chr15.8896_chr15_59798050_59839124_+_2.R 

fitVsDatCorrelation=0.949699966970457
cont.fitVsDatCorrelation=0.233856549644253

fstatistic=10642.4201034451,65,991
cont.fstatistic=1090.41845074546,65,991

residuals=-0.752165916563516,-0.0928566103090755,-0.00562791076871365,0.0833283734411013,1.07278674396107
cont.residuals=-1.04135346500047,-0.3340642282512,-0.124400161322496,0.210643121624893,2.45859147858997

predictedValues:
Include	Exclude	Both
chr15.8896_chr15_59798050_59839124_+_2.R.tl.Lung	60.0013196361963	77.4268345468167	146.103877911753
chr15.8896_chr15_59798050_59839124_+_2.R.tl.cerebhem	64.2365817433334	80.9235721683557	117.297345998812
chr15.8896_chr15_59798050_59839124_+_2.R.tl.cortex	66.1386051891938	75.5996117850221	137.273357951000
chr15.8896_chr15_59798050_59839124_+_2.R.tl.heart	63.2734481145415	88.1445828134475	137.965125629594
chr15.8896_chr15_59798050_59839124_+_2.R.tl.kidney	59.8581807734707	77.2141092713487	148.688104271786
chr15.8896_chr15_59798050_59839124_+_2.R.tl.liver	58.003086617522	78.7465770382292	138.806607747163
chr15.8896_chr15_59798050_59839124_+_2.R.tl.stomach	62.881971842591	100.695108344754	136.767827448212
chr15.8896_chr15_59798050_59839124_+_2.R.tl.testicle	60.6627867067677	81.8444860112546	147.023193977457


diffExp=-17.4255149106204,-16.6869904250223,-9.46100659582835,-24.871134698906,-17.355928497878,-20.7434904207071,-37.8131365021629,-21.1816993044869
diffExpScore=0.993995396920118
diffExp1.5=0,0,0,0,0,0,-1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,-1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,-1,0,-1,-1,-1
diffExp1.3Score=0.8
diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	81.8865909874398	76.8364788159815	71.9691079117778
cerebhem	78.8025182581786	86.2674212348336	73.0919221051333
cortex	75.2873894375243	78.4461188723704	80.842871307837
heart	75.4174401723154	85.4078178252141	84.0329668080035
kidney	80.7617076392087	76.9918798628936	70.5755007658523
liver	76.1646069006697	84.6404339532465	61.9438268413733
stomach	76.0122562198058	76.8759979030524	67.8337644056959
testicle	85.4766227207367	112.284840040464	74.5030327467258
cont.diffExp=5.05011217145831,-7.46490297665501,-3.15872943484617,-9.99037765289869,3.76982777631511,-8.4758270525768,-0.863741683246516,-26.8082173197274
cont.diffExpScore=1.33999282407696

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

tran.correlation=0.182749267485141
cont.tran.correlation=0.604546284272083

tran.covariance=0.000778248854349214
cont.tran.covariance=0.00333943254383017

tran.mean=72.2281789126778
cont.tran.mean=81.722507552746

weightedLogRatios:
wLogRatio
Lung	-1.07643185240960
cerebhem	-0.987937379873962
cortex	-0.569368809463235
heart	-1.4298877916915
kidney	-1.07424599449893
liver	-1.28818840714311
stomach	-2.06070524284071
testicle	-1.27435471790608

cont.weightedLogRatios:
wLogRatio
Lung	0.278398854435263
cerebhem	-0.399335143684852
cortex	-0.178447743315426
heart	-0.545519628209689
kidney	0.208784355409593
liver	-0.462753283644306
stomach	-0.0489991048131741
testicle	-1.25067140870171

varWeightedLogRatios=0.182496025242103
cont.varWeightedLogRatios=0.238799286794214

