fitVsDatCorrelation=0.906620662319447
cont.fitVsDatCorrelation=0.251534761624252

fstatistic=7800.66566796003,57,807
cont.fstatistic=1471.16101524272,57,807

residuals=-0.920605105208071,-0.0991392201165574,-0.00215397552863098,0.100059062282874,1.34958395421824
cont.residuals=-0.965679411880395,-0.29692358537075,-0.0528137591047561,0.22322826483702,1.78245818818355

predictedValues:
Include	Exclude	Both
Lung	58.3414796130003	84.811136624474	104.078738784761
cerebhem	64.8711724241387	84.5081588706641	125.106135101669
cortex	56.6358029182174	72.9578751621637	227.269310655342
heart	55.3688977456583	69.3638945051798	174.551525296603
kidney	58.1934657527443	84.4222650899848	106.619180781378
liver	61.30143506554	82.903487557688	79.0917621164785
stomach	59.2941538313954	70.1819624510722	102.068001965759
testicle	56.5835328025263	69.926016679744	92.9999733463036


diffExp=-26.4696570114736,-19.6369864465254,-16.3220722439463,-13.9949967595215,-26.2287993372405,-21.602052492148,-10.8878086196768,-13.3424838772178
diffExpScore=0.993310359179593
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=-1,0,0,0,-1,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=-1,-1,0,0,-1,-1,0,0
diffExp1.3Score=0.8
diffExp1.2=-1,-1,-1,-1,-1,-1,0,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	74.6381226626129	77.1181017625006	79.472472470814
cerebhem	71.17870826494	71.9418656271798	81.1244600295078
cortex	71.5242833484301	77.3189166379814	75.4136234651299
heart	71.8098469387038	73.34228169505	71.553619185953
kidney	76.3703399658154	65.3280786938059	81.1675227397661
liver	75.2468017630181	84.8714216091988	74.8641205575916
stomach	71.6873956550407	69.5405787744666	66.6065043500439
testicle	69.6531128462072	85.9541559908517	79.232124580083
cont.diffExp=-2.47997909988771,-0.763157362239838,-5.79463328955124,-1.53243475634625,11.0422612720095,-9.62461984618074,2.14681688057406,-16.3010431446445
cont.diffExpScore=2.04407686032236

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,-1
cont.diffExp1.2Score=0.5

tran.correlation=0.633567315336264
cont.tran.correlation=-0.264097626059262

tran.covariance=0.00310626764398000
cont.tran.covariance=-0.000849208421663271

tran.mean=68.104046068387
cont.tran.mean=74.2202507647377

weightedLogRatios:
wLogRatio
Lung	-1.59124332703197
cerebhem	-1.13833564403279
cortex	-1.05430806280297
heart	-0.929943729308756
kidney	-1.58117328247463
liver	-1.28801740607641
stomach	-0.702441891882356
testicle	-0.876853941513888

cont.weightedLogRatios:
wLogRatio
Lung	-0.141500255595109
cerebhem	-0.0455436167915170
cortex	-0.335677147871696
heart	-0.0904717343534505
kidney	0.664905529802957
liver	-0.527309418220925
stomach	0.129435339878842
testicle	-0.914467585523667

varWeightedLogRatios=0.104587453556961
cont.varWeightedLogRatios=0.216781676372014

