chrX.25976_chrX_54598672_54612639_+_2.R 

fitVsDatCorrelation=0.74853830873336
cont.fitVsDatCorrelation=0.261100065085220

fstatistic=11563.5000765298,53,715
cont.fstatistic=5449.21118376054,53,715

residuals=-0.531932993678821,-0.0873983764252825,-0.00338873169240999,0.0842814431078483,0.516669826505077
cont.residuals=-0.456271520419945,-0.135728643915577,-0.0411353241367568,0.104185232022405,0.698681683543487

predictedValues:
Include	Exclude	Both
chrX.25976_chrX_54598672_54612639_+_2.R.tl.Lung	52.9459952251942	60.7675946570833	49.423131297435
chrX.25976_chrX_54598672_54612639_+_2.R.tl.cerebhem	59.106026276851	56.8639975959844	68.0071260084314
chrX.25976_chrX_54598672_54612639_+_2.R.tl.cortex	55.7146798920419	49.7486814252939	56.0808389158892
chrX.25976_chrX_54598672_54612639_+_2.R.tl.heart	51.7485415684224	53.9442857276368	51.8217586731654
chrX.25976_chrX_54598672_54612639_+_2.R.tl.kidney	52.1943091173953	63.320919700611	47.917438929653
chrX.25976_chrX_54598672_54612639_+_2.R.tl.liver	51.8045321174109	58.2431431451003	52.2250664149437
chrX.25976_chrX_54598672_54612639_+_2.R.tl.stomach	51.4543953958413	53.0037267171541	47.726343135693
chrX.25976_chrX_54598672_54612639_+_2.R.tl.testicle	51.2470792546589	59.7082514682701	54.7080970088813


diffExp=-7.82159943188918,2.24202868086660,5.96599846674805,-2.19574415921444,-11.1266105832157,-6.43861102768946,-1.54933132131283,-8.46117221361119
diffExpScore=1.50735669556065
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,-1,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	53.3630916269208	50.5043048590082	53.0472093046049
cerebhem	55.9461076940913	50.273889063388	53.6999068361308
cortex	54.5784671355678	52.451706083253	54.6522876539766
heart	51.956911182342	60.688831992465	56.6670755242299
kidney	54.2341380434766	52.9682176182817	57.0167154090121
liver	53.6016603644746	57.4927340860567	53.8468025985733
stomach	52.6968549611661	55.9616864969869	51.5766883337523
testicle	54.9060163806824	54.183392095711	57.0774935834076
cont.diffExp=2.85878676791261,5.67221863070328,2.12676105231483,-8.73192081012297,1.26592042519489,-3.89107372158215,-3.26483153582080,0.722624284971403
cont.diffExpScore=6.72734573804596

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=-0.253446532564095
cont.tran.correlation=-0.732644674429845

tran.covariance=-0.00102858071408846
cont.tran.covariance=-0.00112194518765089

tran.mean=55.1135099553094
cont.tran.mean=54.113000605242

weightedLogRatios:
wLogRatio
Lung	-0.556395385994778
cerebhem	0.157002045582717
cortex	0.448917734225681
heart	-0.164857973583382
kidney	-0.782939426798935
liver	-0.469303917549257
stomach	-0.117346059753363
testicle	-0.613242796511162

cont.weightedLogRatios:
wLogRatio
Lung	0.217467375977546
cerebhem	0.424505221092868
cortex	0.158182045919476
heart	-0.625743008550543
kidney	0.0940370779404947
liver	-0.281478783070180
stomach	-0.240122136109226
testicle	0.0529806857520789

varWeightedLogRatios=0.176531490637081
cont.varWeightedLogRatios=0.112514700811384

