chr5.18705_chr5_119527008_119531207_+_2.R 

fitVsDatCorrelation=0.864504850720119
cont.fitVsDatCorrelation=0.275024431717439

fstatistic=9992.12385166078,66,1014
cont.fstatistic=2719.71894297129,66,1014

residuals=-0.637776443791067,-0.0961797724022847,-0.00916099973320877,0.0792387176213165,0.851145347529124
cont.residuals=-0.598478728720173,-0.188486694929637,-0.072508213437412,0.106057035065576,1.51012751155135

predictedValues:
Include	Exclude	Both
chr5.18705_chr5_119527008_119531207_+_2.R.tl.Lung	76.7645211307804	45.7131689004571	56.6912915428135
chr5.18705_chr5_119527008_119531207_+_2.R.tl.cerebhem	74.0827982222847	46.7417807390397	63.3801074443229
chr5.18705_chr5_119527008_119531207_+_2.R.tl.cortex	70.0859657523535	46.2855115396543	54.1103409880887
chr5.18705_chr5_119527008_119531207_+_2.R.tl.heart	75.636147232259	47.6327230463174	56.9223777219467
chr5.18705_chr5_119527008_119531207_+_2.R.tl.kidney	102.934193480706	45.5449279321686	67.0334991258488
chr5.18705_chr5_119527008_119531207_+_2.R.tl.liver	72.4559956719398	46.749534939054	55.2219750518662
chr5.18705_chr5_119527008_119531207_+_2.R.tl.stomach	69.8441416037326	47.0019013231205	55.5692617052067
chr5.18705_chr5_119527008_119531207_+_2.R.tl.testicle	72.6641332759721	44.5069331397239	57.3874653567789


diffExp=31.0513522303233,27.3410174832450,23.8004542126992,28.0034241859416,57.3892655485373,25.7064607328857,22.8422402806121,28.1572001362482
diffExpScore=0.995923216469795
diffExp1.5=1,1,1,1,1,1,0,1
diffExp1.5Score=0.875
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	56.5228244981829	67.5789819443273	55.3966016502055
cerebhem	60.9447561029088	57.9149473913608	54.7976890476407
cortex	51.9313652952052	58.8505788237312	55.129418476202
heart	52.8454774753159	55.470670719111	56.1665914029319
kidney	57.1941860343868	64.7393187107473	55.6134596292004
liver	51.1718868275491	63.5209623269241	53.5057003088272
stomach	50.5080108920181	50.5241984493515	54.3580138969567
testicle	53.4198317925279	52.5219124032514	53.7384586121487
cont.diffExp=-11.0561574461444,3.02980871154801,-6.91921352852602,-2.62519324379505,-7.54513267636053,-12.3490754993750,-0.0161875573333958,0.897919389276488
cont.diffExpScore=1.18240730943205

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

tran.correlation=-0.291740079420489
cont.tran.correlation=0.380908586205618

tran.covariance=-0.000772659652271452
cont.tran.covariance=0.00265420873770033

tran.mean=61.5402736205977
cont.tran.mean=56.6037443554312

weightedLogRatios:
wLogRatio
Lung	2.11570406790739
cerebhem	1.87667957483278
cortex	1.67711428665971
heart	1.89346048044531
kidney	3.44616249333471
liver	1.78069627894695
stomach	1.60341433991075
testicle	1.98078575603719

cont.weightedLogRatios:
wLogRatio
Lung	-0.736757633838849
cerebhem	0.208276629592758
cortex	-0.501873652278026
heart	-0.193522551628483
kidney	-0.509099743484085
liver	-0.874074968404155
stomach	-0.00125687303980305
testicle	0.0672928618233428

varWeightedLogRatios=0.346263506902557
cont.varWeightedLogRatios=0.156591909874228

