chr9.23963_chr9_111482592_111483371_-_0.R 

fitVsDatCorrelation=0.876678959053406
cont.fitVsDatCorrelation=0.261755046402499

fstatistic=10733.9433790103,40,416
cont.fstatistic=2659.10973482019,40,416

residuals=-0.399080191051281,-0.0897298920671884,0.00132459362111349,0.0846829782756781,0.527414774901183
cont.residuals=-0.655094078570065,-0.202346907270276,-0.0104483459357228,0.197391070620733,0.964446634491352

predictedValues:
Include	Exclude	Both
chr9.23963_chr9_111482592_111483371_-_0.R.tl.Lung	76.4008277088212	92.448591017876	74.447422597833
chr9.23963_chr9_111482592_111483371_-_0.R.tl.cerebhem	60.7614206668451	102.122856928384	69.9541753536653
chr9.23963_chr9_111482592_111483371_-_0.R.tl.cortex	83.6626513359499	86.2521927692168	77.056868223818
chr9.23963_chr9_111482592_111483371_-_0.R.tl.heart	70.8957876506763	91.0965661233796	63.9100961509701
chr9.23963_chr9_111482592_111483371_-_0.R.tl.kidney	69.5902548034975	95.497874751683	70.4148597740828
chr9.23963_chr9_111482592_111483371_-_0.R.tl.liver	62.483895938056	102.074537598512	63.5487080214247
chr9.23963_chr9_111482592_111483371_-_0.R.tl.stomach	67.7770987355875	99.495525898167	70.8167832276586
chr9.23963_chr9_111482592_111483371_-_0.R.tl.testicle	64.3136749500749	101.091310927837	69.8753749521719


diffExp=-16.0477633090548,-41.3614362615388,-2.5895414332669,-20.2007784727032,-25.9076199481855,-39.5906416604565,-31.7184271625795,-36.7776359777622
diffExpScore=0.995353026925102
diffExp1.5=0,-1,0,0,0,-1,0,-1
diffExp1.5Score=0.75
diffExp1.4=0,-1,0,0,0,-1,-1,-1
diffExp1.4Score=0.8
diffExp1.3=0,-1,0,0,-1,-1,-1,-1
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	83.1763848402153	71.5304534901201	82.0659859092155
cerebhem	80.4179187738975	75.699960110682	86.6793230500992
cortex	68.6202089561507	82.0469822439177	81.5649560916325
heart	85.587688069733	81.4406458926894	78.4766933362344
kidney	80.1294094959801	78.6338282040583	78.0988645095063
liver	76.8979132035371	71.8052735165024	79.2548685093567
stomach	86.3964216249841	90.1390636767136	78.1066977321091
testicle	85.1395677061341	83.763541751949	81.8975505493489
cont.diffExp=11.6459313500953,4.71795866321547,-13.4267732877669,4.14704217704352,1.49558129192174,5.0926396870347,-3.74264205172955,1.37602595418505
cont.diffExpScore=3.70920450483068

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=-0.947333225935765
cont.tran.correlation=0.266499303012135

tran.covariance=-0.00631082741971953
cont.tran.covariance=0.00137072106109528

tran.mean=82.8728167377853
cont.tran.mean=80.089078847329

weightedLogRatios:
wLogRatio
Lung	-0.844872465150795
cerebhem	-2.26721489134375
cortex	-0.135405732431989
heart	-1.09975186462004
kidney	-1.39278311582597
liver	-2.14982990876307
stomach	-1.69224439028078
testicle	-1.98533978979503

cont.weightedLogRatios:
wLogRatio
Lung	0.655482586528096
cerebhem	0.263421997284083
cortex	-0.771637194051808
heart	0.219761785295568
kidney	0.0824145300754967
liver	0.295202648354142
stomach	-0.189991381713755
testicle	0.0722826897167695

