chr8.22991_chr8_94579439_94585392_+_2.R 

fitVsDatCorrelation=0.919420652282584
cont.fitVsDatCorrelation=0.258578136064416

fstatistic=8973.47018137137,48,600
cont.fstatistic=1476.90673078843,48,600

residuals=-0.570077844923043,-0.0899871718009229,-0.0143793287657964,0.0818629457669812,0.780839225357625
cont.residuals=-0.815471859441055,-0.282349871097557,-0.0474710827492784,0.236954874476003,2.48313684289146

predictedValues:
Include	Exclude	Both
chr8.22991_chr8_94579439_94585392_+_2.R.tl.Lung	64.2445285905048	99.7247323885569	66.5521327923114
chr8.22991_chr8_94579439_94585392_+_2.R.tl.cerebhem	64.2174932066532	83.1883355853349	80.6380732441947
chr8.22991_chr8_94579439_94585392_+_2.R.tl.cortex	60.3878089207684	124.828568943746	412.841895673284
chr8.22991_chr8_94579439_94585392_+_2.R.tl.heart	61.0308815243629	85.8840433358403	60.6283296473507
chr8.22991_chr8_94579439_94585392_+_2.R.tl.kidney	65.1186892937482	112.139606700439	73.7895632697216
chr8.22991_chr8_94579439_94585392_+_2.R.tl.liver	68.3452557039121	100.973502174127	50.1850525442589
chr8.22991_chr8_94579439_94585392_+_2.R.tl.stomach	63.2893459066436	86.3552474001258	61.2601160753575
chr8.22991_chr8_94579439_94585392_+_2.R.tl.testicle	61.4898358306535	89.9234758586221	62.1296648264496


diffExp=-35.480203798052,-18.9708423786816,-64.4407600229775,-24.8531618114774,-47.0209174066904,-32.6282464702150,-23.0659014934821,-28.4336400279687
diffExpScore=0.996375415254573
diffExp1.5=-1,0,-1,0,-1,0,0,0
diffExp1.5Score=0.75
diffExp1.4=-1,0,-1,-1,-1,-1,0,-1
diffExp1.4Score=0.857142857142857
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	65.9102349111577	84.3212808399732	93.3927551068752
cerebhem	79.396704971774	83.6147624426111	68.2774016499949
cortex	73.0266279485432	72.4583198424918	75.7999564025544
heart	80.9236961238261	82.0443123226219	67.3085324963131
kidney	71.9510490139335	79.3287849262588	71.8021050134108
liver	73.1137220899465	74.1607190784138	79.9816057939212
stomach	75.2226053431849	81.0652721449522	75.334324271488
testicle	67.1925482692967	67.1603311552474	79.7884277527896
cont.diffExp=-18.4110459288155,-4.21805747083711,0.568308106051433,-1.12061619879579,-7.37773591232528,-1.04699698846733,-5.84266680176725,0.0322171140493168
cont.diffExpScore=1.00523342698674

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

tran.correlation=-0.0260850406932242
cont.tran.correlation=0.404378225911713

tran.covariance=2.48283312164996e-05
cont.tran.covariance=0.00233510499864024

tran.mean=80.6963344602524
cont.tran.mean=75.6806857140146

weightedLogRatios:
wLogRatio
Lung	-1.9270846650137
cerebhem	-1.11082486721849
cortex	-3.24145336991125
heart	-1.46287311717068
kidney	-2.41762282792394
liver	-1.72495284852403
stomach	-1.33719728883022
testicle	-1.63776340008337

cont.weightedLogRatios:
wLogRatio
Lung	-1.06208835326088
cerebhem	-0.227775690706435
cortex	0.0334921595093963
heart	-0.0605176235862796
kidney	-0.422164882813767
liver	-0.0611273359077058
stomach	-0.325979929779545
testicle	0.00201778769260562

varWeightedLogRatios=0.469104070586145
cont.varWeightedLogRatios=0.129846553076701