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.6193763428491	0.0735815434388907	62.7790085252218	0	***
df.mm.trans1	-0.0500073411772189	0.0618759768077486	-0.808186694694032	0.419172675769792	   
df.mm.trans2	-0.796004845496739	0.0549940277251821	-14.4743871002604	2.65816812649378e-43	***
df.mm.exp2	-0.124836634170977	0.0693669432164143	-1.79965597996016	0.0722121570605051	.  
df.mm.exp3	-0.0319821307131566	0.0693669432164143	-0.461057230291627	0.644856419565142	   
df.mm.exp4	0.0222573710284966	0.0693669432164143	0.320864232968389	0.74837939585956	   
df.mm.exp5	0.122088295779493	0.0693669432164143	1.76003569017876	0.0787032356766561	.  
df.mm.exp6	-0.00908561811405059	0.0693669432164143	-0.130979075807115	0.895817840640229	   
df.mm.exp7	-0.0466843101719104	0.0693669432164143	-0.67300515212646	0.501097359824754	   
df.mm.exp8	-0.093841392117644	0.0693669432164143	-1.35282582403658	0.176412840668659	   
df.mm.trans1:exp2	0.0892774278023103	0.0608994403466723	1.46598108774227	0.142963461796984	   
df.mm.trans2:exp2	0.147088645242542	0.0432476227491443	3.40108047315623	0.000697385051379212	***
df.mm.trans1:exp3	-0.0590378675093136	0.0608994403466723	-0.969432020610345	0.332560855190491	   
df.mm.trans2:exp3	0.0444247009869347	0.0432476227491443	1.02721717779999	0.30456318288574	   
df.mm.trans1:exp4	-0.0370656330939276	0.0608994403466723	-0.608636678480625	0.542901602959131	   
df.mm.trans2:exp4	0.0188761968359257	0.0432476227491443	0.436467848080717	0.662590238767694	   
df.mm.trans1:exp5	0.171259021049896	0.0608994403466722	2.81216083555116	0.00501592337386083	** 
df.mm.trans2:exp5	-0.125775446064932	0.0432476227491443	-2.90826265282803	0.00371368529119292	** 
df.mm.trans1:exp6	-0.0486775295191541	0.0608994403466722	-0.799309964788765	0.424297892903921	   
df.mm.trans2:exp6	0.0315035098340121	0.0432476227491443	0.728444890872885	0.466509482101214	   
df.mm.trans1:exp7	-0.0477920477137443	0.0608994403466723	-0.784769900046476	0.432771793101506	   
df.mm.trans2:exp7	0.0744859486096233	0.0432476227491443	1.72231313248535	0.0853177426640677	.  
df.mm.trans1:exp8	0.0389467334839779	0.0608994403466723	0.639525310286469	0.522625704755251	   
df.mm.trans2:exp8	0.06709995390928	0.0432476227491443	1.55152930135582	0.121086928555601	   
df.mm.trans1:probe2	-0.566827481213203	0.0469687337843242	-12.0681873992179	1.97504211086824e-31	***
df.mm.trans1:probe3	-0.152670362601972	0.0469687337843242	-3.25046792410925	0.00119006912772239	** 
df.mm.trans1:probe4	-0.572645962505564	0.0469687337843242	-12.192067283208	5.27055188536603e-32	***
df.mm.trans1:probe5	-0.629853544620711	0.0469687337843242	-13.4100601373019	7.22655517071186e-38	***
df.mm.trans1:probe6	-0.656820388528288	0.0469687337843242	-13.9842047167876	9.15822325981389e-41	***
df.mm.trans1:probe7	-0.509605159528608	0.0469687337843242	-10.8498807285005	5.03782775438448e-26	***
df.mm.trans1:probe8	-0.61415676017254	0.0469687337843241	-13.0758636797127	3.2120914436672e-36	***
df.mm.trans1:probe9	-0.693145520491423	0.0469687337843242	-14.7575943536030	8.5675573295385e-45	***
df.mm.trans1:probe10	-0.562561855400173	0.0469687337843242	-11.9773689872800	5.16940638910877e-31	***
