chrX.26031_chrX_141400611_141411386_+_2.R 

fitVsDatCorrelation=0.89260491434281
cont.fitVsDatCorrelation=0.266474218061932

fstatistic=12365.1354176918,63,945
cont.fstatistic=2693.6820586817,63,945

residuals=-0.534249085278313,-0.0870389225336034,0.000782339463464762,0.0836947945260195,0.869501501991924
cont.residuals=-0.644209130394721,-0.235182399481154,-0.0383791120217903,0.192598268572363,1.02129459910829

predictedValues:
Include	Exclude	Both
chrX.26031_chrX_141400611_141411386_+_2.R.tl.Lung	70.8009199636652	47.1400120258062	76.9339024505984
chrX.26031_chrX_141400611_141411386_+_2.R.tl.cerebhem	60.6050647605855	46.1890417783213	63.5413760520266
chrX.26031_chrX_141400611_141411386_+_2.R.tl.cortex	65.9340967679058	45.8214277252422	67.9481249358254
chrX.26031_chrX_141400611_141411386_+_2.R.tl.heart	68.0498887130423	46.717077451915	72.0696905227705
chrX.26031_chrX_141400611_141411386_+_2.R.tl.kidney	65.3490332709044	48.1958844548378	72.8535070376856
chrX.26031_chrX_141400611_141411386_+_2.R.tl.liver	75.9119238559326	47.2023825226425	79.421898528495
chrX.26031_chrX_141400611_141411386_+_2.R.tl.stomach	71.4672833179424	47.8745235422142	70.9370522668012
chrX.26031_chrX_141400611_141411386_+_2.R.tl.testicle	63.4497825771042	47.2567920330126	65.9396949883023


diffExp=23.6609079378591,14.4160229822643,20.1126690426636,21.3328112611274,17.1531488160666,28.7095413332901,23.5927597757282,16.1929905440916
diffExpScore=0.993982097402696
diffExp1.5=1,0,0,0,0,1,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,0,1,1,0,1,1,0
diffExp1.4Score=0.833333333333333
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	62.5292318301239	64.0394177078122	74.4485990790694
cerebhem	61.532855905359	69.0694660879254	61.2590989572293
cortex	62.9705840232561	60.6928392478858	53.3459168023394
heart	63.78308253472	63.7169852669087	61.0156132077154
kidney	62.6416699554829	58.8707744003439	55.3379666665254
liver	58.4745850950495	65.9166606860436	59.6887180751417
stomach	63.8444287532607	67.4762614159388	55.9002053481739
testicle	67.9756982806067	58.8509256022585	55.5591424405949
cont.diffExp=-1.51018587768833,-7.53661018256638,2.27774477537036,0.0660972678113225,3.77089555513901,-7.44207559099418,-3.63183266267804,9.1247726783482
cont.diffExpScore=6.01242100950672

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=0.337805440921187
cont.tran.correlation=-0.521167298229248

tran.covariance=0.000423798201264757
cont.tran.covariance=-0.00132976709731705

tran.mean=57.3728209225671
cont.tran.mean=63.274091674561

weightedLogRatios:
wLogRatio
Lung	1.64997954306853
cerebhem	1.07800345962387
cortex	1.45805502230157
heart	1.51662743645981
kidney	1.22625143301084
liver	1.94423382702284
stomach	1.63023497246521
testicle	1.17947065335875

cont.weightedLogRatios:
wLogRatio
Lung	-0.0989801757350333
cerebhem	-0.482656272629924
cortex	0.151945396365457
heart	0.00430795318794768
kidney	0.254948916530477
liver	-0.49458922205069
stomach	-0.231492430057656
testicle	0.597771157069947

varWeightedLogRatios=0.0831457807545693
cont.varWeightedLogRatios=0.139320088123436

