chr7.22377_chr7_80141413_80145698_+_2.R 

fitVsDatCorrelation=0.944388501720451
cont.fitVsDatCorrelation=0.279634510313529

fstatistic=6989.58899198419,69,1083
cont.fstatistic=806.0446428607,69,1083

residuals=-1.37442681798523,-0.116687401268528,-0.007760543978958,0.108265851082182,1.03554633748056
cont.residuals=-1.08001440360744,-0.457086270699489,-0.165526235353118,0.302981806134050,2.65531276945359

predictedValues:
Include	Exclude	Both
chr7.22377_chr7_80141413_80145698_+_2.R.tl.Lung	65.3112152868819	209.004673680302	80.0159757154991
chr7.22377_chr7_80141413_80145698_+_2.R.tl.cerebhem	63.0995973639918	179.941407673678	72.1877372004157
chr7.22377_chr7_80141413_80145698_+_2.R.tl.cortex	91.5765054137616	155.826909459508	108.162619102078
chr7.22377_chr7_80141413_80145698_+_2.R.tl.heart	58.9893833404622	149.378470928711	72.5747941062355
chr7.22377_chr7_80141413_80145698_+_2.R.tl.kidney	64.1121852144844	237.909288135526	79.3216024343661
chr7.22377_chr7_80141413_80145698_+_2.R.tl.liver	60.0891437195854	197.853979119351	76.3931772625926
chr7.22377_chr7_80141413_80145698_+_2.R.tl.stomach	106.278377972496	185.051081952762	122.078568497498
chr7.22377_chr7_80141413_80145698_+_2.R.tl.testicle	59.0983432042012	185.885510828208	75.4121933751753


diffExp=-143.69345839342,-116.841810309687,-64.2504040457463,-90.3890875882486,-173.797102921042,-137.764835399766,-78.7727039802658,-126.787167624007
diffExpScore=0.998928529224404
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	100.844888089603	68.4562102323787	83.5477787207234
cerebhem	90.8782150342212	75.0699620503643	84.1829105445398
cortex	104.467954931240	94.5266667799795	86.8322162466566
heart	85.8218322269073	84.5065487921712	103.751005414033
kidney	97.4369228475347	105.013997542198	91.6200205095371
liver	81.8046389786976	106.558815763368	91.9730139295858
stomach	90.8955997259213	83.4918469487405	94.5817300402424
testicle	96.5767293712845	96.3332669810408	84.6496328372101
cont.diffExp=32.3886778572244,15.808252983857,9.94128815126038,1.31528343473612,-7.57707469466298,-24.7541767846703,7.40375277718076,0.243462390243764
cont.diffExpScore=2.77980020036330

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=1,0,0,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=1,0,0,0,0,-1,0,0
cont.diffExp1.3Score=2
cont.diffExp1.2=1,1,0,0,0,-1,0,0
cont.diffExp1.2Score=1.5

tran.correlation=-0.221921585329997
cont.tran.correlation=-0.170300271031141

tran.covariance=-0.00683711528968883
cont.tran.covariance=-0.00241113487165793

tran.mean=129.337879580869
cont.tran.mean=91.4177560184781

weightedLogRatios:
wLogRatio
Lung	-5.53768217920531
cerebhem	-4.89238114928745
cortex	-2.5424834102749
heart	-4.22001478194802
kidney	-6.31534764093368
liver	-5.5910745706239
stomach	-2.74143211128932
testicle	-5.33104861770919

cont.weightedLogRatios:
wLogRatio
Lung	1.71221767960533
cerebhem	0.843508897022884
cortex	0.459880714338342
heart	0.0686434142478996
kidney	-0.345733617638264
liver	-1.19928748915932
stomach	0.379547528734079
testicle	0.0115328258577545

varWeightedLogRatios=1.89122895286236
cont.varWeightedLogRatios=0.727949759396278

