chr4.17391_chr4_85293054_85444771_-_2.R 

fitVsDatCorrelation=0.905335075950163
cont.fitVsDatCorrelation=0.189384555258179

fstatistic=10261.3830392642,62,922
cont.fstatistic=1907.59258593998,62,922

residuals=-0.542758944063693,-0.0873429503328444,-0.00270105630012322,0.0825764846101363,1.34601099235096
cont.residuals=-0.55472938691129,-0.22371086844369,-0.0921010860941115,0.136744949135475,1.79706716997744

predictedValues:
Include	Exclude	Both
chr4.17391_chr4_85293054_85444771_-_2.R.tl.Lung	60.9204385283817	66.5227759091328	79.3435130710668
chr4.17391_chr4_85293054_85444771_-_2.R.tl.cerebhem	65.6879358193086	66.6559760616537	80.0933743054994
chr4.17391_chr4_85293054_85444771_-_2.R.tl.cortex	58.6143869678407	81.2728736105956	85.4135651561242
chr4.17391_chr4_85293054_85444771_-_2.R.tl.heart	60.6108407814107	65.8532164373215	73.7326834686876
chr4.17391_chr4_85293054_85444771_-_2.R.tl.kidney	60.2658549080317	56.1217015733388	71.0270846507155
chr4.17391_chr4_85293054_85444771_-_2.R.tl.liver	64.0473765424532	52.5843758492167	65.1908950116034
chr4.17391_chr4_85293054_85444771_-_2.R.tl.stomach	61.0900272861638	67.4995628943698	76.7259021299107
chr4.17391_chr4_85293054_85444771_-_2.R.tl.testicle	63.4250295369034	56.8396272682207	71.6170763337295


diffExp=-5.60233738075117,-0.968040242345026,-22.6584866427549,-5.24237565591076,4.14415333469295,11.4630006932365,-6.40953560820598,6.58540226868268
diffExpScore=3.20360775542977
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,-1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,-1,0,0,1,0,0
diffExp1.2Score=2

cont.predictedValues:
Include	Exclude	Both
Lung	62.5348077639743	62.2237538686579	65.866949398494
cerebhem	60.1453876484313	70.6090759472484	62.9717549970316
cortex	59.4194700514055	61.8923135738647	60.2681810347319
heart	59.1085590614776	58.8362205204306	63.1644088691361
kidney	66.0445842776366	60.8760897326366	62.5329893281372
liver	63.5358203757194	61.450893929501	64.7770579137403
stomach	61.1759056902979	61.5938374067204	56.2356488435378
testicle	65.6038864482027	58.9058415332687	66.0861186107577
cont.diffExp=0.311053895316427,-10.4636882988171,-2.47284352245916,0.272338541047006,5.16849454499995,2.08492644621830,-0.41793171642248,6.69804491493397
cont.diffExpScore=12.7909504364079

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=-0.521853602647822
cont.tran.correlation=-0.325918318498823

tran.covariance=-0.00266959300691584
cont.tran.covariance=-0.000786868320611715

tran.mean=63.0007499983965
cont.tran.mean=62.1222779893421

weightedLogRatios:
wLogRatio
Lung	-0.365411868765013
cerebhem	-0.0613299042002254
cortex	-1.38393672178514
heart	-0.343925310660862
kidney	0.289471043516033
liver	0.800849715371402
stomach	-0.415276533148815
testicle	0.448918720682887

cont.weightedLogRatios:
wLogRatio
Lung	0.0206103687299596
cerebhem	-0.669959315145762
cortex	-0.167377823836668
heart	0.0188282071285849
kidney	0.338147839206145
liver	0.13796356197036
stomach	-0.0280312942552773
testicle	0.444756137446332

varWeightedLogRatios=0.448173065154728
cont.varWeightedLogRatios=0.115497877710841