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.31733814974190	0.0755405258932919	43.9146816958616	1.96231943698351e-230	***
df.mm.trans1	1.21368260172316	0.0676228321697162	17.9478226921511	3.30836840283637e-62	***
df.mm.trans2	0.499485007673978	0.06075539438767	8.221245417104	6.60787688421774e-16	***
df.mm.exp2	0.0153822568306287	0.0816425527794332	0.188409797427398	0.850595871418054	   
df.mm.exp3	0.0246154962681804	0.0816425527794332	0.301503265517456	0.763097092655519	   
df.mm.exp4	0.0166697973504345	0.0816425527794332	0.204180256287061	0.838256602651473	   
df.mm.exp5	-0.00348189608823405	0.0816425527794332	-0.0426480550852053	0.965991097052438	   
df.mm.exp6	0.0391965061798569	0.0816425527794332	0.480098978356921	0.631268120686839	   
df.mm.exp7	0.105982864747332	0.0816425527794332	1.29813266659676	0.194558480987379	   
df.mm.exp8	0.0470570421828802	0.0816425527794332	0.576378868382647	0.564496375103558	   
df.mm.trans1:exp2	-0.170875784792697	0.0787430701231014	-2.17004219578386	0.0302521675512039	*  
df.mm.trans2:exp2	-0.0357618304388553	0.0646940336304506	-0.552784058003499	0.580542016171807	   
df.mm.trans1:exp3	-0.0958317813807096	0.0787430701231014	-1.21701860533115	0.223900979149314	   
df.mm.trans2:exp3	-0.0529858129110638	0.0646940336304506	-0.819021630553024	0.412980566384839	   
df.mm.trans1:exp4	-0.0563006980770596	0.0787430701231014	-0.714992417606312	0.474790323571951	   
df.mm.trans2:exp4	-0.0256821679428419	0.0646940336304506	-0.396978925283052	0.691472638179778	   
df.mm.trans1:exp5	-0.0766474515034504	0.0787430701231014	-0.973386627974058	0.330610269395284	   
df.mm.trans2:exp5	0.0256333761286293	0.0646940336304506	0.396224731867145	0.69202869459456	   
df.mm.trans1:exp6	0.0305052709817054	0.0787430701231014	0.387402611226811	0.698545404712038	   
df.mm.trans2:exp6	-0.0378742903039199	0.0646940336304506	-0.585437144331853	0.558393590310955	   
df.mm.trans1:exp7	-0.09661509019237	0.0787430701231014	-1.22696625926991	0.220140862629415	   
df.mm.trans2:exp7	-0.0905215220331679	0.0646940336304506	-1.39922519826559	0.16207360376855	   
df.mm.trans1:exp8	-0.156680269161473	0.0787430701231014	-1.98976581579218	0.0469045733150004	*  
df.mm.trans2:exp8	-0.044582804459546	0.0646940336304506	-0.689133169748153	0.490908630914396	   
df.mm.trans1:probe2	-0.677109897456453	0.0431293557536337	-15.6995133737745	1.57025751782318e-49	***
df.mm.trans1:probe3	-0.648762019382913	0.0431293557536337	-15.0422376603261	5.25422194587169e-46	***
df.mm.trans1:probe4	0.238316556623823	0.0431293557536337	5.52562291876442	4.24584350575698e-08	***
df.mm.trans1:probe5	-0.340800189198509	0.0431293557536337	-7.90181497598179	7.62147284413982e-15	***
df.mm.trans1:probe6	-0.295772504408507	0.0431293557536337	-6.85780019757395	1.26209751367159e-11	***
df.mm.trans1:probe7	-0.128720482383022	0.0431293557536337	-2.98452133433913	0.00291333652032665	** 
df.mm.trans1:probe8	-0.145133134970235	0.0431293557536337	-3.36506614657715	0.000796052208312704	***
df.mm.trans1:probe9	-0.612656761772494	0.0431293557536337	-14.2050988489638	1.19013632115798e-41	***