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.46087444024518	0.0985766822763464	55.3972228943185	0	***
df.mm.trans1	-1.21328952298000	0.0840714166269347	-14.4316531308608	2.59272410096612e-43	***
df.mm.trans2	-0.206987360826425	0.0732302426558698	-2.82652840301402	0.00479211555017723	** 
df.mm.exp2	-0.0812185873785682	0.0918108140503247	-0.88462985780792	0.376552649605329	   
df.mm.exp3	-0.257009317274295	0.0918108140503247	-2.79933600341916	0.00521172742952876	** 
df.mm.exp4	-0.340071104194386	0.0918108140503247	-3.70404192264302	0.000222900972752415	***
df.mm.exp5	0.119719321588021	0.0918108140503247	1.30397843463624	0.19251801844272	   
df.mm.exp6	-0.0918289959898198	0.0918108140503248	-1.0001980370143	0.317438098012981	   
df.mm.exp7	-0.057265151315677	0.0918108140503247	-0.623729915784082	0.532936318000597	   
df.mm.exp8	-0.157929210885526	0.0918108140503247	-1.72015913941205	0.0856891895206955	.  
df.mm.trans1:exp2	0.0467692042691005	0.0834716128913219	0.560300713608983	0.575390221425045	   
df.mm.trans2:exp2	-0.0685067414675533	0.0554039924337561	-1.23649467228311	0.216542828706700	   
df.mm.trans1:exp3	0.595020293221798	0.0834716128913219	7.1284149498405	1.85143833947288e-12	***
df.mm.trans2:exp3	-0.0366014600559787	0.0554039924337561	-0.660628565707451	0.50899103684575	   
df.mm.trans1:exp4	0.238264816809386	0.0834716128913218	2.85444127118522	0.00439345412848314	** 
df.mm.trans2:exp4	0.00419764913154335	0.0554039924337561	0.0757643799147197	0.939620548183948	   
df.mm.trans1:exp5	-0.138248650494669	0.0834716128913219	-1.65623552374225	0.097963808499741	.  
df.mm.trans2:exp5	0.00981352332107559	0.0554039924337561	0.177126645391289	0.859442032620377	   
df.mm.trans1:exp6	0.00849441255185871	0.0834716128913219	0.101764087905169	0.918962768733569	   
df.mm.trans2:exp6	0.0370016615961132	0.0554039924337561	0.667851899668681	0.504370425921101	   
df.mm.trans1:exp7	0.544163238541864	0.0834716128913219	6.51914129478188	1.08201618413317e-10	***
df.mm.trans2:exp7	-0.0644595568776547	0.0554039924337561	-1.16344606311045	0.244904836426965	   
df.mm.trans1:exp8	0.0579683294177938	0.0834716128913219	0.694467584965289	0.487537969648398	   
df.mm.trans2:exp8	0.0407035480772354	0.0554039924337561	0.73466814013273	0.46270066876094	   
df.mm.trans1:probe2	0.060835167952958	0.0634012413067576	0.959526449310605	0.337507764162353	   
df.mm.trans1:probe3	-0.000832529374208015	0.0634012413067576	-0.0131311210482448	0.98952560080069	   
df.mm.trans1:probe4	-0.409898573305708	0.0634012413067576	-6.46515060048231	1.52785057836101e-10	***
df.mm.trans1:probe5	-0.285931232832133	0.0634012413067576	-4.50986805524354	7.19380379592516e-06	***
df.mm.trans1:probe6	0.0959315321040933	0.0634012413067576	1.51308602366226	0.130549562436429	   
df.mm.trans1:probe7	-0.158992491681569	0.0634012413067576	-2.50771890904008	0.0122967432536485	*  
df.mm.trans1:probe8	-0.204539033854906	0.0634012413067576	-3.2261045626106	0.00129244711025185	** 
df.mm.trans1:probe9	-0.335237652973791	0.0634012413067576	-5.2875566166251	1.49893492131722e-07	***
df.mm.trans1:probe10	-0.274617710234673	0.0634012413067576	-4.33142482031188	1.61897160625126e-05	***
df.mm.trans1:probe11	-0.179717061480427	0.0634012413067576	-2.83459846804721	0.00467361363507028	** 
df.mm.trans1:probe12	-0.283642520693244	0.0634012413067576	-4.47376920147164	8.49642035760534e-06	***
