chr6.20431_chr6_5843438_5878591_-_2.R 

fitVsDatCorrelation=0.841570165596241
cont.fitVsDatCorrelation=0.250770165151878

fstatistic=10102.7317601486,62,922
cont.fstatistic=3135.12708352263,62,922

residuals=-0.635710620738414,-0.0906676160094425,-0.00846033470597334,0.0756505635661977,1.58339057701559
cont.residuals=-0.736101003336529,-0.192953361799955,-0.0622800767508044,0.126501489513793,1.54144770407009

predictedValues:
Include	Exclude	Both
chr6.20431_chr6_5843438_5878591_-_2.R.tl.Lung	48.1438315369787	46.8426329989223	59.3223897583111
chr6.20431_chr6_5843438_5878591_-_2.R.tl.cerebhem	58.5750205427256	58.3202930690957	60.2206286978273
chr6.20431_chr6_5843438_5878591_-_2.R.tl.cortex	47.7598853565182	48.8324489897482	57.5101582124132
chr6.20431_chr6_5843438_5878591_-_2.R.tl.heart	63.5684186462879	50.484229454037	93.1271344999855
chr6.20431_chr6_5843438_5878591_-_2.R.tl.kidney	58.6332164607641	46.854563291876	81.2331410770335
chr6.20431_chr6_5843438_5878591_-_2.R.tl.liver	62.9710837541275	54.9415826764097	95.5816033741387
chr6.20431_chr6_5843438_5878591_-_2.R.tl.stomach	51.755159221738	51.7824525798441	65.0640170227019
chr6.20431_chr6_5843438_5878591_-_2.R.tl.testicle	53.4797544135959	52.856830896879	66.069936487428


diffExp=1.30119853805643,0.254727473629899,-1.07256363323003,13.0841891922509,11.7786531688881,8.02950107771778,-0.0272933581061068,0.622923516716874
diffExpScore=1.03430563772280
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,1,1,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	60.1982405498533	61.0572010369015	59.1260357386207
cerebhem	60.927689461819	63.2990657809337	62.451566930874
cortex	58.3124278099668	57.8035739315802	59.4591008545626
heart	58.6461117618027	65.3644746040057	59.5614449358524
kidney	63.4214564253307	62.1083371625607	61.165770696369
liver	62.8441044738263	64.4716554345157	56.5003649054833
stomach	60.234431524897	62.5450978997884	53.3931184197747
testicle	60.5214552695174	61.6269702254712	54.681886435907
cont.diffExp=-0.858960487048229,-2.3713763191147,0.508853878386589,-6.71836284220296,1.31311926277009,-1.62755096068939,-2.31066637489148,-1.10551495595379
cont.diffExpScore=1.18658155814600

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=0.431323832281797
cont.tran.correlation=0.325260215064459

tran.covariance=0.00385025337526706
cont.tran.covariance=0.000375204810188415

tran.mean=53.4875877430967
cont.tran.mean=61.4613933345481

weightedLogRatios:
wLogRatio
Lung	0.105774704330019
cerebhem	0.0177298111934570
cortex	-0.086110596639615
heart	0.930324400145382
kidney	0.887856702348002
liver	0.555779090143443
stomach	-0.0020808108573564
testicle	0.0465536988025716

cont.weightedLogRatios:
wLogRatio
Lung	-0.0581558625684902
cerebhem	-0.157648464775182
cortex	0.0355969736110563
heart	-0.447469084595311
kidney	0.0866032966984799
liver	-0.106197470306729
stomach	-0.154981687548305
testicle	-0.0744349429821092

varWeightedLogRatios=0.175698124974044
cont.varWeightedLogRatios=0.0259994602526562

