chr16.9183_chr16_91604826_91611290_-_2.R 

fitVsDatCorrelation=0.901521530731239
cont.fitVsDatCorrelation=0.266864130061567

fstatistic=4381.0029894722,43,485
cont.fstatistic=874.281456534692,43,485

residuals=-0.82735081032727,-0.117800222803840,-0.0044904910260712,0.111929026287433,0.752627821047565
cont.residuals=-1.02969361190266,-0.388557371251337,-0.0977458955043316,0.329836209401192,1.53754177192185

predictedValues:
Include	Exclude	Both
chr16.9183_chr16_91604826_91611290_-_2.R.tl.Lung	114.655105593267	55.706233085767	71.2679607678131
chr16.9183_chr16_91604826_91611290_-_2.R.tl.cerebhem	94.9147396593623	56.4248139980277	87.0017396463881
chr16.9183_chr16_91604826_91611290_-_2.R.tl.cortex	190.483002409619	56.5475943836435	177.031371039979
chr16.9183_chr16_91604826_91611290_-_2.R.tl.heart	135.579699264318	77.3297043208716	70.3784466331679
chr16.9183_chr16_91604826_91611290_-_2.R.tl.kidney	100.897323034313	56.7193942441309	65.2346851220882
chr16.9183_chr16_91604826_91611290_-_2.R.tl.liver	112.015843934465	63.4999579091885	64.2510801738678
chr16.9183_chr16_91604826_91611290_-_2.R.tl.stomach	199.881826487997	57.9028658920073	62.3501916195598
chr16.9183_chr16_91604826_91611290_-_2.R.tl.testicle	129.83576614512	71.6997008948663	61.3232156689169


diffExp=58.9488725075005,38.4899256613347,133.935408025975,58.2499949434465,44.1779287901817,48.515886025277,141.97896059599,58.1360652502536
diffExpScore=0.998286007256437
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
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	71.6847787495686	61.3078119938341	78.3251121729359
cerebhem	81.1093827168242	79.704444573307	73.6986931328187
cortex	80.805557317467	87.040428829221	84.0790338388306
heart	79.8844761672097	70.6083869524479	87.2454629601858
kidney	78.4663800040294	77.6046312098694	81.8310310662701
liver	77.1704876723554	89.45921646608	74.8487075921607
stomach	108.836993432191	96.7052642841995	89.9208357856288
testicle	87.7365877602678	80.3489900950287	88.6532960491926
cont.diffExp=10.3769667557345,1.40493814351731,-6.23487151175402,9.27608921476183,0.861748794160079,-12.2887287937246,12.1317291479911,7.38759766523908
cont.diffExpScore=2.50727547864704

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=-0.0490618239892546
cont.tran.correlation=0.701727964550402

tran.covariance=0.00095546231736202
cont.tran.covariance=0.0123784166368468

tran.mean=98.3808482035603
cont.tran.mean=81.7796136389938

weightedLogRatios:
wLogRatio
Lung	3.16237311449413
cerebhem	2.23263133191525
cortex	5.63800961386899
heart	2.59899617635180
kidney	2.49178384300423
liver	2.51721985963914
stomach	5.79615780826219
testicle	2.71322261558415

cont.weightedLogRatios:
wLogRatio
Lung	0.655835167734207
cerebhem	0.07665643760631
cortex	-0.329209692655301
heart	0.533088809933221
kidney	0.048116632926679
liver	-0.653109787883869
stomach	0.547279139093571
testicle	0.389692103033839

varWeightedLogRatios=2.12665649902765
cont.varWeightedLogRatios=0.215387440916282

