chr14.7436_chr14_45356046_45357947_+_1.R 

fitVsDatCorrelation=0.885254417639796
cont.fitVsDatCorrelation=0.268923605999058

fstatistic=9185.07921121074,45,531
cont.fstatistic=2132.80930744473,45,531

residuals=-0.602814699977677,-0.0928422371594772,0.00980942594714243,0.097102004960643,0.719186645339511
cont.residuals=-0.709153171206275,-0.251585266120610,-0.0257402841939785,0.223907851744108,1.03794629703458

predictedValues:
Include	Exclude	Both
chr14.7436_chr14_45356046_45357947_+_1.R.tl.Lung	58.4513560387119	116.814950343583	82.631573191086
chr14.7436_chr14_45356046_45357947_+_1.R.tl.cerebhem	55.8434164125298	88.2933055491547	77.0529507657025
chr14.7436_chr14_45356046_45357947_+_1.R.tl.cortex	56.6549992067534	98.2171655983514	89.3674090515152
chr14.7436_chr14_45356046_45357947_+_1.R.tl.heart	62.0995291672063	91.6610299624564	84.9966651826728
chr14.7436_chr14_45356046_45357947_+_1.R.tl.kidney	62.786665628151	135.186789238290	74.6489395613694
chr14.7436_chr14_45356046_45357947_+_1.R.tl.liver	64.7413348563434	116.677022095884	67.6547864057015
chr14.7436_chr14_45356046_45357947_+_1.R.tl.stomach	56.8737430818869	97.7054445774884	77.8043570884853
chr14.7436_chr14_45356046_45357947_+_1.R.tl.testicle	57.4684104611952	97.8471118453025	116.014788067800


diffExp=-58.3635943048715,-32.4498891366249,-41.562166391598,-29.5615007952501,-72.4001236101386,-51.9356872395404,-40.8317014956015,-40.3787013841072
diffExpScore=0.997286173280189
diffExp1.5=-1,-1,-1,0,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
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	78.4586127544962	87.1987487867246	73.4273377361757
cerebhem	86.550407530445	89.1735082666086	77.6981006484886
cortex	71.0629860817622	79.59086696125	74.7937037957922
heart	67.2888257709783	81.0078552228889	86.5632185097516
kidney	79.6356084732274	89.6006914705361	86.3392100967833
liver	74.7335464836556	80.2484195661342	78.3035511637087
stomach	69.733788262444	86.5543568823644	86.3160054843432
testicle	82.3833425652686	97.0601439369648	76.0137455791603
cont.diffExp=-8.74013603222838,-2.62310073616362,-8.52788087948774,-13.7190294519106,-9.96508299730877,-5.51487308247864,-16.8205686199203,-14.6768013716963
cont.diffExpScore=0.987743216438365

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,-1,0,0,-1,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.60701854068925
cont.tran.correlation=0.717275932492697

tran.covariance=0.00493603508243469
cont.tran.covariance=0.00427448139238901

tran.mean=82.3326421289555
cont.tran.mean=81.2676068134843

weightedLogRatios:
wLogRatio
Lung	-3.05650880660663
cerebhem	-1.94771539612303
cortex	-2.37251008284015
heart	-1.68336126049540
kidney	-3.46890875209762
liver	-2.62987245292978
stomach	-2.33299747264048
testicle	-2.29755190486877

cont.weightedLogRatios:
wLogRatio
Lung	-0.466346859809454
cerebhem	-0.133629703110369
cortex	-0.489623832113271
heart	-0.798201803551666
kidney	-0.523060546680676
liver	-0.309677672742891
stomach	-0.940570946967893
testicle	-0.73667494081576

varWeightedLogRatios=0.330979467888717
cont.varWeightedLogRatios=0.070212089948973