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.22439733285548	0.0744037152860527	43.3365097489951	1.2684185587282e-224	***
df.mm.trans1	0.588831094573027	0.0639000578139194	9.21487577190833	2.05580647510624e-19	***
df.mm.trans2	0.634632746433788	0.0561083687761187	11.3108393681177	7.21580591043096e-28	***
df.mm.exp2	0.400243516802362	0.071390729350513	5.60637943390735	2.72949306324578e-08	***
df.mm.exp3	0.0646195339485798	0.071390729350513	0.905153015475047	0.365620788892905	   
df.mm.exp4	-0.0981877810803036	0.071390729350513	-1.37535758457128	0.169354676142269	   
df.mm.exp5	-0.116973497494315	0.071390729350513	-1.63849702277169	0.101659229646749	   
df.mm.exp6	-0.0490342157684707	0.071390729350513	-0.686842902636887	0.492354439237855	   
df.mm.exp7	0.0802038302170674	0.071390729350513	1.12344881396692	0.261539361180079	   
df.mm.exp8	0.118176321810464	0.071390729350513	1.65534548933160	0.098194742146939	.  
df.mm.trans1:exp2	-0.204128202433225	0.0655373869405368	-3.11468326649083	0.00189832408454508	** 
df.mm.trans2:exp2	-0.181087153689738	0.046452664291529	-3.89831576835434	0.000103881213765649	***
df.mm.trans1:exp3	-0.0726264860077614	0.0655373869405368	-1.10816877813051	0.268077925415955	   
df.mm.trans2:exp3	-0.023018253513904	0.046452664291529	-0.495520630839285	0.620350743076015	   
df.mm.trans1:exp4	0.376111545200538	0.0655373869405368	5.73888527996684	1.29240370402643e-08	***
df.mm.trans2:exp4	0.173055030785275	0.046452664291529	3.72540592503394	0.000206854634689522	***
df.mm.trans1:exp5	0.314081846556383	0.0655373869405368	4.79240722309168	1.91997715002002e-06	***
df.mm.trans2:exp5	0.117228153844568	0.046452664291529	2.52360452586453	0.0117829017517668	*  
df.mm.trans1:exp6	0.317516828030296	0.0655373869405368	4.84481977162722	1.48606540527624e-06	***
df.mm.trans2:exp6	0.208510953616201	0.046452664291529	4.48867587675105	8.07328173374286e-06	***
df.mm.trans1:exp7	-0.00787272866750508	0.0655373869405368	-0.120125763858241	0.904409694206177	   
df.mm.trans2:exp7	0.0200537586082431	0.046452664291529	0.431703087736563	0.66605815186518	   
df.mm.trans1:exp8	-0.0130661824367580	0.0655373869405368	-0.199369902382791	0.842017361004291	   
df.mm.trans2:exp8	0.00261688303873056	0.046452664291529	0.056334401452357	0.955087619305116	   
df.mm.trans1:probe2	0.67571407523213	0.0469477333112316	14.3929009469447	1.61092229981609e-42	***
df.mm.trans1:probe3	0.0677027535064017	0.0469477333112316	1.44208780129125	0.149617190549731	   
df.mm.trans1:probe4	0.0917941019134405	0.0469477333112316	1.95524033726843	0.0508564907332799	.  
df.mm.trans1:probe5	0.116576720625043	0.0469477333112316	2.48311712627782	0.0132006793545592	*  
df.mm.trans1:probe6	0.135083561913541	0.0469477333112316	2.87731808089708	0.00410325856936697	** 
df.mm.trans1:probe7	-0.101123108070700	0.0469477333112316	-2.15395080738665	0.0315026104639746	*  
df.mm.trans1:probe8	-0.0235266353135484	0.0469477333112316	-0.501123987341045	0.61640348249194	   
df.mm.trans1:probe9	0.128379774099224	0.0469477333112316	2.73452550409948	0.00636682533033061	** 
df.mm.trans1:probe10	0.172967430715073	0.0469477333112316	3.68425520287458	0.000242732398816949	***
df.mm.trans1:probe11	0.0113749487366699	0.0469477333112316	0.242289625811361	0.808609634469944	   
df.mm.trans1:probe12	0.0396410491411702	0.0469477333112316	0.844365560279064	0.398684192244519	   
df.mm.trans1:probe13	0.075065875364692	0.0469477333112316	1.59892437973642	0.110180011616505	   
df.mm.trans1:probe14	0.0719232361791066	0.0469477333112316	1.53198527610064	0.125869097735552	   
df.mm.trans1:probe15	0.0221487767545916	0.0469477333112316	0.471775210269691	0.637198870817692	   
df.mm.trans1:probe16	0.0789391114541618	0.0469477333112316	1.68142540409457	0.0930189736088933	.  
df.mm.trans1:probe17	0.0370919432587647	0.0469477333112316	0.790068883898404	0.429690688723147	   
df.mm.trans1:probe18	0.0570952288804719	0.0469477333112316	1.21614452612588	0.22424113121473	   
df.mm.trans1:probe19	0.0321797885686117	0.0469477333112316	0.685438599458713	0.49323948931138	   
