chr3.15663_chr3_132063186_132066492_+_2.R 

fitVsDatCorrelation=0.853494315379926
cont.fitVsDatCorrelation=0.232814247126234

fstatistic=9380.51418848231,60,876
cont.fstatistic=2682.82652445128,60,876

residuals=-0.593656256958763,-0.100457136849455,-0.00267684872210027,0.0840330106259213,1.22554552360592
cont.residuals=-0.616699903966627,-0.224230372758523,-0.0334363325786951,0.170447202088164,1.29433733500441

predictedValues:
Include	Exclude	Both
chr3.15663_chr3_132063186_132066492_+_2.R.tl.Lung	52.9094320459238	78.7827797394986	86.328597362978
chr3.15663_chr3_132063186_132066492_+_2.R.tl.cerebhem	63.9764793792365	100.321831236322	85.748075288967
chr3.15663_chr3_132063186_132066492_+_2.R.tl.cortex	49.5927325636249	90.1656580988901	118.029229205662
chr3.15663_chr3_132063186_132066492_+_2.R.tl.heart	51.4500188752077	71.8013623847083	76.137079426505
chr3.15663_chr3_132063186_132066492_+_2.R.tl.kidney	52.9336996452113	80.7507267234944	107.886232313953
chr3.15663_chr3_132063186_132066492_+_2.R.tl.liver	52.0047429018385	72.3522683599256	71.862164810319
chr3.15663_chr3_132063186_132066492_+_2.R.tl.stomach	53.5316573523494	84.7835417903035	87.174844831116
chr3.15663_chr3_132063186_132066492_+_2.R.tl.testicle	53.579130281897	85.8485342416605	99.0928797465313


diffExp=-25.8733476935748,-36.3453518570856,-40.5729255352652,-20.3513435095006,-27.8170270782831,-20.3475254580871,-31.2518844379541,-32.2694039597636
diffExpScore=0.995759635974947
diffExp1.5=0,-1,-1,0,-1,0,-1,-1
diffExp1.5Score=0.833333333333333
diffExp1.4=-1,-1,-1,0,-1,0,-1,-1
diffExp1.4Score=0.857142857142857
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	67.707301388271	62.7638952222996	66.2552332574237
cerebhem	65.4663574742423	65.7208393809357	73.9391002663846
cortex	70.2790164845042	66.7109739745586	64.2595030363595
heart	64.7359613775601	79.6901845492804	71.50031747297
kidney	71.8015350200632	64.6437169345467	63.4004747862845
liver	68.4791302797327	61.6616548822256	63.5919793538357
stomach	67.860606313491	67.8198534047469	68.3940317447981
testicle	61.7827331216482	66.9305651930795	69.4933025610487
cont.diffExp=4.94340616597135,-0.254481906693456,3.56804250994563,-14.9542231717203,7.15781808551652,6.81747539750711,0.0407529087440111,-5.14783207143121
cont.diffExpScore=13.5239991600853

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=0.684404152641281
cont.tran.correlation=-0.393863389157315

tran.covariance=0.0054533689233333
cont.tran.covariance=-0.00148462707309144

tran.mean=68.4240372262558
cont.tran.mean=67.1283953125741

weightedLogRatios:
wLogRatio
Lung	-1.65919012655462
cerebhem	-1.97197285390507
cortex	-2.51241999928317
heart	-1.36891849910151
kidney	-1.76541191960656
liver	-1.35929768135998
stomach	-1.93596301084561
testicle	-1.98793841878337

cont.weightedLogRatios:
wLogRatio
Lung	0.316696970681555
cerebhem	-0.0162305524804732
cortex	0.220212623106183
heart	-0.888311677067713
kidney	0.443309899335835
liver	0.437723930050744
stomach	0.00253332502021753
testicle	-0.333223655070247

varWeightedLogRatios=0.141250371740175
cont.varWeightedLogRatios=0.204735579632359

