fitVsDatCorrelation=0.888076710632004
cont.fitVsDatCorrelation=0.277237441122321

fstatistic=4064.23253194765,59,853
cont.fstatistic=919.21263783868,59,853

residuals=-0.89892364894116,-0.120677984248857,0.00483282803378552,0.120504549192733,1.44081930154705
cont.residuals=-0.885366725661717,-0.320648055686866,-0.145112718317168,0.136726357050454,2.52783613007358

predictedValues:
Include	Exclude	Both
Lung	61.2098682447332	46.718863338436	73.0646052559404
cerebhem	56.6163380382661	44.1833882293504	60.770983699125
cortex	63.9005603491441	42.8437203122919	72.317420935714
heart	69.576765425212	47.9474784992037	81.5575820511574
kidney	162.705689856153	62.3823354806528	218.484437835319
liver	62.4229507560842	47.5497441835536	69.2118307875787
stomach	58.7166249153345	46.1818810868281	66.0023957182797
testicle	60.5311084901161	46.9907195302993	66.1949281733712


diffExp=14.4910049062972,12.4329498089157,21.0568400368523,21.6292869260082,100.323354375501,14.8732065725306,12.5347438285064,13.5403889598168
diffExpScore=0.995280386913674
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,1,1,0,0,0
diffExp1.4Score=0.75
diffExp1.3=1,0,1,1,1,1,0,0
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	67.3671687796778	84.8946214219834	59.6244366069853
cerebhem	69.1217085710812	92.8968695523618	59.0071491852791
cortex	71.2902794422117	63.6213881359358	66.7988216948564
heart	61.1445280604144	55.7109397095268	65.8740535994937
kidney	72.0358055064447	57.2346535903897	56.2312601751808
liver	64.6392398381908	58.3821086122099	65.3758570001444
stomach	58.5073219268289	73.2007064509549	72.3951227896431
testicle	63.7325093163961	74.5870158788322	66.7174959722144
cont.diffExp=-17.5274526423056,-23.7751609812806,7.66889130627583,5.43358835088766,14.8011519160550,6.2571312259809,-14.6933845241261,-10.8545065624361
cont.diffExpScore=2.99827964774403

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,-1,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=-1,-1,0,0,1,0,-1,0
cont.diffExp1.2Score=1.33333333333333

tran.correlation=0.963331841064001
cont.tran.correlation=0.0687454509996482

tran.covariance=0.0376346749049734
cont.tran.covariance=0.000763078324565855

tran.mean=61.2798772959787
cont.tran.mean=68.02292904959

weightedLogRatios:
wLogRatio
Lung	1.0750299395727
cerebhem	0.970055520816487
cortex	1.58206345808605
heart	1.51024799025975
kidney	4.42193101563310
liver	1.08804254201403
stomach	0.949172316694146
testicle	1.00689243408775

cont.weightedLogRatios:
wLogRatio
Lung	-1.00035037322518
cerebhem	-1.29590814775799
cortex	0.479124757242842
heart	0.378463514861245
kidney	0.957312723010332
liver	0.419253922817797
stomach	-0.936806271299612
testicle	-0.665782930421674

varWeightedLogRatios=1.38238848480497
cont.varWeightedLogRatios=0.731366870965942

