chrX.25898_chrX_144534433_144539430_-_2.R 

fitVsDatCorrelation=0.75209567369696
cont.fitVsDatCorrelation=0.257114092276252

fstatistic=11310.0924828994,65,991
cont.fstatistic=5252.15391312241,65,991

residuals=-0.500833814498936,-0.08194204516967,-0.00481116774487972,0.0768203690260344,1.01853858665090
cont.residuals=-0.537962479351548,-0.155712388915549,-0.0382962375658387,0.130919352691933,1.39781602807447

predictedValues:
Include	Exclude	Both
chrX.25898_chrX_144534433_144539430_-_2.R.tl.Lung	57.7616718545477	45.2171222615131	63.9482499620069
chrX.25898_chrX_144534433_144539430_-_2.R.tl.cerebhem	53.0933587517425	51.8163788426815	61.8885501388723
chrX.25898_chrX_144534433_144539430_-_2.R.tl.cortex	57.3361773937342	44.0715932296099	63.0471879027988
chrX.25898_chrX_144534433_144539430_-_2.R.tl.heart	56.7293968564616	46.7573353315895	60.9962061930727
chrX.25898_chrX_144534433_144539430_-_2.R.tl.kidney	58.3270367680553	44.9507641629627	64.6177938360362
chrX.25898_chrX_144534433_144539430_-_2.R.tl.liver	53.111267049663	46.6443465595251	56.3857471613502
chrX.25898_chrX_144534433_144539430_-_2.R.tl.stomach	61.3305761853508	46.0695540750872	72.1913030183269
chrX.25898_chrX_144534433_144539430_-_2.R.tl.testicle	52.7920374143407	48.4922395727314	59.6862503170357


diffExp=12.5445495930346,1.27697990906108,13.2645841641244,9.9720615248721,13.3762726050926,6.4669204901379,15.2610221102636,4.29979784160922
diffExpScore=0.987090475717972
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=0,0,1,0,0,0,1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,1,1,1,0,1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	55.597479296531	48.9399743935673	58.7062982772169
cerebhem	51.9019569522716	57.6303364364493	53.3978373260442
cortex	54.493943900039	51.9800660102778	49.947114886308
heart	53.7321917429032	53.1758627564448	55.3988192684098
kidney	56.8604198009039	48.9720049247292	53.3391674773508
liver	55.1924580012498	53.6521360847675	56.5121180194964
stomach	53.2051538015698	54.0920381274387	51.7119728085263
testicle	53.6492039754735	54.0345158816443	56.623678854337
cont.diffExp=6.65750490296367,-5.72837948417773,2.51387788976123,0.556328986458368,7.8884148761747,1.54032191648229,-0.886884325868905,-0.38531190617077
cont.diffExpScore=1.98823936466352

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=-0.650368400576598
cont.tran.correlation=-0.90823319969068

tran.covariance=-0.00183400449107001
cont.tran.covariance=-0.00140759364199881

tran.mean=51.5313035193498
cont.tran.mean=53.5693588803913

weightedLogRatios:
wLogRatio
Lung	0.96321404673953
cerebhem	0.0964054603669224
cortex	1.03072509392909
heart	0.762001749115466
kidney	1.02527230211506
liver	0.507335641387488
stomach	1.13684108507622
testicle	0.333359665973776

cont.weightedLogRatios:
wLogRatio
Lung	0.504353029311857
cerebhem	-0.418948701042872
cortex	0.187711570822985
heart	0.0414102839740146
kidney	0.592313478185002
liver	0.113125972027836
stomach	-0.0658363422332912
testicle	-0.0285257063521579

varWeightedLogRatios=0.143838598423882
cont.varWeightedLogRatios=0.10407551001285

