chr2.13024_chr2_165127055_165128458_-_2.R 

fitVsDatCorrelation=0.874274532891348
cont.fitVsDatCorrelation=0.278381830663847

fstatistic=7368.76771421455,56,784
cont.fstatistic=1871.85204574106,56,784

residuals=-0.916048124595718,-0.097590296747726,-0.00271798962926029,0.0828155226508815,1.81707452497661
cont.residuals=-0.679518944652503,-0.22172532063838,-0.0708518368462328,0.117619158061672,2.34094944700429

predictedValues:
Include	Exclude	Both
chr2.13024_chr2_165127055_165128458_-_2.R.tl.Lung	63.5411230511315	53.8590994584649	64.7271681933245
chr2.13024_chr2_165127055_165128458_-_2.R.tl.cerebhem	59.9778793832304	72.6765730159444	56.2612106800399
chr2.13024_chr2_165127055_165128458_-_2.R.tl.cortex	58.272030762254	50.7236089937092	60.6906817425639
chr2.13024_chr2_165127055_165128458_-_2.R.tl.heart	60.5384089097308	52.9343438808441	63.9205741879144
chr2.13024_chr2_165127055_165128458_-_2.R.tl.kidney	65.3128923836786	52.2941726820617	73.51432602415
chr2.13024_chr2_165127055_165128458_-_2.R.tl.liver	66.382059873046	52.7374285087767	67.9999340425285
chr2.13024_chr2_165127055_165128458_-_2.R.tl.stomach	72.0727239987394	52.6401451118206	72.9261978776912
chr2.13024_chr2_165127055_165128458_-_2.R.tl.testicle	65.8103108921149	54.6517281178281	67.9021142290983


diffExp=9.68202359266664,-12.698693632714,7.54842176854488,7.60406502888669,13.0187197016169,13.6446313642693,19.4325788869188,11.1585827742867
diffExpScore=1.34660140738237
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=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,1,1,1,1
diffExp1.2Score=1.25

cont.predictedValues:
Include	Exclude	Both
Lung	64.242245687568	63.5921685328151	60.7418726406976
cerebhem	64.4311181721948	55.1927003644648	67.7696210697337
cortex	58.1375069469095	58.651679633933	72.2238159509603
heart	65.541271092975	60.5181036315049	54.3022880355683
kidney	62.4272604253854	57.9786796980036	70.899975418765
liver	59.7392637794958	60.3638990936029	59.6111092621975
stomach	57.4823774791528	67.4203671601751	60.5707736635836
testicle	62.9610627310594	50.1867566514518	58.3144792400689
cont.diffExp=0.650077154752857,9.23841780773008,-0.514172687023581,5.02316746147012,4.44858072738182,-0.624635314107032,-9.9379896810223,12.7743060796076
cont.diffExpScore=1.95900959431501

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

tran.correlation=-0.306731532946538
cont.tran.correlation=-0.379407944074549

tran.covariance=-0.00231081932050183
cont.tran.covariance=-0.00166378615235698

tran.mean=59.651533063961
cont.tran.mean=60.5541538175433

weightedLogRatios:
wLogRatio
Lung	0.672675393972886
cerebhem	-0.804660826770797
cortex	0.554334418609983
heart	0.541756979186528
kidney	0.904342999688268
liver	0.93890032318028
stomach	1.29467148166179
testicle	0.760625017887627

cont.weightedLogRatios:
wLogRatio
Lung	0.0422854562498938
cerebhem	0.632716536689562
cortex	-0.0358126248677931
heart	0.330337346843787
kidney	0.302880527060251
liver	-0.0425971060019299
stomach	-0.658799271062133
testicle	0.913667755249712

varWeightedLogRatios=0.385598248347445
cont.varWeightedLogRatios=0.228478018633138

