chr19.12245_chr19_41964702_41968780_+_2.R 

fitVsDatCorrelation=0.881712522328501
cont.fitVsDatCorrelation=0.301912308801175

fstatistic=13743.0187579302,57,807
cont.fstatistic=3355.06425091864,57,807

residuals=-0.543215171955756,-0.0815713399611488,0.00304037444934239,0.0784254390548628,0.471097013585495
cont.residuals=-0.514450374096827,-0.204114072222682,-0.0515674468166397,0.161363433601526,0.963146878902064

predictedValues:
Include	Exclude	Both
chr19.12245_chr19_41964702_41968780_+_2.R.tl.Lung	59.3932359878058	41.7971191784337	75.201856937781
chr19.12245_chr19_41964702_41968780_+_2.R.tl.cerebhem	62.1419864615534	44.7346928546025	68.4532788207566
chr19.12245_chr19_41964702_41968780_+_2.R.tl.cortex	65.2169360154052	44.9857190969015	79.8092158336989
chr19.12245_chr19_41964702_41968780_+_2.R.tl.heart	58.171452299648	45.4922158864278	70.3187700748595
chr19.12245_chr19_41964702_41968780_+_2.R.tl.kidney	59.1581419628804	43.5608867557958	73.46659087859
chr19.12245_chr19_41964702_41968780_+_2.R.tl.liver	62.2133030577351	48.2008024347194	72.0666412583823
chr19.12245_chr19_41964702_41968780_+_2.R.tl.stomach	58.7397429945419	44.2732292156360	67.9476055642806
chr19.12245_chr19_41964702_41968780_+_2.R.tl.testicle	63.3828463359079	45.2749216217549	73.2710197074126


diffExp=17.5961168093721,17.4072936069509,20.2312169185036,12.6792364132203,15.5972552070845,14.0125006230156,14.4665137789059,18.1079247141529
diffExpScore=0.992372121946636
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,0,1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,1,1,0,1,0,1,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	55.7330957249849	56.5548480891932	55.8586423925491
cerebhem	59.3779277670197	59.7036001265961	67.7967668556702
cortex	55.9637181892882	60.9278019056222	63.3733269787438
heart	60.1326298153913	54.2773590023421	61.8894974753915
kidney	61.1877050387858	61.3568146762332	57.9586965848842
liver	57.7908017268303	52.3713660003925	60.6106792072206
stomach	56.5464625904222	59.5584942691169	52.1588141232362
testicle	57.1676656365527	54.1242611911958	56.8482054762868
cont.diffExp=-0.821752364208336,-0.325672359576423,-4.96408371633402,5.85527081304922,-0.169109637447455,5.4194357264378,-3.01203167869468,3.04340444535688
cont.diffExpScore=3.91849849254733

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=0.380249404282714
cont.tran.correlation=0.0912346735620983

tran.covariance=0.000646560855733689
cont.tran.covariance=0.000165742540099378

tran.mean=52.9210770099843
cont.tran.mean=57.673409484373

weightedLogRatios:
wLogRatio
Lung	1.37326428975562
cerebhem	1.30321477054116
cortex	1.48253735855129
heart	0.968777813215012
kidney	1.20193353224211
liver	1.02153123451147
stomach	1.11164823299209
testicle	1.33935860510352

cont.weightedLogRatios:
wLogRatio
Lung	-0.0589553819635632
cerebhem	-0.0223529999176849
cortex	-0.345654755493681
heart	0.414425605051009
kidney	-0.0113581868005382
liver	0.394626384877141
stomach	-0.210751351429263
testicle	0.219843136972062

varWeightedLogRatios=0.0325618031935968
cont.varWeightedLogRatios=0.075110596860449