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04394653005826	0.089951081684413	44.9571751037543	2.92987146097512e-194	***
df.mm.trans1	-0.129059219587047	0.0803735488768517	-1.60574245371187	0.108856656330278	   
df.mm.trans2	0.446557269247099	0.0744740565986797	5.99614536446529	3.49087633607595e-09	***
df.mm.exp2	-0.373712800617412	0.102177562691974	-3.65748399914386	0.000277089266931905	***
df.mm.exp3	-1.66246079143187	0.102177562691974	-16.2703116773646	1.41374694194355e-49	***
df.mm.exp4	-0.107508910092448	0.102177562691974	-1.05217728100000	0.293141778652965	   
df.mm.exp5	0.0276141908902014	0.102177562691974	0.270256895571562	0.787055556144784	   
df.mm.exp6	0.356588368377143	0.102177562691974	3.48988915944415	0.000518665052466731	***
df.mm.exp7	-0.07606710463965	0.102177562691974	-0.744459963964528	0.456889721941575	   
df.mm.exp8	-0.0785173228528065	0.102177562691974	-0.76843996650719	0.442528186493012	   
df.mm.trans1:exp2	0.373291892027114	0.0969341471613846	3.85098443591425	0.000130289570042127	***
df.mm.trans2:exp2	0.192406227030538	0.0854878823130538	2.25068421189746	0.0247668213749022	*  
df.mm.trans1:exp3	1.60055147491554	0.0969341471613846	16.5117404112588	9.0978122812874e-51	***
df.mm.trans2:exp3	1.88698842469416	0.0854878823130538	22.0731684261879	1.72055617891083e-79	***
df.mm.trans1:exp4	0.0561923386352052	0.0969341471613846	0.579696013022648	0.562337232124245	   
df.mm.trans2:exp4	-0.0419067510713249	0.0854878823130538	-0.490206915149259	0.624166690345628	   
df.mm.trans1:exp5	-0.0140991589762169	0.0969341471613846	-0.145450900318371	0.884403727783666	   
df.mm.trans2:exp5	0.0897166782861422	0.0854878823130538	1.04946661279552	0.294385940252624	   
df.mm.trans1:exp6	-0.294712781434559	0.0969341471613846	-3.04034016974323	0.00246612775500386	** 
df.mm.trans2:exp6	-0.344143954962344	0.0854878823130537	-4.02564604071136	6.40984118087544e-05	***
df.mm.trans1:exp7	0.0610875464876691	0.0969341471613846	0.630196357801189	0.528806049397833	   
df.mm.trans2:exp7	-0.0678770379428743	0.0854878823130537	-0.793996015649456	0.427511658364706	   
df.mm.trans1:exp8	0.0346926509354971	0.0969341471613846	0.357899171256313	0.720544639936814	   
df.mm.trans2:exp8	-0.0249373510313606	0.0854878823130537	-0.291706267094567	0.770612041798722	   
df.mm.trans1:probe2	-0.119588358643009	0.0484670735806923	-2.46741446941103	0.0138873124554804	*  
df.mm.trans1:probe3	0.183887467676686	0.0484670735806923	3.79406995494649	0.000163223698199793	***
df.mm.trans1:probe4	0.467299656231724	0.0484670735806923	9.6415900880362	1.50392899261019e-20	***
df.mm.trans1:probe5	-0.0638279560094713	0.0484670735806923	-1.31693439058591	0.188363366666042	   
df.mm.trans1:probe6	-0.0741344222856248	0.0484670735806923	-1.52958321616425	0.126646943276699	   
df.mm.trans1:probe7	0.224112155516568	0.0484670735806923	4.62400840321927	4.61241805125998e-06	***
df.mm.trans1:probe8	0.24524145357942	0.0484670735806923	5.05995999884581	5.58287827130361e-07	***
df.mm.trans1:probe9	0.0494876410540714	0.0484670735806923	1.02105692376248	0.307638980801842	   
