chr14.7546_chr14_63466730_63482324_-_2.R 

fitVsDatCorrelation=0.912843782614545
cont.fitVsDatCorrelation=0.255824254477887

fstatistic=11262.5284117729,53,715
cont.fstatistic=1998.05232594525,53,715

residuals=-0.627015830444707,-0.0840955234123865,0.00487395611589783,0.0875685989202492,0.567729849900871
cont.residuals=-0.708806855026786,-0.272414439932864,-0.0591238654721165,0.232446559766794,1.13641862855341

predictedValues:
Include	Exclude	Both
chr14.7546_chr14_63466730_63482324_-_2.R.tl.Lung	64.2475015724163	44.1743969214757	85.762918818423
chr14.7546_chr14_63466730_63482324_-_2.R.tl.cerebhem	54.6548400409736	45.3051484691821	74.8288347827116
chr14.7546_chr14_63466730_63482324_-_2.R.tl.cortex	64.5004525147664	44.4880262788295	86.1587795395303
chr14.7546_chr14_63466730_63482324_-_2.R.tl.heart	58.5889119357936	44.6472436709869	69.6178472225237
chr14.7546_chr14_63466730_63482324_-_2.R.tl.kidney	60.6943859817504	45.4366384792131	74.7998851212037
chr14.7546_chr14_63466730_63482324_-_2.R.tl.liver	60.9763901409965	48.5816679562205	80.3967995752218
chr14.7546_chr14_63466730_63482324_-_2.R.tl.stomach	67.6110627691942	43.9186294740161	90.2949662449676
chr14.7546_chr14_63466730_63482324_-_2.R.tl.testicle	55.6172050709225	42.778441718632	73.3564907389357


diffExp=20.0731046509406,9.34969157179156,20.0124262359369,13.9416682648067,15.2577475025374,12.3947221847760,23.6924332951782,12.8387633522906
diffExpScore=0.99222156450717
diffExp1.5=0,0,0,0,0,0,1,0
diffExp1.5Score=0.5
diffExp1.4=1,0,1,0,0,0,1,0
diffExp1.4Score=0.75
diffExp1.3=1,0,1,1,1,0,1,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	64.3475396519051	75.3977116953967	69.4124383160453
cerebhem	71.96889838122	60.8130984075863	62.0813855118016
cortex	70.8926429684898	71.9819925062881	61.4489140417156
heart	73.1374902584555	59.680977341257	71.8171613324847
kidney	63.9386904699229	58.0615843507208	71.7362021634782
liver	68.204851546695	63.8405641286695	60.1488630232862
stomach	67.1308095052537	58.5021625952516	61.4198518189329
testicle	68.4112132480146	59.3607846154897	66.8425328093792
cont.diffExp=-11.0501720434916,11.1557999736337,-1.08934953779831,13.4565129171984,5.87710611920212,4.36428741802551,8.62864691000212,9.05042863252492
cont.diffExpScore=1.56238728101252

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

tran.correlation=-0.0198255148499348
cont.tran.correlation=-0.155246740589791

tran.covariance=-2.89012816795463e-06
cont.tran.covariance=-0.000683789248720408

tran.mean=52.8888089372106
cont.tran.mean=65.9794382294135

weightedLogRatios:
wLogRatio
Lung	1.48919130616459
cerebhem	0.733063001992957
cortex	1.47873104573300
heart	1.06925767307937
kidney	1.14686249145848
liver	0.908249882618967
stomach	1.72489302216365
testicle	1.02024462953859

cont.weightedLogRatios:
wLogRatio
Lung	-0.672508345444452
cerebhem	0.706057081662539
cortex	-0.0650961726346212
heart	0.852081035304351
kidney	0.396260379109316
liver	0.277035530112449
stomach	0.569283288531683
testicle	0.5895478281214

varWeightedLogRatios=0.113157699258956
cont.varWeightedLogRatios=0.243844101163591