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.58917644936122	0.0729914065491223	49.1725891998772	4.16469215140673e-268	***
df.mm.trans1	0.623519718498145	0.064484419324553	9.6693081061945	3.39338815194808e-21	***
df.mm.trans2	0.201401473969223	0.0569967127315853	3.53356297788054	0.000428917736591233	***
df.mm.exp2	0.0846958506497532	0.074817318991216	1.13203536015100	0.257893407940413	   
df.mm.exp3	-0.0188633405727957	0.074817318991216	-0.252125321077201	0.800996477591606	   
df.mm.exp4	0.0627249163955312	0.074817318991216	0.838374286078007	0.402022690460388	   
df.mm.exp5	-0.00658342315803024	0.074817318991216	-0.0879933048496855	0.92989977490091	   
df.mm.exp6	0.0729973027935338	0.074817318991216	0.975673865059293	0.32946420705597	   
df.mm.exp7	-0.0426158710835738	0.074817318991216	-0.569599013412619	0.569078785359196	   
df.mm.exp8	0.0489353649078157	0.074817318991216	0.654064668015717	0.513221890217933	   
df.mm.trans1:exp2	-0.168969439828372	0.071539040105599	-2.3619193041863	0.0183731611436349	*  
df.mm.trans2:exp2	0.0515346163082963	0.0551245325732392	0.934876250239886	0.350079817671757	   
df.mm.trans1:exp3	0.0114696941766031	0.071539040105599	0.160327761732232	0.872655564865254	   
df.mm.trans2:exp3	-0.00679705518066542	0.0551245325732392	-0.123303633851856	0.90189166561932	   
df.mm.trans1:exp4	-0.0807578159563251	0.071539040105599	-1.12886356648228	0.259228583821822	   
df.mm.trans2:exp4	-0.0292295936834683	0.0551245325732392	-0.530246558456227	0.596059780148197	   
df.mm.trans1:exp5	0.0163237224524408	0.071539040105599	0.228179221140588	0.819553986480212	   
df.mm.trans2:exp5	0.00067535801673766	0.0551245325732392	0.0122514964791832	0.990227430536582	   
df.mm.trans1:exp6	-0.156933650576967	0.071539040105599	-2.19367844949159	0.0284901748827644	*  
df.mm.trans2:exp6	-0.0419213974815095	0.0551245325732392	-0.760485314334633	0.447145438257568	   
df.mm.trans1:exp7	0.102568946498417	0.071539040105599	1.43374787174967	0.151959657042628	   
df.mm.trans2:exp7	0.0612923444912677	0.0551245325732392	1.11188869329339	0.266455674532512	   
df.mm.trans1:exp8	-0.138900431130510	0.071539040105599	-1.94160322706984	0.0524682993718463	.  
df.mm.trans2:exp8	0.0209925852203867	0.0551245325732393	0.380821101611984	0.703417618755247	   
df.mm.trans1:probe2	-0.0218109292502719	0.0438085362368048	-0.497869390850541	0.618686561552608	   
df.mm.trans1:probe3	-0.0094550324681458	0.0438085362368048	-0.215826258540963	0.82916759248241	   
df.mm.trans1:probe4	-0.358333893279996	0.0438085362368048	-8.17954499422305	8.6739023087988e-16	***
df.mm.trans1:probe5	-0.0712608378638084	0.0438085362368048	-1.62664275013919	0.104130900000449	   
df.mm.trans1:probe6	0.0218338576365021	0.0438085362368048	0.498392767986593	0.618317825258188	   
df.mm.trans1:probe7	-0.269846945979081	0.0438085362368048	-6.15968870816493	1.05810995109150e-09	***
df.mm.trans1:probe8	-0.366524781516865	0.0438085362368048	-8.36651513612859	1.99955225393634e-16	***
df.mm.trans1:probe9	-0.0024439098991333	0.0438085362368048	-0.0557861574265543	0.955523407340504	   
df.mm.trans1:probe10	-0.137613280024281	0.0438085362368048	-3.14124350743928	0.00173220355693148	** 