df.mm.trans1:probe10	0.150038313007007	0.0484670735806923	3.09567510316483	0.00205520170381875	** 
df.mm.trans1:probe11	0.139609369448989	0.0484670735806923	2.88049925722368	0.0041122503582118	** 
df.mm.trans1:probe12	0.171473499517506	0.0484670735806923	3.53793796177979	0.000434438844554442	***
df.mm.trans1:probe13	0.63115185318997	0.0484670735806923	13.0222810366129	2.55790070504103e-34	***
df.mm.trans1:probe14	0.507210340957093	0.0484670735806923	10.4650498469367	1.17771792110767e-23	***
df.mm.trans1:probe15	0.562070555069923	0.0484670735806923	11.5969567284507	3.37198380310182e-28	***
df.mm.trans1:probe16	0.718519176663685	0.0484670735806923	14.8248929341160	1.35886669221048e-42	***
df.mm.trans1:probe17	0.686436392791168	0.0484670735806923	14.1629428409439	1.71238182797543e-39	***
df.mm.trans1:probe18	0.725007144858207	0.0484670735806923	14.9587563534479	3.14953684862188e-43	***
df.mm.trans2:probe2	-0.0689499358295046	0.0484670735806923	-1.42261396728875	0.155367771790262	   
df.mm.trans2:probe3	0.261823447004283	0.0484670735806923	5.40208904027135	9.50907671901591e-08	***
df.mm.trans2:probe4	0.38781283105237	0.0484670735806923	8.00157307634233	6.36936326611333e-15	***
df.mm.trans2:probe5	0.303162628427455	0.0484670735806923	6.25502234878537	7.55758132316248e-10	***
df.mm.trans2:probe6	0.123340264241645	0.0484670735806923	2.54482590198679	0.0111825322007145	*  
df.mm.trans3:probe2	-0.243299335804411	0.0484670735806923	-5.0198891294591	6.82475092749592e-07	***
df.mm.trans3:probe3	-0.247077626461890	0.0484670735806923	-5.09784495345139	4.61152652319112e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.99984189760797	0.220944004291111	18.1034190560694	9.02999567462931e-59	***
df.mm.trans1	0.136805271767609	0.197419012594443	0.692969081193046	0.488597089892519	   
df.mm.trans2	0.47482712302495	0.182928275820461	2.59570107953666	0.00967086751684417	** 
df.mm.exp2	0.490983873866058	0.250975523886141	1.95630181885306	0.0508931365405579	.  
df.mm.exp3	0.159623681084328	0.250975523886141	0.636012941073663	0.525010510447702	   
df.mm.exp4	0.505364931082672	0.250975523886141	2.01360245516188	0.0444972631061985	*  
df.mm.exp5	0.289559029821296	0.250975523886141	1.15373413844395	0.249068547113651	   
df.mm.exp6	0.130339762382713	0.250975523886141	0.51933256424576	0.603720361195271	   
df.mm.exp7	0.307656342998193	0.250975523886141	1.22584201931088	0.220739049074902	   
df.mm.exp8	-0.0508476030485137	0.250975523886141	-0.202599848228951	0.83951648884247	   
df.mm.trans1:exp2	-0.304820744748531	0.238096287730260	-1.28024148404138	0.200954944180509	   
df.mm.trans2:exp2	-0.499398060027873	0.209981188473982	-2.37829904505827	0.0177047996245796	*  
df.mm.trans1:exp3	-0.0570932792070385	0.23809628773026	-0.239790715560083	0.81057440059681	   
df.mm.trans2:exp3	-0.311246458170653	0.209981188473982	-1.48225877009558	0.138796460962873	   
df.mm.trans1:exp4	-0.300151982733949	0.238096287730260	-1.26063277002450	0.207931190278546	   
df.mm.trans2:exp4	-0.532739710505523	0.209981188473982	-2.53708303290003	0.0114301786190796	*  
