chr9.24388_chr9_104955162_104960031_-_2.R 

fitVsDatCorrelation=0.869727669167076
cont.fitVsDatCorrelation=0.267235242352959

fstatistic=13262.3550053244,57,807
cont.fstatistic=3468.35508935902,57,807

residuals=-0.399172410523817,-0.0854629651576998,-0.00374596244742296,0.0753994790266592,0.777712041217896
cont.residuals=-0.656590562915148,-0.165771470659357,-0.0362066860492687,0.127093674528472,1.54517361442248

predictedValues:
Include	Exclude	Both
chr9.24388_chr9_104955162_104960031_-_2.R.tl.Lung	59.4835789537408	43.1995914023088	63.6753049928973
chr9.24388_chr9_104955162_104960031_-_2.R.tl.cerebhem	61.5598223773035	50.8843942600819	73.7806351303832
chr9.24388_chr9_104955162_104960031_-_2.R.tl.cortex	64.9316362843145	40.7232612710277	68.8814816516967
chr9.24388_chr9_104955162_104960031_-_2.R.tl.heart	65.0669017821833	46.1806067975604	69.0735358337281
chr9.24388_chr9_104955162_104960031_-_2.R.tl.kidney	68.6420207604418	43.6583841455314	71.7807533253111
chr9.24388_chr9_104955162_104960031_-_2.R.tl.liver	59.1505310183218	49.2273544466645	61.0285980592584
chr9.24388_chr9_104955162_104960031_-_2.R.tl.stomach	62.1360084286103	44.4525901328598	69.197656252534
chr9.24388_chr9_104955162_104960031_-_2.R.tl.testicle	61.2422572093487	47.4724206189926	66.6754731278256


diffExp=16.2839875514320,10.6754281172216,24.2083750132868,18.8862949846229,24.9836366149104,9.92317657165734,17.6834182957505,13.7698365903561
diffExpScore=0.992722729261953
diffExp1.5=0,0,1,0,1,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,1,1,1,0,0,0
diffExp1.4Score=0.75
diffExp1.3=1,0,1,1,1,0,1,0
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	58.3059559741581	71.1070864753307	57.1488838235873
cerebhem	60.2527441716166	60.4863057325991	67.0840323119365
cortex	60.6650395405804	57.7748429118337	59.6366177032201
heart	63.6024611671414	66.9568667392195	62.1046595779744
kidney	59.5155517619964	60.1853990726378	63.800158739836
liver	62.0147170856547	54.2522071883625	62.2070872978713
stomach	58.6013774624666	60.8604556497338	59.9320073523571
testicle	60.1861524869166	56.0811836790416	58.053267078606
cont.diffExp=-12.8011305011725,-0.233561560982494,2.89019662874672,-3.35440557207802,-0.669847310641472,7.76250989729218,-2.25907818726712,4.10496880787503
cont.diffExpScore=6.12833939576887

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

tran.correlation=-0.453007095826614
cont.tran.correlation=-0.171621064484663

tran.covariance=-0.00168120374478859
cont.tran.covariance=-0.000479455234883963

tran.mean=54.2507099930807
cont.tran.mean=60.6780216937056

weightedLogRatios:
wLogRatio
Lung	1.25573173252059
cerebhem	0.766532544588801
cortex	1.83818125785228
heart	1.37279152042116
kidney	1.81123742051233
liver	0.732389948014144
stomach	1.32684140324871
testicle	1.01556867437409

cont.weightedLogRatios:
wLogRatio
Lung	-0.826669869758693
cerebhem	-0.0158642344987786
cortex	0.199208577930609
heart	-0.214752439529394
kidney	-0.0457963350075350
liver	0.543003785815744
stomach	-0.154693556079717
testicle	0.286956176595441

varWeightedLogRatios=0.176685791103446
cont.varWeightedLogRatios=0.166589868345279