varWeightedLogRatios=0.53186079475542
cont.varWeightedLogRatios=0.1755563840303

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.41636558417626	0.073447860545797	60.1292611024732	2.81436384143871e-207	***
df.mm.trans1	-0.104121268290537	0.0595740815837662	-1.74776119954336	0.0812431721829789	.  
df.mm.trans2	0.0355713776895330	0.0595740815837662	0.59709485641867	0.550768885683309	   
df.mm.exp2	-0.0672618705816968	0.080558861635302	-0.834940678360104	0.404230346136211	   
df.mm.exp3	-0.0130286692558693	0.080558861635302	-0.161728567055122	0.87159812626627	   
df.mm.exp4	0.0631006732186154	0.080558861635302	0.783286555168547	0.433904556331056	   
df.mm.exp5	-0.0052288914954945	0.080558861635302	-0.064907713308639	0.948278658257138	   
df.mm.exp6	0.0562523904663365	0.080558861635302	0.698276878849115	0.485394196412615	   
df.mm.exp7	0.00368790664160816	0.080558861635302	0.0457790312169961	0.963508339234858	   
df.mm.exp8	-0.0194699456210134	0.080558861635302	-0.241685957643688	0.80914260039753	   
df.mm.trans1:exp2	-0.161776600349315	0.0649486306413325	-2.49083927947146	0.0131331796917749	*  
df.mm.trans2:exp2	0.166785721512571	0.0649486306413325	2.56796363319214	0.0105780148163073	*  
df.mm.trans1:exp3	0.103827796568409	0.0649486306413325	1.59861409768252	0.110665589531481	   
df.mm.trans2:exp3	-0.0563485689843446	0.0649486306413325	-0.86758671319061	0.386120802290543	   
df.mm.trans1:exp4	-0.137883183987764	0.0649486306413325	-2.1229575223718	0.0343473479267817	*  
df.mm.trans2:exp4	-0.0778332803893466	0.0649486306413325	-1.19838216172974	0.231450534197316	   
df.mm.trans1:exp5	-0.0881400981574656	0.0649486306413325	-1.35707400274848	0.175493545629979	   
df.mm.trans2:exp5	0.0376801675857546	0.0649486306413325	0.580153379889359	0.562125284908344	   
df.mm.trans1:exp6	-0.257337061915295	0.0649486306413325	-3.96216301058592	8.73889886859958e-05	***
df.mm.trans2:exp6	0.0427981994722976	0.0649486306413325	0.658954608429594	0.510289485481801	   
df.mm.trans1:exp7	-0.123457075514807	0.0649486306413325	-1.90084185448923	0.0580136209368625	.  
df.mm.trans2:exp7	0.0697720534246541	0.0649486306413325	1.0742651959817	0.283326545039422	   
df.mm.trans1:exp8	-0.152751301561564	0.0649486306413325	-2.35187870865372	0.0191442649177679	*  
df.mm.trans2:exp8	0.108841405391131	0.0649486306413325	1.67580754692410	0.0945272704387958	.  
df.mm.trans1:probe2	-0.0243344966410233	0.0412741344469346	-0.589582239993663	0.555790824755772	   
df.mm.trans1:probe3	0.249752871550649	0.0412741344469346	6.05107472021617	3.21188762475527e-09	***
df.mm.trans1:probe4	-0.061817763212508	0.0412741344469346	-1.497736149791	0.134960184668510	   
df.mm.trans1:probe5	-0.00802858798518768	0.0412741344469346	-0.194518627531969	0.845864768514065	   
df.mm.trans1:probe6	0.153167759759107	0.0412741344469346	3.71098659757558	0.000234510830709460	***
df.mm.trans2:probe2	0.142563457962614	0.0412741344469346	3.45406293488494	0.000608897559315306	***
df.mm.trans2:probe3	0.286014911034696	0.0412741344469346	6.92964043624997	1.6140139161383e-11	***
df.mm.trans2:probe4	0.149054201431795	0.0412741344469346	3.61132228280719	0.000341834076820721	***
df.mm.trans2:probe5	0.193970112876512	0.0412741344469346	4.69955616212609	3.54980078522723e-06	***
df.mm.trans2:probe6	0.199702136555418	0.0412741344469346	4.8384330581704	1.84676658977976e-06	***
df.mm.trans3:probe2	0.154253972986013	0.0412741344469346	3.73730364192942	0.000212000017460709	***
df.mm.trans3:probe3	-0.289430575503869	0.0412741344469346	-7.01239600496007	9.53123581755582e-12	***
df.mm.trans3:probe4	0.317202949193596	0.0412741344469346	7.68527198556806	1.10891848389994e-13	***
df.mm.trans3:probe5	-0.00870724048737907	0.0412741344469346	-0.210961189230359	0.833020856308175	   
df.mm.trans3:probe6	-0.270983318924584	0.0412741344469346	-6.5654512821579	1.54881199313212e-10	***