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09846185324003	0.0793074417907712	51.6781497510988	3.08590336292793e-268	***
df.mm.trans1	-0.244670023956424	0.0683427502544695	-3.5800435751475	0.000362423174740849	***
df.mm.trans2	0.363149819512254	0.0602383263031578	6.02855095416582	2.43597877589554e-09	***
df.mm.exp2	0.438370047049999	0.0771668158338647	5.68081036275725	1.82408541914393e-08	***
df.mm.exp3	-0.242554522408418	0.0771668158338646	-3.14324907393643	0.00172707600700292	** 
df.mm.exp4	0.00486373791102983	0.0771668158338647	0.0630288791687499	0.949757886248181	   
df.mm.exp5	-0.197785274844888	0.0771668158338647	-2.56308716003919	0.0105405834616214	*  
df.mm.exp6	0.081016673084467	0.0771668158338646	1.04989006231501	0.294058212469400	   
df.mm.exp7	0.0753436842767362	0.0771668158338647	0.976374150761208	0.329148632291909	   
df.mm.exp8	-0.0394285935628281	0.0771668158338646	-0.510952708580272	0.609512916170356	   
df.mm.trans1:exp2	-0.248436163453647	0.0711442889208048	-3.49200430873934	0.000503380821773578	***
df.mm.trans2:exp2	-0.196681158139179	0.0517650737435185	-3.79949537237558	0.000155010634154878	***
df.mm.trans1:exp3	0.177817201993755	0.0711442889208048	2.49938828107052	0.0126227879536860	*  
df.mm.trans2:exp3	0.377508704611952	0.0517650737435185	7.29272996851897	6.79500330419216e-13	***
df.mm.trans1:exp4	-0.0328345312816232	0.0711442889208048	-0.4615202678907	0.644539961580047	   
df.mm.trans2:exp4	-0.0976547290772513	0.0517650737435185	-1.88649840549060	0.0595583494765651	.  
df.mm.trans1:exp5	0.198243832699801	0.0711442889208048	2.78650381790277	0.00544316216434698	** 
df.mm.trans2:exp5	0.222457794821146	0.0517650737435185	4.29744958779276	1.92137632774353e-05	***
df.mm.trans1:exp6	-0.0982633715272979	0.0711442889208048	-1.38118425270482	0.167574528262456	   
df.mm.trans2:exp6	-0.166164309665686	0.0517650737435185	-3.20996953445837	0.00137581422136551	** 
df.mm.trans1:exp7	-0.0636521016792123	0.0711442889208048	-0.894690250542352	0.371198445792405	   
df.mm.trans2:exp7	-0.00193668473464014	0.0517650737435185	-0.0374129619564702	0.970164261681865	   
df.mm.trans1:exp8	0.0520066028911156	0.0711442889208048	0.731001794803338	0.464973531082147	   
df.mm.trans2:exp8	0.125318665521841	0.0517650737435186	2.42091156177542	0.0156839752136021	*  
df.mm.trans1:probe2	-0.0302407205193956	0.0495612848370505	-0.610168211312967	0.541908635886038	   
df.mm.trans1:probe3	0.320147350559595	0.0495612848370505	6.45962572625361	1.73866531586685e-10	***
df.mm.trans1:probe4	0.091880063630941	0.0495612848370505	1.85386766975529	0.0640940773273077	.  
df.mm.trans1:probe5	0.126940364255631	0.0495612848370505	2.56128073904844	0.0105951345997010	*  
df.mm.trans1:probe6	0.0637194924787212	0.0495612848370505	1.28567071431301	0.198897764357795	   
df.mm.trans1:probe7	-0.0189373672336648	0.0495612848370505	-0.382100006001213	0.702479862742334	   
df.mm.trans1:probe8	0.264248436445103	0.0495612848370505	5.33175113022008	1.23840821617078e-07	***
df.mm.trans1:probe9	-0.0440164705257141	0.0495612848370505	-0.888122062824503	0.374718882780219	   
df.mm.trans1:probe10	0.292008332508538	0.0495612848370505	5.89186364858405	5.44219390153415e-09	***
df.mm.trans1:probe11	-0.00883692789514109	0.0495612848370505	-0.17830304287299	0.858526214162258	   
df.mm.trans1:probe12	0.0624402561570455	0.0495612848370505	1.25985951256790	0.208055581113459	   
df.mm.trans1:probe13	-0.0134072145765460	0.0495612848370505	-0.27051789760146	0.786825571549614	   
df.mm.trans1:probe14	0.305744046049264	0.0495612848370505	6.16900968274937	1.04877219418224e-09	***
df.mm.trans1:probe15	-0.0307186999528765	0.0495612848370505	-0.619812421204871	0.535542472366181	   
df.mm.trans1:probe16	0.288614753315541	0.0495612848370505	5.82339126728573	8.09139073841807e-09	***
df.mm.trans1:probe17	0.531556505306389	0.0495612848370505	10.7252365844441	2.66168838473244e-25	***