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86136650183216	0.0651811786403315	59.2405136326103	4.40047913038369e-296	***
df.mm.trans1	0.243875397104958	0.0563494369723835	4.32791186936756	1.69432051163615e-05	***
df.mm.trans2	-0.112262231360005	0.0500442771049374	-2.24325812768967	0.0251507483828299	*  
df.mm.exp2	0.0507348867933665	0.0647386004492687	0.783688347311803	0.433452945766921	   
df.mm.exp3	-0.0499874611000175	0.0647386004492687	-0.772143060756917	0.440255758119273	   
df.mm.exp4	0.0750697518866483	0.0647386004492687	1.15958255763461	0.246561775365805	   
df.mm.exp5	0.0339493379072336	0.0647386004492687	0.524406423241068	0.600139897121462	   
df.mm.exp6	0.167457929875792	0.0647386004492687	2.58667825244412	0.00986454678236115	** 
df.mm.exp7	-0.0109526118583950	0.0647386004492687	-0.169182092019085	0.865695812024212	   
df.mm.exp8	0.0774146695967836	0.0647386004492687	1.19580387990390	0.232124345896111	   
df.mm.trans1:exp2	-0.0164257536760499	0.0598143517292794	-0.274612249421228	0.78368443803265	   
df.mm.trans2:exp2	0.112990356758361	0.0452153998594913	2.49893525456998	0.0126544360285432	*  
df.mm.trans1:exp3	0.137622137556724	0.0598143517292794	2.30082135102966	0.0216558576125443	*  
df.mm.trans2:exp3	-0.0090441166372683	0.0452153998594913	-0.200022927263129	0.84151304262711	   
df.mm.trans1:exp4	0.0146459563831347	0.0598143517292794	0.244856894034772	0.806629486132151	   
df.mm.trans2:exp4	-0.00834084513750316	0.0452153998594913	-0.184469122542821	0.853691793282265	   
df.mm.trans1:exp5	0.109255266331996	0.0598143517292793	1.82657277347896	0.0681333114435529	.  
df.mm.trans2:exp5	-0.0233850343469495	0.0452153998594912	-0.517191806765382	0.605163991513523	   
df.mm.trans1:exp6	-0.173072652495529	0.0598143517292793	-2.89349708710141	0.00391238300467057	** 
df.mm.trans2:exp6	-0.0368395131168963	0.0452153998594912	-0.814755884751138	0.41545242674348	   
df.mm.trans1:exp7	0.0545779881332522	0.0598143517292794	0.912456401438119	0.361801008430287	   
df.mm.trans2:exp7	0.0395448059006978	0.0452153998594913	0.874587110223174	0.382058806632278	   
df.mm.trans1:exp8	-0.0482775315615824	0.0598143517292793	-0.80712287546118	0.419833507666573	   
df.mm.trans2:exp8	0.0169032173466182	0.0452153998594912	0.373837617253097	0.708623323252308	   
df.mm.trans1:probe2	-0.0956480258450524	0.0401246869490252	-2.38377002084937	0.0173665956629313	*  
df.mm.trans1:probe3	-0.245774562367994	0.0401246869490252	-6.12527052685117	1.41339364770089e-09	***
df.mm.trans1:probe4	-0.0973034188877524	0.0401246869490252	-2.42502624410173	0.0155257839805064	*  
df.mm.trans1:probe5	-0.027706000631848	0.0401246869490252	-0.690497614773816	0.490079851471368	   
df.mm.trans1:probe6	-0.114009210371048	0.0401246869490252	-2.84137320537569	0.00460517014607199	** 
df.mm.trans1:probe7	-0.111144015883758	0.0401246869490252	-2.76996593206961	0.00573499572848425	** 
df.mm.trans1:probe8	0.365318839440495	0.0401246869490252	9.10459039604718	6.67207038673272e-19	***
df.mm.trans1:probe9	-0.0149611448319379	0.0401246869490252	-0.372866331666206	0.709345841863413	   
df.mm.trans1:probe10	-0.129934439081987	0.0401246869490252	-3.23826673706034	0.00125173432540818	** 
df.mm.trans1:probe11	-0.195905430689817	0.0401246869490252	-4.88241642704148	1.26338385797956e-06	***
df.mm.trans1:probe12	-0.261738755917696	0.0401246869490252	-6.5231351524365	1.21430472867742e-10	***
df.mm.trans1:probe13	-0.273568953012237	0.0401246869490252	-6.81797102516417	1.80895914707017e-11	***
df.mm.trans1:probe14	-0.132120066470638	0.0401246869490252	-3.29273762655108	0.00103534232510483	** 
df.mm.trans1:probe15	-0.163383024781298	0.0401246869490252	-4.07188285328834	5.12296693814769e-05	***
