chr14.7731_chr14_54976381_54986051_+_2.R 

fitVsDatCorrelation=0.864275515675649
cont.fitVsDatCorrelation=0.224084582990504

fstatistic=15058.4586562646,49,623
cont.fstatistic=4002.32260698440,49,623

residuals=-0.425652848445406,-0.0781986759451756,-0.0033590088989137,0.0769284822294114,0.389835521821970
cont.residuals=-0.568784556098111,-0.160131988580482,-0.0384841515461819,0.141235593758361,0.971046051094769

predictedValues:
Include	Exclude	Both
chr14.7731_chr14_54976381_54986051_+_2.R.tl.Lung	50.3188943820684	66.0271172791436	66.7302655313794
chr14.7731_chr14_54976381_54986051_+_2.R.tl.cerebhem	53.5356373391895	64.0787384329575	62.6705281035846
chr14.7731_chr14_54976381_54986051_+_2.R.tl.cortex	54.9257500274234	65.7913987182953	66.532388786275
chr14.7731_chr14_54976381_54986051_+_2.R.tl.heart	52.6190660689687	65.7479746988798	61.4101605615192
chr14.7731_chr14_54976381_54986051_+_2.R.tl.kidney	50.9683614270186	62.0855066285171	68.7832853691632
chr14.7731_chr14_54976381_54986051_+_2.R.tl.liver	52.7432727907157	66.6499269501056	64.2851621629959
chr14.7731_chr14_54976381_54986051_+_2.R.tl.stomach	50.0829916112531	78.2573515904823	59.5949055079189
chr14.7731_chr14_54976381_54986051_+_2.R.tl.testicle	52.1299959878189	63.630447523469	62.1368591563546


diffExp=-15.7082228970752,-10.5431010937680,-10.8656486908718,-13.1289086299111,-11.1171452014986,-13.9066541593898,-28.1743599792292,-11.5004515356500
diffExpScore=0.991375183235235
diffExp1.5=0,0,0,0,0,0,-1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,-1,0
diffExp1.4Score=0.5
diffExp1.3=-1,0,0,0,0,0,-1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,0,0,-1,-1,-1,-1,-1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	64.0518784584939	59.0549598957761	59.0591066669976
cerebhem	59.4659305525583	56.8992295891205	66.3125289796696
cortex	62.2295206896059	58.6023549588537	58.7856790647524
heart	59.7399146518257	60.5897356730645	60.5092363768144
kidney	61.7433214469925	55.7485352091843	59.8882922392122
liver	59.889349374666	65.5700841530783	58.9665732747695
stomach	59.5613964611434	59.0961674530289	59.489360072555
testicle	59.5854432769049	64.0589795800488	59.9165488234115
cont.diffExp=4.99691856271782,2.56670096343780,3.62716573075222,-0.849821021238796,5.99478623780814,-5.68073477841228,0.465229008114562,-4.47353630314395
cont.diffExpScore=3.74734998466693

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=-0.411805265777406
cont.tran.correlation=-0.361107010898484

tran.covariance=-0.000906332662270129
cont.tran.covariance=-0.000551415208124646

tran.mean=59.3495269660192
cont.tran.mean=60.3679250890216

weightedLogRatios:
wLogRatio
Lung	-1.10147113245508
cerebhem	-0.731685173618303
cortex	-0.73939855073919
heart	-0.90758594907027
kidney	-0.795121362198795
liver	-0.955364477899778
stomach	-1.84636080755404
testicle	-0.808054898895297

cont.weightedLogRatios:
wLogRatio
Lung	0.334571616350817
cerebhem	0.179281795833328
cortex	0.246272090635172
heart	-0.0578714904264311
kidney	0.415884087345728
liver	-0.374971883618242
stomach	0.032017852428489
testicle	-0.298519969553893

varWeightedLogRatios=0.136318275116323
cont.varWeightedLogRatios=0.0833401286591674