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.84058852524819	0.0893135801435048	31.804665322833	3.21037336021825e-143	***
df.mm.trans1	1.32707626164939	0.0778447915605436	17.0477206637166	1.13851489094088e-55	***
df.mm.trans2	1.17346009806366	0.0694680759776455	16.8920771382997	7.90952511828765e-55	***
df.mm.exp2	0.382111938854736	0.0908858155258883	4.20430775301668	2.92067782016087e-05	***
df.mm.exp3	-0.0821541256645333	0.0908858155258883	-0.903926813982674	0.366311871621711	   
df.mm.exp4	-0.0531885587849454	0.0908858155258883	-0.585223981071007	0.558565518517247	   
df.mm.exp5	-0.129283552294530	0.0908858155258883	-1.42248327251577	0.155283742481286	   
df.mm.exp6	-0.0266321230016105	0.0908858155258883	-0.293028376843078	0.769578002438271	   
df.mm.exp7	-0.0161708802653639	0.0908858155258883	-0.177925236977796	0.858827652074814	   
df.mm.exp8	0.00181254376846403	0.0908858155258883	0.0199430874661375	0.984093847411387	   
df.mm.trans1:exp2	-0.439823424952937	0.0848676551951241	-5.18246231667075	2.7878947756698e-07	***
df.mm.trans2:exp2	-0.0824642153181125	0.0661963145274639	-1.24575236411234	0.213227474717715	   
df.mm.trans1:exp3	-0.00441094627851339	0.0848676551951241	-0.0519744096661081	0.958562323398874	   
df.mm.trans2:exp3	0.0221742213005195	0.0661963145274639	0.334976674438873	0.737732285014313	   
df.mm.trans1:exp4	0.00477927700063508	0.084867655195124	0.056314469742881	0.955105640256417	   
df.mm.trans2:exp4	0.0358695424952614	0.0661963145274639	0.54186615601356	0.588064675424716	   
df.mm.trans1:exp5	0.156785698827906	0.0848676551951241	1.84741405270867	0.0650637170003397	.  
df.mm.trans2:exp5	0.0997971289642285	0.0661963145274639	1.50759343139600	0.13206144714785	   
df.mm.trans1:exp6	0.0703716569471579	0.084867655195124	0.829192897875667	0.407247644341659	   
df.mm.trans2:exp6	0.00558617764574554	0.0661963145274639	0.0843880461566772	0.932769454391852	   
df.mm.trans1:exp7	0.142159241660513	0.0848676551951241	1.67506974634407	0.094319324132496	.  
df.mm.trans2:exp7	-0.00672144323816745	0.0661963145274639	-0.101538028002734	0.919149333590805	   
df.mm.trans1:exp8	0.0332766793785478	0.084867655195124	0.392100845746704	0.695090265878337	   
df.mm.trans2:exp8	0.0127969247060241	0.0661963145274639	0.193317782075539	0.846760171633894	   
df.mm.trans1:probe2	-0.00245623398798016	0.0539324536349882	-0.0455427821734907	0.963686272492196	   
df.mm.trans1:probe3	0.0857426280786512	0.0539324536349882	1.58981507978392	0.11227963631913	   
df.mm.trans1:probe4	-0.0618551629276525	0.0539324536349882	-1.14690059062183	0.251772599523856	   
df.mm.trans1:probe5	-0.440196384328782	0.0539324536349882	-8.16199439595325	1.31024846232862e-15	***
df.mm.trans1:probe6	0.262269248166273	0.0539324536349882	4.86292075530805	1.39790934553281e-06	***
df.mm.trans1:probe7	0.156110654087988	0.0539324536349882	2.89455872237032	0.00390233944118234	** 
df.mm.trans1:probe8	0.31075211926252	0.0539324536349882	5.76187616765357	1.19507778847151e-08	***
df.mm.trans1:probe9	0.848619637288637	0.0539324536349882	15.7348605541303	1.09797419456722e-48	***
df.mm.trans1:probe10	0.0792863280344469	0.0539324536349882	1.47010422650251	0.141934714465814	   
df.mm.trans1:probe11	-0.232577022084207	0.0539324536349882	-4.31237606318221	1.82113254517613e-05	***
df.mm.trans1:probe12	-0.0926865786582589	0.0539324536349882	-1.71856780864369	0.0860878830937498	.  
df.mm.trans1:probe13	-0.152205016770173	0.0539324536349882	-2.82214152169467	0.00489114882085038	** 
df.mm.trans1:probe14	-0.0496642360839306	0.0539324536349882	-0.920859941215642	0.357406817000339	   