df.mm.trans1:probe10	-0.730078766067299	0.0431293557536337	-16.9276529479759	2.41510241885346e-56	***
df.mm.trans1:probe11	-0.316817161903486	0.0431293557536337	-7.34574297175291	4.41796117668508e-13	***
df.mm.trans1:probe12	-0.117920240992719	0.0431293557536337	-2.73410624694490	0.00637194143422831	** 
df.mm.trans1:probe13	-0.264558516391362	0.0431293557536337	-6.13407067572698	1.25783484525559e-09	***
df.mm.trans1:probe14	-0.344763449091686	0.0431293557536337	-7.99370737325793	3.80339225534487e-15	***
df.mm.trans1:probe15	-0.240326410168654	0.0431293557536337	-5.57222351155585	3.28004262361723e-08	***
df.mm.trans1:probe16	-0.400751371195135	0.0431293557536337	-9.29184691476342	1.01692672740922e-19	***
df.mm.trans1:probe17	-0.478171851098688	0.0431293557536337	-11.0869231117231	6.12520976801011e-27	***
df.mm.trans1:probe18	-0.0541567412306272	0.0431293557536337	-1.25568166471080	0.209541787536067	   
df.mm.trans1:probe19	-0.344469162539323	0.0431293557536337	-7.98688402643948	4.00578143515073e-15	***
df.mm.trans1:probe20	-0.132900380126391	0.0431293557536337	-3.08143671066067	0.00211971255955119	** 
df.mm.trans1:probe21	-0.485003957582358	0.0431293557536337	-11.2453327694675	1.26802152956319e-27	***
df.mm.trans1:probe22	-0.23801901108586	0.0431293557536337	-5.5187240088975	4.41049050125359e-08	***
df.mm.trans1:probe23	-0.438547758638859	0.0431293557536337	-10.1681963705639	4.04642566030696e-23	***
df.mm.trans1:probe24	-0.70078489643225	0.0431293557536337	-16.2484434136999	1.53449963772404e-52	***
df.mm.trans1:probe25	-0.118516245509419	0.0431293557536337	-2.74792524577494	0.00611157074557015	** 
df.mm.trans1:probe26	-0.648759441892559	0.0431293557536337	-15.04217789847	5.25805083242078e-46	***
df.mm.trans1:probe27	0.248654584976103	0.0431293557536337	5.76532110510725	1.10311669052545e-08	***
df.mm.trans1:probe28	-0.375383917806194	0.0431293557536337	-8.703675518607	1.41137964561421e-17	***
df.mm.trans1:probe29	0.0152321572732068	0.0431293557536337	0.353173772411925	0.724036934810162	   
df.mm.trans1:probe30	-0.180870648588972	0.0431293557536337	-4.19367842223642	3.00273833110579e-05	***
df.mm.trans1:probe31	-0.145278916358942	0.0431293557536337	-3.36844624317630	0.000786470613419085	***
df.mm.trans1:probe32	-0.658524717058469	0.0431293557536337	-15.2685961928144	3.28698328108264e-47	***
df.mm.trans2:probe2	0.142446680007891	0.0431293557536337	3.30277783006043	0.000993236783265132	***
df.mm.trans2:probe3	0.0118638370707293	0.0431293557536337	0.275075684842099	0.783318221268457	   
df.mm.trans2:probe4	0.0646428206762502	0.0431293557536337	1.49881257316958	0.134256228456981	   
df.mm.trans2:probe5	0.0409993331874714	0.0431293557536337	0.950613160596936	0.342043839681772	   
df.mm.trans2:probe6	0.103037281061547	0.0431293557536337	2.38902898643151	0.0170882761171971	*  
df.mm.trans3:probe2	-0.978556446425213	0.0431293557536337	-22.6888723312953	2.53886106364266e-91	***
df.mm.trans3:probe3	-0.878622072187389	0.0431293557536337	-20.3717875408645	9.19900025407708e-77	***
