chr2.13865_chr2_144960000_144983627_+_2.R 

fitVsDatCorrelation=0.955470259373845
cont.fitVsDatCorrelation=0.278944884649346

fstatistic=7347.6346094521,55,761
cont.fstatistic=681.261083060602,55,761

residuals=-0.838423496475869,-0.0873873311207468,0.00524212951502305,0.0957590783687966,1.02195636081118
cont.residuals=-1.07965598842163,-0.441652925311909,-0.201860561726361,0.302433954414012,2.31399773237419

predictedValues:
Include	Exclude	Both
chr2.13865_chr2_144960000_144983627_+_2.R.tl.Lung	51.520758425604	153.569693627067	141.520661080546
chr2.13865_chr2_144960000_144983627_+_2.R.tl.cerebhem	61.4721493608876	103.584632090534	84.6635156364477
chr2.13865_chr2_144960000_144983627_+_2.R.tl.cortex	49.569071846713	129.749032113062	113.989617216662
chr2.13865_chr2_144960000_144983627_+_2.R.tl.heart	50.7208255852289	147.083233244393	105.734884349244
chr2.13865_chr2_144960000_144983627_+_2.R.tl.kidney	50.8060239873033	110.329526053404	118.841483620564
chr2.13865_chr2_144960000_144983627_+_2.R.tl.liver	53.3942067536559	115.624069361086	109.056359250453
chr2.13865_chr2_144960000_144983627_+_2.R.tl.stomach	53.2440277061638	345.665930365049	191.807026412011
chr2.13865_chr2_144960000_144983627_+_2.R.tl.testicle	54.9975498714811	310.279993979375	181.622645944132


diffExp=-102.048935201463,-42.1124827296461,-80.1799602663494,-96.3624076591645,-59.523502066101,-62.22986260743,-292.421902658886,-255.282444107894
diffExpScore=0.998991082681554
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
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	76.9065634888865	108.009062481021	92.9609458749323
cerebhem	70.148066828006	89.182810052772	65.6518007460879
cortex	90.7447048869463	77.2718513733889	83.6252634663705
heart	79.6345001674671	113.465488986533	61.8731243985742
kidney	76.4816275799721	106.940193661878	73.2049926733793
liver	81.8666337612323	102.19370139516	55.6733141257592
stomach	84.2555981094645	94.6693922840855	99.3901165975416
testicle	66.4725642302784	74.5006212270324	73.6930904288816
cont.diffExp=-31.1024989921347,-19.0347432247659,13.4728535135573,-33.8309888190661,-30.4585660819058,-20.3270676339277,-10.4137941746210,-8.02805699675402
cont.diffExpScore=1.18437449738332

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=-1,0,0,-1,0,0,0,0
cont.diffExp1.4Score=0.666666666666667
cont.diffExp1.3=-1,0,0,-1,-1,0,0,0
cont.diffExp1.3Score=0.75
cont.diffExp1.2=-1,-1,0,-1,-1,-1,0,0
cont.diffExp1.2Score=0.833333333333333

tran.correlation=0.0288215445284804
cont.tran.correlation=0.0993221552409296

tran.covariance=-0.000596211900694654
cont.tran.covariance=0.00244552302650103

tran.mean=115.100670273188
cont.tran.mean=87.0464612821328

weightedLogRatios:
wLogRatio
Lung	-4.90173364310084
cerebhem	-2.28523696904958
cortex	-4.21890440383953
heart	-4.74697420927218
kidney	-3.34667180236265
liver	-3.37182715355438
stomach	-9.18491813054114
testicle	-8.43012913195923

cont.weightedLogRatios:
wLogRatio
Lung	-1.53252001874570
cerebhem	-1.04930550149052
cortex	0.71162001567871
heart	-1.61251716146318
kidney	-1.51004852875498
liver	-1.00154762319042
stomach	-0.523492806982472
testicle	-0.485010304209881

varWeightedLogRatios=6.09827442493242
cont.varWeightedLogRatios=0.602708404210034