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94187810761232	0.0590974359134548	66.7013390121527	7.09159404349249e-286	***
df.mm.trans1	-0.0303136835878897	0.049778150277109	-0.608975693534955	0.54276253986414	   
df.mm.trans2	0.183170820939815	0.0458699086372809	3.99326762100727	7.29435650226776e-05	***
df.mm.exp2	0.094781315063418	0.0599051832109073	1.58218888555474	0.114113959732132	   
df.mm.exp3	0.0869949465489083	0.0599051832109073	1.45221067503669	0.146946455593666	   
df.mm.exp4	0.123544531642029	0.0599051832109074	2.06233459310303	0.0395899300978557	*  
df.mm.exp5	-0.079030679911548	0.0599051832109073	-1.31926280290815	0.187565920553751	   
df.mm.exp6	0.093773779872455	0.0599051832109073	1.56537005391181	0.118003940985400	   
df.mm.exp7	0.278326584514267	0.0599051832109073	4.64611857598976	4.12926327407391e-06	***
df.mm.exp8	0.0697056943755946	0.0599051832109074	1.16360038713483	0.245031444503439	   
df.mm.trans1:exp2	-0.0328144048416851	0.0522895307779392	-0.627552099884771	0.53052733312509	   
df.mm.trans2:exp2	-0.124734225382082	0.0433561885135412	-2.87696473464508	0.00415263526890182	** 
df.mm.trans1:exp3	0.000606686700585309	0.0522895307779392	0.0116024506542573	0.990746505808896	   
df.mm.trans2:exp3	-0.0905713608381435	0.0433561885135412	-2.08900652809589	0.0371123529506705	*  
df.mm.trans1:exp4	-0.0788466451929786	0.0522895307779392	-1.50788588116847	0.132090650561019	   
df.mm.trans2:exp4	-0.127781189626590	0.0433561885135412	-2.94724222786976	0.00332614702534355	** 
df.mm.trans1:exp5	0.0918551155429885	0.0522895307779392	1.75666360314218	0.0794664101171759	.  
df.mm.trans2:exp5	0.0174777284781797	0.0433561885135412	0.403119579404933	0.686998561213871	   
df.mm.trans1:exp6	-0.0467181860940534	0.0522895307779392	-0.893452004617409	0.371960160681534	   
df.mm.trans2:exp6	-0.0843853547799086	0.0433561885135412	-1.94632779478651	0.0520645660779772	.  
df.mm.trans1:exp7	-0.283025763271461	0.0522895307779392	-5.4126659593371	8.87031552447839e-08	***
df.mm.trans2:exp7	-0.108389334978068	0.0433561885135412	-2.49997379138195	0.0126762307614562	*  
df.mm.trans1:exp8	-0.0343458129718026	0.0522895307779392	-0.656839188663995	0.511526878882397	   
df.mm.trans2:exp8	-0.106679129449798	0.0433561885135412	-2.46052831457912	0.0141435056847944	*  
df.mm.trans1:probe2	-0.0581095531777812	0.0358001944105474	-1.62316306194865	0.105060410746144	   
df.mm.trans1:probe3	-0.0119480375746602	0.0358001944105474	-0.333742253956589	0.738686363275387	   
df.mm.trans1:probe4	-0.0226163774481408	0.0358001944105474	-0.631738956185043	0.52778920284121	   
df.mm.trans1:probe5	-0.0582487700286554	0.0358001944105474	-1.62705177968235	0.104231732489847	   
df.mm.trans1:probe6	-0.0606464211291696	0.0358001944105474	-1.69402491041507	0.0907603633405991	.  
df.mm.trans1:probe7	0.166789938835306	0.0358001944105474	4.65891153893195	3.88891363045975e-06	***
df.mm.trans1:probe8	-0.0129583282385761	0.0358001944105474	-0.361962510314143	0.717502783627045	   
df.mm.trans1:probe9	0.00785340939875518	0.0358001944105474	0.219367786350384	0.826435413311752	   
df.mm.trans1:probe10	0.146889161913072	0.0358001944105474	4.1030269341162	4.6197462629458e-05	***