df.mm.trans3:probe7	-0.166393667231689	0.0412741344469346	-4.0314271749446	6.59417276000085e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.32036187532709	0.147351083991668	29.3201906514063	2.85048089548196e-103	***
df.mm.trans1	0.105850854926252	0.119517511251434	0.88565143147576	0.376317036903549	   
df.mm.trans2	-0.0442281547374893	0.119517511251434	-0.370055854363004	0.711529199619009	   
df.mm.exp2	-0.0317638352703598	0.16161717303794	-0.196537500769817	0.84428546111964	   
df.mm.exp3	-0.0490837137063036	0.16161717303794	-0.303703577928449	0.761505571058955	   
df.mm.exp4	0.203051121415174	0.16161717303794	1.25637095117056	0.209686570523093	   
df.mm.exp5	0.106906296180013	0.16161717303794	0.661478567967009	0.508671658381573	   
df.mm.exp6	-0.0397953064252004	0.16161717303794	-0.246231917544173	0.805624157975114	   
df.mm.exp7	0.318660953196743	0.16161717303794	1.97170230865217	0.0493057618730886	*  
df.mm.exp8	0.183257526974782	0.16161717303794	1.13389885202213	0.257489981299235	   
df.mm.trans1:exp2	-0.00196261453749412	0.130299930558324	-0.0150622838330342	0.987989711663043	   
df.mm.trans2:exp2	0.0884181868148753	0.130299930558324	0.678574320308622	0.497784923643057	   
df.mm.trans1:exp3	-0.143292675230773	0.130299930558324	-1.09971413351317	0.272092681754911	   
df.mm.trans2:exp3	0.186252469129812	0.130299930558324	1.42941341819398	0.153635954841587	   
df.mm.trans1:exp4	-0.174473151056188	0.130299930558324	-1.33901184988039	0.181298008663983	   
df.mm.trans2:exp4	-0.073299919701313	0.130299930558324	-0.562547649774096	0.57404596270992	   
df.mm.trans1:exp5	-0.144226821225264	0.130299930558324	-1.10688333145892	0.268984009485331	   
df.mm.trans2:exp5	-0.0122275870173245	0.130299930558324	-0.0938418536750588	0.925279964032866	   
df.mm.trans1:exp6	-0.0386894254093502	0.130299930558324	-0.296925909657583	0.766671273527193	   
df.mm.trans2:exp6	0.0436299451280281	0.130299930558324	0.33484242808939	0.737912720816803	   
df.mm.trans1:exp7	-0.280678166104896	0.130299930558324	-2.15409298302934	0.0318065876986154	*  
df.mm.trans2:exp7	-0.0874306053813085	0.130299930558324	-0.670995026679415	0.50259607654997	   
df.mm.trans1:exp8	-0.159929115517981	0.130299930558324	-1.22739217766808	0.220369330083156	   
df.mm.trans2:exp8	-0.0253829587391312	0.130299930558324	-0.194804084932106	0.845641425384066	   
df.mm.trans1:probe2	-0.00805369140298465	0.0828041607526672	-0.0972619169106817	0.922565240175425	   
df.mm.trans1:probe3	0.00543921200502393	0.0828041607526672	0.0656876653972818	0.947658048082789	   
df.mm.trans1:probe4	0.0184166205034345	0.0828041607526672	0.222411776606785	0.824102498069699	   
df.mm.trans1:probe5	0.00998140708312592	0.0828041607526672	0.120542337394615	0.904111741191757	   
df.mm.trans1:probe6	-0.0940239122032813	0.0828041607526672	-1.13549743574030	0.256820729625522	   
df.mm.trans2:probe2	-0.0228665446509427	0.0828041607526672	-0.276152121380038	0.782568366201928	   
df.mm.trans2:probe3	0.0288953953006000	0.0828041607526672	0.348960668617963	0.727295393127515	   
df.mm.trans2:probe4	-0.0506971732612004	0.0828041607526672	-0.61225393507255	0.540704261774715	   
df.mm.trans2:probe5	-0.0193490110240887	0.0828041607526672	-0.233671965855478	0.81535459802957	   
df.mm.trans2:probe6	-0.0141183685585761	0.0828041607526672	-0.170503129676624	0.864697366343101	   
df.mm.trans3:probe2	-0.0473938858560479	0.0828041607526672	-0.572361164285108	0.567386501446824	   
df.mm.trans3:probe3	0.0555767046797494	0.0828041607526672	0.671182512745402	0.502476768006318	   
df.mm.trans3:probe4	0.0842320838470014	0.0828041607526672	1.01724458144802	0.309628335536457	   
df.mm.trans3:probe5	0.00697179430472006	0.0828041607526672	0.0841961833964423	0.932940954589763	   
df.mm.trans3:probe6	-0.024137691143377	0.0828041607526672	-0.291503360748687	0.770811671430531	   
df.mm.trans3:probe7	0.103524374659223	0.0828041607526672	1.25023155501142	0.211917788500293