chr4.17420_chr4_81711591_81854362_-_2.R 

fitVsDatCorrelation=0.83562892950606
cont.fitVsDatCorrelation=0.258690860267868

fstatistic=11799.6949790640,62,922
cont.fstatistic=3805.53587813203,62,922

residuals=-0.470893771850328,-0.0813053368125042,-0.0117866594349109,0.0803149630464687,0.958157238296732
cont.residuals=-0.567977599626546,-0.191080908885983,-0.0312982180170367,0.155706972990577,1.03565480511439

predictedValues:
Include	Exclude	Both
chr4.17420_chr4_81711591_81854362_-_2.R.tl.Lung	58.8235005634142	68.6985962340811	67.1900094952261
chr4.17420_chr4_81711591_81854362_-_2.R.tl.cerebhem	54.5970325359196	60.4646389734224	61.0170540693862
chr4.17420_chr4_81711591_81854362_-_2.R.tl.cortex	54.0660695551925	55.5344596935682	62.211668832281
chr4.17420_chr4_81711591_81854362_-_2.R.tl.heart	57.5049542308084	63.290642839604	61.9985939072504
chr4.17420_chr4_81711591_81854362_-_2.R.tl.kidney	63.1891587972695	70.0987502399976	71.7914190491297
chr4.17420_chr4_81711591_81854362_-_2.R.tl.liver	55.9279025208479	62.4523555553244	68.2029619252668
chr4.17420_chr4_81711591_81854362_-_2.R.tl.stomach	62.8003703161248	61.4083356399499	66.9289886773629
chr4.17420_chr4_81711591_81854362_-_2.R.tl.testicle	57.2523361767263	63.902301831952	63.7872786669233


diffExp=-9.87509567066696,-5.86760643750281,-1.46839013837569,-5.7856886087956,-6.90959144272806,-6.5244530344765,1.39203467617492,-6.64996565522576
diffExpScore=1.04179248838564
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	61.0267982494323	65.5974717099143	58.011627897396
cerebhem	58.266553403483	54.6576333310154	63.9488410079097
cortex	67.8009729922159	66.501147369835	67.1881585385637
heart	62.2334268967888	56.8419826134682	60.4767272574076
kidney	59.7126604755518	58.1644307534317	61.4337389720647
liver	62.4658113072528	68.3377765205161	61.7583384834952
stomach	59.3914864867246	55.8133443467753	64.6140272709935
testicle	63.7529856604635	65.1443878603101	63.2059619534415
cont.diffExp=-4.57067346048205,3.60892007246763,1.29982562238092,5.39144428332057,1.54822972212003,-5.87196521326327,3.57814213994931,-1.39140219984660
cont.diffExpScore=5.93586897301333

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=0.65116452638457
cont.tran.correlation=0.697377835317529

tran.covariance=0.00282625700151700
cont.tran.covariance=0.00307121184551356

tran.mean=60.6257128565127
cont.tran.mean=61.6068043735737

weightedLogRatios:
wLogRatio
Lung	-0.644358724398228
cerebhem	-0.413524719456784
cortex	-0.107284267967720
heart	-0.393033442709472
kidney	-0.435637724706843
liver	-0.450104882398876
stomach	0.092547592904119
testicle	-0.450801471363012

cont.weightedLogRatios:
wLogRatio
Lung	-0.299543925964588
cerebhem	0.257871139034149
cortex	0.0814343716922327
heart	0.37022367570647
kidney	0.107087484431423
liver	-0.375503854891554
stomach	0.251850017075719
testicle	-0.0899404496200479

varWeightedLogRatios=0.0535441092376139
cont.varWeightedLogRatios=0.0731996297209506

