321573849001	0.329834256096429	0.741625020148753	   
df.mm.trans1:probe16	-0.0891349759362571	0.119321573849001	-0.747014752328491	0.455308390291144	   
df.mm.trans1:probe17	-0.103281822069855	0.119321573849001	-0.865575425618805	0.387023016188855	   
df.mm.trans1:probe18	0.0772666229750998	0.119321573849001	0.647549478964124	0.517491145980306	   
df.mm.trans1:probe19	0.0591839544840142	0.119321573849001	0.496003803628256	0.620049221549576	   
df.mm.trans1:probe20	0.0799252278579241	0.119321573849001	0.669830486472362	0.503189299368639	   
df.mm.trans1:probe21	0.0362535819435158	0.119321573849001	0.303830906466202	0.761348051223159	   
df.mm.trans1:probe22	0.0164149864816374	0.119321573849001	0.137569309154522	0.890620829984905	   
df.mm.trans2:probe2	0.0269866981575236	0.119321573849001	0.226167802577552	0.821137696976738	   
df.mm.trans2:probe3	0.00416716541610193	0.119321573849001	0.0349238220858149	0.972150556129225	   
df.mm.trans2:probe4	0.171699149274087	0.119321573849001	1.43896148647326	0.150613580189391	   
df.mm.trans2:probe5	-0.0836107721437133	0.119321573849001	-0.700717979545938	0.483714569030845	   
df.mm.trans2:probe6	0.0268855415130026	0.119321573849001	0.225320037657446	0.821796836141298	   
df.mm.trans3:probe2	-0.178222512403898	0.119321573849001	-1.49363192803202	0.135727652561648	   
df.mm.trans3:probe3	0.029384724521392	0.119321573849001	0.246264976009936	0.805550149347114