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29548345100632	0.0811757165520956	52.9158673733374	6.96129543350177e-214	***
df.mm.trans1	-0.223893666183562	0.0646253954245758	-3.46448427452747	0.000574185398034093	***
df.mm.trans2	0.503978295675061	0.0646253954245758	7.79845589127967	3.33787775977272e-14	***
df.mm.exp2	-0.255671028984354	0.0861671938994344	-2.96715045963719	0.00314119460011324	** 
df.mm.exp3	-0.282988930208025	0.0861671938994344	-3.28418412392889	0.00109019620368230	** 
df.mm.exp4	-0.210170399557255	0.0861671938994344	-2.4390999642224	0.0150503378619395	*  
df.mm.exp5	0.319209748023731	0.0861671938994344	3.7045392054461	0.000233963212436552	***
df.mm.exp6	0.300997285276873	0.0861671938994344	3.49317729469253	0.000517128984671262	***
df.mm.exp7	-0.145800470785778	0.0861671938994344	-1.69206474282941	0.0912202911223842	.  
df.mm.exp8	-0.533470175341198	0.0861671938994344	-6.19110535227375	1.19658584500643e-09	***
df.mm.trans1:exp2	0.210027780112805	0.066744821392387	3.14672772705571	0.00174371055844417	** 
df.mm.trans2:exp2	-0.0242557428450513	0.066744821392387	-0.363410109414391	0.716443182514027	   
df.mm.trans1:exp3	0.251774273412277	0.066744821392387	3.77219188185579	0.000180019780814682	***
df.mm.trans2:exp3	0.109578870999615	0.0667448213923869	1.64175839733559	0.101232164380881	   
df.mm.trans1:exp4	0.270713918992490	0.066744821392387	4.05595390541218	5.74208805101073e-05	***
df.mm.trans2:exp4	-0.0323233463734779	0.066744821392387	-0.484282461158323	0.628385100877715	   
df.mm.trans1:exp5	-0.247661915483797	0.066744821392387	-3.71057874329777	0.000228589504897993	***
df.mm.trans2:exp5	-0.173143363643481	0.066744821392387	-2.59410932610915	0.0097455136721738	** 
df.mm.trans1:exp6	-0.19879230589192	0.066744821392387	-2.97839295611022	0.00303006886078851	** 
df.mm.trans2:exp6	-0.302178724298540	0.066744821392387	-4.52737333016531	7.37842537282829e-06	***
df.mm.trans1:exp7	0.118439360526635	0.066744821392387	1.77451011263241	0.0765516962582714	.  
df.mm.trans2:exp7	-0.032833305930516	0.066744821392387	-0.49192289747083	0.622977145901603	   
df.mm.trans1:exp8	0.516510701267727	0.066744821392387	7.73858841019599	5.10202261899319e-14	***
df.mm.trans2:exp8	0.356285290883731	0.066744821392387	5.33802148917533	1.39466544390835e-07	***
df.mm.trans1:probe2	-0.0697755545194469	0.0471957158156418	-1.47842983867450	0.139885683648259	   
df.mm.trans1:probe3	0.0875383764629576	0.0471957158156418	1.85479497344429	0.064179614208109	.  
df.mm.trans1:probe4	-0.0677395393339752	0.0471957158156418	-1.43529000807155	0.151793225229104	   
df.mm.trans1:probe5	-0.0146846963405664	0.0471957158156418	-0.311144689444451	0.755812737612158	   
df.mm.trans1:probe6	0.00355326420595998	0.0471957158156418	0.0752878549366624	0.94001403953337	   
df.mm.trans2:probe2	-0.171005630074176	0.0471957158156418	-3.62332951452981	0.000318784298285202	***
df.mm.trans2:probe3	-0.224064717997686	0.0471957158156418	-4.74756477628052	2.65166394517434e-06	***
df.mm.trans2:probe4	-0.28219430168773	0.0471957158156418	-5.97923554735458	4.11877820879115e-09	***
df.mm.trans2:probe5	-0.0499941880856054	0.0471957158156418	-1.05929504874754	0.28994703915804	   
df.mm.trans2:probe6	0.0275865085379118	0.0471957158156418	0.584512980916987	0.559123486906538	   
df.mm.trans3:probe2	-0.0659557139424928	0.0471957158156418	-1.39749366658898	0.162848767308066	   
df.mm.trans3:probe3	-0.345798943155389	0.0471957158156418	-7.32691383485243	8.81232475989665e-13	***
df.mm.trans3:probe4	-0.62528848618914	0.0471957158156418	-13.2488399716549	8.07750328480301e-35	***
df.mm.trans3:probe5	-0.527642537792463	0.0471957158156418	-11.1798820861954	3.32835355006655e-26	***
df.mm.trans3:probe6	0.421278267451432	0.0471957158156418	8.9261972230075	7.17435257243577e-18	***
df.mm.trans3:probe7	-0.260353869343676	0.0471957158156418	-5.5164725196813	5.41092611725831e-08	***
df.mm.trans3:probe8	-0.0701053841127142	0.0471957158156418	-1.48541838811309	0.138026449735089	   
df.mm.trans3:probe9	-0.0225130696440015	0.0471957158156418	-0.477015111539852	0.633547615603706	   
df.mm.trans3:probe10	-0.35292175243595	0.0471957158156418	-7.4778345096947	3.14460677632747e-13	***
df.mm.trans3:probe11	-0.0576726355974988	0.0471957158156418	-1.22198878861765	0.222254046405306	   