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93868168339453	0.0694445063847346	56.7169656527409	6.84223868218557e-303	***
df.mm.trans1	0.0469420597278716	0.0596409460976939	0.787077717563013	0.43143864019207	   
df.mm.trans2	0.306992713587149	0.0523685942123997	5.86215303664692	6.35887318701783e-09	***
df.mm.exp2	-0.105860795507198	0.0666323441125512	-1.58872987161286	0.112464224967602	   
df.mm.exp3	-0.220077693530564	0.0666323441125512	-3.30286584483389	0.000993834553384477	***
df.mm.exp4	-0.0242487474592756	0.0666323441125512	-0.363918571111894	0.716002250502152	   
df.mm.exp5	0.0255271149969402	0.0666323441125513	0.383103961550885	0.701730991383898	   
df.mm.exp6	-0.160766274705201	0.0666323441125513	-2.41273628965604	0.0160275256753884	*  
df.mm.exp7	-0.0428712730850672	0.0666323441125512	-0.643400343422584	0.52012435090881	   
df.mm.exp8	-0.0474755872060124	0.0666323441125512	-0.71250063071201	0.476335085485882	   
df.mm.trans1:exp2	0.0312988831182636	0.0611691428087069	0.511677647930166	0.608999154389094	   
df.mm.trans2:exp2	-0.0218092558546526	0.0433564685523939	-0.503021961493412	0.61506896956673	   
df.mm.trans1:exp3	0.135743057993595	0.0611691428087068	2.21914272067049	0.0267197680027683	*  
df.mm.trans2:exp3	0.0073526512411599	0.0433564685523939	0.169586026875658	0.865372912724775	   
df.mm.trans1:exp4	0.00157840758293129	0.0611691428087069	0.0258039840098368	0.97941926782014	   
df.mm.trans2:exp4	-0.0577425225021356	0.0433564685523939	-1.33180871113515	0.183252189783424	   
df.mm.trans1:exp5	0.0460641889198133	0.0611691428087069	0.753062521472125	0.451604582220552	   
df.mm.trans2:exp5	-0.00535091510633325	0.0433564685523939	-0.123416765363788	0.901803979809989	   
df.mm.trans1:exp6	0.110288236469458	0.0611691428087069	1.80300444644713	0.0717139211096897	.  
df.mm.trans2:exp6	0.0654414638802684	0.0433564685523939	1.50938178466233	0.131543843139110	   
df.mm.trans1:exp7	0.108290798791572	0.0611691428087068	1.77035011149704	0.0769992400651044	.  
df.mm.trans2:exp7	-0.0693119071136463	0.0433564685523939	-1.59865204496272	0.110240550860697	   
df.mm.trans1:exp8	0.0204025908843247	0.0611691428087068	0.333543841674051	0.738799640223904	   
df.mm.trans2:exp8	-0.0248977953956383	0.0433564685523939	-0.574257918759013	0.56593335119982	   
df.mm.trans1:probe2	0.506474506122324	0.0438185398826688	11.5584523692138	5.93299439060346e-29	***
df.mm.trans1:probe3	0.113090405869005	0.0438185398826688	2.58088028884172	0.0100083971355649	*  
df.mm.trans1:probe4	0.470053324598374	0.0438185398826688	10.7272703713318	2.21708696426506e-25	***
df.mm.trans1:probe5	-0.188851199465721	0.0438185398826688	-4.30984692715459	1.80928629156834e-05	***
df.mm.trans1:probe6	-0.167168808865661	0.0438185398826688	-3.81502462914744	0.000145241595201720	***
df.mm.trans1:probe7	0.253945445047342	0.0438185398826688	5.79538811031407	9.3524107378893e-09	***
df.mm.trans1:probe8	0.0212188771990739	0.0438185398826688	0.484244277784948	0.628327519970056	   
df.mm.trans1:probe9	0.135049192441176	0.0438185398826688	3.08201032719922	0.00211717181863871	** 
df.mm.trans1:probe10	0.137484336426274	0.0438185398826688	3.13758369846211	0.00175755447099362	** 
df.mm.trans1:probe11	0.339177266838008	0.0438185398826688	7.74049677935891	2.5977694435465e-14	***
df.mm.trans1:probe12	0.466243937147518	0.0438185398826688	10.6403348536022	5.10275432428722e-25	***
df.mm.trans1:probe13	0.353332499031846	0.0438185398826688	8.06353885770613	2.29204822752756e-15	***
df.mm.trans1:probe14	0.418362129866957	0.0438185398826688	9.54760544251791	1.15289219674929e-20	***
df.mm.trans1:probe15	0.103002846856938	0.0438185398826688	2.35066816769214	0.0189495689241972	*  
df.mm.trans1:probe16	0.241093637889628	0.0438185398826688	5.50209200341215	4.86327359568469e-08	***
df.mm.trans1:probe17	-0.00438055704553002	0.0438185398826688	-0.099970401963636	0.920389556021478	   
df.mm.trans1:probe18	-0.0812938397861796	0.0438185398826688	-1.85523844481941	0.0638811920366215	.  