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.68967939229782	0.0741653487916017	49.7493701899186	2.47719027165468e-263	***
df.mm.trans1	0.643266495315504	0.0635114103003963	10.1283610657200	6.2179742574512e-23	***
df.mm.trans2	0.509807026529808	0.0558296463489483	9.13147511885345	4.17811506439701e-19	***
df.mm.exp2	0.0679404388932869	0.0709156373575891	0.958045946209298	0.338290737026077	   
df.mm.exp3	0.0879611325038841	0.0709156373575891	1.24036299723774	0.21515671059098	   
df.mm.exp4	0.0581294550034772	0.0709156373575891	0.819698689449294	0.412599739081698	   
df.mm.exp5	-0.070099430994752	0.0709156373575891	-0.988490460027576	0.323171837987949	   
df.mm.exp6	0.0113958768557961	0.0709156373575891	0.160696248111441	0.8723678460043	   
df.mm.exp7	0.0509039795969438	0.0709156373575891	0.717810365861377	0.473056051678500	   
df.mm.exp8	-0.0145676425185421	0.0709156373575891	-0.205422147517132	0.837287662318516	   
df.mm.trans1:exp2	0.00740611802772687	0.0647368237676793	0.114403481615118	0.90894286191443	   
df.mm.trans2:exp2	-0.0659401168454519	0.0457758470786045	-1.44050019942224	0.150065416016804	   
df.mm.trans1:exp3	-0.126549681088790	0.0647368237676793	-1.95483302583609	0.0509046403518343	.  
df.mm.trans2:exp3	0.112306786549431	0.0457758470786045	2.4534070632616	0.0143351621969497	*  
df.mm.trans1:exp4	-0.0632244135763815	0.0647368237676794	-0.976637559532957	0.329004789693570	   
df.mm.trans2:exp4	-0.0682455669950963	0.0457758470786045	-1.49086409865682	0.136339205365773	   
df.mm.trans1:exp5	0.0592963943161473	0.0647368237676793	0.915960822065413	0.359926946616645	   
df.mm.trans2:exp5	-0.0999223778624502	0.0457758470786045	-2.18286245344335	0.0292975663900443	*  
df.mm.trans1:exp6	0.0386584646920614	0.0647368237676794	0.597163444885631	0.550544877908607	   
df.mm.trans2:exp6	-0.246521222221526	0.0457758470786045	-5.3853994618212	9.17694389511028e-08	***
df.mm.trans1:exp7	-0.0481240727116878	0.0647368237676793	-0.74338019554976	0.457440909907532	   
df.mm.trans2:exp7	-0.0363272412763987	0.0457758470786045	-0.793589711491715	0.427638507569649	   
df.mm.trans1:exp8	0.0548574872704204	0.0647368237676793	0.847392319822287	0.396996393329399	   
df.mm.trans2:exp8	-0.142742995733276	0.0457758470786045	-3.11830375281016	0.00187539971366706	** 
df.mm.trans1:probe2	-0.294161487639529	0.0469062851284556	-6.27125953023033	5.49728248036465e-10	***
df.mm.trans1:probe3	-0.50210245477714	0.0469062851284556	-10.7043747634694	2.76276439043987e-25	***
df.mm.trans1:probe4	-0.469334193101995	0.0469062851284556	-10.0057847645938	1.91051964094936e-22	***
df.mm.trans1:probe5	-0.519385716134279	0.0469062851284556	-11.0728384205211	7.66863783151681e-27	***
df.mm.trans1:probe6	-0.356803076686279	0.0469062851284556	-7.60672212069562	6.92681063474583e-14	***
df.mm.trans1:probe7	0.192319301150978	0.0469062851284556	4.10007530172769	4.49459279343504e-05	***
df.mm.trans1:probe8	-0.460164536576912	0.0469062851284556	-9.81029589780397	1.11935394724131e-21	***
df.mm.trans1:probe9	-0.321825428885292	0.0469062851284556	-6.86102998785672	1.25297433141877e-11	***
df.mm.trans1:probe10	-0.41406028400612	0.0469062851284556	-8.82739451380964	5.30403223256256e-18	***
df.mm.trans1:probe11	-0.395871332522556	0.0469062851284556	-8.43962235419922	1.22268917592507e-16	***
df.mm.trans1:probe12	-0.462846594037567	0.0469062851284556	-9.86747496140516	6.69303942845916e-22	***
df.mm.trans1:probe13	-0.569712813366568	0.0469062851284556	-12.1457670716488	1.35415283517802e-31	***
df.mm.trans1:probe14	-0.481997250728572	0.0469062851284556	-10.2757498149468	1.58977348369860e-23	***
df.mm.trans1:probe15	-0.541737604652805	0.0469062851284556	-11.5493606703073	6.50755787736921e-29	***
df.mm.trans1:probe16	-0.366244051726144	0.0469062851284556	-7.8079952552875	1.57507593832004e-14	***
df.mm.trans1:probe17	-0.419173221597503	0.0469062851284556	-8.93639776523707	2.15030449498594e-18	***
df.mm.trans1:probe18	-0.445647222232203	0.0469062851284556	-9.50079975448432	1.73759504136047e-20	***