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.77091629838876	0.0898816128930838	41.9542571279217	5.48662854074114e-205	***
df.mm.trans1	0.163049846579623	0.0780503485539524	2.08903418883408	0.0370173840842468	*  
df.mm.trans2	0.710458364003853	0.0693755476492898	10.2407604419268	3.19073934148045e-23	***
df.mm.exp2	-0.081503504888111	0.0901670023979938	-0.903917206078977	0.366309052252299	   
df.mm.exp3	-0.961204609117438	0.0901670023979938	-10.6602702047775	6.48448276475773e-25	***
df.mm.exp4	-0.770427940271908	0.0901670023979938	-8.54445550791706	6.4026412600359e-17	***
df.mm.exp5	-0.0312516524139054	0.0901670023979938	-0.346597442332193	0.728984120190842	   
df.mm.exp6	0.301279187810822	0.0901670023979937	3.34134638834934	0.000872068128386913	***
df.mm.exp7	-0.153629701310896	0.0901670023979938	-1.70383507519503	0.0887968761211064	.  
df.mm.exp8	-0.111035896929385	0.0901670023979938	-1.23144713671722	0.218514410591832	   
df.mm.trans1:exp2	0.187593519377319	0.0838683291693007	2.23676233013577	0.0255742942369648	*  
df.mm.trans2:exp2	0.0779247269786798	0.0641148876175506	1.21539208558714	0.224572028374843	   
df.mm.trans1:exp3	0.931532628723357	0.0838683291693007	11.1070846164459	9.01680435510797e-27	***
df.mm.trans2:exp3	0.810659968839942	0.0641148876175506	12.6438647709340	1.46028852284964e-33	***
df.mm.trans1:exp4	0.718132637827122	0.0838683291693007	8.56262006099423	5.54203507375367e-17	***
df.mm.trans2:exp4	0.569367558025976	0.0641148876175506	8.88042667129497	4.26140909912228e-18	***
df.mm.trans1:exp5	0.0287114025449185	0.0838683291693007	0.342339031065711	0.732184904914435	   
df.mm.trans2:exp5	0.0266559613153937	0.0641148876175506	0.415753069308930	0.677701231657735	   
df.mm.trans1:exp6	-0.251789260625603	0.0838683291693007	-3.00219717168001	0.00276305567869995	** 
df.mm.trans2:exp6	-0.324028919394925	0.0641148876175506	-5.05387955021894	5.35843830982383e-07	***
df.mm.trans1:exp7	0.169827090123758	0.0838683291693007	2.02492516312011	0.0432038454389384	*  
df.mm.trans2:exp7	-0.0357058281086898	0.0641148876175506	-0.556903855492618	0.577747528588906	   
df.mm.trans1:exp8	0.0804405739088514	0.0838683291693007	0.959129324568635	0.337780861108504	   
df.mm.trans2:exp8	-0.0819531868077031	0.0641148876175506	-1.2782239796872	0.201537978540051	   
df.mm.trans1:probe2	0.557516552144274	0.0549047095468276	10.1542573805763	7.02189262471607e-23	***
df.mm.trans1:probe3	0.505712165557736	0.0549047095468276	9.2107247216456	2.73805524339054e-19	***
df.mm.trans1:probe4	0.0195854903949923	0.0549047095468276	0.356717858206462	0.721396284138234	   
df.mm.trans1:probe5	-0.00855856015043855	0.0549047095468276	-0.155880255465864	0.876166392268772	   
df.mm.trans1:probe6	-0.0195293980280582	0.0549047095468276	-0.355696226958486	0.722161026780496	   
df.mm.trans1:probe7	0.278018146073233	0.0549047095468276	5.06364842593538	5.09892065339492e-07	***
df.mm.trans1:probe8	-0.130611103464252	0.0549047095468276	-2.37886885373385	0.0175975465812376	*  
df.mm.trans1:probe9	0.370308092030575	0.0549047095468276	6.74455971240033	2.92574967314149e-11	***
df.mm.trans1:probe10	0.169265528382148	0.0549047095468276	3.08289634494439	0.00211971329070077	** 
df.mm.trans1:probe11	-0.123940738815443	0.0549047095468276	-2.25737900880317	0.0242509608074194	*  
df.mm.trans1:probe12	0.0619716621563266	0.0549047095468276	1.12871304971519	0.259354324718355	   
df.mm.trans1:probe13	-0.128196325074644	0.0549047095468276	-2.33488759220750	0.0197936270458126	*  
df.mm.trans1:probe14	-0.170797870898084	0.0549047095468276	-3.11080547202262	0.00193141990084626	** 
df.mm.trans1:probe15	-0.124511815682890	0.0549047095468276	-2.2677802452756	0.0236061953421599	*  