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.16739191307441	0.0721866339263985	43.8778169973112	5.23045010899726e-205	***
df.mm.trans1	0.680288901977079	0.0626309380351451	10.8618667278357	1.52395976694619e-25	***
df.mm.trans2	0.613163572035635	0.0563128780125784	10.8885142027136	1.18599847734368e-25	***
df.mm.exp2	-4.63202296414781e-05	0.0738587586484535	-0.000627146062147471	0.999499784800402	   
df.mm.exp3	0.00639898079514906	0.0738587586484535	0.0866380766783039	0.930983455143292	   
df.mm.exp4	0.127015604416833	0.0738587586484535	1.71970943922022	0.0859180222942198	.  
df.mm.exp5	0.108052179629292	0.0738587586484535	1.46295688699007	0.143918605665866	   
df.mm.exp6	0.107457158874827	0.0738587586484535	1.45490068938597	0.146135620933619	   
df.mm.exp7	-0.0062729739161072	0.0738587586484535	-0.0849320247306722	0.932339206910447	   
df.mm.exp8	-0.0201055875963588	0.0738587586484535	-0.272216700690241	0.785533990456493	   
df.mm.trans1:exp2	-0.161658741720012	0.0682812651579891	-2.36754168725442	0.0181719515506502	*  
df.mm.trans2:exp2	0.0253216329216439	0.0542644470498568	0.466633943553853	0.640903943000643	   
df.mm.trans1:exp3	-0.00246957827203733	0.0682812651579892	-0.0361677286781846	0.971158712848437	   
df.mm.trans2:exp3	0.000675733704392837	0.0542644470498568	0.0124526046265982	0.990067989502575	   
df.mm.trans1:exp4	-0.219212978837047	0.0682812651579891	-3.21044108262834	0.00138459299532303	** 
df.mm.trans2:exp4	-0.116368396972936	0.0542644470498568	-2.14446849271346	0.0323318086198647	*  
df.mm.trans1:exp5	-0.164943810772733	0.0682812651579891	-2.4156525276895	0.0159567517419527	*  
df.mm.trans2:exp5	-0.0798787511631611	0.0542644470498568	-1.47202736793338	0.141453496007127	   
df.mm.trans1:exp6	-0.159713253479214	0.0682812651579891	-2.33904941728408	0.0196073671572667	*  
df.mm.trans2:exp6	-0.0123562677543864	0.0542644470498568	-0.227704665322283	0.81994094239877	   
df.mm.trans1:exp7	0.0573017570208055	0.0682812651579891	0.839201747188204	0.401636595627319	   
df.mm.trans2:exp7	0.000466199495887756	0.0542644470498568	0.00859125120098293	0.99314765409588	   
df.mm.trans1:exp8	-0.124144652280127	0.0682812651579891	-1.81813638035091	0.0694616041266895	.  
df.mm.trans2:exp8	-0.0120055009575572	0.0542644470498568	-0.221240639318168	0.824968233813481	   
df.mm.trans1:probe2	0.249945719128603	0.0433919867210277	5.76018149930617	1.24825210714479e-08	***
df.mm.trans1:probe3	0.0770754502579887	0.0433919867210277	1.77625999826917	0.0761152556097934	.  
df.mm.trans1:probe4	0.373056812013196	0.0433919867210277	8.59736647717061	5.12007345209826e-17	***
df.mm.trans1:probe5	0.513982037844502	0.0433919867210277	11.8450911489477	1.10932119422074e-29	***
df.mm.trans1:probe6	0.239599427277966	0.0433919867210277	5.52174365323209	4.70014986447195e-08	***
df.mm.trans1:probe7	0.84670507104879	0.0433919867210278	19.5129362592305	2.62985547616585e-68	***
df.mm.trans1:probe8	0.178489200898596	0.0433919867210277	4.11341389012963	4.35185828125567e-05	***
df.mm.trans1:probe9	0.167609510308897	0.0433919867210277	3.86268348085739	0.000122326691350790	***
df.mm.trans1:probe10	0.191012998276477	0.0433919867210277	4.40203394014943	1.23614904074436e-05	***
df.mm.trans1:probe11	0.26974330189154	0.0433919867210277	6.21643124168874	8.64145042004959e-10	***
df.mm.trans1:probe12	0.192127175181548	0.0433919867210277	4.42771096001565	1.10127589228879e-05	***