df.mm.trans1:probe11	0.00497831446361783	0.0358001944105474	0.13905830807866	0.88944901196969	   
df.mm.trans1:probe12	0.0479734234974923	0.0358001944105474	1.340032485504	0.180723311081943	   
df.mm.trans2:probe2	0.482319135702823	0.0358001944105474	13.4725283938883	1.72040346735361e-36	***
df.mm.trans2:probe3	0.134645506776536	0.0358001944105474	3.76102725120585	0.000185166038996199	***
df.mm.trans2:probe4	-0.0745569565323793	0.0358001944105474	-2.08258524178331	0.0376964079314527	*  
df.mm.trans2:probe5	0.262497448887949	0.0358001944105474	7.3322911567936	7.06178646274499e-13	***
df.mm.trans2:probe6	0.235360416477302	0.0358001944105474	6.57427760805567	1.03485826675150e-10	***
df.mm.trans3:probe2	-0.0945795091504404	0.0358001944105474	-2.64187138387649	0.00845196124056725	** 
df.mm.trans3:probe3	0.253133867805545	0.0358001944105474	7.07074003293587	4.1501408820168e-12	***
df.mm.trans3:probe4	-0.017434182975645	0.0358001944105474	-0.486985706717518	0.626439748544734	   
df.mm.trans3:probe5	-0.150784948050643	0.0358001944105474	-4.21184718500352	2.90684294324981e-05	***
df.mm.trans3:probe6	0.0409608229003738	0.0358001944105474	1.14415085098828	0.253000486726695	   
df.mm.trans3:probe7	0.257713443956763	0.0358001944105474	7.19866045981123	1.75677417075958e-12	***
df.mm.trans3:probe8	0.290845286011033	0.0358001944105474	8.12412588254953	2.42653275004213e-15	***
df.mm.trans3:probe9	0.0281290235491454	0.0358001944105474	0.785722647943442	0.432328712594563	   
df.mm.trans3:probe10	0.451249807292007	0.0358001944105474	12.604674771237	1.31261180572100e-32	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15600698788106	0.114497866672055	36.2976805479242	7.66819027980463e-156	***
df.mm.trans1	0.0112716162613199	0.0964422893398717	0.116874208798566	0.90699740237545	   
df.mm.trans2	-0.0937071663901636	0.0888702970311946	-1.05442616397773	0.292096597467049	   
df.mm.exp2	-0.227316638815642	0.116062830378837	-1.95856535700246	0.0506094747966895	.  
df.mm.exp3	-0.0319170323538958	0.116062830378837	-0.274997880455926	0.783409040040755	   
df.mm.exp4	-0.0682932757782059	0.116062830378837	-0.588416425442083	0.556466068765114	   
df.mm.exp5	-0.108267219769798	0.116062830378837	-0.932832840767426	0.351267655478976	   
df.mm.exp6	0.0390243720080438	0.116062830378837	0.336234881405750	0.736806966584033	   
df.mm.exp7	-0.0792468861512744	0.116062830378837	-0.682792982840478	0.494991475650911	   
df.mm.exp8	-0.00536036183176467	0.116062830378837	-0.0461850000923473	0.963177598114374	   
df.mm.trans1:exp2	0.153026835702196	0.101307943920350	1.51051171093263	0.131419917304835	   
df.mm.trans2:exp2	0.190129906157971	0.0840001095665422	2.26344831142579	0.0239512728632947	*  
df.mm.trans1:exp3	0.00305317275122533	0.101307943920350	0.0301375453204911	0.975967009312917	   
df.mm.trans2:exp3	0.0242233811765112	0.0840001095665421	0.288373209291141	0.773156988378713	   
df.mm.trans1:exp4	-0.00139969600943573	0.101307943920350	-0.0138162512757755	0.98898100032494	   
df.mm.trans2:exp4	0.0939502422110456	0.0840001095665422	1.11845380554678	0.263804298300695	   
df.mm.trans1:exp5	0.0715596788100155	0.101307943920350	0.706358021304594	0.480229592989239	   
