chr10.1949_chr10_127943791_127948486_+_2.R 

fitVsDatCorrelation=0.778903412998317
cont.fitVsDatCorrelation=0.216846334404771

fstatistic=10789.2530497745,51,669
cont.fstatistic=4445.197457566,51,669

residuals=-0.530072571964474,-0.0918069970268404,-0.0088024720512595,0.0740669521351484,0.609151449715834
cont.residuals=-0.671954870427037,-0.149551993463655,-0.00506623723782181,0.117802503597269,1.17726027771921

predictedValues:
Include	Exclude	Both
chr10.1949_chr10_127943791_127948486_+_2.R.tl.Lung	58.0081760301282	67.2996652150943	66.5501291507162
chr10.1949_chr10_127943791_127948486_+_2.R.tl.cerebhem	60.7236917480318	72.300349001536	60.9550640151528
chr10.1949_chr10_127943791_127948486_+_2.R.tl.cortex	68.1893261909044	68.6772515676265	83.1275580160602
chr10.1949_chr10_127943791_127948486_+_2.R.tl.heart	57.0081121089447	68.0694684733622	73.9739487525773
chr10.1949_chr10_127943791_127948486_+_2.R.tl.kidney	54.1860105604185	65.1616770369587	64.5912845272598
chr10.1949_chr10_127943791_127948486_+_2.R.tl.liver	53.6711168928858	67.1505695128564	66.0793754871047
chr10.1949_chr10_127943791_127948486_+_2.R.tl.stomach	55.9455407685439	75.7203120492214	68.2482102901858
chr10.1949_chr10_127943791_127948486_+_2.R.tl.testicle	59.859076346965	68.5495454735993	77.2940737471965


diffExp=-9.29148918496615,-11.5766572535042,-0.487925376722131,-11.0613563644175,-10.9756664765402,-13.4794526199706,-19.7747712806775,-8.69046912663432
diffExpScore=0.988417586009192
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,-1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,0,-1,-1,-1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	60.933974836849	57.4082892197585	58.6115955966504
cerebhem	61.2732595768279	61.2967341992717	59.2972797977466
cortex	59.9303468042954	59.2690402518499	59.152369772638
heart	59.1878909398783	65.5296050307567	62.8988839615026
kidney	59.2385103839298	62.739677592315	64.2641895212328
liver	58.6275120310363	68.3256387741688	57.6892447722648
stomach	59.4060773807621	59.2650367910593	58.9271517529915
testicle	61.0326225207444	62.4106969487503	62.9557555974027
cont.diffExp=3.52568561709052,-0.0234746224438425,0.661306552445474,-6.34171409087842,-3.50116720838525,-9.6981267431325,0.141040589702804,-1.37807442800597
cont.diffExpScore=1.43464503346652

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=0.130402024896942
cont.tran.correlation=-0.599439477047651

tran.covariance=0.000575762469204743
cont.tran.covariance=-0.00056787703807472

tran.mean=63.7824930610673
cont.tran.mean=60.9921820801408

weightedLogRatios:
wLogRatio
Lung	-0.614322935226525
cerebhem	-0.731759100946382
cortex	-0.0301302144171697
heart	-0.732724442644038
kidney	-0.753408073093413
liver	-0.917514542896862
stomach	-1.26385339267288
testicle	-0.563914493639903

cont.weightedLogRatios:
wLogRatio
Lung	0.243176343086634
cerebhem	-0.00157641572062621
cortex	0.045356064968427
heart	-0.420536078370632
kidney	-0.236021573347873
liver	-0.63494091192587
stomach	0.00970578803645615
testicle	-0.092049499824993

varWeightedLogRatios=0.120679037007641
cont.varWeightedLogRatios=0.0796861740254473