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.14324449543571	0.0800621582865374	39.2600519734459	4.16551601384726e-204	***
df.mm.trans1	0.79067609345219	0.0700803573621342	11.2824209694942	7.37083740895244e-28	***
df.mm.trans2	1.18966617472428	0.0614582025432465	19.3573213256138	4.66111665765702e-71	***
df.mm.exp2	0.331983768890096	0.0794909584239897	4.17637144490518	3.22283699442041e-05	***
df.mm.exp3	0.135847400542792	0.0794909584239897	1.70896669553546	0.0877702191544359	.  
df.mm.exp4	0.240061177730351	0.0794909584239897	3.01998091971555	0.00259269726103522	** 
df.mm.exp5	-0.0226726533061293	0.0794909584239897	-0.285223046188444	0.775532863434507	   
df.mm.exp6	0.0342672693329813	0.0794909584239897	0.431083861767098	0.666501145239828	   
df.mm.exp7	0.375689815685989	0.0794909584239897	4.72619557160364	2.61821784803774e-06	***
df.mm.exp8	0.0601789043052493	0.0794909584239897	0.757053449830941	0.449197792558382	   
df.mm.trans1:exp2	-0.263777467304389	0.0753042438431502	-3.5028233980253	0.000480904222008396	***
df.mm.trans2:exp2	-0.287812033202088	0.0554832931546538	-5.18736392232239	2.58600814480365e-07	***
df.mm.trans1:exp3	-0.0384613378916858	0.0753042438431502	-0.510745954395295	0.609642711227534	   
df.mm.trans2:exp3	-0.159729672569793	0.0554832931546538	-2.87887873065795	0.00407673241293224	** 
df.mm.trans1:exp4	-0.186961953561161	0.0753042438431502	-2.48275454369585	0.0132016820873883	*  
df.mm.trans2:exp4	-0.110416145042048	0.0554832931546538	-1.99007915291319	0.0468566260952871	*  
df.mm.trans1:exp5	0.020284207996695	0.0753042438431502	0.269363411163713	0.78770611232325	   
df.mm.trans2:exp5	0.0199214361395036	0.0554832931546538	0.35905287892654	0.719632015767511	   
df.mm.trans1:exp6	-0.068137598575924	0.0753042438431502	-0.904830791712702	0.365774951352357	   
df.mm.trans2:exp6	-0.0173658788336854	0.0554832931546538	-0.31299293618495	0.754351885141647	   
df.mm.trans1:exp7	-0.328796865153729	0.0753042438431502	-4.36624615524423	1.39676444713835e-05	***
df.mm.trans2:exp7	-0.112926013738741	0.0554832931546538	-2.03531562958910	0.0420847834035042	*  
df.mm.trans1:exp8	-0.0492150192415905	0.0753042438431502	-0.65354907943966	0.513553966340475	   
df.mm.trans2:exp8	-0.00469138983541087	0.0554832931546538	-0.084554999688539	0.932632253736614	   
df.mm.trans1:probe2	-0.0491388130507578	0.0492981996518069	-0.996766887996421	0.319120990359921	   
df.mm.trans1:probe3	-0.0681585521824904	0.0492981996518069	-1.38257690268395	0.167106112625845	   
df.mm.trans1:probe4	0.105597717074860	0.0492981996518069	2.14201974556264	0.0324350907770501	*  
df.mm.trans1:probe5	0.408145552464211	0.0492981996518069	8.2791167902062	3.98418339549786e-16	***
df.mm.trans1:probe6	0.0252761332648077	0.0492981996518068	0.512719195494622	0.608262020706027	   
df.mm.trans1:probe7	-0.084216794737912	0.0492981996518069	-1.70831379913943	0.0878912837489077	.  
df.mm.trans1:probe8	0.0499566047671269	0.0492981996518069	1.01335556105437	0.311137701688061	   
df.mm.trans1:probe9	0.139447319122346	0.0492981996518069	2.82864932405773	0.00476875400409812	** 
df.mm.trans1:probe10	-0.0186767363553024	0.0492981996518069	-0.378852300636051	0.704878698125976	   