df.mm.trans1:probe16	0.181494939387447	0.0401246869490252	4.52327365489529	7.00285939800912e-06	***
df.mm.trans1:probe17	0.0665135864059627	0.0401246869490252	1.65767240727541	0.0977722503994625	.  
df.mm.trans1:probe18	0.335857757919683	0.0401246869490252	8.37035210633193	2.52203467966461e-16	***
df.mm.trans1:probe19	0.166713879047525	0.0401246869490252	4.15489544527338	3.60166586392911e-05	***
df.mm.trans1:probe20	0.088240881804847	0.0401246869490252	2.19916685996701	0.0281490531290139	*  
df.mm.trans1:probe21	0.0728089113611601	0.0401246869490252	1.81456646512052	0.0699617322615176	.  
df.mm.trans2:probe2	0.00236076677750887	0.0401246869490252	0.0588357681271883	0.953097484587349	   
df.mm.trans2:probe3	0.0198470333747314	0.0401246869490252	0.494633974339669	0.6209931424602	   
df.mm.trans2:probe4	0.0422232817490277	0.0401246869490252	1.05230183609080	0.292976072322469	   
df.mm.trans2:probe5	0.06577248416495	0.0401246869490252	1.63920242539233	0.101560858351819	   
df.mm.trans2:probe6	0.120697930733104	0.0401246869490252	3.00807158661298	0.0027107884469479	** 
df.mm.trans3:probe2	0.849387108079489	0.0401246869490252	21.1686912139292	1.88771709809405e-79	***
df.mm.trans3:probe3	0.0107644924179875	0.0401246869490252	0.268276047403506	0.788555402278324	   
df.mm.trans3:probe4	0.0548024373478121	0.0401246869490252	1.36580348695140	0.172381171690496	   
df.mm.trans3:probe5	0.429369206849225	0.0401246869490252	10.7008736889262	4.42020617451068e-25	***
df.mm.trans3:probe6	-0.122742654609646	0.0401246869490252	-3.05903083469735	0.00229394860190279	** 
df.mm.trans3:probe7	0.101202345809387	0.0401246869490252	2.52219652051007	0.0118537693854602	*  
df.mm.trans3:probe8	0.102077255446416	0.0401246869490252	2.54400129217446	0.0111444387505841	*  
df.mm.trans3:probe9	0.0224940966884454	0.0401246869490252	0.560604914301814	0.575222503580366	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.26574104755021	0.127267598175671	33.5178875746684	5.31973301358339e-155	***
df.mm.trans1	-0.186984283189044	0.110023440073070	-1.69949497184292	0.0896113336825429	.  
df.mm.trans2	-0.0137039200618192	0.0977124851443267	-0.140247380276714	0.888499533982328	   
df.mm.exp2	-0.289212331995647	0.126403455112344	-2.28800970462874	0.0223947582246832	*  
df.mm.exp3	-0.210580152423451	0.126403455112344	-1.66593668057801	0.0961141085750915	.  
df.mm.exp4	-0.0563516108295359	0.126403455112344	-0.445807519892965	0.655855865405288	   
df.mm.exp5	-0.256319633807974	0.126403455112344	-2.02778977505135	0.0429098119561035	*  
df.mm.exp6	-0.293684921132131	0.126403455112344	-2.32339314515660	0.0204056815179797	*  
df.mm.exp7	-0.198100265576688	0.126403455112344	-1.56720609733825	0.117458534234243	   
df.mm.exp8	-0.221349727288039	0.126403455112344	-1.75113668444671	0.0803022779914557	.  
df.mm.trans1:exp2	0.322056200815955	0.116788757733661	2.75759591133258	0.00595441418860957	** 
df.mm.trans2:exp2	0.127442318908586	0.0882840025404111	1.44354940013340	0.149253836099668	   
df.mm.trans1:exp3	0.250243480782667	0.116788757733661	2.14270179457985	0.0324355739648858	*  
df.mm.trans2:exp3	0.00294658864063553	0.0882840025404111	0.03337624661146	0.973382804686312	   
df.mm.trans1:exp4	0.143299529093320	0.116788757733661	1.22699763122848	0.220181317075926	   
df.mm.trans2:exp4	-0.00378675808167845	0.0882840025404111	-0.0428929134691769	0.96579750798178	   
df.mm.trans1:exp5	0.276853037428362	0.116788757733661	2.37054527165816	0.0179959509245529	*  
df.mm.trans2:exp5	0.0895624154082618	0.0882840025404111	1.01448068541371	0.310657495635140	   
df.mm.trans1:exp6	0.355352401428011	0.116788757733661	3.04269356335134	0.00242069823257508	** 