df.mm.trans3:probe12	-0.0271406470452494	0.0471957158156418	-0.575065905373013	0.565490302573387	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.46881648579315	0.168101767174760	26.5839946890464	1.20964776916771e-99	***
df.mm.trans1	-0.109104443107578	0.133828731505770	-0.815254257288351	0.415292296386012	   
df.mm.trans2	0.0357363357890874	0.133828731505770	0.267030370735799	0.789549450839575	   
df.mm.exp2	0.06401525404177	0.178438308674360	0.358752862641141	0.719922571962814	   
df.mm.exp3	-0.208732673030595	0.178438308674360	-1.16977500280795	0.242616308827086	   
df.mm.exp4	-0.391799608751383	0.178438308674360	-2.19571465153481	0.0285442114466919	*  
df.mm.exp5	-0.11992439536066	0.178438308674360	-0.672077628686314	0.501826588468906	   
df.mm.exp6	-0.196001739958961	0.178438308674360	-1.09842859089555	0.272515185486630	   
df.mm.exp7	-0.287022383764234	0.178438308674360	-1.60852445809736	0.108314721129347	   
df.mm.exp8	0.121334976540217	0.178438308674360	0.67998277635352	0.496811704472993	   
df.mm.trans1:exp2	0.034140476483783	0.138217719564243	0.247005062675157	0.804999773562326	   
df.mm.trans2:exp2	-0.0416212331295254	0.138217719564243	-0.301128055510858	0.763434726194377	   
df.mm.trans1:exp3	0.109728025020193	0.138217719564243	0.793878132023386	0.427621002945234	   
df.mm.trans2:exp3	0.117442040594569	0.138217719564243	0.849688744430358	0.395881085197743	   
df.mm.trans1:exp4	0.238222535825638	0.138217719564243	1.72353108253182	0.0853746301114742	.  
df.mm.trans2:exp4	0.318155754746215	0.138217719564243	2.30184491358461	0.0217300463394148	*  
df.mm.trans1:exp5	0.134814471113136	0.138217719564243	0.975377625518381	0.329817107352681	   
df.mm.trans2:exp5	0.147097450580962	0.138217719564243	1.06424451976718	0.2877016513557	   
df.mm.trans1:exp6	0.147359554371640	0.138217719564243	1.06614083082993	0.286844491976194	   
df.mm.trans2:exp6	0.112938825859317	0.138217719564243	0.817108155274	0.414233074854478	   
df.mm.trans1:exp7	0.16913609140717	0.138217719564243	1.22369325684437	0.221610675765426	   
df.mm.trans2:exp7	0.279605021567847	0.138217719564243	2.02293180967936	0.0435807735474401	*  
df.mm.trans1:exp8	-0.0725229730931117	0.138217719564243	-0.524700981334044	0.600010053207658	   
df.mm.trans2:exp8	-0.0141941316312075	0.138217719564243	-0.102694008235392	0.918244599032356	   
df.mm.trans1:probe2	0.0165994827299797	0.0977346867840165	0.169842287075241	0.865198856219243	   
df.mm.trans1:probe3	0.0800471073010608	0.0977346867840165	0.819024544253737	0.413139835573271	   
df.mm.trans1:probe4	0.0233976162579363	0.0977346867840165	0.239399306713312	0.810888343630467	   
df.mm.trans1:probe5	-0.0583233289745682	0.0977346867840165	-0.596751582203938	0.550927583644309	   
df.mm.trans1:probe6	-0.0102549710823834	0.0977346867840165	-0.104926627585617	0.916473651484766	   
df.mm.trans2:probe2	-0.095574775451906	0.0977346867840165	-0.977900258309687	0.328568960894638	   
df.mm.trans2:probe3	-0.0564035986922223	0.0977346867840165	-0.5771093206332	0.564110195289274	   
df.mm.trans2:probe4	-0.139062452682064	0.0977346867840165	-1.42285668740493	0.155364914099146	   
df.mm.trans2:probe5	-0.229135112956044	0.0977346867840165	-2.34446050318255	0.0194221600222809	*  
df.mm.trans2:probe6	-0.134355172051905	0.0977346867840165	-1.37469281861838	0.169806517586408	   
df.mm.trans3:probe2	-0.130480904126990	0.0977346867840165	-1.33505215415832	0.182431351070733	   
df.mm.trans3:probe3	-0.139815626334418	0.0977346867840165	-1.43056299595451	0.153143682272448	   
df.mm.trans3:probe4	-0.160340219495209	0.0977346867840165	-1.64056615692179	0.101479685239837	   
df.mm.trans3:probe5	-0.0699035056534566	0.0977346867840165	-0.715237424435974	0.474776702213779	   
df.mm.trans3:probe6	-0.146246360482272	0.0977346867840165	-1.49636086526232	0.135153753026211	   
df.mm.trans3:probe7	-0.0744245932598998	0.0977346867840165	-0.761496206811103	0.446698802473396	   
df.mm.trans3:probe8	-0.162382931465133	0.0977346867840165	-1.66146674029848	0.0972099801431276	.  
df.mm.trans3:probe9	-0.0961754709721008	0.0977346867840165	-0.984046443865304	0.325540830994886	   
df.mm.trans3:probe10	-0.0831580113145666	0.0977346867840165	-0.85085463565599	0.39523362100729	   
df.mm.trans3:probe11	-0.119680119351448	0.0977346867840165	-1.22454087990203	0.221291229602241	   
df.mm.trans3:probe12	-0.00721701923558023	0.0977346867840165	-0.0738429668427658	0.941163145713024