df.mm.trans1:probe20	0.142840261371428	0.0469477333112316	3.04253797354804	0.0024123829210869	** 
df.mm.trans1:probe21	0.179132475440395	0.0469477333112316	3.81557239947813	0.000144924778912887	***
df.mm.trans1:probe22	0.122759395877275	0.0469477333112316	2.61480985809184	0.00907344414555155	** 
df.mm.trans2:probe2	-0.105240582282017	0.0469477333112316	-2.24165417282967	0.025220912096994	*  
df.mm.trans2:probe3	0.0290236027610493	0.0469477333112316	0.618210948942785	0.53658897273824	   
df.mm.trans2:probe4	-0.0369428723257507	0.0469477333112316	-0.786893630856352	0.431546349860874	   
df.mm.trans2:probe5	-0.0934728681134606	0.0469477333112316	-1.99099853221452	0.0467758186057948	*  
df.mm.trans2:probe6	-0.0258575409932179	0.0469477333112316	-0.550772937679438	0.581922762091561	   
df.mm.trans3:probe2	-0.585377680756603	0.0469477333112316	-12.4687101904568	4.36005744770669e-33	***
df.mm.trans3:probe3	-0.168963989316390	0.0469477333112316	-3.598980768598	0.000336478338203501	***
df.mm.trans3:probe4	-0.711918581811399	0.0469477333112316	-15.1640671785337	1.54074273735913e-46	***
df.mm.trans3:probe5	-0.553495031750177	0.0469477333112316	-11.7896007477269	5.5579665090272e-30	***
df.mm.trans3:probe6	-0.599186028602717	0.0469477333112316	-12.7628319056539	1.80612679756928e-34	***
df.mm.trans3:probe7	-0.562861730362633	0.0469477333112316	-11.9891140778031	7.00594070136643e-31	***
df.mm.trans3:probe8	-0.275149399524593	0.0469477333112316	-5.86076004352626	6.4105162610746e-09	***
df.mm.trans3:probe9	-0.430676852542196	0.0469477333112316	-9.17353878806248	2.92363465771681e-19	***
df.mm.trans3:probe10	-0.120323129752433	0.0469477333112316	-2.56291670046715	0.0105374119461809	*  
df.mm.trans3:probe11	-0.216600280262485	0.0469477333112316	-4.61364724099825	4.51590263659671e-06	***
df.mm.trans3:probe12	-0.419924672998785	0.0469477333112316	-8.94451432223511	2.0097670324998e-18	***
df.mm.trans3:probe13	-0.0988894521517075	0.0469477333112316	-2.10637330446046	0.0354416774460523	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17090510432426	0.133345477526122	31.2789393514091	5.7481331812963e-147	***
df.mm.trans1	-0.0843614291566625	0.114520944154265	-0.736646294524288	0.461524864832972	   
df.mm.trans2	-0.0487305152948742	0.100556769227165	-0.484607010243023	0.62807024212529	   
df.mm.exp2	-0.00661592183487176	0.127945638999116	-0.0517088498414351	0.958771885934368	   
df.mm.exp3	-0.0922057558949732	0.127945638999116	-0.720663530357688	0.471299228356358	   
df.mm.exp4	0.0347088112810983	0.127945638999116	0.271277798544881	0.78623810459703	   
df.mm.exp5	0.0353118292530302	0.127945638999116	0.275990878073415	0.782616920018169	   
df.mm.exp6	0.142852800059202	0.127945638999116	1.11651167774612	0.264494056817820	   
df.mm.exp7	0.126667227462371	0.127945638999116	0.990008166384212	0.32242986342225	   
df.mm.exp8	0.0927821195520555	0.127945638999116	0.725168284576675	0.468532799072973	   
df.mm.trans1:exp2	0.0186605390439849	0.117455346467602	0.158873474943366	0.87380335164982	   
df.mm.trans2:exp2	0.042675345577854	0.0832519273869611	0.51260489597431	0.608350506082934	   
df.mm.trans1:exp3	0.0603778713751617	0.117455346467602	0.514049578763245	0.607340505682592	   
df.mm.trans2:exp3	0.0374452156848476	0.0832519273869611	0.449781967338719	0.652973383715258	   
df.mm.trans1:exp4	-0.0608306591417456	0.117455346467602	-0.517904556677841	0.604649107005535	   
df.mm.trans2:exp4	0.033458951117715	0.0832519273869611	0.401900018028356	0.687850753175491	   
df.mm.trans1:exp5	0.0168472792164082	0.117455346467602	0.143435609557844	0.885977503108797	   
df.mm.trans2:exp5	-0.0182427422025758	0.0832519273869611	-0.219126965286727	0.826599644092043	   
df.mm.trans1:exp6	-0.0998387976850768	0.117455346467602	-0.850014926418149	0.395537457409860	   
df.mm.trans2:exp6	-0.0884382701750582	0.0832519273869611	-1.06229696958235	0.288379050718804	   
df.mm.trans1:exp7	-0.126066211550168	0.117455346467602	-1.07331181884463	0.283412062454289	   