df.mm.trans1:exp5	-0.201866756009807	0.238096287730260	-0.847836637581276	0.396867038017908	   
df.mm.trans2:exp5	-0.350592254228874	0.209981188473982	-1.66963648875773	0.0955129037387993	.  
df.mm.trans1:exp6	-0.0266174357332245	0.238096287730260	-0.111792737244940	0.911025134170648	   
df.mm.trans2:exp6	-0.258739419766324	0.209981188473982	-1.23220285420179	0.218355962991338	   
df.mm.trans1:exp7	-0.175498293424941	0.238096287730260	-0.737089582949582	0.461355998360467	   
df.mm.trans2:exp7	-0.347035958786625	0.209981188473982	-1.65270023142871	0.0989150222984928	.  
df.mm.trans1:exp8	0.0701162164008364	0.238096287730260	0.294486810648099	0.768487796005076	   
df.mm.trans2:exp8	-0.17670390881888	0.209981188473982	-0.841522567345478	0.400390513766926	   
df.mm.trans1:probe2	-0.068460493164449	0.11904814386513	-0.575065607423566	0.565462500349485	   
df.mm.trans1:probe3	0.195545570263769	0.11904814386513	1.64257554897541	0.100994724128139	   
df.mm.trans1:probe4	-0.0756623222398594	0.11904814386513	-0.635560704966366	0.525305109488928	   
df.mm.trans1:probe5	0.059232364032586	0.11904814386513	0.4975496644424	0.618983777121146	   
df.mm.trans1:probe6	0.219640280913373	0.11904814386513	1.84497022618180	0.0655345021346011	.  
df.mm.trans1:probe7	-0.000476035207202119	0.11904814386513	-0.00399867811245693	0.996810854071127	   
df.mm.trans1:probe8	0.0790261707082825	0.11904814386513	0.663816907534581	0.507062489824599	   
df.mm.trans1:probe9	0.0966461709996733	0.119048143865130	0.811824257496733	0.417214428746256	   
df.mm.trans1:probe10	-0.0103555446221179	0.11904814386513	-0.0869861913500281	0.930711509628374	   
df.mm.trans1:probe11	0.0106689309706217	0.11904814386513	0.089618625072463	0.9286201898242	   
df.mm.trans1:probe12	0.0158208651224188	0.11904814386513	0.132894681166489	0.894321230078166	   
df.mm.trans1:probe13	0.113502982231387	0.11904814386513	0.953420847619221	0.34076081760176	   
df.mm.trans1:probe14	0.0574506902932685	0.11904814386513	0.482583671009223	0.629567388130702	   
df.mm.trans1:probe15	0.0445479866370751	0.11904814386513	0.374201438096705	0.708386657177026	   
df.mm.trans1:probe16	0.106275179562975	0.11904814386513	0.892707572857036	0.372371685480934	   
df.mm.trans1:probe17	0.0693157960542789	0.11904814386513	0.58225012002545	0.560616937003854	   
df.mm.trans1:probe18	0.171859373303165	0.11904814386513	1.44361237162895	0.149369951535618	   
df.mm.trans2:probe2	0.0489366655178131	0.11904814386513	0.411066178177912	0.681170783438307	   
df.mm.trans2:probe3	-0.0272564242009218	0.11904814386513	-0.228952953956181	0.81898349877315	   
df.mm.trans2:probe4	-0.128309743870328	0.11904814386513	-1.07779709707772	0.281557440091438	   
df.mm.trans2:probe5	-0.138378439937753	0.11904814386513	-1.16237377119060	0.245545801855364	   
df.mm.trans2:probe6	-0.115304769059505	0.11904814386513	-0.968555790253518	0.333157123783221	   
df.mm.trans3:probe2	-0.131082468836568	0.11904814386513	-1.10108788411747	0.271299933579501	   
df.mm.trans3:probe3	-0.0928990870330242	0.11904814386513	-0.780348890934998	0.435493278672324