df.mm.trans1:probe11	-0.599924811310704	0.0469687337843242	-12.7728546838307	9.45416790554822e-35	***
df.mm.trans1:probe12	-0.702849329290506	0.0469687337843242	-14.9641958098747	6.80810284197e-46	***
df.mm.trans1:probe13	-0.686247659662456	0.0469687337843242	-14.6107336598350	5.1136361775335e-44	***
df.mm.trans1:probe14	-0.671083018875838	0.0469687337843242	-14.2878669447932	2.49339180368062e-42	***
df.mm.trans1:probe15	-0.629516747486944	0.0469687337843242	-13.4028894706343	7.84495090552383e-38	***
df.mm.trans1:probe16	-0.668522273598027	0.0469687337843242	-14.2333467337616	4.7798073156632e-42	***
df.mm.trans2:probe2	0.058111004953432	0.0469687337843242	1.23722741218173	0.216289104108331	   
df.mm.trans2:probe3	-0.0729983808418658	0.0469687337843242	-1.55419094704718	0.120450915661015	   
df.mm.trans2:probe4	0.00989581717184805	0.0469687337843241	0.210689460296900	0.833171929943127	   
df.mm.trans2:probe5	-0.0134805880355290	0.0469687337843241	-0.287011953471656	0.774161805075442	   
df.mm.trans2:probe6	-0.0100952084778815	0.0469687337843242	-0.214934652576281	0.829861486389552	   
df.mm.trans3:probe2	0.231105353774544	0.0469687337843242	4.92040843246397	1.00729111917145e-06	***
df.mm.trans3:probe3	0.12533857739375	0.0469687337843242	2.6685534672766	0.00773945685857097	** 
df.mm.trans3:probe4	0.0485125428198497	0.0469687337843242	1.03286886639556	0.301911547211812	   
df.mm.trans3:probe5	0.286030113337166	0.0469687337843242	6.08979826134101	1.60237411261820e-09	***
df.mm.trans3:probe6	0.15975659176268	0.0469687337843242	3.40133912266545	0.000696732982275186	***
df.mm.trans3:probe7	0.0806433375262248	0.0469687337843241	1.71695787875677	0.0862922386851509	.  
df.mm.trans3:probe8	0.258328343830658	0.0469687337843242	5.50000655791312	4.80730749138208e-08	***
df.mm.trans3:probe9	0.0994114353537588	0.0469687337843242	2.11654492987285	0.0345415967204941	*  
df.mm.trans3:probe10	0.174977632977806	0.0469687337843242	3.72540664564827	0.000205759161581824	***
df.mm.trans3:probe11	1.31539245211916	0.0469687337843242	28.0057039254947	2.64365215105211e-128	***
df.mm.trans3:probe12	0.094470482688639	0.0469687337843242	2.01134829655912	0.0445529067070403	*  
df.mm.trans3:probe13	0.141686002262245	0.0469687337843242	3.01660255336782	0.00262000533199107	** 
df.mm.trans3:probe14	-0.0169063258227723	0.0469687337843241	-0.359948511714292	0.718960592718239	   
df.mm.trans3:probe15	0.0211925231155621	0.0469687337843242	0.451204906073817	0.651938350480386	   
df.mm.trans3:probe16	0.804841694467712	0.0469687337843241	17.1356906950796	5.3061964363566e-58	***
df.mm.trans3:probe17	0.263973429056773	0.0469687337843242	5.62019470801392	2.46424914436707e-08	***
df.mm.trans3:probe18	0.36552138405753	0.0469687337843242	7.78222776317472	1.75027603646404e-14	***
df.mm.trans3:probe19	0.159908819881740	0.0469687337843242	3.40458017488881	0.000688610080648324	***
df.mm.trans3:probe20	0.721612673431296	0.0469687337843242	15.3636816513911	4.77866413230321e-48	***
df.mm.trans3:probe21	0.00166960896689918	0.0469687337843242	0.0355472424393185	0.971650372439976	   
df.mm.trans3:probe22	0.32187500511325	0.0469687337843242	6.85296321998521	1.25372258683914e-11	***