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.40364724272845	0.117360468951955	37.5224066677105	1.62181698830181e-145	***
df.mm.trans1	0.341740249943285	0.0939531928255918	3.63734578533875	0.000304968876034264	***
df.mm.trans2	-0.322352380920485	0.0939531928255918	-3.43098910453078	0.000652766634929486	***
df.mm.exp2	-0.375613794019839	0.125809725926097	-2.98557040208865	0.00297355675507455	** 
df.mm.exp3	-0.387255045209686	0.125809725926097	-3.07810101611037	0.00220121435214967	** 
df.mm.exp4	0.508177031121828	0.125809725926097	4.03925076047254	6.23259430465428e-05	***
df.mm.exp5	-0.0213454265822923	0.125809725926097	-0.169664359612635	0.865344848387725	   
df.mm.exp6	0.211307315124446	0.125809725926097	1.67957853471974	0.0936833541609828	.  
df.mm.exp7	0.728152742333562	0.125809725926097	5.78773013750379	1.28197074380023e-08	***
df.mm.exp8	0.52702467975227	0.125809725926097	4.18906150436934	3.32822331494044e-05	***
df.mm.trans1:exp2	0.186664265068911	0.098693268845681	1.89135760981616	0.0591722104544053	.  
df.mm.trans2:exp2	0.388430775007633	0.0986932688456811	3.93573725493874	9.5071579099685e-05	***
df.mm.trans1:exp3	0.894889469291572	0.0986932688456811	9.06738098512919	3.02855101150203e-18	***
df.mm.trans2:exp3	0.402245661967776	0.0986932688456811	4.07571526075133	5.35943729569533e-05	***
df.mm.trans1:exp4	-0.34054591757114	0.098693268845681	-3.45054856885554	0.000608303412558892	***
df.mm.trans2:exp4	-0.180190921374262	0.098693268845681	-1.82576708099529	0.0685000873721204	.  
df.mm.trans1:exp5	-0.106479717596806	0.0986932688456811	-1.07889543878924	0.281170609526537	   
df.mm.trans2:exp5	0.039369583726965	0.0986932688456811	0.398908498902028	0.69013620100975	   
df.mm.trans1:exp6	-0.234595530441561	0.0986932688456811	-2.37701651982345	0.0178402742108593	*  
df.mm.trans2:exp6	-0.0803601173376985	0.098693268845681	-0.814241115707205	0.415906433489447	   
df.mm.trans1:exp7	-0.172354958284317	0.0986932688456811	-1.74636994295745	0.0813797807905565	.  
df.mm.trans2:exp7	-0.689477906972531	0.0986932688456811	-6.98606819934816	9.3735701376786e-12	***
df.mm.trans1:exp8	-0.402682905633033	0.0986932688456811	-4.08014558989506	5.26164363249978e-05	***
df.mm.trans2:exp8	-0.274630149039366	0.0986932688456811	-2.78266342022558	0.00560175057225264	** 
df.mm.trans1:probe2	0.160328152041433	0.0675706620258767	2.37274798314146	0.0180453091202778	*  
df.mm.trans1:probe3	0.0272490588180769	0.0675706620258767	0.403267601664783	0.68692909020069	   
df.mm.trans1:probe4	-0.0296426779744683	0.0675706620258767	-0.43869154283432	0.661080407091167	   
df.mm.trans1:probe5	-0.302467027822643	0.0675706620258767	-4.47630700594306	9.4756930853091e-06	***
df.mm.trans1:probe6	0.0891892571138304	0.0675706620258767	1.31994055466964	0.187477514089208	   
df.mm.trans2:probe2	-0.140308224603755	0.0675706620258767	-2.07646662615237	0.0383768554174271	*  
df.mm.trans2:probe3	-0.237545126137396	0.0675706620258767	-3.51550686370997	0.00048010304118015	***
df.mm.trans2:probe4	-0.320454414527738	0.0675706620258767	-4.74250813770357	2.77973631894153e-06	***
df.mm.trans2:probe5	-0.17726166163544	0.0675706620258767	-2.62335244795376	0.00898112901963289	** 
df.mm.trans2:probe6	-0.103675637795061	0.0675706620258767	-1.53432917018569	0.125600741427489	   
df.mm.trans3:probe2	-0.358839283919359	0.0675706620258767	-5.31057818823706	1.66751808977675e-07	***
df.mm.trans3:probe3	0.496621241274198	0.0675706620258767	7.34965777135664	8.49949915007454e-13	***
df.mm.trans3:probe4	-0.115971240975661	0.0675706620258767	-1.71629576355562	0.0867464646334388	.  
df.mm.trans3:probe5	-0.510998538224967	0.0675706620258767	-7.56243202159652	1.99782946198196e-13	***
df.mm.trans3:probe6	-0.129769427632667	0.0675706620258767	-1.92049957395668	0.0553808684287245	.  
df.mm.trans3:probe7	0.0095898961416615	0.0675706620258767	0.141923963065346	0.887199000290477	   
df.mm.trans3:probe8	-0.0611995564217839	0.0675706620258767	-0.905711955261694	0.365538048102811	   
df.mm.trans3:probe9	-0.531933028581961	0.0675706620258767	-7.87224828991985	2.29503341838272e-14	***