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20163260504761	0.120225643546432	34.9478903261176	8.91514684846e-167	***
df.mm.trans1	0.0823264080242393	0.103450863985302	0.795802034441546	0.426368508256153	   
df.mm.trans2	-0.390091256523807	0.091416648880417	-4.26717957069383	2.20134125659604e-05	***
df.mm.exp2	0.0504216766618849	0.117224338045796	0.430129762321086	0.667209894764575	   
df.mm.exp3	-0.0332902173470918	0.117224338045796	-0.283987249593906	0.77648907011899	   
df.mm.exp4	0.0441152183297536	0.117224338045796	0.376331562755502	0.706764001084124	   
df.mm.exp5	0.171397967772673	0.117224338045796	1.46213636715708	0.144072154030705	   
df.mm.exp6	0.0914252261389142	0.117224338045796	0.779916761851939	0.435656217688485	   
df.mm.exp7	0.0485070237464061	0.117224338045796	0.413796525150399	0.6791271459839	   
df.mm.exp8	0.0933913238296533	0.117224338045796	0.796688856482756	0.425853438296722	   
df.mm.trans1:exp2	-0.128432497535076	0.107691571221132	-1.19259563286857	0.233359417275143	   
df.mm.trans2:exp2	-0.106220799131226	0.0789765611848145	-1.34496612080458	0.178993653336919	   
df.mm.trans1:exp3	0.0763099255007737	0.107691571221132	0.708597011219015	0.47876819043443	   
df.mm.trans2:exp3	-0.0532987078156201	0.0789765611848145	-0.6748674165604	0.499942877852439	   
df.mm.trans1:exp4	0.0840070408308766	0.107691571221132	0.780070713782958	0.435565648614794	   
df.mm.trans2:exp4	-0.0181570134220484	0.0789765611848145	-0.229903823991005	0.818221623933078	   
df.mm.trans1:exp5	0.8062365949499	0.107691571221132	7.48653386525849	1.76353117307617e-13	***
df.mm.trans2:exp5	0.117736173285237	0.0789765611848145	1.49077361078967	0.136390625646529	   
df.mm.trans1:exp6	-0.071800640155854	0.107691571221132	-0.666724789523406	0.50512823559448	   
df.mm.trans2:exp6	-0.073796826103613	0.0789765611848145	-0.934414274267015	0.350354672648795	   
df.mm.trans1:exp7	-0.0900925410404792	0.107691571221132	-0.836579316458155	0.403063373338106	   
df.mm.trans2:exp7	-0.060067495846195	0.0789765611848145	-0.760573706237094	0.447121910362646	   
df.mm.trans1:exp8	-0.104542323372464	0.107691571221132	-0.970756784277932	0.331944586667236	   
df.mm.trans2:exp8	-0.087589207414229	0.0789765611848145	-1.10905319376036	0.267719816082487	   
df.mm.trans1:probe2	0.707731273036683	0.0750212380614465	9.43374558091153	3.64640370275572e-20	***
df.mm.trans1:probe3	0.119048065983271	0.0750212380614465	1.58685818922056	0.112915365151151	   
df.mm.trans1:probe4	-0.261132974330357	0.0750212380614465	-3.48078732207104	0.000525311710366146	***
df.mm.trans1:probe5	-0.203586567897572	0.0750212380614465	-2.71371911685626	0.00678745743550044	** 
df.mm.trans1:probe6	-0.320190836150454	0.0750212380614465	-4.26800256066423	2.19340730699690e-05	***
df.mm.trans1:probe7	-0.461316595427579	0.0750212380614465	-6.14914665963971	1.19573635355425e-09	***
df.mm.trans1:probe8	-0.360497765024305	0.0750212380614465	-4.80527613699253	1.82560327474612e-06	***
df.mm.trans1:probe9	-0.325917676888069	0.0750212380614465	-4.34433882071	1.56504784515313e-05	***
df.mm.trans1:probe10	-0.517285532467689	0.0750212380614465	-6.8951878939135	1.04676737852729e-11	***
df.mm.trans1:probe11	-0.443842721573497	0.0750212380614465	-5.91622763156701	4.76612034661979e-09	***
df.mm.trans1:probe12	-0.160490315668445	0.0750212380614465	-2.13926509099990	0.0326974912741634	*  
df.mm.trans1:probe13	-0.190848193506896	0.0750212380614465	-2.54392220707662	0.0111369125667166	*  
df.mm.trans1:probe14	0.188333424794276	0.0750212380614465	2.51040144978707	0.0122434344874827	*  
df.mm.trans1:probe15	-0.113049308897833	0.0750212380614465	-1.50689740424224	0.132207190947386	   
df.mm.trans1:probe16	-0.494762992651306	0.0750212380614465	-6.5949723763032	7.45285142829535e-11	***