df.mm.trans3:probe4	-0.893057944037236	0.0431293557536337	-20.7064985885395	7.84756154137191e-79	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97044819222273	0.161496712458674	24.5853189936528	1.41917854154140e-103	***
df.mm.trans1	0.131950014629044	0.144569619464664	0.912709150910473	0.361628313617545	   
df.mm.trans2	0.158563733636128	0.129887849491529	1.22077418524408	0.222476037664071	   
df.mm.exp2	0.254547624660105	0.174542124438441	1.45837359020963	0.145069860214785	   
df.mm.exp3	0.286671969733153	0.174542124438441	1.64242282861785	0.100835095057850	   
df.mm.exp4	0.213785367114958	0.174542124438441	1.2248353674093	0.22094247568194	   
df.mm.exp5	0.214292247024211	0.174542124438441	1.22773942229510	0.219850526353035	   
df.mm.exp6	0.18281640857504	0.174542124438441	1.04740565730605	0.295180336951652	   
df.mm.exp7	0.359633112967108	0.174542124438441	2.06043735358536	0.0396300221838287	*  
df.mm.exp8	0.291685731765008	0.174542124438441	1.67114805496642	0.095023529973444	.  
df.mm.trans1:exp2	-0.27061050682316	0.168343373353626	-1.60749129254235	0.108280675563883	   
df.mm.trans2:exp2	-0.178933667057847	0.138308194488443	-1.29373149378216	0.196074295922191	   
df.mm.trans1:exp3	-0.27963842933105	0.168343373353626	-1.66111931678852	0.0970211842824274	.  
df.mm.trans2:exp3	-0.340345043638363	0.138308194488443	-2.46077280451234	0.0140417000541785	*  
df.mm.trans1:exp4	-0.193931532641456	0.168343373353626	-1.15199980122817	0.249612505957525	   
df.mm.trans2:exp4	-0.218832990842410	0.138308194488443	-1.58221276513515	0.113935609775360	   
df.mm.trans1:exp5	-0.212495692809178	0.168343373353626	-1.26227536359750	0.20716114090634	   
df.mm.trans2:exp5	-0.298446265111336	0.138308194488443	-2.15783501632128	0.0311920374498623	*  
df.mm.trans1:exp6	-0.249858348236964	0.168343373353626	-1.48421849496924	0.138084422037314	   
df.mm.trans2:exp6	-0.153923976780086	0.138308194488443	-1.11290569115880	0.266031946821272	   
df.mm.trans1:exp7	-0.338817946212720	0.168343373353626	-2.01265983604232	0.044433690700822	*  
df.mm.trans2:exp7	-0.307356055124966	0.138308194488443	-2.22225484369727	0.0265021753078453	*  
df.mm.trans1:exp8	-0.208169625372533	0.168343373353626	-1.2365774857989	0.216551218052638	   
df.mm.trans2:exp8	-0.376176965476322	0.138308194488443	-2.71984582596627	0.00665107102890436	** 
df.mm.trans1:probe2	-0.0491483472684275	0.0922054629922952	-0.533030751903869	0.594137617887284	   
df.mm.trans1:probe3	-0.0094135262209207	0.0922054629922952	-0.102092933709441	0.918704568168107	   
df.mm.trans1:probe4	-0.0663153657773078	0.0922054629922952	-0.719212979634939	0.47218751307418	   
df.mm.trans1:probe5	0.131066594200665	0.0922054629922952	1.42146235100643	0.155512308448027	   
df.mm.trans1:probe6	0.0618145806709891	0.0922054629922952	0.670400415170134	0.502766366099982	   
df.mm.trans1:probe7	0.181932327100125	0.0922054629922952	1.97311874151456	0.0487730978030041	*  
df.mm.trans1:probe8	0.104514397675968	0.0922054629922952	1.13349463561287	0.257294069158217	   
df.mm.trans1:probe9	0.0104309927754803	0.0922054629922952	0.11312770889022	0.909953334672168	   