df.mm.trans1:probe13	-0.235145897552027	0.0634012413067576	-3.70885321336704	0.000218760823978082	***
df.mm.trans1:probe14	-0.0969749535690412	0.0634012413067576	-1.52954345325578	0.126421790639199	   
df.mm.trans1:probe15	-0.268946696680992	0.0634012413067576	-4.24197840827331	2.40493534650773e-05	***
df.mm.trans1:probe16	-0.188094893637638	0.0634012413067576	-2.96673834393192	0.00307584974098134	** 
df.mm.trans1:probe17	-0.0355758570013839	0.0634012413067576	-0.561122404989759	0.574830173914759	   
df.mm.trans1:probe18	-0.0937073703342056	0.0634012413067576	-1.47800529457801	0.139697115592852	   
df.mm.trans1:probe19	-0.093697404983073	0.0634012413067576	-1.47784811546089	0.139739185075981	   
df.mm.trans1:probe20	0.118355376879715	0.0634012413067576	1.86676750234385	0.0622041146128878	.  
df.mm.trans1:probe21	0.065426389905701	0.0634012413067576	1.03194178153619	0.302329781261393	   
df.mm.trans1:probe22	-0.068684699678111	0.0634012413067576	-1.08333367395427	0.278901469876333	   
df.mm.trans2:probe2	0.201172773548968	0.0634012413067576	3.17301001372548	0.00155113647149267	** 
df.mm.trans2:probe3	0.528719913069752	0.0634012413067576	8.33926753123993	2.24953617254840e-16	***
df.mm.trans2:probe4	0.711868265512946	0.0634012413067576	11.2279862482294	9.47064626115068e-28	***
df.mm.trans2:probe5	0.198921456238266	0.0634012413067576	3.13750097219413	0.00174994261271952	** 
df.mm.trans2:probe6	0.659525486101184	0.0634012413067576	10.4024065224554	3.19951584034431e-24	***
df.mm.trans3:probe2	-0.0474808994613867	0.0634012413067576	-0.748895423540011	0.454082856102536	   
df.mm.trans3:probe3	1.94962096090880	0.0634012413067576	30.750517193754	9.01406384269354e-150	***
df.mm.trans3:probe4	0.232768685139642	0.0634012413067576	3.67135848355753	0.000253035783572912	***
df.mm.trans3:probe5	0.789424562090644	0.0634012413067576	12.4512477330078	2.34223038319044e-33	***
df.mm.trans3:probe6	0.239075249338702	0.0634012413067576	3.77082915746036	0.000171501040530489	***
df.mm.trans3:probe7	-0.0824642693985276	0.0634012413067576	-1.30067278966253	0.193647167891491	   
df.mm.trans3:probe8	1.28786079526976	0.0634012413067576	20.3128640500687	5.62214681036284e-78	***
df.mm.trans3:probe9	1.28397229931737	0.0634012413067576	20.2515325071485	1.38793059935726e-77	***
df.mm.trans3:probe10	-0.093586475561667	0.0634012413067576	-1.47609847430057	0.140208141479508	   
df.mm.trans3:probe11	0.174946956469691	0.0634012413067576	2.75936169172518	0.00588907103630515	** 
df.mm.trans3:probe12	0.315442149322216	0.0634012413067576	4.97533081089053	7.5712083140111e-07	***
df.mm.trans3:probe13	-0.00871193375331853	0.0634012413067576	-0.137409513974137	0.890732652508939	   
df.mm.trans3:probe14	0.146342685354785	0.0634012413067576	2.30819905633595	0.0211757642110053	*  
df.mm.trans3:probe15	0.249019331054279	0.0634012413067576	3.92767280137995	9.11978446087063e-05	***
df.mm.trans3:probe16	-0.230181203904169	0.0634012413067576	-3.63054727573031	0.000296042793383015	***
df.mm.trans3:probe17	-0.158548984951059	0.0634012413067576	-2.50072367170136	0.0125408407206129	*  
df.mm.trans3:probe18	0.981080217321115	0.0634012413067576	15.4741484094026	6.00324211470996e-49	***
df.mm.trans3:probe19	-0.0814546801116201	0.0634012413067576	-1.28474898018974	0.199154733411046	   
