fitVsDatCorrelation=0.837524553326914
cont.fitVsDatCorrelation=0.250880773196561

fstatistic=7702.43536275376,77,1267
cont.fstatistic=2442.83054198248,77,1267

residuals=-0.724113692168163,-0.110300379530957,-0.0147050158462170,0.090014816348489,1.19628181956561
cont.residuals=-0.76146850770741,-0.280359869829697,-0.0549323432687801,0.235545965241361,1.52709761654090

predictedValues:
Include	Exclude	Both
Lung	86.5362465310709	86.6012870533558	64.0756042902671
cerebhem	85.9382488968878	85.7480957904882	65.0639180319025
cortex	74.4714877502333	77.8470197342467	59.0221749665618
heart	80.7526452638093	87.771378108618	60.3343181186725
kidney	88.2676972463381	89.83460061488	64.4716723217262
liver	89.2280613713566	86.4398649433182	60.5088207362394
stomach	92.4721833237946	89.7019542189348	64.4179139669642
testicle	83.3046269456497	89.61745486432	61.9472800791511


diffExp=-0.0650405222849457,0.190153106399606,-3.37553198401335,-7.01873284480872,-1.56690336854187,2.78819642803848,2.77022910485982,-6.31282791867031
diffExpScore=1.77239172361607
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	70.4510173761946	70.0972088524276	76.4320678870164
cerebhem	66.3594963757209	68.621809725943	74.9872932525786
cortex	69.032816343755	79.0807397248989	72.6245469217555
heart	67.5212018687207	71.3827406376222	79.254077946486
kidney	66.3731931414107	79.9378441776667	72.9625068050235
liver	66.774045245785	69.5733410662492	73.4465481357982
stomach	66.1320519890146	73.1537385481397	73.4320330974157
testicle	72.0252282347241	63.8905839180933	68.8385689095465
cont.diffExp=0.353808523767015,-2.2623133502221,-10.0479233811439,-3.86153876890148,-13.5646510362561,-2.79929582046427,-7.02168655912509,8.13464431663083
cont.diffExpScore=1.49820473242328

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

tran.correlation=0.742893532005417
cont.tran.correlation=-0.439065632039337

tran.covariance=0.00240893202554642
cont.tran.covariance=-0.00107221444997748

tran.mean=85.9083032910814
cont.tran.mean=70.0254410766479

weightedLogRatios:
wLogRatio
Lung	-0.0033515761873435
cerebhem	0.00986288258407312
cortex	-0.192060186658459
heart	-0.369472479887484
kidney	-0.0789914631318685
liver	0.142076312091009
stomach	0.13722482152428
testicle	-0.325714167520525

cont.weightedLogRatios:
wLogRatio
Lung	0.0214095716607153
cerebhem	-0.141196052115806
cortex	-0.584658862032889
heart	-0.2358191604624
kidney	-0.797430329382725
liver	-0.173378640783457
stomach	-0.428069898255897
testicle	0.505395362042226

varWeightedLogRatios=0.0381398314413405
cont.varWeightedLogRatios=0.157446568448173