df.mm.trans1:probe15	-0.203411968357568	0.0539324536349882	-3.77160604882263	0.000174404300037444	***
df.mm.trans1:probe16	-0.270102493232846	0.0539324536349882	-5.00816252605314	6.79241447844171e-07	***
df.mm.trans1:probe17	-0.145205133546909	0.0539324536349882	-2.69235170588843	0.00724639408521076	** 
df.mm.trans1:probe18	-0.146838767820698	0.0539324536349882	-2.72264208141715	0.00662010008221225	** 
df.mm.trans1:probe19	-0.153752952350525	0.0539324536349882	-2.85084289676706	0.00447485940427162	** 
df.mm.trans1:probe20	-0.00742229813519857	0.0539324536349882	-0.137622111269631	0.89057441557423	   
df.mm.trans1:probe21	-0.177925522207557	0.0539324536349882	-3.29904371515797	0.00101393069445148	** 
df.mm.trans1:probe22	-0.0698278659696583	0.0539324536349882	-1.29472815092466	0.195795201897452	   
df.mm.trans2:probe2	-0.0138639052732966	0.0539324536349882	-0.257060532923771	0.797199491848446	   
df.mm.trans2:probe3	-0.116129590872333	0.0539324536349882	-2.15324137963926	0.0316040602158221	*  
df.mm.trans2:probe4	-0.121352382933951	0.0539324536349882	-2.25008088367826	0.0247203407739367	*  
df.mm.trans2:probe5	-0.113929452419492	0.0539324536349882	-2.11244704701478	0.0349637397301194	*  
df.mm.trans2:probe6	0.00547100204915459	0.0539324536349882	0.101441742038699	0.91922573916799	   
df.mm.trans3:probe2	-0.892827428395332	0.0539324536349882	-16.5545486663362	5.14501923780893e-53	***
df.mm.trans3:probe3	-1.47617145446530	0.0539324536349882	-27.3707453485418	2.70043480371927e-116	***
df.mm.trans3:probe4	-1.49940539574698	0.0539324536349882	-27.8015423866096	6.44210997395096e-119	***
df.mm.trans3:probe5	-1.49422441294098	0.0539324536349882	-27.7054780977296	2.47652134415412e-118	***
df.mm.trans3:probe6	-1.40532158582005	0.0539324536349882	-26.0570675187742	2.63161621021167e-108	***
df.mm.trans3:probe7	-1.42865675200935	0.0539324536349882	-26.4897414398836	6.18130016473284e-111	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09793090899125	0.176714949933490	23.1894976091925	5.37190582403317e-91	***
df.mm.trans1	0.0862953909177493	0.154022920378977	0.560276293329703	0.575451052516828	   
df.mm.trans2	-0.0114460197841184	0.137448835313073	-0.0832747673565029	0.933654320385589	   
df.mm.exp2	-0.248204668827408	0.179825759022491	-1.38025091720239	0.167902824700235	   
df.mm.exp3	-0.353860689046085	0.179825759022491	-1.96779755564289	0.0494434220369523	*  
df.mm.exp4	0.0825381643921264	0.179825759022490	0.458989662219657	0.646368812207247	   
df.mm.exp5	-0.275710753225165	0.179825759022490	-1.53321056295768	0.125627396043144	   
df.mm.exp6	-0.105979299341198	0.179825759022490	-0.589344373783197	0.555800065563495	   
df.mm.exp7	-0.0499049798123986	0.179825759022491	-0.277518527288168	0.781455142793	   
df.mm.exp8	-0.216100820096633	0.179825759022490	-1.20172338641210	0.229833590058698	   
df.mm.trans1:exp2	0.251140359861010	0.167918287618546	1.49561053428270	0.135157585322051	   
df.mm.trans2:exp2	0.106545047104128	0.130975361067228	0.813473971256624	0.416193341282859	   
df.mm.trans1:exp3	0.254010676283233	0.167918287618546	1.51270406508825	0.130757829935837	   
df.mm.trans2:exp3	0.272986575507538	0.130975361067228	2.08425900324426	0.0374598053227767	*  
df.mm.trans1:exp4	-0.0625191539577478	0.167918287618546	-0.372318910848891	0.709756039204678	   
df.mm.trans2:exp4	-0.132085937025867	0.130975361067228	-1.00847927388471	0.313535574891874	   
df.mm.trans1:exp5	0.247051772189817	0.167918287618546	1.47126186011994	0.141621565106423	   
df.mm.trans2:exp5	0.183295778291876	0.130975361067228	1.39946763115081	0.162068330112345	   
df.mm.trans1:exp6	0.0333077615045088	0.167918287618546	0.19835696264467	0.842817210070662	   