df.mm.trans3:probe23	0.418556774293126	0.0469687337843242	8.91139148470764	2.29414792219085e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05349234756125	0.140749285710535	28.7993813048377	9.18995619491069e-134	***
df.mm.trans1	-0.0417041073786108	0.118358478652531	-0.352354202701801	0.72464586307393	   
df.mm.trans2	0.178232709762511	0.105194451745812	1.69431663747045	0.0905122037849759	.  
df.mm.exp2	-0.0681279931147504	0.132687454670504	-0.513447132465755	0.607750435174315	   
df.mm.exp3	-0.218182163402524	0.132687454670504	-1.64433151532165	0.100417708193510	   
df.mm.exp4	-0.278518856643396	0.132687454670504	-2.09905945769347	0.0360584340833752	*  
df.mm.exp5	-0.0350275730809819	0.132687454670504	-0.263985567949615	0.791844679724134	   
df.mm.exp6	-0.126651230303231	0.132687454670504	-0.954507949660632	0.340054075187906	   
df.mm.exp7	-0.384430995449505	0.132687454670504	-2.89726708831775	0.00384524946464275	** 
df.mm.exp8	-0.278139696823424	0.132687454670504	-2.09620191685878	0.0363116511919850	*  
df.mm.trans1:exp2	0.143451279297013	0.116490526406043	1.23144159205698	0.218443269048441	   
df.mm.trans2:exp2	-0.086193513598675	0.082725527708943	-1.04192159283569	0.297696381633683	   
df.mm.trans1:exp3	0.133460581985725	0.116490526406042	1.14567755939681	0.252198903376366	   
df.mm.trans2:exp3	0.0798868159172848	0.082725527708943	0.965685177595412	0.334431990568192	   
df.mm.trans1:exp4	0.211246462460105	0.116490526406042	1.81342182044726	0.07006231924742	.  
df.mm.trans2:exp4	0.0810762654818675	0.082725527708943	0.980063442654871	0.327288572545673	   
df.mm.trans1:exp5	0.0468352935490143	0.116490526406042	0.402052381373606	0.687730206463113	   
df.mm.trans2:exp5	-0.00790071866385463	0.082725527708943	-0.095505207191329	0.923932424689483	   
df.mm.trans1:exp6	0.0271969961503997	0.116490526406042	0.233469596107765	0.815443898757026	   
df.mm.trans2:exp6	0.0647241803898372	0.082725527708943	0.78239670609971	0.434164135568923	   
df.mm.trans1:exp7	0.271918420679011	0.116490526406042	2.33425351458371	0.0197769730063302	*  
df.mm.trans2:exp7	0.0935863774563848	0.082725527708943	1.13128776628242	0.258201472462628	   
df.mm.trans1:exp8	0.221677225730667	0.116490526406042	1.90296355051211	0.057328309523235	.  
df.mm.trans2:exp8	0.0260731417315456	0.082725527708943	0.315176493322351	0.752692500521296	   
df.mm.trans1:probe2	0.113233014622801	0.0898433958015867	1.26033765323016	0.207837534854890	   
df.mm.trans1:probe3	0.0189449453158820	0.0898433958015867	0.210866309614127	0.833033961487726	   
df.mm.trans1:probe4	-0.0185441264310670	0.0898433958015867	-0.206405003568882	0.836515998422806	   
df.mm.trans1:probe5	0.137526669797670	0.0898433958015867	1.53073766380546	0.126146138659086	   
df.mm.trans1:probe6	0.0550088238029165	0.0898433958015867	0.612274539626707	0.54049340159102	   
df.mm.trans1:probe7	-0.0145202519425868	0.0898433958015867	-0.161617354431413	0.871639386117825	   
df.mm.trans1:probe8	-0.0483185400794776	0.0898433958015867	-0.537808479392142	0.590827289571088	   
df.mm.trans1:probe9	0.103373964434011	0.0898433958015867	1.15060170546431	0.250167393176587	   
df.mm.trans1:probe10	0.153938796589353	0.0898433958015867	1.71341249087820	0.086942332691287	.  
df.mm.trans1:probe11	-0.0102176373061700	0.0898433958015867	-0.113727194024756	0.909476567222036	   