df.mm.trans1:probe11	0.580028674802056	0.0492981996518069	11.7657171843759	5.23397451825924e-30	***
df.mm.trans1:probe12	0.123109374427447	0.0492981996518069	2.49723874902062	0.0126777758951881	*  
df.mm.trans1:probe13	0.325815235954827	0.0492981996518069	6.60906966696675	6.30639621770288e-11	***
df.mm.trans1:probe14	0.0756237097531695	0.0492981996518069	1.53400550704285	0.125347470551339	   
df.mm.trans1:probe15	-0.0397825059540897	0.0492981996518069	-0.80697685179324	0.419873447898851	   
df.mm.trans1:probe16	0.466956420065194	0.0492981996518069	9.47207856196184	1.94562013179197e-20	***
df.mm.trans1:probe17	0.228221930954627	0.0492981996518069	4.62941715045495	4.15414001128383e-06	***
df.mm.trans1:probe18	0.275725488396296	0.0492981996518069	5.59301334214525	2.88594201560348e-08	***
df.mm.trans1:probe19	0.380240656633582	0.0492981996518069	7.71307389152588	2.98365377381777e-14	***
df.mm.trans1:probe20	0.661545542143027	0.0492981996518069	13.4192637219112	7.55882208364419e-38	***
df.mm.trans1:probe21	0.190680290543966	0.0492981996518069	3.86789562074763	0.000116944192303049	***
df.mm.trans1:probe22	0.708477557091463	0.0492981996518069	14.3712663362038	1.11265343759289e-42	***
df.mm.trans1:probe23	0.651001084158632	0.0492981996518069	13.2053723818852	8.57634684232847e-37	***
df.mm.trans1:probe24	0.88249242362522	0.0492981996518069	17.9011085568695	2.61221270828649e-62	***
df.mm.trans1:probe25	0.0204246762788606	0.0492981996518069	0.414308766306275	0.678737653267169	   
df.mm.trans1:probe26	0.145516178455143	0.0492981996518069	2.95175441462211	0.00323419653763879	** 
df.mm.trans1:probe27	-0.0527948921945787	0.0492981996518069	-1.07092941664136	0.284461914584759	   
df.mm.trans1:probe28	-0.0953373182664052	0.0492981996518069	-1.93389046536735	0.0534110673992411	.  
df.mm.trans1:probe29	0.0236066659318742	0.0492981996518069	0.478854524072036	0.632147760167365	   
df.mm.trans1:probe30	0.0371631239326887	0.0492981996518069	0.753843430291001	0.451122312042941	   
df.mm.trans2:probe2	0.339284819336045	0.0492981996518068	6.88229634616301	1.04319395528606e-11	***
df.mm.trans2:probe3	-0.229060868429718	0.0492981996518069	-4.64643475923208	3.83260694280552e-06	***
df.mm.trans2:probe4	-0.282522772350761	0.0492981996518069	-5.73089431959422	1.32494976695879e-08	***
df.mm.trans2:probe5	0.369148667766334	0.0492981996518068	7.4880760428095	1.54263334354691e-13	***
df.mm.trans2:probe6	0.0330686525659602	0.0492981996518068	0.670788239723237	0.502511740536012	   
df.mm.trans3:probe2	0.789852282507208	0.0492981996518069	16.0219295650944	1.50307943984031e-51	***
df.mm.trans3:probe3	-0.99072802953545	0.0492981996518069	-20.0966371294076	1.28575362865519e-75	***
df.mm.trans3:probe4	-0.399361312388228	0.0492981996518069	-8.10093097129138	1.59415383336283e-15	***
df.mm.trans3:probe5	1.35977379954554	0.0492981996518069	27.5826259204113	9.81960253319335e-125	***
df.mm.trans3:probe6	-0.817378049299088	0.0492981996518069	-16.5802819387367	1.11803581854406e-54	***
df.mm.trans3:probe7	-0.523915440141303	0.0492981996518069	-10.6274761318206	4.67156846262791e-25	***