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03225097472524	0.0752460038299461	53.5875763427652	1.37023248010305e-252	***
df.mm.trans1	0.0127677662876338	0.0676505812138086	0.188731065698926	0.850357157818576	   
df.mm.trans2	-0.0288341710245482	0.0621213136817565	-0.464159067405814	0.642675071928418	   
df.mm.exp2	-0.275527738388540	0.0850631120133809	-3.23909779300337	0.00125453554711295	** 
df.mm.exp3	-0.275477118447166	0.0850631120133808	-3.23850270612992	0.00125711731096593	** 
df.mm.exp4	-0.189372635844735	0.0850631120133808	-2.22626037729428	0.0263076231384198	*  
df.mm.exp5	0.0577991792572379	0.0850631120133808	0.679485829864134	0.497049992119581	   
df.mm.exp6	-0.119369110657782	0.0850631120133809	-1.40330053571288	0.16096135714875	   
df.mm.exp7	-0.130335937713350	0.0850631120133808	-1.53222630383953	0.125908918655763	   
df.mm.exp8	-0.151793412421737	0.0850631120133808	-1.78447988592116	0.0747696367842596	.  
df.mm.trans1:exp2	0.385588189015397	0.0815896708129706	4.72594367857284	2.75869369290268e-06	***
df.mm.trans2:exp2	0.209133483811559	0.0708291952348136	2.95264520679978	0.00325389190502183	** 
df.mm.trans1:exp3	0.326448347429609	0.0815896708129705	4.00109896481790	6.96196039112866e-05	***
df.mm.trans2:exp3	0.075404413413698	0.0708291952348136	1.06459508912556	0.287418587208312	   
df.mm.trans1:exp4	0.166496449294102	0.0815896708129706	2.04065597562913	0.0416518434832257	*  
df.mm.trans2:exp4	0.0702677397358043	0.0708291952348136	0.992073106334924	0.321497475158069	   
df.mm.trans1:exp5	-0.0720981470976683	0.0815896708129705	-0.883667581683718	0.377172710152325	   
df.mm.trans2:exp5	-0.0166400839761312	0.0708291952348136	-0.234932557414579	0.814328343847559	   
df.mm.trans1:exp6	0.0975743126637968	0.0815896708129706	1.19591501830505	0.232126173800817	   
df.mm.trans2:exp6	0.0769388176548832	0.0708291952348136	1.08625853223116	0.277730703901647	   
df.mm.trans1:exp7	0.101759390732393	0.0815896708129705	1.24720923271841	0.212729157214452	   
df.mm.trans2:exp7	-0.00635850006117798	0.0708291952348136	-0.089772304204477	0.928493297961135	   
df.mm.trans1:exp8	0.119179602616979	0.0815896708129705	1.46071924827564	0.144531781543486	   
df.mm.trans2:exp8	0.134206974545678	0.0708291952348136	1.89479739393839	0.0585224395848896	.  
df.mm.trans1:probe2	-0.0907798487689451	0.0407948354064853	-2.22527797610659	0.0263737730912717	*  
df.mm.trans1:probe3	-0.108021130434098	0.0407948354064853	-2.64791190742066	0.00827759758035051	** 
df.mm.trans1:probe4	-0.131592544480807	0.0407948354064853	-3.22571578410847	0.00131379452178083	** 
df.mm.trans1:probe5	-0.166804959987526	0.0407948354064853	-4.08887444514626	4.82695494444856e-05	***
df.mm.trans1:probe6	-0.159747850116990	0.0407948354064853	-3.91588416830808	9.86982553903938e-05	***
df.mm.trans1:probe7	-0.139180154319535	0.0407948354064853	-3.41171015724723	0.000681887831980156	***
df.mm.trans1:probe8	-0.0338922478900548	0.0407948354064853	-0.830797515233188	0.406365359514434	   
df.mm.trans1:probe9	-0.0650715083699611	0.0407948354064853	-1.59509182281482	0.111133597180381	   
df.mm.trans1:probe10	-0.095097986860706	0.0407948354064853	-2.3311280928857	0.0200236682985302	*  
df.mm.trans1:probe11	-0.046188142508986	0.0407948354064853	-1.13220563457999	0.257927545527487	   
df.mm.trans1:probe12	-0.140688531501132	0.0407948354064853	-3.44868486658403	0.000596343175488881	***
df.mm.trans1:probe13	-0.173457443031318	0.0407948354064853	-4.25194614227424	2.40050920131052e-05	***