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.61394661994573	0.0632530562105734	57.1347352436959	7.74486607887676e-286	***
df.mm.trans1	0.587722882006342	0.054456953009711	10.7924305258419	1.85545328177432e-25	***
df.mm.trans2	0.107194598321479	0.0483654118534835	2.21634829961152	0.0269458363411267	*  
df.mm.exp2	0.207187904881615	0.0623370343740163	3.32367278877123	0.00092844570347791	***
df.mm.exp3	0.107593449982551	0.0623370343740163	1.72599564709800	0.0847309567673047	.  
df.mm.exp4	0.131065351531097	0.0623370343740163	2.10252786080130	0.0358162848568716	*  
df.mm.exp5	0.0607112866913405	0.0623370343740163	0.973920034871703	0.330388077899094	   
df.mm.exp6	0.231521415741494	0.0623370343740163	3.71402679107876	0.000218027283311485	***
df.mm.exp7	0.147927994786317	0.0623370343740163	2.37303548800161	0.0178759361573452	*  
df.mm.exp8	0.170949528090018	0.0623370343740163	2.74234297166492	0.00623538929201144	** 
df.mm.trans1:exp2	-0.161946381535406	0.0572043859529175	-2.83101337139948	0.00475546197275101	** 
df.mm.trans2:exp2	-0.139265995770789	0.0429032894646881	-3.24604470912219	0.00121846449740018	** 
df.mm.trans1:exp3	-0.0140546075683862	0.0572043859529175	-0.245691083546529	0.805983845130712	   
df.mm.trans2:exp3	-0.0340757819611132	0.0429032894646881	-0.794246370995853	0.427285511405597	   
df.mm.trans1:exp4	-0.151850975074068	0.0572043859529175	-2.65453378345937	0.0080982342496781	** 
df.mm.trans2:exp4	-0.0463515376085628	0.0429032894646881	-1.08037258184394	0.280299063416457	   
df.mm.trans1:exp5	-0.0646774039505404	0.0572043859529175	-1.13063715086066	0.258543749520834	   
df.mm.trans2:exp5	-0.0193790497012970	0.0429032894646881	-0.451691465691623	0.651612634219448	   
df.mm.trans1:exp6	-0.185132910963681	0.0572043859529175	-3.23634119796437	0.00126009948178103	** 
df.mm.trans2:exp6	-0.0889731647797384	0.0429032894646881	-2.07380753060831	0.038413841808829	*  
df.mm.trans1:exp7	-0.158991791956636	0.0572043859529175	-2.77936366780505	0.00557321158255248	** 
df.mm.trans2:exp7	-0.090375225026316	0.0429032894646882	-2.10648708185185	0.0354702478244733	*  
df.mm.trans1:exp8	-0.105936613406093	0.0572043859529175	-1.85189669710440	0.0644057165281643	.  
df.mm.trans2:exp8	-0.0910236734609569	0.0429032894646882	-2.12160127106045	0.034175419988802	*  
df.mm.trans1:probe2	-0.351623794143786	0.0391651657183057	-8.9779728412955	1.91023260152804e-18	***
df.mm.trans1:probe3	-0.192838563577083	0.0391651657183057	-4.92372648092614	1.02995180460548e-06	***
df.mm.trans1:probe4	-0.319434143915062	0.0391651657183057	-8.15607793447325	1.32066166125466e-15	***
df.mm.trans1:probe5	-0.377565099770673	0.0391651657183057	-9.64032943167657	6.85377306319163e-21	***
df.mm.trans1:probe6	-0.330586481442662	0.0391651657183057	-8.44082937936215	1.45188963624699e-16	***
df.mm.trans1:probe7	0.398120723718279	0.0391651657183057	10.1651739860301	6.35834181815769e-23	***
df.mm.trans1:probe8	-0.149683105544391	0.0391651657183057	-3.82184277275838	0.000142642956750533	***
df.mm.trans1:probe9	-0.414944125438336	0.0391651657183057	-10.5947241082243	1.20105596838689e-24	***
df.mm.trans1:probe10	-0.289344187452431	0.0391651657183057	-7.38779428468476	3.72984525795638e-13	***
df.mm.trans1:probe11	-0.378708951043171	0.0391651657183057	-9.66953526424536	5.30881129233742e-21	***
df.mm.trans1:probe12	-0.415560868749803	0.0391651657183057	-10.6104713494311	1.03600834261181e-24	***
df.mm.trans1:probe13	-0.324510505504850	0.0391651657183057	-8.28569213363943	4.871510993954e-16	***
df.mm.trans1:probe14	-0.202684762191178	0.0391651657183057	-5.17512842021356	2.87654512339975e-07	***
df.mm.trans1:probe15	0.0126019716882938	0.0391651657183057	0.321764799335541	0.74771420830444	   