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.1951634527765	0.0911041828468483	57.024422923697	0	***
df.mm.trans1	-0.651415749575783	0.078389359282658	-8.310002218884	2.43637548126117e-16	***
df.mm.trans2	-0.6961277754296	0.0671864564591927	-10.3611324680058	3.3408822747016e-24	***
df.mm.exp2	-0.0321415950476969	0.0836029318659502	-0.38445535736992	0.700705494751158	   
df.mm.exp3	-0.174565602947124	0.0836029318659502	-2.08803207077742	0.0369942390365175	*  
df.mm.exp4	0.00441077769609654	0.0836029318659502	0.0527586485025289	0.957932526113179	   
df.mm.exp5	0.0503041456449273	0.0836029318659502	0.601703128373362	0.547479414175463	   
df.mm.exp6	0.0860410614550109	0.0836029318659502	1.02916320677569	0.30359941361839	   
df.mm.exp7	0.0961943363175558	0.0836029318659502	1.15060960388082	0.250109988178812	   
df.mm.exp8	0.0299561654198372	0.0836029318659502	0.358314771399037	0.720167454841139	   
df.mm.trans1:exp2	0.0252072360986772	0.0778300939292659	0.323875185369637	0.746085985185112	   
df.mm.trans2:exp2	0.0222407968163191	0.0482681752178483	0.460775587971577	0.645038695450358	   
df.mm.trans1:exp3	0.0244185791082320	0.0778300939292659	0.313742125641326	0.753768548488746	   
df.mm.trans2:exp3	0.067996540833584	0.0482681752178483	1.40872408220729	0.159162123556498	   
df.mm.trans1:exp4	-0.0735834186533917	0.0778300939292659	-0.945436590636346	0.344616290036342	   
df.mm.trans2:exp4	0.00901000264158332	0.0482681752178483	0.186665491308063	0.851952797969372	   
df.mm.trans1:exp5	-0.0304932957752309	0.0778300939292659	-0.391793125714894	0.695276961376387	   
df.mm.trans2:exp5	-0.0136486145392227	0.0482681752178483	-0.282766325381527	0.777402157737877	   
df.mm.trans1:exp6	-0.0554088431853718	0.0778300939292659	-0.711920548826895	0.476645038693103	   
df.mm.trans2:exp6	-0.0879067697561768	0.0482681752178483	-1.8212159328466	0.0688097787689187	.  
df.mm.trans1:exp7	-0.0298498190570434	0.0778300939292659	-0.383525414785851	0.701394573764888	   
df.mm.trans2:exp7	-0.0610164588317914	0.0482681752178483	-1.26411364333552	0.206421747789151	   
df.mm.trans1:exp8	-0.0680154334320663	0.0778300939292659	-0.873896329790897	0.382340319267324	   
df.mm.trans2:exp8	0.0042792668359193	0.0482681752178483	0.0886560723832158	0.929369254942706	   
df.mm.trans1:probe2	-0.525203296374791	0.0591162000494903	-8.8842533169437	2.15915418623046e-18	***
df.mm.trans1:probe3	-0.280252070065197	0.0591162000494903	-4.74069831671485	2.37070567620146e-06	***
df.mm.trans1:probe4	-0.0236873532890050	0.0591162000494903	-0.400691405556762	0.688714895513894	   
df.mm.trans1:probe5	-0.485535195086976	0.0591162000494903	-8.2132341842084	5.25877965881261e-16	***
df.mm.trans1:probe6	-0.6209740987933	0.0591162000494903	-10.5042965933778	8.33942619729626e-25	***
df.mm.trans1:probe7	-0.339706210575368	0.0591162000494903	-5.74641486244001	1.14064123442961e-08	***
df.mm.trans1:probe8	0.141513746260469	0.0591162000494903	2.39382345519499	0.0168184956594652	*  
df.mm.trans1:probe9	-0.133992028394604	0.0591162000494903	-2.26658730233726	0.0235834581302129	*  
df.mm.trans1:probe10	-0.525015270001311	0.0591162000494903	-8.88107269347123	2.21807843897144e-18	***
df.mm.trans1:probe11	0.131209542206931	0.0591162000494903	2.21951921972465	0.0266279459787673	*  
df.mm.trans1:probe12	0.166607231381805	0.0591162000494903	2.81830075753053	0.00490291954047279	** 
df.mm.trans1:probe13	0.0063402630118946	0.0591162000494903	0.107250855206978	0.914606951147156	   
df.mm.trans1:probe14	0.0113757542602536	0.0591162000494903	0.192430404030202	0.8474359162375	   
df.mm.trans1:probe15	-0.0347242649863772	0.0591162000494903	-0.58739000404808	0.557046465871217	   
df.mm.trans1:probe16	0.239037971704354	0.0591162000494903	4.04352734959687	5.58294672691794e-05	***