df.mm.trans1:probe19	-0.403134666549361	0.0469062851284556	-8.59447013220835	3.54169983766697e-17	***
df.mm.trans1:probe20	-0.291872494439891	0.0469062851284556	-6.22246024473226	7.41760249700761e-10	***
df.mm.trans1:probe21	-0.29444551285491	0.0469062851284556	-6.27731469351183	5.29591557815603e-10	***
df.mm.trans2:probe2	-0.0186780010252543	0.0469062851284556	-0.398198257954206	0.690576236900764	   
df.mm.trans2:probe3	-0.0813858199724818	0.0469062851284556	-1.73507281059675	0.0830620641216767	.  
df.mm.trans2:probe4	-0.0582150416214511	0.0469062851284556	-1.24109256279890	0.214887231242510	   
df.mm.trans2:probe5	0.0691420916547718	0.0469062851284556	1.47404748564978	0.140810144254379	   
df.mm.trans2:probe6	0.0502960723718786	0.0469062851284556	1.07226722888286	0.28388059878386	   
df.mm.trans3:probe2	-0.78180493111855	0.0469062851284556	-16.66738111913	1.01649055880383e-54	***
df.mm.trans3:probe3	-0.459822909646525	0.0469062851284556	-9.80301271753396	1.19492917260579e-21	***
df.mm.trans3:probe4	-0.8257489776503	0.0469062851284556	-17.6042288445768	5.03465275204392e-60	***
df.mm.trans3:probe5	-0.339665449611948	0.0469062851284556	-7.24136325615542	9.36498743958918e-13	***
df.mm.trans3:probe6	-0.462889204854083	0.0469062851284556	-9.8683833858604	6.63845112728478e-22	***
df.mm.trans3:probe7	-0.527980169911383	0.0469062851284556	-11.2560644797489	1.24724966458182e-27	***
df.mm.trans3:probe8	-0.79665120450961	0.0469062851284556	-16.9838903747746	1.70388259699975e-56	***
df.mm.trans3:probe9	-0.648207384679532	0.0469062851284556	-13.8192010495902	1.29419159792200e-39	***
df.mm.trans3:probe10	-0.46672748286165	0.0469062851284556	-9.95021203626528	3.16691607599999e-22	***
df.mm.trans3:probe11	-0.769504990877793	0.0469062851284556	-16.4051574063147	2.91592922949073e-53	***
df.mm.trans3:probe12	-0.566671710426274	0.0469062851284556	-12.0809334799038	2.6784772807783e-31	***
df.mm.trans3:probe13	-0.830992883824864	0.0469062851284556	-17.7160242289310	1.14610875345604e-60	***
df.mm.trans3:probe14	0.911140201076475	0.0469062851284556	19.424693270449	1.01120721883419e-70	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.19209124533199	0.171470494858234	24.4478867854080	3.73302936589607e-102	***
df.mm.trans1	-0.0253932326755380	0.146838559122188	-0.172933000891188	0.862742046591251	   
df.mm.trans2	-0.0517891756364552	0.129078299275771	-0.401222947056417	0.688348956054031	   
df.mm.exp2	0.132413798253431	0.163957152889029	0.807612207947112	0.419522158568555	   
df.mm.exp3	0.0323903029703728	0.163957152889030	0.197553460764810	0.843437993710188	   
df.mm.exp4	-0.0704309525686123	0.163957152889029	-0.429569258355455	0.66760937005213	   
df.mm.exp5	0.0846529103252308	0.163957152889029	0.516311175411336	0.605760895397603	   
df.mm.exp6	0.0200673756487481	0.163957152889029	0.122394023652815	0.902613643293694	   
df.mm.exp7	0.125941002047938	0.163957152889029	0.768133624113233	0.442604487815992	   
df.mm.exp8	-0.0102068758609404	0.163957152889029	-0.0622533124117415	0.950374600065988	   
df.mm.trans1:exp2	-0.171372365538158	0.149671718502741	-1.14498829339639	0.252511135215139	   
df.mm.trans2:exp2	-0.00599192935935305	0.105833887105132	-0.0566163591194646	0.954863069400062	   
df.mm.trans1:exp3	-0.083491677607472	0.149671718502741	-0.55783202359598	0.577094507630808	   
df.mm.trans2:exp3	-0.0377311277236419	0.105833887105132	-0.356512727215252	0.721538190938282	   
df.mm.trans1:exp4	0.0140833639288233	0.149671718502741	0.0940950238943465	0.925054117027303	   
df.mm.trans2:exp4	0.0144517912351180	0.105833887105132	0.136551643622066	0.891415011058302	   
df.mm.trans1:exp5	-0.0300462029423815	0.149671718502741	-0.200747363917193	0.840940396154005	   
df.mm.trans2:exp5	-0.106549249766860	0.105833887105132	-1.00675929686884	0.314314596067808	   
df.mm.trans1:exp6	-0.00418685433110289	0.149671718502741	-0.0279735836067534	0.977689273648314	   
df.mm.trans2:exp6	-0.032565814312728	0.105833887105132	-0.307706871622111	0.758374885101616	   
df.mm.trans1:exp7	-0.147910913861166	0.149671718502741	-0.988235555392896	0.323296564740738	   