df.mm.trans1:probe16	-0.104110655681191	0.0391651657183057	-2.6582462699125	0.00801033964564983	** 
df.mm.trans1:probe17	-0.0714301080701276	0.0391651657183057	-1.82381733257269	0.0685494143589961	.  
df.mm.trans1:probe18	-0.0478222577219570	0.0391651657183057	-1.22104060700055	0.222427262084326	   
df.mm.trans1:probe19	0.0849225726840897	0.0391651657183057	2.16831899282369	0.0304259637195743	*  
df.mm.trans1:probe20	-0.0494722876410802	0.0391651657183057	-1.26317064497845	0.206892683332243	   
df.mm.trans2:probe2	0.0301573501700362	0.0391651657183057	0.770004406133299	0.441522615839203	   
df.mm.trans2:probe3	0.111791731649998	0.0391651657183057	2.85436636357055	0.00442277289311018	** 
df.mm.trans2:probe4	-0.0224505478049571	0.0391651657183057	-0.57322744314251	0.56665039707941	   
df.mm.trans2:probe5	0.0198595525225738	0.0391651657183057	0.507071836882117	0.612242942473366	   
df.mm.trans2:probe6	0.0476211088581759	0.0391651657183057	1.21590469451066	0.224376779987828	   
df.mm.trans3:probe2	-0.0970823005681935	0.0391651657183057	-2.47879202826448	0.0133861510551277	*  
df.mm.trans3:probe3	-0.248859869368373	0.0391651657183057	-6.35411250799472	3.5011776973942e-10	***
df.mm.trans3:probe4	0.276259587160825	0.0391651657183057	7.05370658068484	3.74912271016142e-12	***
df.mm.trans3:probe5	-0.0644926500431505	0.0391651657183057	-1.64668395652943	0.100012350773272	   
df.mm.trans3:probe6	0.387047277726326	0.0391651657183057	9.88243687030848	8.10104979533172e-22	***
df.mm.trans3:probe7	-0.150886707344187	0.0391651657183057	-3.85257420916932	0.000126153935395846	***
df.mm.trans3:probe8	-0.158716130524432	0.0391651657183057	-4.05248203635836	5.55773433154991e-05	***
df.mm.trans3:probe9	0.275730363522901	0.0391651657183057	7.04019396997023	4.10810022198362e-12	***
df.mm.trans3:probe10	-0.105881333572469	0.0391651657183057	-2.70345679969841	0.00700659272751989	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87617867694179	0.127814859785168	30.3265104187174	1.95623881466725e-135	***
df.mm.trans1	0.110875194552557	0.110040656218919	1.00758390909609	0.313956267862365	   
df.mm.trans2	0.144451733870696	0.0977315359106952	1.47804628797293	0.139785559979520	   
df.mm.exp2	-0.0761604363081258	0.125963863017360	-0.604621313476464	0.545600594706132	   
df.mm.exp3	-0.0476107057423602	0.125963863017360	-0.377971146659725	0.705551422653386	   
df.mm.exp4	-0.0676515198821952	0.125963863017360	-0.537070857162197	0.591366711564166	   
df.mm.exp5	0.137961063814497	0.125963863017360	1.09524319522880	0.273736867336818	   
df.mm.exp6	-0.122242309257945	0.125963863017360	-0.970455385614031	0.332110320074997	   
df.mm.exp7	0.134767569438125	0.125963863017360	1.06989073064194	0.284988388692473	   
df.mm.exp8	-0.0360744871997591	0.125963863017360	-0.286387590342377	0.774654782748644	   
df.mm.trans1:exp2	0.139508858541861	0.115592368301194	1.20690371338659	0.227822875630194	   
df.mm.trans2:exp2	0.130341828608915	0.0866942762258958	1.50346521458105	0.133110343560614	   
df.mm.trans1:exp3	0.0517401483637135	0.115592368301194	0.447608688394516	0.654555755218865	   
df.mm.trans2:exp3	0.122089363711924	0.0866942762258959	1.40827479075782	0.159434839939235	   
df.mm.trans1:exp4	0.143629990807869	0.115592368301194	1.24255600018176	0.214392421954556	   
df.mm.trans2:exp4	0.0265477686790734	0.0866942762258959	0.306222853858298	0.759513956306586	   
df.mm.trans1:exp5	-0.0445889412037056	0.115592368301194	-0.385742950499310	0.699788712672929	   
df.mm.trans2:exp5	-0.0564657500667976	0.0866942762258958	-0.651320393051867	0.515025109580914	   
df.mm.trans1:exp6	0.158497783932284	0.115592368301194	1.37117861898368	0.170700447076381	   