df.mm.trans2:exp7	-0.102590511230464	0.0832519273869611	-1.23228992349469	0.218154952987086	   
df.mm.trans1:exp8	-0.0874273099695587	0.117455346467602	-0.744345086016787	0.456857395431484	   
df.mm.trans2:exp8	-0.0834936631990634	0.0832519273869611	-1.00290366625362	0.316170474074377	   
df.mm.trans1:probe2	-0.115881982125629	0.0841391843550685	-1.37726533735585	0.168764484270856	   
df.mm.trans1:probe3	0.131736552380143	0.0841391843550685	1.56569799659826	0.117762468112501	   
df.mm.trans1:probe4	0.0336721016732888	0.0841391843550685	0.400195247094292	0.689105417849384	   
df.mm.trans1:probe5	-0.062182902699989	0.0841391843550685	-0.739048080589612	0.460065901713761	   
df.mm.trans1:probe6	0.0208922980821432	0.0841391843550685	0.248306401378665	0.803952639569414	   
df.mm.trans1:probe7	0.0882764313741728	0.0841391843550685	1.04917146571858	0.294374139923498	   
df.mm.trans1:probe8	-0.00417109028075344	0.0841391843550685	-0.0495736952137708	0.960472845800486	   
df.mm.trans1:probe9	0.0122157069739053	0.0841391843550685	0.145184518575256	0.884596920383725	   
df.mm.trans1:probe10	-0.0851800262154758	0.0841391843550685	-1.01237047718474	0.311626543346568	   
df.mm.trans1:probe11	0.0565830152955974	0.0841391843550685	0.672493033172467	0.501438368646041	   
df.mm.trans1:probe12	0.0378647895438388	0.0841391843550685	0.450025631150034	0.652797746325326	   
df.mm.trans1:probe13	-0.0355782324624214	0.0841391843550685	-0.422849742781922	0.672503493544905	   
df.mm.trans1:probe14	-0.0264231924055873	0.0841391843550685	-0.314041461277793	0.753560589336555	   
df.mm.trans1:probe15	0.0236560560042042	0.0841391843550685	0.281153854598534	0.778655478412992	   
df.mm.trans1:probe16	0.0301543022938576	0.0841391843550685	0.358385959229246	0.72013653719638	   
df.mm.trans1:probe17	-0.0110179395355063	0.0841391843550685	-0.13094897008997	0.895844241817475	   
df.mm.trans1:probe18	0.149831537549836	0.0841391843550685	1.78075814138564	0.0752811545012744	.  
df.mm.trans1:probe19	-0.0712363630219109	0.0841391843550685	-0.84664907994939	0.397410442556140	   
df.mm.trans1:probe20	-0.00208437043721905	0.0841391843550685	-0.0247728861789648	0.98024147854799	   
df.mm.trans1:probe21	0.123079519240960	0.0841391843550685	1.46280856160387	0.143860557423311	   
df.mm.trans1:probe22	0.0942745378430673	0.0841391843550685	1.12045937413925	0.262809822660789	   
df.mm.trans2:probe2	-0.00542870497064141	0.0841391843550685	-0.0645205324041673	0.9485697445059	   
df.mm.trans2:probe3	0.0285018933671705	0.0841391843550685	0.338746965348417	0.734877486307895	   
df.mm.trans2:probe4	-0.0671915921865506	0.0841391843550685	-0.798576700042648	0.424741551930918	   
df.mm.trans2:probe5	-0.0617248989468886	0.0841391843550685	-0.733604674445246	0.463376203636036	   
df.mm.trans2:probe6	-0.0910621014607496	0.0841391843550685	-1.08227934652262	0.279411390124603	   
df.mm.trans3:probe2	0.0610983321474356	0.0841391843550685	0.726157884887495	0.467926280815692	   
df.mm.trans3:probe3	0.0468293528042023	0.0841391843550685	0.556570082811615	0.577956257329136	   
df.mm.trans3:probe4	0.0180840903031768	0.0841391843550685	0.214930658548598	0.829868911399449	   
df.mm.trans3:probe5	0.0194109512566426	0.0841391843550685	0.230700492350011	0.817598670901498	   
df.mm.trans3:probe6	0.0325858694468386	0.0841391843550685	0.387285302283485	0.698634384739826	   
df.mm.trans3:probe7	0.0659070424909098	0.0841391843550685	0.783309738454098	0.43364640427788	   
df.mm.trans3:probe8	0.0108930375018562	0.0841391843550685	0.129464500819112	0.897018349816501	   
df.mm.trans3:probe9	0.168129721723711	0.0841391843550685	1.99823332033029	0.0459846741011652	*  
df.mm.trans3:probe10	0.0646909752890787	0.0841391843550685	0.768856696020274	0.442175285623184	   
df.mm.trans3:probe11	0.0406712519940454	0.0841391843550685	0.483380630627606	0.628940267176205	   
df.mm.trans3:probe12	0.0461288169408814	0.0841391843550685	0.548244165836183	0.583656972523286	   
df.mm.trans3:probe13	-0.030272782568387	0.0841391843550685	-0.359794105450743	0.719083505547548