df.mm.trans1:probe17	0.0139173649947481	0.0591162000494903	0.235423876756234	0.81391780113371	   
df.mm.trans1:probe18	-0.0208789049450627	0.0591162000494903	-0.353184151342331	0.724009140609167	   
df.mm.trans1:probe19	-0.0233288631656774	0.0591162000494903	-0.394627245089286	0.693184420851441	   
df.mm.trans1:probe20	0.238063355113502	0.0591162000494903	4.02704089427606	5.98371207866849e-05	***
df.mm.trans1:probe21	0.195380898041456	0.0591162000494903	3.30503141064359	0.000976223584762101	***
df.mm.trans1:probe22	0.221204330772442	0.0591162000494903	3.74185638771194	0.000190838963713943	***
df.mm.trans1:probe23	0.00236887026584832	0.0591162000494903	0.0400714231270815	0.96804249764806	   
df.mm.trans1:probe24	-0.492608306878422	0.0591162000494903	-8.33288178986514	2.02881921809070e-16	***
df.mm.trans1:probe25	-0.478741687482639	0.0591162000494903	-8.09831631738594	1.29796691665533e-15	***
df.mm.trans1:probe26	-0.55698207791261	0.0591162000494903	-9.42181800329387	2.02618504297909e-20	***
df.mm.trans1:probe27	-0.300185312109851	0.0591162000494903	-5.07788578864245	4.38729648888155e-07	***
df.mm.trans1:probe28	-0.357743957978912	0.0591162000494903	-6.05153845611558	1.88639121125976e-09	***
df.mm.trans1:probe29	0.200300280155167	0.0591162000494903	3.38824687627895	0.000725009547253665	***
df.mm.trans1:probe30	-0.526977809742169	0.0591162000494903	-8.91427069569761	1.67398174286823e-18	***
df.mm.trans2:probe2	-0.393547213536453	0.0591162000494903	-6.65718048871522	4.14653362442233e-11	***
df.mm.trans2:probe3	0.0385309615680468	0.0591162000494903	0.651783462668268	0.514659082085327	   
df.mm.trans2:probe4	-0.324416855182007	0.0591162000494903	-5.48778261983034	4.91076935808619e-08	***
df.mm.trans2:probe5	-0.275896114448769	0.0591162000494903	-4.66701368182998	3.38064143211089e-06	***
df.mm.trans2:probe6	-0.0254167742658235	0.0591162000494903	-0.429946008785161	0.667308028822466	   
df.mm.trans3:probe2	0.447437658450881	0.0591162000494903	7.56878246701072	7.22090380167125e-14	***
df.mm.trans3:probe3	0.140565290156136	0.0591162000494903	2.37777952639816	0.0175648909715915	*  
df.mm.trans3:probe4	0.137835358369675	0.0591162000494903	2.33160044546645	0.0198778370704294	*  
df.mm.trans3:probe5	0.0412463975372450	0.0591162000494903	0.697717334718991	0.485482001110075	   
df.mm.trans3:probe6	0.607104554815458	0.0591162000494903	10.2696816491454	8.04071114355012e-24	***
df.mm.trans3:probe7	0.181450518934704	0.0591162000494903	3.06938738929091	0.00219056592150803	** 
df.mm.trans3:probe8	0.192324739233155	0.0591162000494903	3.25333392660805	0.00117067607492921	** 
df.mm.trans3:probe9	0.0191849073252525	0.0591162000494903	0.324528763844622	0.74559131998641	   
df.mm.trans3:probe10	0.128125226671123	0.0591162000494903	2.16734544107809	0.0303943078320934	*  
df.mm.trans3:probe11	0.680805551300544	0.0591162000494903	11.5163956873174	2.94482104923496e-29	***
df.mm.trans3:probe12	0.36014300210624	0.0591162000494903	6.09212029536301	1.47558289737435e-09	***
df.mm.trans3:probe13	0.133274734422536	0.0591162000494903	2.25445367447438	0.0243378575410996	*  
df.mm.trans3:probe14	0.184061205186176	0.0591162000494903	3.11354933219803	0.00188992892505397	** 
df.mm.trans3:probe15	0.247382055879752	0.0591162000494903	4.18467451684397	3.0526783914238e-05	***
df.mm.trans3:probe16	0.280798269765377	0.0591162000494903	4.7499377417747	2.26673305898028e-06	***
df.mm.trans3:probe17	0.169883980127748	0.0591162000494903	2.87372970497978	0.00412426245725034	** 
df.mm.trans3:probe18	0.493727829115085	0.0591162000494903	8.35181944546082	1.74299431382533e-16	***
df.mm.trans3:probe19	0.535941887552023	0.0591162000494903	9.06590557416324	4.5761867588124e-19	***