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.88301665862976	0.0759616203570204	51.1181388756524	3.1062894705622e-233	***
df.mm.trans1	0.226779863414167	0.0672839230928389	3.3704910919248	0.000793476274856732	***
df.mm.trans2	0.409056656265367	0.0614423547246871	6.65756802613248	5.8058842252046e-11	***
df.mm.exp2	0.205242206567267	0.0829161836430723	2.47529731289575	0.0135591331042571	*  
df.mm.exp3	-0.0404539524819663	0.0829161836430723	-0.48788970626178	0.625787819049873	   
df.mm.exp4	-0.111774376304416	0.0829161836430723	-1.34804053193727	0.178101602507921	   
df.mm.exp5	-0.0705689425161062	0.0829161836430723	-0.851087681747184	0.395025170149650	   
df.mm.exp6	-0.0728280210961761	0.0829161836430723	-0.878333009267255	0.380078365771082	   
df.mm.exp7	0.0564902245723378	0.0829161836430723	0.681293109382725	0.495921799558789	   
df.mm.exp8	-0.099851182815681	0.0829161836430723	-1.20424238584724	0.228921788318952	   
df.mm.trans1:exp2	-0.159492242437054	0.0783484291364753	-2.03567887952462	0.0421761353380281	*  
df.mm.trans2:exp2	-0.133568512406310	0.0664808466452585	-2.00912772845736	0.0449248109841714	*  
df.mm.trans1:exp3	0.202158030969733	0.0783484291364753	2.58024357600831	0.0100851021285751	*  
df.mm.trans2:exp3	0.0607167076441768	0.0664808466452584	0.91329624558124	0.361415770382857	   
df.mm.trans1:exp4	0.094383985065213	0.0783484291364753	1.20466978222123	0.228756797166916	   
df.mm.trans2:exp4	0.123147893081156	0.0664808466452585	1.85238153987829	0.0644113340922071	.  
df.mm.trans1:exp5	0.00240774325647776	0.0783484291364753	0.0307312256673801	0.975493053960733	   
df.mm.trans2:exp5	0.03828520101158	0.0664808466452584	0.575883174531	0.564887814603133	   
df.mm.trans1:exp6	-0.00488094920384671	0.0783484291364753	-0.0622979842434949	0.950344141111254	   
df.mm.trans2:exp6	0.07061016311095	0.0664808466452585	1.06211287421963	0.28856769843598	   
df.mm.trans1:exp7	-0.0926954599330991	0.0783484291364753	-1.18311829547511	0.237182530777438	   
df.mm.trans2:exp7	0.061400960731454	0.0664808466452585	0.923588730135904	0.356033471530405	   
df.mm.trans1:exp8	0.131260288251753	0.0783484291364753	1.67534039544189	0.0943347575108725	.  
df.mm.trans2:exp8	0.118252696097332	0.0664808466452585	1.77874834729960	0.0757350442097309	.  
df.mm.trans1:probe2	-0.318955641738897	0.0429132019831425	-7.43257615370186	3.2662716349847e-13	***
df.mm.trans1:probe3	-0.231030011934058	0.0429132019831425	-5.38365820441021	1.01122924086757e-07	***
df.mm.trans1:probe4	-0.302856788940157	0.0429132019831425	-7.05742696755949	4.25200994551532e-12	***
df.mm.trans1:probe5	-0.262104343234240	0.0429132019831425	-6.10777875156464	1.71349978578494e-09	***
df.mm.trans1:probe6	-0.111659463215038	0.0429132019831425	-2.60198395959594	0.00947374749199827	** 
df.mm.trans1:probe7	-0.232227804278547	0.0429132019831425	-5.41157018228964	8.71225556120454e-08	***
df.mm.trans1:probe8	-0.183792569193843	0.0429132019831425	-4.28289106149761	2.11514805335251e-05	***
df.mm.trans1:probe9	-0.0550094869028802	0.0429132019831425	-1.28187793873991	0.200329528064362	   
df.mm.trans1:probe10	-0.0977144702792213	0.0429132019831425	-2.27702585133606	0.0230995659324170	*  
df.mm.trans1:probe11	-0.0374822855699502	0.0429132019831425	-0.873444157923109	0.382734437989375	   