df.mm.trans1:probe17	-0.548426773930607	0.0750212380614465	-7.3102869014427	6.1362639168912e-13	***
df.mm.trans1:probe18	-0.526790398297403	0.0750212380614465	-7.02188356137142	4.47045295222923e-12	***
df.mm.trans1:probe19	-0.496503968130459	0.0750212380614465	-6.61817881122936	6.42083317493635e-11	***
df.mm.trans1:probe20	-0.532676924623301	0.0750212380614465	-7.10034835984724	2.62202106979809e-12	***
df.mm.trans1:probe21	-0.486612121494147	0.0750212380614465	-6.486324860376	1.48857287360215e-10	***
df.mm.trans2:probe2	0.0181743783316154	0.0750212380614465	0.242256443658389	0.808639693847095	   
df.mm.trans2:probe3	0.096080333731337	0.0750212380614465	1.28070845288693	0.200644200429388	   
df.mm.trans2:probe4	0.0619144567221325	0.0750212380614465	0.825292388155753	0.409436352787651	   
df.mm.trans2:probe5	0.129392972079490	0.0750212380614465	1.72475122276055	0.0849345601327465	.  
df.mm.trans2:probe6	0.248751087151402	0.0750212380614465	3.31574222952254	0.000952598536938708	***
df.mm.trans3:probe2	0.0213128319166973	0.0750212380614465	0.284090645094939	0.776409860909719	   
df.mm.trans3:probe3	0.242421880554016	0.0750212380614465	3.23137669836185	0.00127918263447009	** 
df.mm.trans3:probe4	-0.117949609180384	0.0750212380614465	-1.57221624473562	0.116271234352080	   
df.mm.trans3:probe5	1.24320608216651	0.0750212380614465	16.5713885066553	1.11001178415083e-53	***
df.mm.trans3:probe6	0.316512631889827	0.0750212380614465	4.21897372088935	2.71692234332432e-05	***
df.mm.trans3:probe7	0.0202102107240101	0.0750212380614465	0.269393191131514	0.787692275802956	   
df.mm.trans3:probe8	1.09928550862933	0.0750212380614465	14.652990766814	1.57414417831296e-43	***
df.mm.trans3:probe9	0.209707249491543	0.0750212380614465	2.79530510173375	0.00530151367509828	** 
df.mm.trans3:probe10	0.511432022608866	0.0750212380614465	6.81716319037517	1.75604466539864e-11	***
df.mm.trans3:probe11	0.82610045513808	0.0750212380614465	11.0115545475464	1.84641412395085e-26	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.55573478251499	0.251281608879198	18.1299968701853	2.18179910277098e-62	***
df.mm.trans1	-0.329455790127817	0.216220922386914	-1.52369986442975	0.127954340407536	   
df.mm.trans2	-0.132625702379092	0.191068410460571	-0.694126789768111	0.487791691128703	   
df.mm.exp2	0.126197153881539	0.245008630397311	0.515072280012729	0.60663595771849	   
df.mm.exp3	-0.3454787792267	0.245008630397311	-1.41006779502610	0.158884185250416	   
df.mm.exp4	-0.617830762447781	0.245008630397311	-2.52166938546570	0.0118610475094407	*  
df.mm.exp5	-0.268653020401354	0.245008630397311	-1.09650431483046	0.273167598029556	   
df.mm.exp6	-0.507824921870453	0.245008630397311	-2.07268177062560	0.0385017398897905	*  
df.mm.exp7	-0.483284981214187	0.245008630397311	-1.97252227576834	0.0488726120660761	*  
df.mm.exp8	-0.29730903035827	0.245008630397311	-1.21346350075974	0.225288559372463	   
df.mm.trans1:exp2	-0.100486099928726	0.225084353727944	-0.446437516710658	0.655394547670425	   
df.mm.trans2:exp2	-0.0361179449804682	0.165067591013578	-0.218807003595862	0.826852731760364	   
df.mm.trans1:exp3	0.402080974996123	0.225084353727944	1.78635684060969	0.0743965806873668	.  
df.mm.trans2:exp3	0.0570177450946316	0.165067591013578	0.345420592525286	0.729863361537164	   
df.mm.trans1:exp4	0.520913347543951	0.225084353727944	2.31430278878279	0.0208878529842116	*  
df.mm.trans2:exp4	0.196596554826641	0.165067591013578	1.19100638483583	0.233982385668014	   
df.mm.trans1:exp5	0.335658525358213	0.225084353727944	1.4912565880253	0.136263843954420	   
df.mm.trans2:exp5	-0.125598172045576	0.165067591013578	-0.7608893500799	0.446933441494708	   
df.mm.trans1:exp6	0.466488786937367	0.225084353727944	2.07250650350048	0.0385181071332476	*  
df.mm.trans2:exp6	0.133423665996662	0.165067591013578	0.808297165890587	0.419144898098387	   