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.34630959907421	0.0958930480838901	45.3245536138546	2.24468609789089e-218	***
df.mm.trans1	-0.459327353306269	0.0839683702714524	-5.47024256659214	6.1011735776829e-08	***
df.mm.trans2	0.545581702737139	0.0753002859742073	7.24541342278591	1.05912587084226e-12	***
df.mm.exp2	0.296594859799403	0.0993030764250219	2.98676406086316	0.00290999566233774	** 
df.mm.exp3	0.00916820726850034	0.099303076425022	0.0923255109364383	0.926463718692493	   
df.mm.exp4	0.232706780597108	0.099303076425022	2.34339950960947	0.0193654464440724	*  
df.mm.exp5	-0.169997601360024	0.099303076425022	-1.7119066949389	0.0873212880498926	.  
df.mm.exp6	0.0124880177237863	0.099303076425022	0.125756604662850	0.899957808449132	   
df.mm.exp7	0.540175060495365	0.099303076425022	5.43966088405348	7.20010406540083e-08	***
df.mm.exp8	0.519138996255293	0.099303076425022	5.22782389976876	2.21789376115771e-07	***
df.mm.trans1:exp2	-0.119995446159281	0.0931545429325746	-1.28813305697967	0.198091176198133	   
df.mm.trans2:exp2	-0.6903603740023	0.0743116178870121	-9.29007325680846	1.58439621983861e-19	***
df.mm.trans1:exp3	-0.0477859221858675	0.0931545429325747	-0.512974683590633	0.608117902092304	   
df.mm.trans2:exp3	-0.177720639010417	0.0743116178870121	-2.3915592751679	0.0170188496331271	*  
df.mm.trans1:exp4	-0.248354995970928	0.0931545429325747	-2.66605350799357	0.0078376103429888	** 
df.mm.trans2:exp4	-0.275862635639834	0.0743116178870121	-3.71224101269431	0.000220418880336276	***
df.mm.trans1:exp5	0.156027728659737	0.0931545429325747	1.67493418729637	0.09435798344431	.  
df.mm.trans2:exp5	-0.160685313725764	0.0743116178870121	-2.16231752577476	0.0309046388255638	*  
df.mm.trans1:exp6	0.023229431916276	0.0931545429325747	0.249364455935225	0.803146167059248	   
df.mm.trans2:exp6	-0.29629836482309	0.0743116178870121	-3.98724147378409	7.32886994665333e-05	***
df.mm.trans1:exp7	-0.507274220630162	0.0931545429325747	-5.4455124211959	6.97595262067734e-08	***
df.mm.trans2:exp7	0.271143234841801	0.0743116178870121	3.64873276281057	0.000281524788889353	***
df.mm.trans1:exp8	-0.453835162487971	0.0931545429325747	-4.8718521738275	1.34551855830668e-06	***
df.mm.trans2:exp8	0.184181605833018	0.0743116178870121	2.47850351089191	0.0134092313524354	*  
df.mm.trans1:probe2	-0.138374503979901	0.0570452743517492	-2.42569617820864	0.0155106000760018	*  
df.mm.trans1:probe3	-0.141574231451118	0.0570452743517492	-2.48178719552038	0.0132873093694096	*  
df.mm.trans1:probe4	0.039574478095126	0.0570452743517492	0.693738062352092	0.48805818687817	   
df.mm.trans1:probe5	0.0692236706421473	0.0570452743517492	1.21348650574111	0.225320367714827	   
df.mm.trans1:probe6	-0.0571668166593737	0.0570452743517492	-1.00213062885586	0.316599042740438	   
df.mm.trans1:probe7	-0.0125000793717963	0.0570452743517492	-0.219125589522439	0.826610974072992	   
df.mm.trans1:probe8	0.0213002221920331	0.0570452743517492	0.373391528642547	0.708961052973947	   
df.mm.trans1:probe9	-0.0896076390133682	0.0570452743517492	-1.57081616368141	0.116641019288433	   
df.mm.trans1:probe10	0.0528840966648647	0.0570452743517492	0.92705482208349	0.354191986714584	   
df.mm.trans1:probe11	0.148880620478564	0.0570452743517492	2.60986772647538	0.00923577343894104	** 
df.mm.trans1:probe12	-0.0259456090535239	0.0570452743517492	-0.454824862328467	0.649364958080718	   
df.mm.trans1:probe13	0.0103925259905613	0.0570452743517492	0.182180313946419	0.855489738966164	   
df.mm.trans1:probe14	0.157446634386263	0.0570452743517492	2.76002940077779	0.00591862452713299	** 