df.mm.trans2:exp7	-0.136116000448005	0.105833887105132	-1.28612870764910	0.198721027002267	   
df.mm.trans1:exp8	0.0581184887521197	0.149671718502741	0.388306417094123	0.69787893028758	   
df.mm.trans2:exp8	-0.0445896831002552	0.105833887105132	-0.421317635777292	0.67362135339966	   
df.mm.trans1:probe2	-0.0270306888210933	0.108447463053642	-0.249251462966202	0.803221788606512	   
df.mm.trans1:probe3	-0.00629170519661261	0.108447463053642	-0.0580161584185747	0.953748328580115	   
df.mm.trans1:probe4	-0.0907784773294648	0.108447463053642	-0.837073314334358	0.402768270600365	   
df.mm.trans1:probe5	-0.0720459666926212	0.108447463053642	-0.664339807165289	0.506638954419423	   
df.mm.trans1:probe6	0.0262288650576449	0.108447463053642	0.241857802101569	0.808944130238084	   
df.mm.trans1:probe7	-0.130217799419010	0.108447463053642	-1.20074546469197	0.230158342687518	   
df.mm.trans1:probe8	0.0233036213190101	0.108447463053642	0.214883969277210	0.829905302885889	   
df.mm.trans1:probe9	0.0364552346228311	0.108447463053642	0.336155716291852	0.736829931111493	   
df.mm.trans1:probe10	-0.0631877530717937	0.108447463053642	-0.58265773391618	0.560266195001456	   
df.mm.trans1:probe11	-0.153069242797346	0.108447463053642	-1.41145987639778	0.158446419177909	   
df.mm.trans1:probe12	-0.0482160851554389	0.108447463053642	-0.444603163575985	0.656710904532621	   
df.mm.trans1:probe13	-0.0743619159481069	0.108447463053642	-0.685695302169724	0.493077640897011	   
df.mm.trans1:probe14	0.00072964261765422	0.108447463053642	0.00672807456356368	0.994633269107467	   
df.mm.trans1:probe15	-0.137172571988586	0.108447463053642	-1.26487580369432	0.206235457388036	   
df.mm.trans1:probe16	-0.0477204613821945	0.108447463053642	-0.440032989601521	0.6600163449439	   
df.mm.trans1:probe17	-0.088627826370994	0.108447463053642	-0.817242043985443	0.414001242880084	   
df.mm.trans1:probe18	-0.0723925684594058	0.108447463053642	-0.667535841051422	0.504596965749792	   
df.mm.trans1:probe19	-0.0193237814498140	0.108447463053642	-0.178185647738535	0.858616322676364	   
df.mm.trans1:probe20	-0.0830140718012969	0.108447463053642	-0.765477305450984	0.4441832769929	   
df.mm.trans1:probe21	-0.0573804827667989	0.108447463053642	-0.529108576181413	0.596857489327177	   
df.mm.trans2:probe2	0.0504849824507853	0.108447463053642	0.465524789877414	0.641665627298187	   
df.mm.trans2:probe3	-0.0321188949746514	0.108447463053642	-0.296170090754122	0.76716688420518	   
df.mm.trans2:probe4	-0.127583605910424	0.108447463053642	-1.17645542198914	0.239716584283095	   
df.mm.trans2:probe5	-0.079976297590405	0.108447463053642	-0.737465822975001	0.461026752183327	   
df.mm.trans2:probe6	-0.00211114115992197	0.108447463053642	-0.0194669483312646	0.98447281528092	   
df.mm.trans3:probe2	0.122859283808364	0.108447463053642	1.13289218898181	0.257554044746016	   
df.mm.trans3:probe3	0.0428273340488704	0.108447463053642	0.394913194305767	0.692998291906558	   
df.mm.trans3:probe4	0.0208622201919061	0.108447463053642	0.192371675689517	0.847493439446423	   
df.mm.trans3:probe5	0.144472854772660	0.108447463053642	1.33219211131936	0.183126251058190	   
df.mm.trans3:probe6	0.141723573315239	0.108447463053642	1.30684083633323	0.19159262399603	   
df.mm.trans3:probe7	0.0564474850405204	0.108447463053642	0.520505353016876	0.602836363196462	   
df.mm.trans3:probe8	0.0496267182707478	0.108447463053642	0.457610688838343	0.647339976900674	   
df.mm.trans3:probe9	0.0615901529042318	0.108447463053642	0.567926175218753	0.570223369278117	   
df.mm.trans3:probe10	0.0886714235006152	0.108447463053642	0.817644055506908	0.413771704454701	   
df.mm.trans3:probe11	0.0536501302917608	0.108447463053642	0.494710791576781	0.6209221415677	   
df.mm.trans3:probe12	0.0842246455683345	0.108447463053642	0.776640072499198	0.437570345761104	   
df.mm.trans3:probe13	0.133315201000388	0.108447463053642	1.22930677441893	0.219270433876096	   
df.mm.trans3:probe14	0.0179203067382841	0.108447463053642	0.165244130509721	0.868788054632464