df.mm.trans3:probe20	1.26758234141162	0.0591162000494903	21.4422161835577	2.87095225157819e-87	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.99832118676341	0.161402888369172	24.7723025725424	8.42073272291843e-111	***
df.mm.trans1	0.162216279905966	0.138876927603851	1.16805780992427	0.243003193653884	   
df.mm.trans2	0.246844011449657	0.119029530730031	2.07380479395084	0.0382990720487029	*  
df.mm.exp2	-0.0620197007204667	0.148113448336167	-0.418731056613462	0.675483666232949	   
df.mm.exp3	0.151350055465716	0.148113448336167	1.02185221643211	0.307045917744155	   
df.mm.exp4	-0.060559416587244	0.148113448336167	-0.408871829449238	0.682702823014748	   
df.mm.exp5	0.118198627239974	0.148113448336167	0.798027650883551	0.425003987860295	   
df.mm.exp6	-0.0212602923772505	0.148113448336167	-0.143540594159936	0.885886079034346	   
df.mm.exp7	0.0194581952285213	0.148113448336167	0.131373588604579	0.895500637805767	   
df.mm.exp8	0.0340259502190861	0.148113448336167	0.229728971955731	0.81833946146927	   
df.mm.trans1:exp2	0.00218889738871365	0.137886116418441	0.0158746757510455	0.987336872590517	   
df.mm.trans2:exp2	0.0407471343666602	0.08551333926749	0.476500329839782	0.633800134988918	   
df.mm.trans1:exp3	-0.171685744095393	0.137886116418441	-1.24512712776957	0.213315258774122	   
df.mm.trans2:exp3	-0.0307636796653025	0.08551333926749	-0.359752992092522	0.719091812808959	   
df.mm.trans1:exp4	0.0180833875545763	0.137886116418441	0.131147268661182	0.89567962953807	   
df.mm.trans2:exp4	0.0787325523554953	0.08551333926749	0.920704921944587	0.357379772278093	   
df.mm.trans1:exp5	-0.177823049373724	0.137886116418441	-1.28963708597091	0.197412049851900	   
df.mm.trans2:exp5	0.0131677810823628	0.08551333926749	0.153985111506093	0.877645997507003	   
df.mm.trans1:exp6	-0.0323429261581384	0.137886116418441	-0.234562601357107	0.814586138785915	   
df.mm.trans2:exp6	0.0137587790932783	0.08551333926749	0.160896290697293	0.87220073364929	   
df.mm.trans1:exp7	-0.082722343750016	0.137886116418441	-0.599932363741248	0.548658589110399	   
df.mm.trans2:exp7	0.0232220623114512	0.08551333926749	0.271560700475179	0.78600406039153	   
df.mm.trans1:exp8	-0.0119271797774253	0.137886116418441	-0.0865002226999424	0.931082446583953	   
df.mm.trans2:exp8	-0.126736932810026	0.08551333926749	-1.48207208250384	0.138569778022212	   
df.mm.trans1:probe2	0.210319564254402	0.104732023703427	2.00816862710463	0.0448372988879854	*  
df.mm.trans1:probe3	0.153276766909031	0.104732023703427	1.46351384694971	0.143574862378130	   
df.mm.trans1:probe4	0.163597778283088	0.104732023703427	1.56206070023389	0.118523415007126	   
df.mm.trans1:probe5	0.0686293056939302	0.104732023703427	0.655284823754287	0.512403344881101	   
df.mm.trans1:probe6	0.128665219733924	0.104732023703427	1.22851841475220	0.219480563956299	   
df.mm.trans1:probe7	0.276296965675959	0.104732023703427	2.63813259694434	0.00843852600384944	** 
df.mm.trans1:probe8	0.0803987596438986	0.104732023703427	0.767661664512146	0.442831252788362	   
df.mm.trans1:probe9	0.0828483471535133	0.104732023703427	0.791050761972454	0.429062420932468	   
df.mm.trans1:probe10	0.150187125281874	0.104732023703427	1.43401339887372	0.151815183574042	   
df.mm.trans1:probe11	0.169762128340870	0.104732023703427	1.62091901156794	0.105283831668365	   
df.mm.trans1:probe12	0.0927371637339785	0.104732023703427	0.885470942455814	0.376070701547559	   
df.mm.trans1:probe13	0.358027060835612	0.104732023703427	3.4185060898799	0.000649652193622648	***
df.mm.trans1:probe14	-0.0448265460205371	0.104732023703427	-0.428011838551632	0.66871524333146	   
df.mm.trans1:probe15	0.114545717090487	0.104732023703427	1.09370289086411	0.274293195840577	   
df.mm.trans1:probe16	0.0440956734753099	0.104732023703427	0.421033337426735	0.67380214736705	   