df.mm.trans1:probe15	0.158285586316758	0.0570452743517492	2.77473617430160	0.00566011520443027	** 
df.mm.trans1:probe16	0.0256214052493258	0.0570452743517492	0.449141590438159	0.653457397587367	   
df.mm.trans1:probe17	0.116409471235072	0.0570452743517492	2.04065056322237	0.0416301941854754	*  
df.mm.trans1:probe18	0.220874823450664	0.0570452743517492	3.87192148623422	0.000117257806839434	***
df.mm.trans1:probe19	0.196962038987137	0.0570452743517492	3.45273190856514	0.000585605521336636	***
df.mm.trans1:probe20	0.162733238734843	0.0570452743517492	2.85270323587905	0.00445249195737664	** 
df.mm.trans1:probe21	0.38293093494618	0.0570452743517492	6.71275472504477	3.73347115270742e-11	***
df.mm.trans1:probe22	0.241720725742331	0.0570452743517492	4.23734881616744	2.53918640068846e-05	***
df.mm.trans2:probe2	0.110542448033050	0.0570452743517492	1.93780202285345	0.053016995028085	.  
df.mm.trans2:probe3	0.069562777207249	0.0570452743517492	1.21943102207407	0.223058454147748	   
df.mm.trans2:probe4	0.754381565905192	0.0570452743517492	13.2242604576423	4.36819479499469e-36	***
df.mm.trans2:probe5	0.477382424765674	0.0570452743517492	8.36848328263034	2.78415560688268e-16	***
df.mm.trans2:probe6	0.295289091918813	0.0570452743517492	5.17639883889451	2.89740075749801e-07	***
df.mm.trans3:probe2	1.24825748215196	0.0570452743517492	21.8818735878985	1.02607802323049e-82	***
df.mm.trans3:probe3	0.489714136671759	0.0570452743517492	8.58465740127942	5.10174317977956e-17	***
df.mm.trans3:probe4	0.843869625825393	0.0570452743517492	14.7929804074913	1.04302173819857e-43	***
df.mm.trans3:probe5	-0.337227983222598	0.0570452743517492	-5.9115849131201	5.11332477761933e-09	***
df.mm.trans3:probe6	0.874677374309125	0.0570452743517492	15.3330382621309	1.92777563935988e-46	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.61449553746769	0.312066089489788	14.7869175565091	1.118609040751e-43	***
df.mm.trans1	-0.223493676379892	0.273259443463711	-0.817880888385737	0.413681207122333	   
df.mm.trans2	0.181599786322601	0.245050775327074	0.741070033670437	0.458879585652677	   
df.mm.exp2	0.064304636047221	0.323163392482341	0.198984902198459	0.842327705347644	   
df.mm.exp3	-0.0635926956385632	0.323163392482341	-0.196781866751935	0.84405078288027	   
df.mm.exp4	0.491233348185256	0.323163392482341	1.52007733429181	0.128906893138613	   
df.mm.exp5	0.223429774363336	0.323163392482341	0.69138330504296	0.489535548836411	   
df.mm.exp6	0.519833728130562	0.323163392482341	1.60857863304851	0.108123259705902	   
df.mm.exp7	-0.107433815039620	0.323163392482341	-0.332444260515958	0.739645373134667	   
df.mm.exp8	-0.284939174773015	0.323163392482341	-0.881718602420556	0.378207399941291	   
df.mm.trans1:exp2	-0.156287611399273	0.303154133819435	-0.51553844715956	0.606326541230043	   
df.mm.trans2:exp2	-0.255831462910675	0.241833338923295	-1.05788335078076	0.290444307904116	   
df.mm.trans1:exp3	0.229051594897758	0.303154133819435	0.75556150929542	0.450145931868681	   
df.mm.trans2:exp3	-0.271292698372721	0.241833338923295	-1.12181678332932	0.262294188483157	   
df.mm.trans1:exp4	-0.456377153874825	0.303154133819435	-1.50542942669109	0.132628640952804	   
df.mm.trans2:exp4	-0.441949754757741	0.241833338923295	-1.82749722071165	0.0680164931579543	.  
df.mm.trans1:exp5	-0.228970448557973	0.303154133819435	-0.755293835756673	0.450306391897636	   
df.mm.trans2:exp5	-0.233375169369942	0.241833338923296	-0.965024799347305	0.334838950243919	   
df.mm.trans1:exp6	-0.457333446295335	0.303154133819435	-1.50858390262866	0.131820186073589	   