df.mm.trans1:probe10	0.0590040062217905	0.0922054629922952	0.639918767358947	0.522380520110091	   
df.mm.trans1:probe11	-0.0106215980706821	0.0922054629922952	-0.115194888957606	0.90831512159357	   
df.mm.trans1:probe12	0.115061638420182	0.0922054629922952	1.24788309375765	0.212382988789229	   
df.mm.trans1:probe13	-0.0180756292310160	0.0922054629922952	-0.196036423921286	0.844623803091563	   
df.mm.trans1:probe14	0.100261203282934	0.0922054629922952	1.08736727769928	0.277151784461803	   
df.mm.trans1:probe15	-0.0167283856606087	0.0922054629922952	-0.181425103434560	0.856072820999752	   
df.mm.trans1:probe16	0.071550365239489	0.0922054629922952	0.775988351638859	0.437950022578214	   
df.mm.trans1:probe17	0.149638315714731	0.0922054629922952	1.62287906658237	0.104948783083923	   
df.mm.trans1:probe18	0.0342569463051635	0.0922054629922952	0.371528380135416	0.710327305073834	   
df.mm.trans1:probe19	0.0701603273118563	0.0922054629922952	0.760912911610443	0.446898906694631	   
df.mm.trans1:probe20	0.0162638967926826	0.0922054629922952	0.176387561700564	0.860027268452006	   
df.mm.trans1:probe21	-0.116022291133231	0.0922054629922952	-1.25830170326162	0.208593460949577	   
df.mm.trans1:probe22	0.0346754937309274	0.0922054629922952	0.376067671107783	0.706950996604279	   
df.mm.trans1:probe23	0.0162151159259569	0.0922054629922952	0.175858516401700	0.860442772486972	   
df.mm.trans1:probe24	0.0286403299131776	0.0922054629922952	0.310614241105984	0.756162394436255	   
df.mm.trans1:probe25	0.06540981960565	0.0922054629922952	0.709392019550032	0.478256222370174	   
df.mm.trans1:probe26	0.115325456112768	0.0922054629922952	1.25074428748766	0.211337369340124	   
df.mm.trans1:probe27	0.0673041724100678	0.0922054629922952	0.729936928093857	0.465609636424409	   
df.mm.trans1:probe28	-0.066881404764535	0.0922054629922952	-0.72535186738473	0.468415770942734	   
df.mm.trans1:probe29	-0.0469764654897848	0.0922054629922952	-0.509475946058751	0.61053760249142	   
df.mm.trans1:probe30	0.141826481199474	0.0922054629922952	1.53815703101372	0.124344972116521	   
df.mm.trans1:probe31	0.0196250478059247	0.0922054629922952	0.212840401957144	0.831497353123039	   
df.mm.trans1:probe32	0.00169969910466846	0.0922054629922952	0.0184338221349258	0.985296662027297	   
df.mm.trans2:probe2	0.0762414236262474	0.0922054629922952	0.826864495356618	0.408522599137899	   
df.mm.trans2:probe3	0.108554555524374	0.0922054629922952	1.17731153883415	0.239367610396602	   
df.mm.trans2:probe4	0.00149891069929685	0.0922054629922952	0.0162562027308739	0.987033429444239	   
df.mm.trans2:probe5	0.132968725271901	0.0922054629922952	1.44209161753260	0.149607851298846	   
df.mm.trans2:probe6	-0.0143949191640337	0.0922054629922952	-0.156117855676692	0.875973467383902	   
df.mm.trans3:probe2	0.039360883536134	0.0922054629922952	0.426882337106459	0.669562265675125	   
df.mm.trans3:probe3	0.110807191565185	0.0922054629922952	1.20174215246274	0.229764515291033	   
df.mm.trans3:probe4	0.046419931012543	0.0922054629922952	0.503440137992929	0.614772184158072