df.mm.trans1:probe11	-0.172397336119761	0.0438085362368048	-3.93524529529761	8.89124089110214e-05	***
df.mm.trans1:probe12	-0.262167483221033	0.0438085362368048	-5.98439267187335	3.03178379386599e-09	***
df.mm.trans1:probe13	-0.228931687728985	0.0438085362368048	-5.22573241186391	2.11479375004796e-07	***
df.mm.trans1:probe14	0.00251852934015736	0.0438085362368048	0.0574894656727077	0.954166886648115	   
df.mm.trans1:probe15	-0.227188475993741	0.0438085362368048	-5.18594081221261	2.60530903829607e-07	***
df.mm.trans1:probe16	-0.246381243741221	0.0438085362368048	-5.62404647371507	2.42564730755108e-08	***
df.mm.trans1:probe17	-0.36823748717994	0.0438085362368048	-8.40561038582644	1.46603787105037e-16	***
df.mm.trans1:probe18	-0.263901373642757	0.0438085362368048	-6.02397149761525	2.39603746310699e-09	***
df.mm.trans1:probe19	-0.418069600237129	0.0438085362368048	-9.54310817365079	1.04083297433606e-20	***
df.mm.trans1:probe20	-0.359620259312108	0.0438085362368048	-8.20890835905129	6.9013716764295e-16	***
df.mm.trans1:probe21	-0.365293570369769	0.0438085362368048	-8.33841076988268	2.49747223728095e-16	***
df.mm.trans1:probe22	-0.351249552761594	0.0438085362368048	-8.01783357615357	3.01693685017283e-15	***
df.mm.trans1:probe23	-0.137697907966315	0.0438085362368048	-3.14317527574982	0.00172092523390797	** 
df.mm.trans1:probe24	0.0336963579157060	0.0438085362368048	0.769173334930937	0.441973696653883	   
df.mm.trans1:probe25	-0.279754405029117	0.0438085362368048	-6.3858423280093	2.61589082879554e-10	***
df.mm.trans1:probe26	-0.0232592460725919	0.0438085362368048	-0.530929541833246	0.595586568373581	   
df.mm.trans1:probe27	-0.375693472498432	0.0438085362368048	-8.5758051916558	3.74309285915159e-17	***
df.mm.trans1:probe28	-0.0872865467409367	0.0438085362368048	-1.99245522080705	0.0465950968855898	*  
df.mm.trans1:probe29	-0.338681878469131	0.0438085362368048	-7.73095628300392	2.61374821240871e-14	***
df.mm.trans1:probe30	-0.0227829232167310	0.0438085362368048	-0.520056709806031	0.603140199793989	   
df.mm.trans1:probe31	-0.127223946232563	0.0438085362368048	-2.90409032488237	0.00376498248572385	** 
df.mm.trans1:probe32	-0.107028434129167	0.0438085362368048	-2.44309541753758	0.0147355621190056	*  
df.mm.trans2:probe2	0.0313906181750354	0.0438085362368048	0.716541132654033	0.473826103153973	   
df.mm.trans2:probe3	0.177642344337845	0.0438085362368048	4.05497100787866	5.40737885686431e-05	***
df.mm.trans2:probe4	-0.00156506527931447	0.0438085362368048	-0.0357251214889854	0.971508733806727	   
df.mm.trans2:probe5	0.0467724951547668	0.0438085362368048	1.06765710915198	0.285935223430168	   
df.mm.trans2:probe6	-0.00346555792000663	0.0438085362368048	-0.0791069096961773	0.936963558010744	   
df.mm.trans3:probe2	-0.345550646081271	0.0438085362368048	-7.88774690424293	8.0980376883705e-15	***
df.mm.trans3:probe3	-0.494571089141032	0.0438085362368048	-11.2893771768053	6.8718172339526e-28	***
df.mm.trans3:probe4	-0.4079374767105	0.0438085362368048	-9.3118262273228	7.86907773644192e-20	***
df.mm.trans3:probe5	-0.216712186813033	0.0438085362368048	-4.94680273364092	8.85882944906386e-07	***