df.mm.trans1:probe17	0.243912336733864	0.104732023703427	2.32891839677	0.0200199976962140	*  
df.mm.trans1:probe18	0.281440307067397	0.104732023703427	2.68724213583764	0.00729877395995545	** 
df.mm.trans1:probe19	0.0783853296324409	0.104732023703427	0.748437076461034	0.454335472215105	   
df.mm.trans1:probe20	0.192322980074521	0.104732023703427	1.83633403875712	0.0665424491013671	.  
df.mm.trans1:probe21	0.170159932443643	0.104732023703427	1.62471731593280	0.104471522237686	   
df.mm.trans1:probe22	0.311578977896605	0.104732023703427	2.97501152826868	0.00298527641760398	** 
df.mm.trans1:probe23	0.122346034023966	0.104732023703427	1.16818170505725	0.242953243585249	   
df.mm.trans1:probe24	0.168227431201289	0.104732023703427	1.60626545017084	0.108464777211676	   
df.mm.trans1:probe25	0.123087990230296	0.104732023703427	1.17526603495076	0.240109108128936	   
df.mm.trans1:probe26	0.162403067215801	0.104732023703427	1.55065338635757	0.121234506682731	   
df.mm.trans1:probe27	0.209398014048687	0.104732023703427	1.99936950174519	0.0457816288494274	*  
df.mm.trans1:probe28	0.091648166766656	0.104732023703427	0.875073005618402	0.381700035222712	   
df.mm.trans1:probe29	0.257763361527413	0.104732023703427	2.46117044637015	0.0139809840863736	*  
df.mm.trans1:probe30	0.257775675100729	0.104732023703427	2.46128801855944	0.0139764252295662	*  
df.mm.trans2:probe2	-0.0687295260467037	0.104732023703427	-0.65624174551737	0.511787751279133	   
df.mm.trans2:probe3	-0.00674299733297929	0.104732023703427	-0.0643833384913255	0.948675152635982	   
df.mm.trans2:probe4	-0.0821992214152892	0.104732023703427	-0.784852793908147	0.432686605483174	   
df.mm.trans2:probe5	0.195737594840146	0.104732023703427	1.86893738819008	0.0618621037491127	.  
df.mm.trans2:probe6	0.0845963878078705	0.104732023703427	0.807741365214373	0.419391104548199	   
df.mm.trans3:probe2	0.0153185177755399	0.104732023703427	0.1462639337412	0.883736303550544	   
df.mm.trans3:probe3	-0.142823175716984	0.104732023703427	-1.36370109797000	0.172903889326546	   
df.mm.trans3:probe4	-0.156974760165010	0.104732023703427	-1.49882294463745	0.134168675264823	   
df.mm.trans3:probe5	-0.0598505282793163	0.104732023703427	-0.571463494764473	0.567786855526221	   
df.mm.trans3:probe6	-0.0695933001662167	0.104732023703427	-0.664489214524166	0.506498163761984	   
df.mm.trans3:probe7	-0.102354936983668	0.104732023703427	-0.97730315298317	0.328605653293834	   
df.mm.trans3:probe8	-0.144040319747552	0.104732023703427	-1.37532260577181	0.169274565993255	   
df.mm.trans3:probe9	-0.0397575170745808	0.104732023703427	-0.379611848112123	0.70429718092961	   
df.mm.trans3:probe10	-0.139685358143929	0.104732023703427	-1.33374065738937	0.182528571015599	   
df.mm.trans3:probe11	-0.128727169467465	0.104732023703427	-1.22910992183236	0.219258814790025	   
df.mm.trans3:probe12	-0.119854926157029	0.104732023703427	-1.14439616383644	0.252675524679507	   
df.mm.trans3:probe13	-0.0871952110295501	0.104732023703427	-0.83255539181085	0.405252425601446	   
df.mm.trans3:probe14	-0.0789710370705339	0.104732023703427	-0.75402951531003	0.45097160699859	   
df.mm.trans3:probe15	-0.0665482755594177	0.104732023703427	-0.635414777698408	0.525272673090154	   
df.mm.trans3:probe16	-0.0312875672984521	0.104732023703427	-0.298739260372262	0.765187987439068	   
df.mm.trans3:probe17	-0.0553560159614193	0.104732023703427	-0.528549091328289	0.597210852672517	   
df.mm.trans3:probe18	0.102935547180420	0.104732023703427	0.982846922464758	0.325870423461634	   
df.mm.trans3:probe19	-0.0426209186733328	0.104732023703427	-0.406952116136165	0.68411189423146	   
df.mm.trans3:probe20	-0.0721957128680575	0.104732023703427	-0.68933751411599	0.490737116899038