df.mm.trans2:exp6	-0.575178817909644	0.241833338923295	-2.37840994327122	0.0176333546319366	*  
df.mm.trans1:exp7	0.198697604637541	0.303154133819435	0.655434257597454	0.512386284846739	   
df.mm.trans2:exp7	-0.0243905796135842	0.241833338923295	-0.100856977462981	0.919690561281123	   
df.mm.trans1:exp8	0.139137245509282	0.303154133819435	0.45896535784056	0.646390102061503	   
df.mm.trans2:exp8	-0.0864684967234756	0.241833338923295	-0.357554078806734	0.720776188409847	   
df.mm.trans1:probe2	-0.0457958154838104	0.185643235318256	-0.24668722997259	0.805216818862812	   
df.mm.trans1:probe3	-0.190108856475489	0.185643235318256	-1.02405485526891	0.306134775095512	   
df.mm.trans1:probe4	-0.120771042011437	0.185643235318256	-0.650554499356758	0.51553046772522	   
df.mm.trans1:probe5	-0.134649360972142	0.185643235318256	-0.725312509994275	0.468483304494534	   
df.mm.trans1:probe6	-0.0398853888037968	0.185643235318256	-0.214849675160097	0.829942083296397	   
df.mm.trans1:probe7	-0.0702422829429787	0.185643235318256	-0.378372434754001	0.705259467729431	   
df.mm.trans1:probe8	0.0863974026879375	0.185643235318256	0.465394834020332	0.641781883811847	   
df.mm.trans1:probe9	-0.231520424016890	0.185643235318256	-1.24712556113334	0.212735137656564	   
df.mm.trans1:probe10	-0.166758528511957	0.185643235318256	-0.898274199035991	0.369323496756106	   
df.mm.trans1:probe11	0.223014622226904	0.185643235318256	1.20130756094926	0.230005659374638	   
df.mm.trans1:probe12	-0.218266727355979	0.185643235318256	-1.17573218858094	0.240069602320991	   
df.mm.trans1:probe13	-0.202169444869773	0.185643235318256	-1.08902133990062	0.276489218786147	   
df.mm.trans1:probe14	-0.204619749990035	0.185643235318256	-1.10222034020925	0.270714353163568	   
df.mm.trans1:probe15	0.236721767305087	0.185643235318256	1.27514351330532	0.202647599942465	   
df.mm.trans1:probe16	-0.0103612733962681	0.185643235318256	-0.0558128249516084	0.955505567851381	   
df.mm.trans1:probe17	0.0514828153532768	0.185643235318256	0.277321256899114	0.781608753676792	   
df.mm.trans1:probe18	-0.272428103460357	0.185643235318256	-1.46748198496606	0.142658139836028	   
df.mm.trans1:probe19	0.0504210160066586	0.185643235318256	0.271601687614524	0.786001963806103	   
df.mm.trans1:probe20	0.00369878942448764	0.185643235318256	0.0199241810139036	0.984109077957758	   
df.mm.trans1:probe21	-0.115281559493709	0.185643235318256	-0.62098443445072	0.534795809742189	   
df.mm.trans1:probe22	0.0156243015683105	0.185643235318256	0.0841630536201604	0.932948927505495	   
df.mm.trans2:probe2	-0.25044329670496	0.185643235318256	-1.34905694934488	0.177719933478076	   
df.mm.trans2:probe3	-0.316260648883705	0.185643235318256	-1.70359371480208	0.0888652826664822	.  
df.mm.trans2:probe4	-0.326842166897722	0.185643235318256	-1.7605929261974	0.0787089666522643	.  
df.mm.trans2:probe5	-0.231593882224616	0.185643235318256	-1.24752125671363	0.212590187431724	   
df.mm.trans2:probe6	-0.24142226519098	0.185643235318256	-1.30046357346175	0.193835819110437	   
df.mm.trans3:probe2	-0.0704446927310422	0.185643235318256	-0.379462750744867	0.704450122067235	   
df.mm.trans3:probe3	-0.160101935499041	0.185643235318256	-0.86241728778628	0.388729616223522	   
df.mm.trans3:probe4	-0.101668276999523	0.185643235318256	-0.547654089443271	0.584090024845983	   
df.mm.trans3:probe5	0.0785830818187791	0.185643235318256	0.423301617664983	0.672194815187892	   
df.mm.trans3:probe6	0.0110987477068881	0.185643235318256	0.0597853602791451	0.952342281633117