df.mm.trans1:probe14	-0.140726103733126	0.0407948354064853	-3.44960587120678	0.000594346037280063	***
df.mm.trans1:probe15	-0.0871223508743998	0.0407948354064853	-2.13562207093866	0.0330493654504642	*  
df.mm.trans1:probe16	-0.208740700124734	0.0407948354064853	-5.11684133652737	3.9962084854767e-07	***
df.mm.trans1:probe17	-0.172508972360408	0.0407948354064853	-4.2286963690748	2.65570832204321e-05	***
df.mm.trans1:probe18	0.195664425745054	0.0407948354064853	4.79630384080307	1.96760268715992e-06	***
df.mm.trans1:probe19	-0.196376030136887	0.0407948354064853	-4.81374733296922	1.80826209701172e-06	***
df.mm.trans1:probe20	0.286508469548021	0.0407948354064853	7.02315542379841	5.05738012874799e-12	***
df.mm.trans1:probe21	0.0466492604976023	0.0407948354064853	1.14350897687863	0.253210039619715	   
df.mm.trans1:probe22	-0.154780280462663	0.0407948354064853	-3.79411459613482	0.000160711665457806	***
df.mm.trans1:probe23	-0.187449298959995	0.0407948354064853	-4.59492720321641	5.1166452930828e-06	***
df.mm.trans2:probe2	0.38928275582285	0.0407948354064853	9.5424519291225	2.14739081982217e-20	***
df.mm.trans2:probe3	-0.083658679047586	0.0407948354064853	-2.05071740611280	0.0406584750690869	*  
df.mm.trans2:probe4	0.335359988234355	0.0407948354064853	8.22064815050195	9.51427857354803e-16	***
df.mm.trans2:probe5	0.279592111271613	0.0407948354064853	6.85361537767513	1.55636813567678e-11	***
df.mm.trans2:probe6	0.0121825688662612	0.0407948354064853	0.298630175728677	0.765309006820062	   
df.mm.trans3:probe2	-0.240699847943113	0.0407948354064853	-5.90025294978513	5.60159899131936e-09	***
df.mm.trans3:probe3	-0.106598183555481	0.0407948354064853	-2.6130313431424	0.00916293343374368	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94686512157866	0.109541210688285	36.0308699965898	6.95968248788104e-163	***
df.mm.trans1	0.0064562567939786	0.0984839884211573	0.0655564107169283	0.94774931854955	   
df.mm.trans2	0.00333270496531487	0.0904346219584646	0.0368520915235931	0.970613225391072	   
df.mm.exp2	0.0304677786885089	0.123832706065258	0.24603983597397	0.805722007721403	   
df.mm.exp3	0.0305455607650555	0.123832706065258	0.246667958212579	0.805236008265566	   
df.mm.exp4	0.0909854170000373	0.123832706065258	0.734744639692276	0.462735931392294	   
df.mm.exp5	-0.00833750583369796	0.123832706065258	-0.0673287865429044	0.946338783079586	   
df.mm.exp6	0.119099937203936	0.123832706065258	0.961780946151432	0.33648479241778	   
df.mm.exp7	0.118157610898074	0.123832706065258	0.954171273910519	0.340319421340287	   
df.mm.exp8	0.0255918343689569	0.123832706065258	0.206664581451289	0.836330657164184	   
df.mm.trans1:exp2	0.0168017471087131	0.118776159072976	0.141457235524766	0.88754858706858	   
df.mm.trans2:exp2	-0.0350405178526162	0.103111333535172	-0.339831875422924	0.734082944352558	   
df.mm.trans1:exp3	-0.00802546957105531	0.118776159072976	-0.067568017299873	0.946148405421408	   
df.mm.trans2:exp3	0.00728872396993657	0.103111333535172	0.0706879032599294	0.943665914719309	   
df.mm.trans1:exp4	-0.117690011996162	0.118776159072976	-0.990855512711547	0.322091333388265	   
df.mm.trans2:exp4	0.0927156998121518	0.103111333535172	0.899180493873893	0.368859244924431	   
df.mm.trans1:exp5	0.0245287302414839	0.118776159072976	0.206512236402706	0.836449599453027	   
df.mm.trans2:exp5	0.0559709945526902	0.103111333535172	0.542820974510993	0.587422232594367	   