df.mm.trans1:probe19	0.148899662638665	0.0438185398826688	3.39809731308637	0.000707613733418822	***
df.mm.trans1:probe20	-0.174838598025873	0.0438185398826688	-3.99005988090957	7.12959593718447e-05	***
df.mm.trans1:probe21	0.117818717432907	0.0438185398826688	2.68878693238948	0.00730061678635312	** 
df.mm.trans1:probe22	-0.0965942338667829	0.0438185398826688	-2.20441470951404	0.0277415634340142	*  
df.mm.trans2:probe2	-0.0206662798573875	0.0438185398826688	-0.471633238184678	0.637300183164317	   
df.mm.trans2:probe3	0.0263463518930319	0.0438185398826688	0.601260378907614	0.547814366947384	   
df.mm.trans2:probe4	0.0136762138982081	0.0438185398826688	0.312110214873165	0.755027332152186	   
df.mm.trans2:probe5	-0.140191191513635	0.0438185398826688	-3.19935789483218	0.00142436809104236	** 
df.mm.trans2:probe6	-0.182132087905547	0.0438185398826688	-4.15650745992987	3.53240726586861e-05	***
df.mm.trans3:probe2	0.43337727981512	0.0438185398826688	9.8902720395421	5.44867889413079e-22	***
df.mm.trans3:probe3	0.136191460176163	0.0438185398826688	3.10807846497938	0.00194080996432697	** 
df.mm.trans3:probe4	-0.375953137627361	0.0438185398826688	-8.57977327939351	3.98686744011317e-17	***
df.mm.trans3:probe5	0.303665453664108	0.0438185398826688	6.93006783149829	7.8952359628338e-12	***
df.mm.trans3:probe6	0.0983310164283344	0.0438185398826688	2.24405050217628	0.025065736939436	*  
df.mm.trans3:probe7	-0.394894137271654	0.0438185398826688	-9.01203322449919	1.14318091354533e-18	***
df.mm.trans3:probe8	-0.173446443661439	0.0438185398826688	-3.95828898283397	8.12921028000877e-05	***
df.mm.trans3:probe9	-0.351561404171160	0.0438185398826688	-8.02312001067408	3.11984424328501e-15	***
df.mm.trans3:probe10	-0.115060902648266	0.0438185398826688	-2.62584976487943	0.00878652947305771	** 
df.mm.trans3:probe11	-0.177281458763315	0.0438185398826688	-4.04580935918939	5.65091494320852e-05	***
df.mm.trans3:probe12	-0.175570418507855	0.0438185398826688	-4.00676104174107	6.65199069727527e-05	***
df.mm.trans3:probe13	-0.313991857392782	0.0438185398826688	-7.165730721141	1.58368692479193e-12	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30004551907128	0.122121455944017	35.2112205495036	8.47340223111607e-173	***
df.mm.trans1	-0.200478258856722	0.104881430519176	-1.91147525223797	0.0562533141240724	.  
df.mm.trans2	-0.126907067971461	0.0920926550406803	-1.37803680342804	0.168526259137047	   
df.mm.exp2	-0.32617305824253	0.117176135300117	-2.78361338174466	0.00548566128693621	** 
df.mm.exp3	-0.0279080388469254	0.117176135300117	-0.238171695759089	0.811800849324073	   
df.mm.exp4	-0.165297935092362	0.117176135300117	-1.41067918539038	0.158676528191829	   
df.mm.exp5	-0.199347866270785	0.117176135300117	-1.701266778941	0.0892300689898651	.  
df.mm.exp6	0.00164633001080740	0.117176135300117	0.0140500453150357	0.988793094141312	   
df.mm.exp7	-0.296474415906096	0.117176135300117	-2.53016038758021	0.0115665192507457	*  
df.mm.exp8	-0.0489832225642822	0.117176135300117	-0.418030705986538	0.676022006349266	   
df.mm.trans1:exp2	0.279888205650870	0.107568836867546	2.60194507815965	0.00941833330357516	** 
df.mm.trans2:exp2	0.143724785599576	0.0762444049191665	1.88505354264293	0.0597368797556578	.  
df.mm.trans1:exp3	0.133171501340995	0.107568836867546	1.23801191143281	0.216026789586472	   
df.mm.trans2:exp3	0.0415900858417041	0.0762444049191665	0.545483775311742	0.585552773503291	   
df.mm.trans1:exp4	0.184877117133776	0.107568836867546	1.71868658728199	0.0860071423433225	.  
df.mm.trans2:exp4	0.0220359641159209	0.0762444049191665	0.289017458255241	0.772632987701387	   
df.mm.trans1:exp5	0.177578849186299	0.107568836867546	1.65083916827100	0.0991119731300859	.  
df.mm.trans2:exp5	0.0790847245373321	0.0762444049191665	1.03725282689500	0.299889995431672	   
df.mm.trans1:exp6	0.0216599747111254	0.107568836867546	0.201359197904094	0.840462130769177	   
df.mm.trans2:exp6	0.0392792263971754	0.0762444049191665	0.515175197954772	0.606554087666631	   
df.mm.trans1:exp7	0.269312223596405	0.107568836867546	2.50362680715809	0.0124646269069709	*  