df.mm.trans3:probe20	-0.114550349601505	0.0634012413067576	-1.80675247424998	0.0710783407629747	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.64207873173158	0.287819224567241	16.1284526379755	1.3039292137515e-52	***
df.mm.trans1	0.124483225811859	0.245467481589609	0.507127155930085	0.61216884427028	   
df.mm.trans2	-0.420875348070358	0.213813968672598	-1.96841839045054	0.0492746597998233	*  
df.mm.exp2	-0.0194102986322648	0.268064583800589	-0.0724090379902777	0.942289762252567	   
df.mm.exp3	0.319425467550936	0.268064583800588	1.19159891628413	0.233679612091819	   
df.mm.exp4	-0.167250569527876	0.268064583800588	-0.623918934596347	0.532812216322747	   
df.mm.exp5	0.301289881805922	0.268064583800589	1.12394512372455	0.261285371030145	   
df.mm.exp6	0.137176658891922	0.268064583800589	0.511729885936617	0.608944389745144	   
df.mm.exp7	-0.0293629315246036	0.268064583800589	-0.109536780682847	0.912797042992101	   
df.mm.exp8	0.285271574368649	0.268064583800589	1.06418971997010	0.287479974867988	   
df.mm.trans1:exp2	-0.0846529623581537	0.243716204897291	-0.347342362375242	0.72840161665524	   
df.mm.trans2:exp2	0.111636530524217	0.161765782454613	0.690112141333341	0.490271524840245	   
df.mm.trans1:exp3	-0.28412866941063	0.243716204897291	-1.16581771626704	0.243944834577735	   
df.mm.trans2:exp3	0.00326224111959445	0.161765782454613	0.0201664472553690	0.983914308312026	   
df.mm.trans1:exp4	0.00594042365098915	0.243716204897291	0.0243743482444781	0.980558499557804	   
df.mm.trans2:exp4	0.377885327217553	0.161765782454613	2.33600283993048	0.0196728285440308	*  
df.mm.trans1:exp5	-0.335668233176643	0.243716204897291	-1.37729140053737	0.168706751980316	   
df.mm.trans2:exp5	0.126609495222806	0.161765782454613	0.782671670743033	0.43399104186972	   
df.mm.trans1:exp6	-0.346426280521936	0.243716204897291	-1.42143309948524	0.155478774775125	   
df.mm.trans2:exp6	0.305326160337137	0.161765782454613	1.88745824799384	0.0593659189904668	.  
df.mm.trans1:exp7	-0.0745090511810293	0.243716204897291	-0.30572054579805	0.759876181754161	   
df.mm.trans2:exp7	0.227917643112230	0.161765782454613	1.40893605343378	0.159141120791368	   
df.mm.trans1:exp8	-0.328517333809776	0.243716204897291	-1.34795030945202	0.177956239678891	   
df.mm.trans2:exp8	0.056347862127803	0.161765782454613	0.348329920412017	0.727660123766943	   
df.mm.trans1:probe2	-0.326470143241987	0.185115746321787	-1.76360007038236	0.0780812713951015	.  
df.mm.trans1:probe3	-0.193112367686655	0.185115746321787	-1.04319795330089	0.297089423515453	   
df.mm.trans1:probe4	-0.235702100843325	0.185115746321787	-1.27326878197387	0.203195908641494	   
df.mm.trans1:probe5	-0.291326199039109	0.185115746321787	-1.57375158422610	0.115836960218484	   
df.mm.trans1:probe6	-0.124636929076520	0.185115746321787	-0.673291881177217	0.500905266762384	   
df.mm.trans1:probe7	-0.423713075512158	0.185115746321787	-2.28890887961318	0.0222764727045905	*  
df.mm.trans1:probe8	-0.286506725544592	0.185115746321787	-1.54771666504565	0.121982679779471	   
df.mm.trans1:probe9	-0.497103494005308	0.185115746321787	-2.68536579887263	0.00735576878584775	** 
df.mm.trans1:probe10	-0.225952081089106	0.185115746321787	-1.22059892569233	0.222503601722846	   
df.mm.trans1:probe11	-0.154093338041215	0.185115746321787	-0.832416156394144	0.405357602370533	   
df.mm.trans1:probe12	-0.285764919642398	0.185115746321787	-1.54370941057414	0.122950880968532	   