df.mm.trans3:probe8	-0.532394469475978	0.0492981996518069	-10.7994708373993	8.83370562200496e-26	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.66968691049113	0.248568818596984	18.7862940204994	1.37281705322387e-67	***
df.mm.trans1	-0.21046101036947	0.217578341743122	-0.967288419809472	0.333635869060304	   
df.mm.trans2	-0.282240356623043	0.190809155363949	-1.47917617519295	0.139411023466922	   
df.mm.exp2	0.061901323808059	0.246795415555462	0.250820395787085	0.802004965314518	   
df.mm.exp3	-0.179560550200783	0.246795415555462	-0.727568418548806	0.467049671130644	   
df.mm.exp4	-0.131510788387953	0.246795415555462	-0.532873708743586	0.594240473625777	   
df.mm.exp5	0.00774203312544498	0.246795415555461	0.0313702469230233	0.97498058557772	   
df.mm.exp6	0.174303222839103	0.246795415555461	0.706266048122487	0.480188708721929	   
df.mm.exp7	-0.0147495658908141	0.246795415555461	-0.0597643431002045	0.952355374302772	   
df.mm.exp8	0.38766414991293	0.246795415555461	1.57079153614104	0.116550286203573	   
df.mm.trans1:exp2	-0.100291623111429	0.233796931384731	-0.428968945475127	0.668039056910138	   
df.mm.trans2:exp2	0.0538711850712736	0.172258866441794	0.312733888153597	0.754548644439551	   
df.mm.trans1:exp3	0.0955379467849238	0.233796931384731	0.408636444537882	0.682894765846402	   
df.mm.trans2:exp3	0.200293043561235	0.172258866441794	1.16274446534172	0.245213111325358	   
df.mm.trans1:exp4	0.0492140854281062	0.233796931384731	0.210499278739978	0.833321286638526	   
df.mm.trans2:exp4	0.237268916646141	0.172258866441794	1.37739741092696	0.168700414357836	   
df.mm.trans1:exp5	-0.0215743485357655	0.233796931384731	-0.0922781509919102	0.926495697476664	   
df.mm.trans2:exp5	-0.00572158512597641	0.172258866441794	-0.0332150399231247	0.973509793067127	   
df.mm.trans1:exp6	-0.246741597665897	0.233796931384731	-1.05536713507956	0.291514666013978	   
df.mm.trans2:exp6	-0.0775706396626429	0.172258866441794	-0.45031435109817	0.652582286733351	   
df.mm.trans1:exp7	-0.0596910940224176	0.233796931384731	-0.255311708622006	0.798535334656007	   
df.mm.trans2:exp7	0.0152637608187104	0.172258866441794	0.0886094349394081	0.929410212465854	   
df.mm.trans1:exp8	-0.344756483074445	0.233796931384731	-1.47459798138719	0.140638258129852	   
df.mm.trans2:exp8	-0.00830480443577117	0.172258866441794	-0.0482111870774288	0.96155764866437	   
df.mm.trans1:probe2	-0.171131189945739	0.153056019331275	-1.11809513074648	0.263797331364168	   
df.mm.trans1:probe3	-0.0227701055454667	0.153056019331275	-0.148769748781869	0.881765588733367	   
df.mm.trans1:probe4	0.091520159655012	0.153056019331275	0.597952044322577	0.550008551350836	   
df.mm.trans1:probe5	-0.274065924592226	0.153056019331275	-1.79062493451523	0.0736586447688668	.  
df.mm.trans1:probe6	-0.0718743689891296	0.153056019331275	-0.469595180269025	0.638747574315205	   
df.mm.trans1:probe7	-0.0584209775051387	0.153056019331275	-0.381696700073533	0.702768174646665	   
df.mm.trans1:probe8	0.246990692588071	0.153056019331275	1.61372740299408	0.106904917671115	   
df.mm.trans1:probe9	-0.219851758447779	0.153056019331275	-1.43641367002973	0.151200265429452	   
df.mm.trans1:probe10	-0.0774219876078826	0.153056019331275	-0.505840854519481	0.613080871401171	   