df.mm.trans3:probe6	-0.0713694806619228	0.0438085362368048	-1.62912269600013	0.103604858084242	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.71481478125649	0.107028435830309	34.7086711343352	2.18602075680000e-173	***
df.mm.trans1	0.261389003617953	0.0945545080171585	2.76442666880059	0.0058080833042622	** 
df.mm.trans2	0.146576797539757	0.0835751672633632	1.75383193763600	0.0797684120577271	.  
df.mm.exp2	0.189449971821938	0.109705799671885	1.72689112506865	0.0844988347966066	.  
df.mm.exp3	0.201799748615128	0.109705799671885	1.83946290185827	0.0661461171056583	.  
df.mm.exp4	0.106873219306948	0.109705799671885	0.974180213139056	0.330204793817160	   
df.mm.exp5	0.118992003731674	0.109705799671885	1.08464642787859	0.278342077880398	   
df.mm.exp6	0.122707122114211	0.109705799671885	1.11851080326848	0.263619947342231	   
df.mm.exp7	0.182967554508842	0.109705799671885	1.66780202191746	0.0956709578320178	.  
df.mm.exp8	0.0994770234142374	0.109705799671885	0.906761754727276	0.364753220918952	   
df.mm.trans1:exp2	-0.258231339986966	0.104898808302196	-2.46171852823195	0.0139967084691764	*  
df.mm.trans2:exp2	-0.0259954033138326	0.0808299603491017	-0.321606038176431	0.747818988679004	   
df.mm.trans1:exp3	-0.221848038056022	0.104898808302196	-2.11487662869263	0.0346895791098955	*  
df.mm.trans2:exp3	-0.141533984279194	0.0808299603491017	-1.75100895346124	0.0802537499691196	.  
df.mm.trans1:exp4	-0.14099878744317	0.104898808302196	-1.34414098429961	0.179210421137171	   
df.mm.trans2:exp4	-0.0238631682948536	0.0808299603491017	-0.295226772248674	0.767882491769206	   
df.mm.trans1:exp5	-0.0965303783095843	0.104898808302196	-0.920223783968037	0.357679770376659	   
df.mm.trans2:exp5	-0.118337731751321	0.0808299603491017	-1.46403302983478	0.143502047838902	   
df.mm.trans1:exp6	-0.130018672413572	0.104898808302196	-1.23946758326377	0.215465828561764	   
df.mm.trans2:exp6	-0.0307803734368382	0.0808299603491017	-0.380804015044655	0.703430294268413	   
df.mm.trans1:exp7	-0.226950150719543	0.104898808302196	-2.16351505219905	0.0307400184898265	*  
df.mm.trans2:exp7	-0.0828750838286271	0.0808299603491017	-1.02530155242799	0.305471124650213	   
df.mm.trans1:exp8	-0.135148255646116	0.104898808302196	-1.28836788361577	0.197918646882507	   
df.mm.trans2:exp8	-0.000448532840446703	0.0808299603491017	-0.00554909143230437	0.995573605152043	   
df.mm.trans1:probe2	0.148178033781548	0.0642371387416018	2.30673465045827	0.0212751075128693	*  
df.mm.trans1:probe3	-0.00239938419538781	0.0642371387416018	-0.0373519780362492	0.970211884495961	   
df.mm.trans1:probe4	0.0981830257985544	0.0642371387416018	1.52844643646882	0.126720837871895	   
df.mm.trans1:probe5	0.044859288512786	0.0642371387416018	0.698338833135696	0.485129151836053	   
df.mm.trans1:probe6	0.10441947143468	0.0642371387416018	1.6255311721575	0.104367374712406	   
df.mm.trans1:probe7	-0.000971227907731927	0.0642371387416018	-0.0151194142011642	0.987939955677683	   
df.mm.trans1:probe8	0.142192742300322	0.0642371387416018	2.21355971149807	0.0270861910206495	*  
df.mm.trans1:probe9	0.110565670797665	0.0642371387416018	1.72121101536640	0.0855246426238539	.  
df.mm.trans1:probe10	0.0492003769718661	0.0642371387416018	0.765917939928457	0.443907516100009	   