df.mm.trans2:exp6	0.0453913709446827	0.0866942762258959	0.523579790047594	0.600714585974153	   
df.mm.trans1:exp7	-0.120279071160125	0.115592368301194	-1.04054508898649	0.298398365474688	   
df.mm.trans2:exp7	-0.0830195727575802	0.0866942762258959	-0.957613078645	0.338544691938063	   
df.mm.trans1:exp8	0.0614887910111315	0.115592368301194	0.531945074876506	0.594910442634479	   
df.mm.trans2:exp8	-0.0078539065255908	0.0866942762258959	-0.0905931379498017	0.92783836491284	   
df.mm.trans1:probe2	0.0958134058038752	0.0791406844925117	1.21067193717463	0.226375611463622	   
df.mm.trans1:probe3	0.192840087052509	0.0791406844925117	2.4366744903597	0.0150382124494369	*  
df.mm.trans1:probe4	-0.0393339326598626	0.0791406844925117	-0.497012793256602	0.61931540052674	   
df.mm.trans1:probe5	0.0418617622198328	0.0791406844925117	0.528953754800969	0.596982983452459	   
df.mm.trans1:probe6	-0.0703614460993979	0.0791406844925117	-0.889067949697296	0.374231495545886	   
df.mm.trans1:probe7	0.0589702620203565	0.0791406844925117	0.74513206953544	0.456408834068425	   
df.mm.trans1:probe8	-0.0287072475507779	0.0791406844925117	-0.362736912561	0.716896403696769	   
df.mm.trans1:probe9	0.0511898699844394	0.0791406844925117	0.646821168059053	0.517931613912555	   
df.mm.trans1:probe10	0.0140886818336093	0.0791406844925117	0.178020722513998	0.85875137767388	   
df.mm.trans1:probe11	0.0501104709026346	0.0791406844925117	0.633182177080817	0.526794071741352	   
df.mm.trans1:probe12	0.163982428084362	0.0791406844925117	2.07203702035049	0.0385790924749123	*  
df.mm.trans1:probe13	0.0532622089948714	0.0791406844925117	0.673006675850915	0.501135716236656	   
df.mm.trans1:probe14	0.157666288829858	0.0791406844925117	1.99222801572783	0.0466825613393378	*  
df.mm.trans1:probe15	0.0632854321300939	0.0791406844925117	0.799657376429212	0.424144644849909	   
df.mm.trans1:probe16	-0.0436154138776977	0.0791406844925117	-0.551112416545053	0.581709157230929	   
df.mm.trans1:probe17	0.0102745913466876	0.0791406844925117	0.129826920408551	0.896735715832127	   
df.mm.trans1:probe18	0.0963936668708041	0.0791406844925117	1.21800395699035	0.223578456984784	   
df.mm.trans1:probe19	0.122061666734708	0.0791406844925117	1.54233776871437	0.123383551909384	   
df.mm.trans1:probe20	0.0158255325202331	0.0791406844925117	0.199967091789944	0.84155669619237	   
df.mm.trans2:probe2	0.14806873531017	0.0791406844925117	1.87095595975266	0.0617128623903899	.  
df.mm.trans2:probe3	0.0585364085174492	0.0791406844925117	0.739650015574328	0.459727485838103	   
df.mm.trans2:probe4	0.0296789404198247	0.0791406844925117	0.375014957352725	0.707747877191624	   
df.mm.trans2:probe5	0.0104294400671404	0.0791406844925117	0.131783546402448	0.89518834888203	   
df.mm.trans2:probe6	-0.0134252187918929	0.0791406844925117	-0.169637385347143	0.86533783336195	   
df.mm.trans3:probe2	-0.138495721988314	0.0791406844925117	-1.74999398699185	0.0804993980968642	.  
df.mm.trans3:probe3	-0.0852952027926076	0.0791406844925117	-1.07776680653651	0.281459889805222	   
df.mm.trans3:probe4	-0.0851334448138382	0.0791406844925116	-1.07572287704807	0.28237270807184	   
df.mm.trans3:probe5	-0.197703001698459	0.0791406844925117	-2.49812094709852	0.0126833138401745	*  
df.mm.trans3:probe6	-0.182052861562322	0.0791406844925117	-2.30037006540609	0.0216815193451516	*  
df.mm.trans3:probe7	-0.0814011936696168	0.0791406844925117	-1.02856317444814	0.303993170129562	   
df.mm.trans3:probe8	-0.0284853073462673	0.0791406844925116	-0.3599325369616	0.718991757640075	   
df.mm.trans3:probe9	-0.128936021505230	0.0791406844925117	-1.62920023161323	0.103660897433467	   
df.mm.trans3:probe10	-0.159310733552521	0.0791406844925117	-2.01300676856788	0.0444455673188756	*