df.mm.trans1:probe12	-0.0705431428838823	0.0429132019831425	-1.64385642701734	0.100675685237925	   
df.mm.trans1:probe13	0.0658641353327665	0.0429132019831425	1.53482220596449	0.125300273330188	   
df.mm.trans1:probe14	-0.0327189030381856	0.0429132019831425	-0.76244375917319	0.446063928756315	   
df.mm.trans1:probe15	0.145348076341792	0.0429132019831425	3.38702472956665	0.000747927023392725	***
df.mm.trans1:probe16	0.0781163025262398	0.0429132019831425	1.82033264627808	0.0691550288793743	.  
df.mm.trans1:probe17	0.0716988449791437	0.0429132019831425	1.67078758204314	0.0952313466708046	.  
df.mm.trans1:probe18	0.124796425294007	0.0429132019831425	2.90811264428674	0.00375667501757947	** 
df.mm.trans1:probe19	0.158974335198331	0.0429132019831425	3.7045554247101	0.000229250747516807	***
df.mm.trans1:probe20	0.110195462042494	0.0429132019831425	2.56786855676213	0.0104486072437606	*  
df.mm.trans2:probe2	-0.179897580899375	0.0429132019831425	-4.19212672524514	3.13504623826549e-05	***
df.mm.trans2:probe3	-0.214958286862146	0.0429132019831425	-5.00914117167458	7.00391280836119e-07	***
df.mm.trans2:probe4	-0.189901492274257	0.0429132019831425	-4.42524639268017	1.12452934716213e-05	***
df.mm.trans2:probe5	-0.136088540745434	0.0429132019831425	-3.17125114082358	0.00158719279536967	** 
df.mm.trans2:probe6	-0.108334626943801	0.0429132019831425	-2.52450579162929	0.011816277200687	*  
df.mm.trans3:probe2	-0.519606829437596	0.0429132019831425	-12.1083211092407	1.18218953875383e-30	***
df.mm.trans3:probe3	-0.356394784535963	0.0429132019831425	-8.30501496196822	5.53506516915834e-16	***
df.mm.trans3:probe4	-0.407589157279086	0.0429132019831425	-9.4979898596054	3.72021286789198e-20	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10377091576307	0.118241077410528	34.7068126038377	9.59682017721933e-152	***
df.mm.trans1	-0.0422924704229993	0.104733463050319	-0.403810484168558	0.68648112890506	   
df.mm.trans2	-0.0400655914233714	0.095640537776067	-0.418918508354492	0.675410152730317	   
df.mm.exp2	0.059459609711229	0.129066479133103	0.460689794209925	0.645170893423488	   
df.mm.exp3	0.00610638216409241	0.129066479133103	0.0473119140237415	0.962278752482039	   
df.mm.exp4	0.0326435327107757	0.129066479133103	0.252920300685596	0.80040749625872	   
df.mm.exp5	-0.0314836889350273	0.129066479133103	-0.243933894737757	0.807356804134057	   
df.mm.exp6	0.151371350208786	0.129066479133103	1.17281691749475	0.241286657744509	   
df.mm.exp7	0.00106705373489853	0.129066479133103	0.00826747380160653	0.99340605012173	   
df.mm.exp8	0.0136659666605392	0.129066479133103	0.105883159998854	0.915706795281296	   
df.mm.trans1:exp2	-0.053906982432943	0.121956359421750	-0.442018626076905	0.6586185770568	   
df.mm.trans2:exp2	0.00607825171330822	0.103483402507132	0.058736488809298	0.953179535524944	   
df.mm.trans1:exp3	-0.0227142791414214	0.121956359421750	-0.186249239064859	0.852305772411967	   
df.mm.trans2:exp3	0.0257919962962284	0.103483402507132	0.249238000214101	0.803253187804596	   
df.mm.trans1:exp4	-0.0617174549310342	0.121956359421750	-0.506061801317001	0.612979978104471	   
df.mm.trans2:exp4	0.0996697886250444	0.103483402507132	0.963147579324864	0.335821468218586	   
df.mm.trans1:exp5	0.00326463432145676	0.121956359421750	0.0267688732013309	0.978652062758145	   