df.mm.trans1:probe13	0.233685502722109	0.0433919867210277	5.38545294605893	9.81318423442158e-08	***
df.mm.trans1:probe14	0.496342921826838	0.0433919867210277	11.4385848478865	6.099650951401e-28	***
df.mm.trans1:probe15	0.66449530530151	0.0433919867210277	15.3137792370197	5.4262372968913e-46	***
df.mm.trans1:probe16	0.95751432743797	0.0433919867210277	22.0666164375912	1.06375490212244e-82	***
df.mm.trans1:probe17	0.607322064188693	0.0433919867210277	13.9961801724645	1.62552207692612e-39	***
df.mm.trans1:probe18	0.844829277843914	0.0433919867210277	19.4697072359333	4.56580505624249e-68	***
df.mm.trans1:probe19	1.08807646686938	0.0433919867210277	25.0755162206505	4.73998277189011e-100	***
df.mm.trans2:probe2	-0.00789963360520951	0.0433919867210277	-0.182052821319226	0.85559287393159	   
df.mm.trans2:probe3	-0.0353546791038018	0.0433919867210277	-0.814774380603987	0.415472763925054	   
df.mm.trans2:probe4	0.0942034307180632	0.0433919867210277	2.17098680739622	0.0302604437933267	*  
df.mm.trans2:probe5	0.0164978518127994	0.0433919867210277	0.380205034603878	0.703906137788698	   
df.mm.trans2:probe6	0.0312214827918036	0.0433919867210277	0.719521855326197	0.472054550513141	   
df.mm.trans3:probe2	0.0893988457576027	0.0433919867210277	2.06026164075774	0.0397348406478637	*  
df.mm.trans3:probe3	-0.219156758779821	0.0433919867210277	-5.05062743931974	5.59534208970308e-07	***
df.mm.trans3:probe4	-0.224363698749890	0.0433919867210277	-5.17062517077982	3.03179896822475e-07	***
df.mm.trans3:probe5	-0.123375521232052	0.0433919867210277	-2.84327892210255	0.00459261170827924	** 
df.mm.trans3:probe6	0.0410175664475216	0.0433919867210277	0.945279752024917	0.344835398104996	   
df.mm.trans3:probe7	0.371675977258238	0.0433919867210277	8.5655441325558	6.57989489372708e-17	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.34456288661912	0.170911010300668	25.4200292829357	4.7379145400354e-102	***
df.mm.trans1	-0.0573928215892622	0.148286688455086	-0.387039606772563	0.698842055994222	   
df.mm.trans2	-0.0391959844488411	0.13332788011533	-0.293981906972017	0.768857048039635	   
df.mm.exp2	0.00858351783598593	0.174869977633687	0.0490851428709305	0.960865151271683	   
df.mm.exp3	0.172366934627745	0.174869977633687	0.985686262217147	0.324620519887179	   
df.mm.exp4	-0.139778580464788	0.174869977633687	-0.799328634659022	0.424365333764260	   
df.mm.exp5	-0.300576169844332	0.174869977633687	-1.71885519693936	0.0860735916934964	.  
df.mm.exp6	0.035072401946126	0.174869977633687	0.200562740504232	0.841097529413814	   
df.mm.exp7	-0.0890358320200923	0.174869977633687	-0.50915447708584	0.610801037354562	   
df.mm.exp8	-0.140178579611874	0.174869977633687	-0.801616043581346	0.423041414830282	   
df.mm.trans1:exp2	0.103351841464403	0.161664554474981	0.6392980935125	0.522833786077666	   
df.mm.trans2:exp2	-0.223555243417164	0.12847796003557	-1.74002796553799	0.0822843729531915	.  
df.mm.trans1:exp3	-0.0754989722491846	0.161664554474981	-0.467010053591363	0.640634961235881	   
df.mm.trans2:exp3	-0.218727876658218	0.12847796003557	-1.70245446454522	0.0891048578557693	.  
df.mm.trans1:exp4	0.267820978920623	0.161664554474981	1.65664625613446	0.0980297359994342	.  
df.mm.trans2:exp4	-0.0939850130982085	0.12847796003557	-0.731526349516977	0.46469739400354	   
df.mm.trans1:exp5	0.294202133194696	0.161664554474981	1.81983078572877	0.0692027917528429	.  
df.mm.trans2:exp5	0.0393034905451478	0.12847796003557	0.305916209552723	0.759757492098455	   