df.mm.trans1:probe18	0.247831291203707	0.0495612848370505	5.00050174281269	6.90753997638277e-07	***
df.mm.trans1:probe19	0.430122769062985	0.0495612848370505	8.67860408536943	1.93109974716260e-17	***
df.mm.trans1:probe20	0.359667450325096	0.0495612848370505	7.25702433880849	8.71984288695542e-13	***
df.mm.trans1:probe21	0.239584856901338	0.0495612848370505	4.83411311246378	1.57903589706991e-06	***
df.mm.trans1:probe22	0.309714609160996	0.0495612848370505	6.24912389134558	6.43644646944073e-10	***
df.mm.trans2:probe2	-0.372130643188148	0.0495612848370505	-7.50849467304274	1.47242288692248e-13	***
df.mm.trans2:probe3	-0.361784671812188	0.0495612848370505	-7.29974360030572	6.46933255848255e-13	***
df.mm.trans2:probe4	-0.286848562418307	0.0495612848370505	-5.78775476385284	9.9305661844658e-09	***
df.mm.trans2:probe5	-0.344540985596864	0.0495612848370505	-6.9518170630495	7.0444214823512e-12	***
df.mm.trans2:probe6	-0.248288063891236	0.0495612848370505	-5.00971806335464	6.59360998871587e-07	***
df.mm.trans3:probe2	0.260032723087272	0.0495612848370505	5.24669051543392	1.94333922248275e-07	***
df.mm.trans3:probe3	0.214467156138014	0.0495612848370505	4.32731227293939	1.68329993969772e-05	***
df.mm.trans3:probe4	0.280252906797653	0.0495612848370505	5.65467396010978	2.11313239282024e-08	***
df.mm.trans3:probe5	0.242248944280108	0.0495612848370505	4.88786650863841	1.21215145221333e-06	***
df.mm.trans3:probe6	0.150459116083621	0.0495612848370505	3.03581952280506	0.00246980339036009	** 
df.mm.trans3:probe7	0.305554951078200	0.0495612848370505	6.16519430605592	1.07329668381751e-09	***
df.mm.trans3:probe8	0.237007410620083	0.0495612848370505	4.78210787713282	2.03438061679201e-06	***
df.mm.trans3:probe9	0.212615876586692	0.0495612848370505	4.28995893237512	1.98595221780526e-05	***
df.mm.trans3:probe10	0.366393720239994	0.0495612848370505	7.39274055232098	3.36035718404173e-13	***
df.mm.trans3:probe11	0.384379120333729	0.0495612848370505	7.75563267977223	2.43904508926448e-14	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12818646292817	0.148009693988408	27.8913248969455	5.01828253226113e-123	***
df.mm.trans1	0.116265660768945	0.127546536908562	0.911554822161072	0.362253886748304	   
df.mm.trans2	0.00607354268181017	0.112421432859051	0.0540247755908336	0.95692774295295	   
df.mm.exp2	-0.0973488957554242	0.144014691934739	-0.675965031397898	0.49924125987981	   
df.mm.exp3	0.128853555443649	0.144014691934739	0.894725070842356	0.371179837655031	   
df.mm.exp4	0.117701837488715	0.144014691934739	0.817290485487774	0.413984664410543	   
df.mm.exp5	0.132265859161258	0.144014691934739	0.918419206987546	0.358652240519204	   
df.mm.exp6	0.0346443804638555	0.144014691934739	0.240561431604179	0.809951331870874	   
df.mm.exp7	0.0479655433612709	0.144014691934739	0.333060069892083	0.739168634490316	   
df.mm.exp8	-0.0750102744409529	0.144014691934739	-0.520851542528344	0.602601821454245	   
df.mm.trans1:exp2	0.0636912568330081	0.132774985479566	0.479693193736482	0.631565308436843	   
df.mm.trans2:exp2	0.143384969616733	0.0966079922774299	1.48419365972303	0.138117266854772	   
df.mm.trans1:exp3	-0.0915743097320427	0.132774985479566	-0.689695497998272	0.490568356120082	   
df.mm.trans2:exp3	-0.0678640800042434	0.09660799227743	-0.702468588823972	0.48257360009732	   
df.mm.trans1:exp4	-0.162578996383093	0.132774985479566	-1.22447007465961	0.221104132964799	   
df.mm.trans2:exp4	0.121064594454412	0.09660799227743	1.25315299076654	0.210484414377291	   
df.mm.trans1:exp5	-0.0735540274910745	0.132774985479566	-0.553975036980096	0.579737308586837	   
df.mm.trans2:exp5	-0.102754935823857	0.0966079922774299	-1.06362769167974	0.287790497337892	   
df.mm.trans1:exp6	-0.0233093723303684	0.132774985479566	-0.175555450043379	0.860683775516545	   
df.mm.trans2:exp6	-0.0523621108402614	0.0966079922774299	-0.542005993560996	0.58795221403888	   
df.mm.trans1:exp7	-0.0457038725401005	0.132774985479566	-0.344220504901762	0.730763075450561	   