df.mm.trans2:exp5	0.120289048501114	0.103483402507132	1.16239943398483	0.245487745222701	   
df.mm.trans1:exp6	-0.189958173470428	0.121956359421750	-1.55759137425146	0.119803057076883	   
df.mm.trans2:exp6	0.0227250262232004	0.103483402507132	0.219600686415721	0.826249143067252	   
df.mm.trans1:exp7	-0.0264614180460166	0.121956359421750	-0.216974483097742	0.828294381512205	   
df.mm.trans2:exp7	0.0307637751921243	0.103483402507132	0.297282215764062	0.766343341568718	   
df.mm.trans1:exp8	-0.0120483482708582	0.121956359421750	-0.0987922919967835	0.921332781988138	   
df.mm.trans2:exp8	0.0698820150906847	0.103483402507132	0.675296843722053	0.499720520140521	   
df.mm.trans1:probe2	0.0799431492206301	0.0667982490865	1.19678510011704	0.231814255901591	   
df.mm.trans1:probe3	0.0239973711785683	0.0667982490865	0.359251499953136	0.719520311803539	   
df.mm.trans1:probe4	0.0292738608570300	0.0667982490865	0.43824293686384	0.661351656910015	   
df.mm.trans1:probe5	0.132040938017302	0.0667982490865	1.97671256092831	0.0484843723321727	*  
df.mm.trans1:probe6	0.0420305078640332	0.0667982490865	0.629215712070627	0.52942263982943	   
df.mm.trans1:probe7	0.0338870681452090	0.0667982490865	0.507304736405998	0.61210820028708	   
df.mm.trans1:probe8	0.132972813079868	0.0667982490865	1.99066315207866	0.0469243452612573	*  
df.mm.trans1:probe9	0.0657202349490121	0.0667982490865	0.983861640803012	0.325539221981776	   
df.mm.trans1:probe10	0.056371886010357	0.0667982490865	0.84391262916725	0.399019885441656	   
df.mm.trans1:probe11	0.073961928492432	0.0667982490865	1.10724352065958	0.268586605778181	   
df.mm.trans1:probe12	0.0722852580300003	0.0667982490865	1.08214300552092	0.279578884762748	   
df.mm.trans1:probe13	0.0617877706675771	0.0667982490865	0.924990871954823	0.355304182770505	   
df.mm.trans1:probe14	0.0396775371912151	0.0667982490865	0.593990676908835	0.55271914233924	   
df.mm.trans1:probe15	0.00686889419769764	0.0667982490865	0.102830452768347	0.918128350819105	   
df.mm.trans1:probe16	0.0282553444076981	0.0667982490865	0.422995284967859	0.672434635236358	   
df.mm.trans1:probe17	0.0507949231527751	0.0667982490865	0.760422972868624	0.447269705072508	   
df.mm.trans1:probe18	0.0929805981775456	0.0667982490865	1.39196160751371	0.164396590900431	   
df.mm.trans1:probe19	0.122255264642273	0.0667982490865	1.83021660468914	0.0676623176288659	.  
df.mm.trans1:probe20	0.0143935216425509	0.0667982490865	0.215477528818340	0.829460704975243	   
df.mm.trans2:probe2	0.0197895227346522	0.0667982490865	0.296258105643246	0.767124919659962	   
df.mm.trans2:probe3	-0.0726362102427095	0.0667982490865	-1.08739691887208	0.277253039395687	   
df.mm.trans2:probe4	-0.055881134201337	0.0667982490865	-0.836565852631468	0.403135358199845	   
df.mm.trans2:probe5	-0.00176753662781106	0.0667982490865	-0.0264608227308803	0.978897672504904	   
df.mm.trans2:probe6	-0.0246708411274227	0.0667982490865	-0.369333649681077	0.71199591497358	   
df.mm.trans3:probe2	0.104290545385445	0.0667982490865	1.56127663242183	0.118931392396774	   
df.mm.trans3:probe3	0.0043815660528031	0.0667982490864999	0.0655940254830515	0.947720641824627	   
df.mm.trans3:probe4	0.0894066892302597	0.0667982490865	1.33845857418333	0.181201682708314