df.mm.trans2:exp6	0.0231415972496414	0.0882840025404111	0.262126733991796	0.793290630368454	   
df.mm.trans1:exp7	0.203154219138703	0.116788757733661	1.73950149895420	0.0823278624561751	.  
df.mm.trans2:exp7	0.0424968958929923	0.0882840025404112	0.481365759029103	0.630387062940406	   
df.mm.trans1:exp8	0.253087779078577	0.116788757733661	2.16705600770023	0.0305224700434261	*  
df.mm.trans2:exp8	-0.0160369242106484	0.0882840025404111	-0.181651530845666	0.855901821080864	   
df.mm.trans1:probe2	0.0251461233913408	0.0783442803900662	0.320969485789408	0.748316617034606	   
df.mm.trans1:probe3	-0.0911490597652612	0.0783442803900662	-1.16344242759576	0.244993925806618	   
df.mm.trans1:probe4	0.130717954203506	0.0783442803900663	1.66850666765561	0.0956030877946423	.  
df.mm.trans1:probe5	0.0395175262916811	0.0783442803900663	0.504408568116631	0.614111988407755	   
df.mm.trans1:probe6	0.0124083584820479	0.0783442803900662	0.158382442474017	0.87419507197688	   
df.mm.trans1:probe7	-0.0510662537406787	0.0783442803900662	-0.651818530803095	0.514703834738002	   
df.mm.trans1:probe8	0.0082077151460414	0.0783442803900662	0.104764701458437	0.916588552230701	   
df.mm.trans1:probe9	0.0249895641005095	0.0783442803900662	0.318971135813484	0.749830942124634	   
df.mm.trans1:probe10	-0.0200009268628677	0.0783442803900662	-0.255295303796087	0.7985600768094	   
df.mm.trans1:probe11	-0.0103697941507683	0.0783442803900662	-0.132361853336816	0.894731080133689	   
df.mm.trans1:probe12	-0.0914395615244456	0.0783442803900662	-1.16715044249790	0.243494371697469	   
df.mm.trans1:probe13	-0.00410986798973449	0.0783442803900662	-0.0524590687319097	0.958175891238862	   
df.mm.trans1:probe14	-0.0277843511739263	0.0783442803900662	-0.354644283355358	0.722948750503682	   
df.mm.trans1:probe15	-0.135759254747383	0.0783442803900662	-1.73285470325919	0.0835035156520693	.  
df.mm.trans1:probe16	-0.0642162037495113	0.0783442803900662	-0.8196667763082	0.412648106035486	   
df.mm.trans1:probe17	-0.158413967090096	0.0783442803900662	-2.02202338577077	0.0435034310195023	*  
df.mm.trans1:probe18	0.00567158495182813	0.0783442803900662	0.0723930952405208	0.942307017335869	   
df.mm.trans1:probe19	0.0150165299407108	0.0783442803900662	0.191673595901901	0.848046105905345	   
df.mm.trans1:probe20	-0.0239215494379812	0.0783442803900662	-0.305338811191817	0.760186857145028	   
df.mm.trans1:probe21	0.0249799707937644	0.0783442803900662	0.318848685180236	0.749923765197854	   
df.mm.trans2:probe2	0.0967363216993707	0.0783442803900662	1.23475920919476	0.217279528318833	   
df.mm.trans2:probe3	0.0011776633807761	0.0783442803900662	0.0150318999027455	0.988010446183362	   
df.mm.trans2:probe4	0.0111429758543236	0.0783442803900662	0.142230878870086	0.886933142637911	   
df.mm.trans2:probe5	-0.0315564539502839	0.0783442803900662	-0.402792058248136	0.68720788242902	   
df.mm.trans2:probe6	0.10474759531792	0.0783442803900662	1.33701649688267	0.181594009917663	   
df.mm.trans3:probe2	-0.002281302323303	0.0783442803900662	-0.0291189390207515	0.976776930486907	   
df.mm.trans3:probe3	0.0146407440005176	0.0783442803900662	0.186876998903088	0.85180404341853	   
df.mm.trans3:probe4	-0.0256910598264553	0.0783442803900662	-0.327925149079713	0.743053315868273	   
df.mm.trans3:probe5	-0.0278446232493729	0.0783442803900662	-0.355413606593080	0.722372631577013	   
df.mm.trans3:probe6	0.00821346278270699	0.0783442803900662	0.104838065290959	0.916530355365388	   
df.mm.trans3:probe7	-0.0216904100467829	0.0783442803900662	-0.276860160547638	0.781958381201171	   
df.mm.trans3:probe8	-0.0917347795193872	0.0783442803900662	-1.17091865625227	0.241977103939178	   
df.mm.trans3:probe9	-0.0281488958742143	0.0783442803900662	-0.359297395215893	0.719466613183312