df.mm.trans2:exp6	0.0538802019763622	0.130975361067228	0.41137662486539	0.680908819714057	   
df.mm.trans1:exp7	-0.0612776247859816	0.167918287618546	-0.364925260107368	0.715265614485446	   
df.mm.trans2:exp7	0.108361808980153	0.130975361067228	0.827344991433407	0.408293296754414	   
df.mm.trans1:exp8	0.195956276723333	0.167918287618546	1.16697400564541	0.243575686807134	   
df.mm.trans2:exp8	-0.0206383262218772	0.130975361067228	-0.157574112059777	0.874832977377954	   
df.mm.trans1:probe2	0.174396268956853	0.106710209450610	1.63429787885077	0.102597766228124	   
df.mm.trans1:probe3	0.0853099192629093	0.106710209450610	0.79945414503562	0.424269266704156	   
df.mm.trans1:probe4	-0.0249650020506051	0.106710209450610	-0.233951392084559	0.81508379157912	   
df.mm.trans1:probe5	0.0888055635089709	0.106710209450610	0.832212437461984	0.405542461378732	   
df.mm.trans1:probe6	-0.0154568072645665	0.106710209450610	-0.144848439002648	0.884867764638877	   
df.mm.trans1:probe7	-0.0577490637670977	0.106710209450610	-0.541176557186185	0.588539629214949	   
df.mm.trans1:probe8	-0.104501737360880	0.106710209450610	-0.97930402253824	0.327731872818766	   
df.mm.trans1:probe9	-0.106787077326065	0.106710209450610	-1.00072034227887	0.317270789749145	   
df.mm.trans1:probe10	-0.166390589534205	0.106710209450610	-1.55927525951692	0.119334798524171	   
df.mm.trans1:probe11	0.00909804980034671	0.106710209450610	0.085259412826452	0.93207692512632	   
df.mm.trans1:probe12	-0.128297548501083	0.106710209450610	-1.2022987225085	0.229610812645596	   
df.mm.trans1:probe13	-0.0214105356943208	0.106710209450610	-0.200641867395365	0.841030655374456	   
df.mm.trans1:probe14	-0.0362163703856431	0.106710209450610	-0.339389928780953	0.734406895738342	   
df.mm.trans1:probe15	0.0517953666852244	0.106710209450610	0.485383422559934	0.62753999630351	   
df.mm.trans1:probe16	-0.0631315774712444	0.106710209450610	-0.591617032674502	0.554277612635833	   
df.mm.trans1:probe17	-0.160694125978595	0.106710209450610	-1.50589270516774	0.132497497536453	   
df.mm.trans1:probe18	-0.00946689016940434	0.106710209450610	-0.0887158803093347	0.929330368060606	   
df.mm.trans1:probe19	-0.0690335686954762	0.106710209450610	-0.646925622683066	0.517869435354066	   
df.mm.trans1:probe20	-0.0718812273927867	0.106710209450610	-0.673611529420308	0.500756853342429	   
df.mm.trans1:probe21	-0.0363318233660809	0.106710209450610	-0.340471858814005	0.7335924192335	   
df.mm.trans1:probe22	0.0375158567013886	0.106710209450610	0.351567641882969	0.725257000371014	   
df.mm.trans2:probe2	0.115920257349917	0.106710209450610	1.08630896656210	0.277676203814658	   
df.mm.trans2:probe3	0.177673632614362	0.106710209450610	1.66501062577895	0.096310168698988	.  
df.mm.trans2:probe4	0.146313884760395	0.106710209450610	1.37113295450999	0.170725852137835	   
df.mm.trans2:probe5	0.207030119665828	0.106710209450610	1.94011539038025	0.0527239318459538	.  
df.mm.trans2:probe6	0.211132790256832	0.106710209450610	1.97856223264704	0.0482149203219219	*  
df.mm.trans3:probe2	-0.0768208143201811	0.106710209450610	-0.71990126076678	0.471800345853804	   
df.mm.trans3:probe3	-0.16265438029947	0.106710209450610	-1.52426259058889	0.127846388748147	   
df.mm.trans3:probe4	0.00321479756065451	0.106710209450610	0.0301264291130686	0.975973890152304	   
df.mm.trans3:probe5	-0.143235104840122	0.106710209450610	-1.34228117044806	0.179893401016446	   
df.mm.trans3:probe6	-0.121088695948791	0.106710209450610	-1.13474330687015	0.256829698302781	   
df.mm.trans3:probe7	0.0375552640239806	0.106710209450610	0.351936934781885	0.724980132331717