df.mm.trans1:probe12	0.0932654707386281	0.0898433958015867	1.03808933207065	0.299475942357565	   
df.mm.trans1:probe13	0.0319182727216118	0.0898433958015867	0.355265653494457	0.722464467793547	   
df.mm.trans1:probe14	0.00328451840893114	0.0898433958015867	0.0365582620695325	0.970844418856558	   
df.mm.trans1:probe15	0.135623332107253	0.0898433958015867	1.50955260425339	0.131469146814078	   
df.mm.trans1:probe16	0.136878049951748	0.0898433958015867	1.52351821444989	0.127940903551048	   
df.mm.trans2:probe2	-0.061418924676993	0.0898433958015867	-0.683622030634646	0.494370050221703	   
df.mm.trans2:probe3	-0.0815857179452034	0.0898433958015867	-0.908088092811854	0.364047444867809	   
df.mm.trans2:probe4	-0.189811835461906	0.0898433958015867	-2.11269658463371	0.0348706657598841	*  
df.mm.trans2:probe5	-0.111706856459598	0.0898433958015867	-1.24335078235795	0.214025989677931	   
df.mm.trans2:probe6	-0.089889842948707	0.0898433958015867	-1.00051697897999	0.317299079323475	   
df.mm.trans3:probe2	-0.0308206146845281	0.0898433958015867	-0.343048194133194	0.731633309219565	   
df.mm.trans3:probe3	-0.140594016863264	0.0898433958015867	-1.56487870487172	0.117923362572099	   
df.mm.trans3:probe4	-0.225971762579605	0.0898433958015867	-2.51517388188052	0.0120510667601869	*  
df.mm.trans3:probe5	-0.241455392120427	0.0898433958015867	-2.68751409011371	0.0073164710478499	** 
df.mm.trans3:probe6	-0.170407187820160	0.0898433958015867	-1.89671356809011	0.0581497296618599	.  
df.mm.trans3:probe7	-0.0709069478794881	0.0898433958015867	-0.789228270446072	0.430163096919402	   
df.mm.trans3:probe8	-0.341936439962178	0.0898433958015867	-3.80591624917342	0.000149743322622960	***
df.mm.trans3:probe9	-0.226176339346902	0.0898433958015867	-2.51745091922391	0.0119739430443024	*  
df.mm.trans3:probe10	-0.206214562004726	0.0898433958015867	-2.29526678243704	0.0219212845126903	*  
df.mm.trans3:probe11	-0.213814725974495	0.0898433958015867	-2.37986024533946	0.0175032902605565	*  
df.mm.trans3:probe12	-0.215147054709410	0.0898433958015867	-2.39468970189583	0.0168151378948348	*  
df.mm.trans3:probe13	-0.19921849477968	0.0898433958015867	-2.21739720546228	0.0268170540590297	*  
df.mm.trans3:probe14	-0.114276946459437	0.0898433958015867	-1.2719571142637	0.203679987319065	   
df.mm.trans3:probe15	-0.211662893505957	0.0898433958015867	-2.35590931996160	0.0186669899841944	*  
df.mm.trans3:probe16	-0.137294723391116	0.0898433958015867	-1.52815598927630	0.126785677317763	   
df.mm.trans3:probe17	-0.198725098992143	0.0898433958015867	-2.21190547417658	0.0271955195012480	*  
df.mm.trans3:probe18	-0.174895109416493	0.0898433958015867	-1.94666628366026	0.0518506199431432	.  
df.mm.trans3:probe19	-0.235871595764274	0.0898433958015867	-2.62536376391183	0.00878594053036838	** 
df.mm.trans3:probe20	-0.17232377419901	0.0898433958015867	-1.91804609188611	0.0553857801722656	.  
df.mm.trans3:probe21	-0.117706299502171	0.0898433958015867	-1.31012745513446	0.190449426333707	   
df.mm.trans3:probe22	-0.186772437167385	0.0898433958015867	-2.07886662676753	0.0378805485423608	*  
df.mm.trans3:probe23	-0.204369685808891	0.0898433958015867	-2.27473242730303	0.0231301154809101	*