df.mm.trans3:probe10	-0.363382495837381	0.0675706620258767	-5.37781464532967	1.17453036590342e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03988957696926	0.26137125433137	15.4565183049833	4.14498605206715e-44	***
df.mm.trans1	0.221948767259346	0.209241357644158	1.06073086964384	0.289340158584008	   
df.mm.trans2	0.0972810147780668	0.209241357644158	0.464922498464696	0.64219561642443	   
df.mm.exp2	0.446821492525902	0.280188432834667	1.59471784043834	0.111426754363188	   
df.mm.exp3	0.399343777386866	0.280188432834667	1.42526860708240	0.154722788628962	   
df.mm.exp4	0.141687477821292	0.280188432834667	0.505686392503215	0.613306566182907	   
df.mm.exp5	0.282323395403107	0.280188432834667	1.00761973842689	0.314139484516916	   
df.mm.exp6	0.497013656478358	0.280188432834667	1.77385501410629	0.0767145070108538	.  
df.mm.exp7	0.73527201040991	0.280188432834667	2.62420544264144	0.00895898667502071	** 
df.mm.exp8	0.348667876772415	0.280188432834667	1.24440496434825	0.213951578389147	   
df.mm.trans1:exp2	-0.323301279185081	0.219797890231838	-1.47090255891023	0.141965950089083	   
df.mm.trans2:exp2	-0.184403415542096	0.219797890231838	-0.83896808721681	0.40190053256474	   
df.mm.trans1:exp3	-0.279576469811135	0.219797890231838	-1.27197067049299	0.203993087378789	   
df.mm.trans2:exp3	-0.0488783410324668	0.219797890231838	-0.222378572337118	0.824112804681103	   
df.mm.trans1:exp4	-0.0333843689670029	0.219797890231838	-0.151886667027558	0.87933948408681	   
df.mm.trans2:exp4	-0.00044581856465912	0.219797890231838	-0.00202831139183765	0.99838247675338	   
df.mm.trans1:exp5	-0.191931576824047	0.219797890231838	-0.873218467300127	0.382976139974794	   
df.mm.trans2:exp5	-0.0466035630025963	0.219797890231838	-0.212029164399348	0.832173356282571	   
df.mm.trans1:exp6	-0.423274990841342	0.219797890231838	-1.92574637725085	0.0547203096009085	.  
df.mm.trans2:exp6	-0.119138090542761	0.219797890231838	-0.54203473207635	0.58804351716454	   
df.mm.trans1:exp7	-0.317699154883623	0.219797890231838	-1.44541494255709	0.148987186116868	   
df.mm.trans2:exp7	-0.279511443639609	0.219797890231838	-1.27167482519867	0.204098138194122	   
df.mm.trans1:exp8	-0.146607306413117	0.219797890231838	-0.667009616236437	0.505083192710305	   
df.mm.trans2:exp8	-0.0781956270388245	0.219797890231838	-0.355761499604684	0.722173868361789	   
df.mm.trans1:probe2	0.254676390411414	0.150485328215028	1.69236691332133	0.0912182912781822	.  
df.mm.trans1:probe3	-0.0605204329748454	0.150485328215028	-0.402168328917541	0.687737325112348	   
df.mm.trans1:probe4	0.0672879979008228	0.150485328215028	0.447139921871156	0.65497365020777	   
df.mm.trans1:probe5	-0.0871099005151845	0.150485328215028	-0.578859756950615	0.562952530722872	   
df.mm.trans1:probe6	-0.00729261419682788	0.150485328215028	-0.0484606325635114	0.961369087342642	   
df.mm.trans2:probe2	-0.128437161527549	0.150485328215028	-0.853486270395917	0.393811017860206	   
df.mm.trans2:probe3	-0.199197299637211	0.150485328215028	-1.32369914064033	0.186226562687617	   
df.mm.trans2:probe4	0.00971954032974948	0.150485328215028	0.0645879598033721	0.948528683495766	   
df.mm.trans2:probe5	-0.0623715228369799	0.150485328215028	-0.414469128497746	0.67871382248891	   
df.mm.trans2:probe6	0.0400733524731633	0.150485328215028	0.266294082941446	0.790125901256705	   
df.mm.trans3:probe2	0.125332103304016	0.150485328215028	0.83285264278342	0.405337772102128	   
df.mm.trans3:probe3	-0.0289657954004913	0.150485328215028	-0.192482521346548	0.847444827001108	   
df.mm.trans3:probe4	0.0265289767598072	0.150485328215028	0.176289456749565	0.860140130236092	   
df.mm.trans3:probe5	0.0387722030863026	0.150485328215028	0.257647729158694	0.796788071446887	   
df.mm.trans3:probe6	-0.0370056280866409	0.150485328215028	-0.245908544876639	0.805857059936242	   
df.mm.trans3:probe7	-0.118735046007234	0.150485328215028	-0.789014101345311	0.43048928693336	   
df.mm.trans3:probe8	0.0106695452154626	0.150485328215028	0.0709009000546346	0.943505852650105	   
df.mm.trans3:probe9	0.0312673698211816	0.150485328215028	0.207776865639046	0.835490413888548	   
df.mm.trans3:probe10	-0.0303746289140386	0.150485328215028	-0.201844454036319	0.840122959597276