df.mm.trans1:exp7	0.342279099614571	0.225084353727944	1.52067033512369	0.128713163880148	   
df.mm.trans2:exp7	0.335079313713829	0.165067591013578	2.02995216478483	0.0426713042918904	*  
df.mm.trans1:exp8	0.241846023940036	0.225084353727944	1.07446839344663	0.282916641307465	   
df.mm.trans2:exp8	0.167864733147559	0.165067591013578	1.01694543499911	0.309467737574864	   
df.mm.trans1:probe2	0.0865209637589704	0.156800636237884	0.551789621744317	0.581237022996648	   
df.mm.trans1:probe3	0.189742019616497	0.156800636237884	1.21008450073275	0.226581693364079	   
df.mm.trans1:probe4	0.0484463562558373	0.156800636237884	0.308967855094278	0.757421426467442	   
df.mm.trans1:probe5	0.0877237855040573	0.156800636237884	0.559460647665806	0.575994244593537	   
df.mm.trans1:probe6	-0.0468803230849211	0.156800636237884	-0.298980439172443	0.765027795174015	   
df.mm.trans1:probe7	-0.0813306632100765	0.156800636237884	-0.518688349495526	0.604112614921614	   
df.mm.trans1:probe8	-0.018693076495894	0.156800636237884	-0.119215565347161	0.905132645226358	   
df.mm.trans1:probe9	-0.138843461710561	0.156800636237884	-0.88547766795997	0.376148602148633	   
df.mm.trans1:probe10	-0.233510475091209	0.156800636237884	-1.48921892598029	0.136799350316461	   
df.mm.trans1:probe11	0.109679249044416	0.156800636237884	0.699482168414293	0.484441448858594	   
df.mm.trans1:probe12	-0.139560521574179	0.156800636237884	-0.89005073526902	0.373689596450224	   
df.mm.trans1:probe13	0.117918102187278	0.156800636237884	0.75202566147999	0.452243080267819	   
df.mm.trans1:probe14	-0.148853647081635	0.156800636237884	-0.94931787684718	0.342727841706774	   
df.mm.trans1:probe15	0.110898928473668	0.156800636237884	0.70726070464039	0.479597653514913	   
df.mm.trans1:probe16	-0.197299806615176	0.156800636237884	-1.25828447734007	0.208633195646391	   
df.mm.trans1:probe17	0.0249102071756959	0.156800636237884	0.158865472573111	0.873812468584138	   
df.mm.trans1:probe18	-0.0337497885753352	0.156800636237884	-0.21524012520034	0.829631559887928	   
df.mm.trans1:probe19	-0.0271471343604833	0.156800636237884	-0.173131531936503	0.86258911359988	   
df.mm.trans1:probe20	-0.090501219093925	0.156800636237884	-0.577173800217394	0.563974349379545	   
df.mm.trans1:probe21	-0.135347998782811	0.156800636237884	-0.863185265252834	0.388278243804885	   
df.mm.trans2:probe2	0.324766819480277	0.156800636237884	2.07120855675336	0.0386394997906132	*  
df.mm.trans2:probe3	0.0850996808593434	0.156800636237884	0.542725354317042	0.587460685604267	   
df.mm.trans2:probe4	0.107282727043940	0.156800636237884	0.684198289101197	0.494035798218357	   
df.mm.trans2:probe5	-0.0672290796990787	0.156800636237884	-0.428755146102115	0.668209676333362	   
df.mm.trans2:probe6	-0.13879194035815	0.156800636237884	-0.88514908923958	0.376325668200737	   
df.mm.trans3:probe2	-0.0581066191424743	0.156800636237884	-0.370576424538992	0.711045047406287	   
df.mm.trans3:probe3	0.0436414064642039	0.156800636237884	0.278324166988678	0.780830978358973	   
df.mm.trans3:probe4	-0.0754102634178597	0.156800636237884	-0.480930850965769	0.630688976633906	   
df.mm.trans3:probe5	0.0213898464071428	0.156800636237884	0.136414283260254	0.891525970739233	   
df.mm.trans3:probe6	-0.110481955632133	0.156800636237884	-0.704601449859677	0.481250621964597	   
df.mm.trans3:probe7	0.0466829878922216	0.156800636237884	0.29772192901946	0.765987900333545	   
df.mm.trans3:probe8	-0.0129031093887411	0.156800636237884	-0.0822899045458311	0.934435487389714	   
df.mm.trans3:probe9	-0.0298743032270993	0.156800636237884	-0.190524119951762	0.848943777769905	   
df.mm.trans3:probe10	-0.0609515424402931	0.156800636237884	-0.388719994399912	0.697580295945866	   
df.mm.trans3:probe11	0.174547864864745	0.156800636237884	1.11318339678122	0.265943265692822