df.mm.trans1:probe16	-0.0280477848153185	0.0549047095468276	-0.51084478994278	0.609599459250596	   
df.mm.trans1:probe17	0.647125174107877	0.0549047095468276	11.7863327107841	1.06784242151150e-29	***
df.mm.trans1:probe18	0.603417879695779	0.0549047095468276	10.9902754185619	2.79136975201646e-26	***
df.mm.trans1:probe19	0.349960032836336	0.0549047095468276	6.37395290358214	3.09577233842976e-10	***
df.mm.trans1:probe20	0.60552301789302	0.0549047095468276	11.0286170875119	1.92814982303274e-26	***
df.mm.trans1:probe21	0.38474495146049	0.0549047095468276	7.00750363012749	5.12207100552152e-12	***
df.mm.trans1:probe22	0.151460335557501	0.0549047095468276	2.75860371191514	0.00593625836455134	** 
df.mm.trans2:probe2	0.115618665822965	0.0549047095468276	2.10580598235120	0.0355295717863279	*  
df.mm.trans2:probe3	-0.158198908054568	0.0549047095468276	-2.88133585188428	0.0040649512352261	** 
df.mm.trans2:probe4	-0.00149698873918667	0.0549047095468276	-0.0272652155259997	0.978254941305041	   
df.mm.trans2:probe5	-0.254188628833144	0.0549047095468276	-4.62963252025493	4.26759744824956e-06	***
df.mm.trans2:probe6	-0.275003341578455	0.0549047095468276	-5.00873866464785	6.73275090023011e-07	***
df.mm.trans3:probe2	0.068975320536432	0.0549047095468276	1.25627329796916	0.209380446577731	   
df.mm.trans3:probe3	-0.192052656044039	0.0549047095468276	-3.49792681956071	0.000494537254626165	***
df.mm.trans3:probe4	0.430508833639034	0.0549047095468276	7.8410183241541	1.41306315670663e-14	***
df.mm.trans3:probe5	0.156750055116972	0.0549047095468276	2.85494735170726	0.0044147723649799	** 
df.mm.trans3:probe6	-0.12135553374086	0.0549047095468276	-2.21029370235275	0.0273646647826294	*  
df.mm.trans3:probe7	-0.0614327738490842	0.0549047095468276	-1.11889807552281	0.263516556333269	   
df.mm.trans3:probe8	-0.275607586375831	0.0549047095468276	-5.01974400102724	6.3692795323922e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.60221057936149	0.206167683213115	22.3226574972189	2.45936877943460e-86	***
df.mm.trans1	-0.270152651704985	0.179029492433403	-1.50898406755791	0.131694250058459	   
df.mm.trans2	-0.249179773925500	0.159131500538479	-1.56587333797715	0.117770291882808	   
df.mm.exp2	-0.137511152249313	0.206822300894604	-0.664875845856621	0.506319856305556	   
df.mm.exp3	0.0124089840887432	0.206822300894604	0.0599982885552887	0.952171852226547	   
df.mm.exp4	0.0161330598800311	0.206822300894604	0.0780044502466513	0.937843849214314	   
df.mm.exp5	-0.164077598058291	0.206822300894604	-0.793326432152516	0.427820845110335	   
df.mm.exp6	0.16365734425201	0.206822300894604	0.791294476195819	0.42900467274355	   
df.mm.exp7	0.0328444163072536	0.206822300894605	0.158805003934227	0.873862238470795	   
df.mm.exp8	0.0423809094210573	0.206822300894604	0.204914601751067	0.837690518773648	   
df.mm.trans1:exp2	0.0900534803942271	0.192374597687266	0.468115237026369	0.639828582920497	   
df.mm.trans2:exp2	0.0680314873561582	0.147064760122888	0.462595439582609	0.64377908074638	   
df.mm.trans1:exp3	-0.0550233691268111	0.192374597687266	-0.286022010121418	0.77493466789472	   
df.mm.trans2:exp3	-0.0098083771780546	0.147064760122888	-0.0666942724406492	0.946841613294969	   
df.mm.trans1:exp4	-0.0547628535646625	0.192374597687266	-0.284667800338627	0.775971694554445	   
df.mm.trans2:exp4	-0.066333822449576	0.147064760122888	-0.451051784221776	0.652073398994585	   
df.mm.trans1:exp5	0.187020594182488	0.192374597687266	0.972168864449132	0.331257841951819	   
df.mm.trans2:exp5	-0.00183849869375136	0.147064760122888	-0.0125012864551311	0.99002876613156	   
df.mm.trans1:exp6	-0.155535347342891	0.192374597687266	-0.808502521708904	0.419039630891172	   