df.mm.trans1:probe13	-0.373686759005225	0.185115746321787	-2.01866543732938	0.0437680887913719	*  
df.mm.trans1:probe14	-0.235947081990253	0.185115746321787	-1.27459217640030	0.202727030761655	   
df.mm.trans1:probe15	-0.220057083045757	0.185115746321787	-1.1887539953691	0.234797076783657	   
df.mm.trans1:probe16	-0.392899158773564	0.185115746321787	-2.12245131265380	0.034026294845657	*  
df.mm.trans1:probe17	-0.370447326383773	0.185115746321787	-2.00116593938921	0.0456236818899694	*  
df.mm.trans1:probe18	-0.606337682826322	0.185115746321787	-3.27545168292882	0.00108836040296728	** 
df.mm.trans1:probe19	-0.362433632285089	0.185115746321787	-1.95787575874324	0.0505010675384257	.  
df.mm.trans1:probe20	-0.208498779112109	0.185115746321787	-1.12631574166400	0.260281408383504	   
df.mm.trans1:probe21	-0.383945862310131	0.185115746321787	-2.07408537598264	0.0383072346234222	*  
df.mm.trans1:probe22	-0.226457333177931	0.185115746321787	-1.22332831040899	0.221471825365056	   
df.mm.trans2:probe2	-0.277468837574687	0.185115746321787	-1.49889376289126	0.13419261600663	   
df.mm.trans2:probe3	0.0793822099312424	0.185115746321787	0.428824729978682	0.668136061622777	   
df.mm.trans2:probe4	0.0808556781576087	0.185115746321787	0.436784443053576	0.662354751428326	   
df.mm.trans2:probe5	0.172160084517476	0.185115746321787	0.9300131833097	0.352571506953983	   
df.mm.trans2:probe6	0.0748340181837495	0.185115746321787	0.404255281739596	0.686104823637451	   
df.mm.trans3:probe2	0.373044274904407	0.185115746321787	2.01519472177123	0.044130952713403	*  
df.mm.trans3:probe3	-0.0968053342936996	0.185115746321787	-0.522944893760809	0.601119568965773	   
df.mm.trans3:probe4	0.204998496496438	0.185115746321787	1.10740712537813	0.268363840559393	   
df.mm.trans3:probe5	0.252408353642272	0.185115746321787	1.36351638722030	0.173003182596035	   
df.mm.trans3:probe6	-0.0376688955456906	0.185115746321787	-0.203488337940797	0.838791586838372	   
df.mm.trans3:probe7	0.0344607994484325	0.185115746321787	0.186158120706433	0.85235562667906	   
df.mm.trans3:probe8	-0.06245110305617	0.185115746321787	-0.337362457257478	0.735909054194849	   
df.mm.trans3:probe9	0.0674790989910683	0.185115746321787	0.364523819998377	0.715538052205068	   
df.mm.trans3:probe10	-0.0599838054538662	0.185115746321787	-0.324034052454923	0.745974821327663	   
df.mm.trans3:probe11	0.187370767914202	0.185115746321787	1.01218168436355	0.311677270438529	   
df.mm.trans3:probe12	0.0386718493855955	0.185115746321787	0.208906320256366	0.834560655117654	   
df.mm.trans3:probe13	-0.102371841227425	0.185115746321787	-0.553015306701526	0.580367058810363	   
df.mm.trans3:probe14	0.0645077720128754	0.185115746321787	0.348472635605733	0.72755298933305	   
df.mm.trans3:probe15	0.522599992126332	0.185115746321787	2.82309853435102	0.0048432996524762	** 
df.mm.trans3:probe16	0.000461028464558734	0.185115746321787	0.00249048756639712	0.998013339127243	   
df.mm.trans3:probe17	0.335570917156006	0.185115746321787	1.81276268401654	0.070145209068844	.  
df.mm.trans3:probe18	-0.0531071599081685	0.185115746321787	-0.286886237196982	0.774254305199146	   
df.mm.trans3:probe19	-0.141793133024062	0.185115746321787	-0.765970134045663	0.443860981026666	   
df.mm.trans3:probe20	0.0672491017222586	0.185115746321787	0.363281368865074	0.716465604841194