df.mm.trans1:exp6	-0.114639231622018	0.118776159072976	-0.965170388710613	0.334785802238024	   
df.mm.trans2:exp6	0.0105000615130237	0.103111333535172	0.101832273456555	0.918918358635922	   
df.mm.trans1:exp7	-0.130721174252025	0.118776159072976	-1.10056744781341	0.271455340601818	   
df.mm.trans2:exp7	-0.0155489013915052	0.103111333535172	-0.150797209757755	0.880178219536536	   
df.mm.trans1:exp8	0.00291175716856153	0.118776159072976	0.0245146601076109	0.980448930124953	   
df.mm.trans2:exp8	0.0447240308414355	0.103111333535172	0.433745053119498	0.664604423950096	   
df.mm.trans1:probe2	-0.00790486699147896	0.0593880795364881	-0.133105280608076	0.894147570742013	   
df.mm.trans1:probe3	0.105746941415781	0.0593880795364881	1.78060887371868	0.0754008868504633	.  
df.mm.trans1:probe4	0.0130634399317976	0.0593880795364881	0.219967374492577	0.825959350501757	   
df.mm.trans1:probe5	0.0688174850918787	0.0593880795364881	1.15877606464101	0.246934343417706	   
df.mm.trans1:probe6	-0.045903426753711	0.0593880795364881	-0.772940076728831	0.439813284236812	   
df.mm.trans1:probe7	-0.0304931910829553	0.0593880795364881	-0.513456426288718	0.607790703092515	   
df.mm.trans1:probe8	0.0306298339675079	0.0593880795364881	0.515757273287292	0.606183387577093	   
df.mm.trans1:probe9	-0.0146464669587382	0.0593880795364881	-0.246623010426519	0.805270783388065	   
df.mm.trans1:probe10	0.085800230502574	0.0593880795364881	1.44473825677185	0.148969502693647	   
df.mm.trans1:probe11	0.0736234267004298	0.0593880795364881	1.23970041252463	0.215492948283733	   
df.mm.trans1:probe12	0.041756151049654	0.0593880795364881	0.70310660616663	0.482218183944889	   
df.mm.trans1:probe13	0.069796599307313	0.0593880795364881	1.17526277751463	0.240280817170332	   
df.mm.trans1:probe14	0.0635589119138644	0.0593880795364881	1.07023012715563	0.284876881136146	   
df.mm.trans1:probe15	0.0506812489396474	0.0593880795364881	0.85339093863961	0.393728341557847	   
df.mm.trans1:probe16	0.0254996877163263	0.0593880795364881	0.429373839251011	0.667780540430008	   
df.mm.trans1:probe17	-0.0304409575024039	0.0593880795364881	-0.5125768965757	0.60840562388971	   
df.mm.trans1:probe18	0.0798996860832472	0.0593880795364881	1.34538255331454	0.178928238502805	   
df.mm.trans1:probe19	-0.00216152802689515	0.0593880795364881	-0.0363966648486604	0.970976232386642	   
df.mm.trans1:probe20	0.0779735052410709	0.0593880795364881	1.31294875755603	0.189621444516108	   
df.mm.trans1:probe21	0.0241273455509509	0.0593880795364881	0.406265798444065	0.684668875640748	   
df.mm.trans1:probe22	-0.00370624907438721	0.0593880795364881	-0.0624072895320699	0.950255931236289	   
df.mm.trans1:probe23	-0.0569708316404473	0.0593880795364881	-0.959297422733536	0.337733204117244	   
df.mm.trans2:probe2	-0.0195898935775141	0.0593880795364881	-0.329862385354253	0.74160056321449	   
df.mm.trans2:probe3	-0.0440911691121822	0.0593880795364881	-0.74242456493466	0.458073967679317	   
df.mm.trans2:probe4	-0.0278387546747592	0.0593880795364881	-0.468759974931587	0.639384096337156	   
df.mm.trans2:probe5	-0.0780808926283031	0.0593880795364881	-1.314756988906	0.189013129156447	   
df.mm.trans2:probe6	-0.0836525324737711	0.0593880795364881	-1.40857446690754	0.159395725570234	   
df.mm.trans3:probe2	0.0402135951218543	0.0593880795364881	0.677132438625954	0.498540947248102	   
df.mm.trans3:probe3	0.00337093088499147	0.0593880795364881	0.056761069078187	0.954751384190646