df.mm.trans2:exp7	0.134950248502156	0.0762444049191665	1.76996920161196	0.0770627202386991	.  
df.mm.trans1:exp8	0.0926861561288239	0.107568836867546	0.861645052859983	0.389106903404983	   
df.mm.trans2:exp8	0.0420522264030064	0.0762444049191665	0.551545079899171	0.58139371448829	   
df.mm.trans1:probe2	-0.0303297919633880	0.0770569792542843	-0.39360214034995	0.693965802897961	   
df.mm.trans1:probe3	0.0185331660459255	0.0770569792542843	0.240512491214676	0.809986447937576	   
df.mm.trans1:probe4	0.0151649710420166	0.0770569792542843	0.196802044263544	0.844025823458205	   
df.mm.trans1:probe5	0.0401869282353512	0.0770569792542843	0.521522237495662	0.602128266000667	   
df.mm.trans1:probe6	0.075634630160077	0.0770569792542843	0.981541592883968	0.326583218287374	   
df.mm.trans1:probe7	0.09611949723926	0.0770569792542843	1.24738210827173	0.212574164518778	   
df.mm.trans1:probe8	-0.00514394722029588	0.0770569792542843	-0.066755111218688	0.94679113202038	   
df.mm.trans1:probe9	-0.0474273169765471	0.0770569792542843	-0.615483729514484	0.538387202104752	   
df.mm.trans1:probe10	0.00856924891332343	0.0770569792542843	0.111206655078514	0.911476677590154	   
df.mm.trans1:probe11	0.0524962925422094	0.0770569792542843	0.681265902845402	0.49587432386633	   
df.mm.trans1:probe12	0.0317985709382395	0.0770569792542843	0.412663087055434	0.679949411393465	   
df.mm.trans1:probe13	0.0331984002521994	0.0770569792542843	0.430829245753929	0.666693231440287	   
df.mm.trans1:probe14	0.0389889921231365	0.0770569792542843	0.50597612961799	0.612994357386362	   
df.mm.trans1:probe15	0.0779377128163562	0.0770569792542843	1.01142964038553	0.312076191514951	   
df.mm.trans1:probe16	0.0405570962084319	0.0770569792542843	0.526326058988057	0.598788262421056	   
df.mm.trans1:probe17	0.0171601857184384	0.0770569792542843	0.222694762817144	0.823822400738281	   
df.mm.trans1:probe18	-0.0305760936871481	0.0770569792542843	-0.396798498760877	0.691607883592682	   
df.mm.trans1:probe19	0.0158112786622478	0.0770569792542843	0.205189443127161	0.837469408084495	   
df.mm.trans1:probe20	-0.0418607685201005	0.0770569792542843	-0.543244348859848	0.587092883976792	   
df.mm.trans1:probe21	-0.00922868455857627	0.0770569792542843	-0.119764421703089	0.904695857224355	   
df.mm.trans1:probe22	0.0135134388623588	0.0770569792542843	0.175369434321648	0.860827861488898	   
df.mm.trans2:probe2	0.0437206375255235	0.0770569792542843	0.56738063117226	0.570593723267514	   
df.mm.trans2:probe3	0.0866979792682623	0.0770569792542843	1.12511520834684	0.260833020373917	   
df.mm.trans2:probe4	-0.0296652559736047	0.0770569792542843	-0.384978184464133	0.700342366939513	   
df.mm.trans2:probe5	0.0925792959574339	0.0770569792542843	1.20143946535883	0.229889297090891	   
df.mm.trans2:probe6	0.00424270212421711	0.0770569792542843	0.0550592842501184	0.956103161628759	   
df.mm.trans3:probe2	-0.00659593771954708	0.0770569792542843	-0.0855981869958957	0.93180442351268	   
df.mm.trans3:probe3	0.154092287490684	0.0770569792542843	1.99971876631949	0.0458236409502276	*  
df.mm.trans3:probe4	0.141041133319214	0.0770569792542843	1.83034858988937	0.0675205107848376	.  
df.mm.trans3:probe5	0.194215699061246	0.0770569792542843	2.52041672202519	0.0118894119293705	*  
df.mm.trans3:probe6	0.126528570175501	0.0770569792542843	1.64201311029806	0.100928293449909	   
df.mm.trans3:probe7	0.126316632527951	0.0770569792542843	1.63926270858753	0.101499697773453	   
df.mm.trans3:probe8	0.0958393969194175	0.0770569792542843	1.24374713162778	0.213908770566922	   
df.mm.trans3:probe9	0.0454953498567573	0.0770569792542843	0.590411800424007	0.555059281250519	   
df.mm.trans3:probe10	0.0464827469847957	0.0770569792542843	0.603225657618954	0.546506938552662	   
df.mm.trans3:probe11	0.0862359533574012	0.0770569792542843	1.11911930874979	0.26338071093972	   
df.mm.trans3:probe12	0.0863957143052208	0.0770569792542843	1.12119259204438	0.262497823116189	   
df.mm.trans3:probe13	0.0477350833120105	0.0770569792542843	0.619477739381491	0.53575472769242