df.mm.trans1:probe11	0.0910051761849264	0.0642371387416018	1.41670656520056	0.156883003146874	   
df.mm.trans1:probe12	-0.0143291443269601	0.0642371387416018	-0.223066353945185	0.823529833584787	   
df.mm.trans1:probe13	-0.00415762102275062	0.0642371387416018	-0.0647230107722408	0.9484075944553	   
df.mm.trans1:probe14	-0.0065263771218965	0.0642371387416018	-0.101598191478442	0.919096167085259	   
df.mm.trans1:probe15	0.0996907149642644	0.0642371387416018	1.55191711394988	0.121001336289874	   
df.mm.trans1:probe16	0.0507132549923765	0.0642371387416018	0.789469393964977	0.430026553574655	   
df.mm.trans1:probe17	-0.00357896190365282	0.0642371387416018	-0.055714839947175	0.955580207673683	   
df.mm.trans1:probe18	0.0252351397592523	0.0642371387416018	0.392843458684584	0.69451958960777	   
df.mm.trans1:probe19	0.0798536161406696	0.0642371387416018	1.24310667792795	0.214122546082494	   
df.mm.trans1:probe20	0.0241054799771621	0.0642371387416018	0.375257685030587	0.707549132416128	   
df.mm.trans1:probe21	0.108492354774756	0.0642371387416018	1.68893504443237	0.0915464558189297	.  
df.mm.trans1:probe22	0.0223431032089504	0.0642371387416018	0.347822204516721	0.728047576524282	   
df.mm.trans1:probe23	0.0241240070086623	0.0642371387416018	0.375546101231294	0.707334734958058	   
df.mm.trans1:probe24	0.0255762244502406	0.0642371387416018	0.398153232713596	0.690602995399817	   
df.mm.trans1:probe25	0.0543943616668252	0.0642371387416018	0.846774354094913	0.397325340025779	   
df.mm.trans1:probe26	0.108059316698866	0.0642371387416018	1.68219380277103	0.0928462959824465	.  
df.mm.trans1:probe27	0.0675746652993403	0.0642371387416018	1.05195633901385	0.293076017045884	   
df.mm.trans1:probe28	0.0432514035485383	0.0642371387416018	0.67330837574382	0.500908113012251	   
df.mm.trans1:probe29	0.0153746486474081	0.0642371387416018	0.239342052722082	0.810889849830582	   
df.mm.trans1:probe30	0.0168339187737744	0.0642371387416018	0.262058975594943	0.793330434859646	   
df.mm.trans1:probe31	0.00587848679782867	0.0642371387416018	0.0915122764336574	0.92710404611674	   
df.mm.trans1:probe32	0.0653532345064854	0.0642371387416018	1.01737461827765	0.309223561227678	   
df.mm.trans2:probe2	0.128851583240057	0.0642371387416018	2.0058736388987	0.0451411466367493	*  
df.mm.trans2:probe3	0.0439751463501672	0.0642371387416018	0.684575110467796	0.493772142899799	   
df.mm.trans2:probe4	0.07482136392119	0.0642371387416018	1.16476800472331	0.244393217357931	   
df.mm.trans2:probe5	0.0106318101509452	0.0642371387416018	0.165508775129484	0.86857730021967	   
df.mm.trans2:probe6	0.0921555683040321	0.0642371387416018	1.43461508574867	0.151712299943008	   
df.mm.trans3:probe2	-0.0170944141064999	0.0642371387416018	-0.266114189414061	0.790206621974901	   
df.mm.trans3:probe3	-0.0878303342572378	0.0642371387416018	-1.36728278964200	0.171846716482941	   
df.mm.trans3:probe4	-0.0273095324207194	0.0642371387416018	-0.425136189993981	0.670829692284982	   
df.mm.trans3:probe5	-0.122661335051165	0.0642371387416018	-1.90950807358620	0.0564851057405446	.  
df.mm.trans3:probe6	-0.0465057759739876	0.0642371387416018	-0.723970227893558	0.469254877216994