df.mm.trans1:probe11	-0.162369414672197	0.153056019331275	-1.06084958554138	0.289016733332324	   
df.mm.trans1:probe12	-0.222562882573861	0.153056019331275	-1.45412695002962	0.14622773680934	   
df.mm.trans1:probe13	-0.191874164220569	0.153056019331275	-1.25362050482494	0.210275634125292	   
df.mm.trans1:probe14	-0.00324749644718468	0.153056019331275	-0.0212176983392975	0.983076267403004	   
df.mm.trans1:probe15	0.0359339917941913	0.153056019331275	0.234776730449362	0.814430528880936	   
df.mm.trans1:probe16	0.00171559921178793	0.153056019331275	0.0112089627006088	0.991058985019624	   
df.mm.trans1:probe17	-0.132296440939985	0.153056019331275	-0.864366142004789	0.387595953614742	   
df.mm.trans1:probe18	-0.0794939379421914	0.153056019331275	-0.519378057063762	0.60361310501777	   
df.mm.trans1:probe19	-0.161831278655116	0.153056019331275	-1.05733364399637	0.290617012055193	   
df.mm.trans1:probe20	-0.089389125101154	0.153056019331275	-0.584028811749505	0.559333838067777	   
df.mm.trans1:probe21	-0.0690778409185791	0.153056019331275	-0.451323908856316	0.651854860252061	   
df.mm.trans1:probe22	0.0153139242535580	0.153056019331275	0.100054374342589	0.920321394186066	   
df.mm.trans1:probe23	-0.00123376273575291	0.153056019331275	-0.00806085733278181	0.99357005839672	   
df.mm.trans1:probe24	0.0599923251649527	0.153056019331275	0.391963187250447	0.695169688680461	   
df.mm.trans1:probe25	-0.141832118425852	0.153056019331275	-0.926668020281319	0.354324543559553	   
df.mm.trans1:probe26	-0.0406338975839889	0.153056019331275	-0.265483825866663	0.790691983551934	   
df.mm.trans1:probe27	-0.121161053971505	0.153056019331275	-0.791612473007437	0.428776100815449	   
df.mm.trans1:probe28	-0.105456988005119	0.153056019331275	-0.689009086123344	0.49097882792294	   
df.mm.trans1:probe29	-0.0284491056379885	0.153056019331275	-0.185873811185518	0.852581757903782	   
df.mm.trans1:probe30	-0.0528654534149682	0.153056019331275	-0.345399374986657	0.729867447772385	   
df.mm.trans2:probe2	-0.221331267703437	0.153056019331275	-1.44608012589421	0.148470908497088	   
df.mm.trans2:probe3	0.0127066349909311	0.153056019331275	0.0830195051880242	0.933852795803603	   
df.mm.trans2:probe4	-0.139649167424852	0.153056019331275	-0.912405588718429	0.361777150227672	   
df.mm.trans2:probe5	-0.0710166649072716	0.153056019331275	-0.463991323030313	0.642755863797643	   
df.mm.trans2:probe6	-0.221448121567151	0.153056019331275	-1.44684359710053	0.148256955979706	   
df.mm.trans3:probe2	0.110386492210662	0.153056019331275	0.721216275537263	0.470946567511658	   
df.mm.trans3:probe3	0.104550110333917	0.153056019331275	0.683083950508526	0.494713454078536	   
df.mm.trans3:probe4	0.170377692786621	0.153056019331275	1.11317211522309	0.265904450306701	   
df.mm.trans3:probe5	0.0350535197240657	0.153056019331275	0.229024117295221	0.818897425854884	   
df.mm.trans3:probe6	0.0899152660538663	0.153056019331275	0.587466382875495	0.557024345816837	   
df.mm.trans3:probe7	0.0227051421417133	0.153056019331275	0.148345306776665	0.882100439870143	   
df.mm.trans3:probe8	0.261023190072135	0.153056019331275	1.70540950439313	0.0884314537084357	.