df.mm.trans2:exp6	-0.0678579559021994	0.147064760122888	-0.461415473329553	0.644624892896478	   
df.mm.trans1:exp7	-0.073180881300295	0.192374597687267	-0.380408235703039	0.703742506468606	   
df.mm.trans2:exp7	-0.136272002786064	0.147064760122888	-0.926612212689125	0.354404937780503	   
df.mm.trans1:exp8	-0.111504921827446	0.192374597687266	-0.579623937712991	0.562330018994392	   
df.mm.trans2:exp8	0.0660951389024739	0.147064760122888	0.4494288016194	0.65324303585435	   
df.mm.trans1:probe2	0.00759686591928025	0.125938736526939	0.0603219162648596	0.951914181940404	   
df.mm.trans1:probe3	-0.193901941682142	0.125938736526939	-1.53965290608315	0.124036975212654	   
df.mm.trans1:probe4	-0.136595926841755	0.125938736526939	-1.08462202026726	0.278413012664084	   
df.mm.trans1:probe5	-0.0192989934205302	0.125938736526939	-0.153241123047173	0.87824643758667	   
df.mm.trans1:probe6	0.0716806837862389	0.125938736526939	0.569171056999653	0.569398435704263	   
df.mm.trans1:probe7	-0.180305349376172	0.125938736526939	-1.43169095028679	0.152619455545389	   
df.mm.trans1:probe8	-0.0203193156774682	0.125938736526939	-0.161342857946822	0.87186375237984	   
df.mm.trans1:probe9	0.126101244580077	0.125938736526939	1.00129037385652	0.316986578722575	   
df.mm.trans1:probe10	0.0456329665599841	0.125938736526939	0.362342578768231	0.717190910758904	   
df.mm.trans1:probe11	-0.0314294649895486	0.125938736526939	-0.249561539652461	0.802989952547922	   
df.mm.trans1:probe12	-0.100144591346898	0.125938736526939	-0.79518497730423	0.42673971784111	   
df.mm.trans1:probe13	-0.145577882965573	0.125938736526939	-1.15594206342091	0.248046958209822	   
df.mm.trans1:probe14	-0.0889972986584206	0.125938736526939	-0.706671363495721	0.47997471253099	   
df.mm.trans1:probe15	-0.105868998813445	0.125938736526939	-0.840638883103287	0.400799267076449	   
df.mm.trans1:probe16	0.00173589855618995	0.125938736526939	0.0137836745393950	0.989005973918181	   
df.mm.trans1:probe17	-0.0358557938079242	0.125938736526939	-0.284708222400297	0.775940734346555	   
df.mm.trans1:probe18	0.120762759176286	0.125938736526939	0.958900831520203	0.337895896751394	   
df.mm.trans1:probe19	0.0840492010410405	0.125938736526939	0.667381644114412	0.504719176472391	   
df.mm.trans1:probe20	-0.0103194867532076	0.125938736526939	-0.0819405294811752	0.93471432314935	   
df.mm.trans1:probe21	0.0607361736065371	0.125938736526939	0.482267611074097	0.629746627366541	   
df.mm.trans1:probe22	-0.031876448732091	0.125938736526939	-0.253110755365347	0.800247089758841	   
df.mm.trans2:probe2	-0.0421341367610404	0.125938736526939	-0.334560580191525	0.738043517343731	   
df.mm.trans2:probe3	-0.0669901074004006	0.125938736526939	-0.531926151141518	0.594923543621589	   
df.mm.trans2:probe4	-0.0782545166937672	0.125938736526939	-0.621369713972221	0.534531869871413	   
df.mm.trans2:probe5	-0.0298441304081956	0.125938736526939	-0.236973398584253	0.812737568287182	   
df.mm.trans2:probe6	0.109524118094156	0.125938736526939	0.869661877787126	0.384743798220174	   
df.mm.trans3:probe2	0.319905933537871	0.125938736526939	2.54017105745253	0.0112662366045827	*  
df.mm.trans3:probe3	0.261448114712496	0.125938736526939	2.07599442334067	0.0382105605182467	*  
df.mm.trans3:probe4	0.289153327109140	0.125938736526939	2.29598402432192	0.0219323099076922	*  
df.mm.trans3:probe5	0.322984897651128	0.125938736526939	2.56461916768586	0.0105087242854485	*  
df.mm.trans3:probe6	0.394255191652396	0.125938736526939	3.13053157848746	0.00180775217291147	** 
df.mm.trans3:probe7	0.411293711691182	0.125938736526939	3.26582370947641	0.00113751204047543	** 
df.mm.trans3:probe8	0.34121922156024	0.125938736526939	2.70940642228255	0.00688329422045306	**