df.mm.trans2:exp5	0.050649821526101	0.0840001095665421	0.602973279290521	0.546745897558865	   
df.mm.trans1:exp6	-0.106219045377134	0.101307943920350	-1.04847696307650	0.294825481183255	   
df.mm.trans2:exp6	0.0656266517108712	0.0840001095665422	0.781268644166279	0.434941287894432	   
df.mm.trans1:exp7	0.0065611840699345	0.101307943920350	0.0647647540363961	0.948382080150944	   
df.mm.trans2:exp7	0.0799444259860317	0.0840001095665422	0.951718115590103	0.341609036464536	   
df.mm.trans1:exp8	-0.0669216901629957	0.101307943920350	-0.660576925888561	0.509127843594114	   
df.mm.trans2:exp8	0.086696042793697	0.0840001095665422	1.03209440131765	0.302428385094344	   
df.mm.trans1:probe2	0.0593492949429335	0.0693608076745553	0.855660378428746	0.392514619655565	   
df.mm.trans1:probe3	-0.0398774084400051	0.0693608076745553	-0.574927106199686	0.565548155544216	   
df.mm.trans1:probe4	-0.0488332317650805	0.0693608076745553	-0.704046469502038	0.48166690031294	   
df.mm.trans1:probe5	-0.0100979775987027	0.0693608076745553	-0.145586217018738	0.884295139413028	   
df.mm.trans1:probe6	-0.0143068132945644	0.0693608076745553	-0.206266532559609	0.836650108263043	   
df.mm.trans1:probe7	0.0317947408980096	0.0693608076745553	0.458396347504952	0.646827572242793	   
df.mm.trans1:probe8	-0.0176036917742526	0.0693608076745553	-0.253798829114708	0.79973471426868	   
df.mm.trans1:probe9	-0.0504022957193401	0.0693608076745553	-0.726668235407961	0.467702155211092	   
df.mm.trans1:probe10	-0.00663871111326953	0.0693608076745553	-0.0957127135026847	0.923779521667508	   
df.mm.trans1:probe11	-0.0603137947541378	0.0693608076745553	-0.869565923123811	0.384872557890651	   
df.mm.trans1:probe12	-0.00994557240251016	0.0693608076745553	-0.143388935855178	0.886029385900452	   
df.mm.trans2:probe2	0.0196536720911637	0.0693608076745553	0.283354141194258	0.776999509741478	   
df.mm.trans2:probe3	0.0626698824806903	0.0693608076745553	0.90353449710016	0.366591514267471	   
df.mm.trans2:probe4	0.0884624469343264	0.0693608076745553	1.27539528301627	0.202644737801612	   
df.mm.trans2:probe5	0.0543579992044535	0.0693608076745553	0.783699051768603	0.433514558132806	   
df.mm.trans2:probe6	0.0335553988595795	0.0693608076745553	0.483780393922506	0.628711728291002	   
df.mm.trans3:probe2	0.105480762104175	0.0693608076745553	1.52075452464591	0.128828798827600	   
df.mm.trans3:probe3	-0.0333867807829007	0.0693608076745553	-0.481349365762193	0.630437237014474	   
df.mm.trans3:probe4	-0.0239780392102533	0.0693608076745553	-0.345700115297959	0.729684797923851	   
df.mm.trans3:probe5	-0.0436980274021763	0.0693608076745553	-0.630010359844854	0.528918798566039	   
df.mm.trans3:probe6	0.0257606932525767	0.0693608076745553	0.371401287214636	0.710464938015163	   
df.mm.trans3:probe7	0.0381184052796582	0.0693608076745553	0.549566917653437	0.582813395167556	   
df.mm.trans3:probe8	-0.0579038559818043	0.0693608076745553	-0.834820959027645	0.404138531193718	   
df.mm.trans3:probe9	0.0405650120429118	0.0693608076745553	0.584840537515726	0.558866684917863	   
df.mm.trans3:probe10	-0.00128504336155194	0.0693608076745553	-0.0185269376847720	0.985224419835995