df.mm.trans2:exp7	0.0295094404958988	0.09660799227743	0.305455478374464	0.760091840389243	   
df.mm.trans1:exp8	-0.0165598227257250	0.132774985479566	-0.124720953016210	0.90077308280665	   
df.mm.trans2:exp8	0.139286024754374	0.09660799227743	1.44176502865711	0.149726061928325	   
df.mm.trans1:probe2	-0.0936902908426732	0.0924951106323264	-1.01292155014656	0.311377321555513	   
df.mm.trans1:probe3	-0.0364566861856452	0.0924951106323264	-0.394147170984667	0.693568314211934	   
df.mm.trans1:probe4	-0.0487275041905133	0.0924951106323264	-0.526811675313391	0.598457744576972	   
df.mm.trans1:probe5	0.00479330413288448	0.0924951106323264	0.0518222433609291	0.958682146121547	   
df.mm.trans1:probe6	-0.127846556608190	0.0924951106323264	-1.38219799656641	0.16726323239486	   
df.mm.trans1:probe7	0.0471410720041259	0.0924951106323264	0.509660150486381	0.610417944429203	   
df.mm.trans1:probe8	0.0379625804769179	0.0924951106323264	0.410427969839632	0.681592395085994	   
df.mm.trans1:probe9	-0.0125123495087727	0.0924951106323264	-0.135275793749899	0.892424899641151	   
df.mm.trans1:probe10	-0.0912301753426702	0.0924951106323264	-0.986324301025117	0.324246340898455	   
df.mm.trans1:probe11	-0.132885407178564	0.0924951106323264	-1.43667493633033	0.151167418820500	   
df.mm.trans1:probe12	0.0112482590332706	0.0924951106323264	0.121609228383791	0.903236394264003	   
df.mm.trans1:probe13	-0.0502969393811138	0.0924951106323264	-0.543779439121351	0.586731619823363	   
df.mm.trans1:probe14	-0.0506161018966869	0.0924951106323264	-0.547230027086394	0.584360089884238	   
df.mm.trans1:probe15	-0.119613038211708	0.0924951106323264	-1.29318228167948	0.196288943236282	   
df.mm.trans1:probe16	-0.115565893213251	0.0924951106323264	-1.24942704996194	0.211842648653364	   
df.mm.trans1:probe17	-0.0625126530552572	0.0924951106323264	-0.675848189465374	0.499315412044049	   
df.mm.trans1:probe18	0.00823767758408527	0.0924951106323264	0.0890606814540774	0.929054041577806	   
df.mm.trans1:probe19	-0.00254815259998171	0.0924951106323264	-0.0275490518640577	0.978028091534556	   
df.mm.trans1:probe20	-0.0089448333180625	0.0924951106323264	-0.0967060124250108	0.922981984670956	   
df.mm.trans1:probe21	-0.0955968542318832	0.0924951106323264	-1.03353413578677	0.301639244062021	   
df.mm.trans1:probe22	-0.0258567717268968	0.0924951106323264	-0.279547443644659	0.779890723205997	   
df.mm.trans2:probe2	0.0193151829138835	0.0924951106323264	0.208823826274045	0.834634317408083	   
df.mm.trans2:probe3	0.0246985161131535	0.0924951106323264	0.267025099427488	0.789512683980215	   
df.mm.trans2:probe4	0.0644015085351108	0.0924951106323264	0.696269328128172	0.486444872218018	   
df.mm.trans2:probe5	-0.0169588276136077	0.0924951106323264	-0.183348368337220	0.854567112800625	   
df.mm.trans2:probe6	-0.0044166231547826	0.0924951106323264	-0.0477498013093788	0.961926527404726	   
df.mm.trans3:probe2	-0.0856950342353278	0.0924951106323264	-0.926481774544503	0.35445083748454	   
df.mm.trans3:probe3	-0.0824945538802341	0.0924951106323264	-0.891880157948618	0.372702084173284	   
df.mm.trans3:probe4	-0.108423249770337	0.0924951106323264	-1.17220520121680	0.241433367883680	   
df.mm.trans3:probe5	-0.0282620477835747	0.0924951106323264	-0.305551802580334	0.76001851360959	   
df.mm.trans3:probe6	-0.172286051408424	0.0924951106323264	-1.86265036314483	0.0628460328082602	.  
df.mm.trans3:probe7	0.00255131427450584	0.0924951106323264	0.0275832339359803	0.978000836338597	   
df.mm.trans3:probe8	0.00989542334862679	0.0924951106323264	0.106983204636207	0.914826799525459	   
df.mm.trans3:probe9	-0.092243685604166	0.0924951106323264	-0.997281747906008	0.318903162555676	   
df.mm.trans3:probe10	0.0127844743450619	0.0924951106323264	0.138217839382678	0.89010002151554	   
df.mm.trans3:probe11	-0.082949922028164	0.0924951106323264	-0.896803317073644	0.370070263040379