df.mm.trans1:exp6	0.0231445979579093	0.161664554474981	0.143164332052089	0.886200734454669	   
df.mm.trans2:exp6	-0.201460537927606	0.12847796003557	-1.56805523586948	0.117310671543204	   
df.mm.trans1:exp7	0.131380229204823	0.161664554474981	0.812671829217547	0.416676738492408	   
df.mm.trans2:exp7	-0.164677372671134	0.12847796003557	-1.28175581730549	0.200343795637525	   
df.mm.trans1:exp8	0.201416627715187	0.161664554474981	1.24589232543463	0.213212010809752	   
df.mm.trans2:exp8	-0.0989645293863814	0.12847796003557	-0.770284096657375	0.441385817240481	   
df.mm.trans1:probe2	-0.107733968961838	0.102736031396139	-1.04864834175293	0.294694328225014	   
df.mm.trans1:probe3	-0.195916558821619	0.102736031396139	-1.90698974993678	0.0569224204883278	.  
df.mm.trans1:probe4	-0.240546905691028	0.102736031396139	-2.34140741492638	0.0194849159710085	*  
df.mm.trans1:probe5	-0.0785847308563667	0.102736031396139	-0.764918887642766	0.444572234821633	   
df.mm.trans1:probe6	-0.106891923739343	0.102736031396139	-1.04045214017641	0.298481580985428	   
df.mm.trans1:probe7	-0.280294454287875	0.102736031396139	-2.72829746758556	0.00652254934039943	** 
df.mm.trans1:probe8	-0.233351916627892	0.102736031396139	-2.27137366955624	0.0234207784581879	*  
df.mm.trans1:probe9	-0.197807915266729	0.102736031396139	-1.92539961470775	0.0545756995952075	.  
df.mm.trans1:probe10	-0.127181269759291	0.102736031396139	-1.23794221005962	0.216143823189502	   
df.mm.trans1:probe11	-0.207933453370811	0.102736031396139	-2.02395839653415	0.0433458297632663	*  
df.mm.trans1:probe12	-0.158132787726323	0.102736031396139	-1.53921448568108	0.124194356690602	   
df.mm.trans1:probe13	-0.207548075612974	0.102736031396139	-2.02020725146265	0.0437343091059244	*  
df.mm.trans1:probe14	-0.184292879890131	0.102736031396139	-1.79384853965712	0.0732597708859527	.  
df.mm.trans1:probe15	-0.122566592748089	0.102736031396139	-1.19302440519126	0.233255547135426	   
df.mm.trans1:probe16	-0.176803746127169	0.102736031396139	-1.72095168291476	0.0856921987906257	.  
df.mm.trans1:probe17	-0.194812740784693	0.102736031396139	-1.89624553467047	0.0583304604533358	.  
df.mm.trans1:probe18	-0.246555464464161	0.102736031396139	-2.39989282351652	0.0166546586460275	*  
df.mm.trans1:probe19	-0.127700119987091	0.102736031396139	-1.24299253389197	0.214278034042707	   
df.mm.trans2:probe2	-0.0250911161555031	0.102736031396139	-0.244228979984194	0.807123548697047	   
df.mm.trans2:probe3	0.078896028516489	0.102736031396139	0.767948960499308	0.442771049438179	   
df.mm.trans2:probe4	0.134428024879186	0.102736031396139	1.30847982983542	0.191131056257171	   
df.mm.trans2:probe5	0.0438406410876799	0.102736031396139	0.426730918957102	0.669703782689302	   
df.mm.trans2:probe6	-0.00574327262408129	0.102736031396139	-0.0559031972136034	0.955434541286892	   
df.mm.trans3:probe2	-0.0596545176847177	0.102736031396139	-0.580658186558682	0.561653810950507	   
df.mm.trans3:probe3	-0.0633377778342529	0.102736031396139	-0.616509874612827	0.537754293288978	   
df.mm.trans3:probe4	0.143674069208247	0.102736031396139	1.39847789773245	0.16240318174505	   
df.mm.trans3:probe5	-0.0097222792538512	0.102736031396139	-0.0946335878632802	0.92463238397872	   
df.mm.trans3:probe6	-0.081452355146318	0.102736031396139	-0.79283143449689	0.428139031497393	   
df.mm.trans3:probe7	0.0151368420109590	0.102736031396139	0.147337227312130	0.882907385733542