chr17.10327_chr17_25304285_25305658_+_2.R 

fitVsDatCorrelation=0.794272887053142
cont.fitVsDatCorrelation=0.241471637278204

fstatistic=9340.11137822834,59,853
cont.fstatistic=3652.40957959063,59,853

residuals=-0.420041127312381,-0.0969862061161937,-0.00448062691388907,0.0797483482689537,0.744439449384592
cont.residuals=-0.52631642643689,-0.177353846144855,-0.0509188956227357,0.118437785875515,1.29165354697750

predictedValues:
Include	Exclude	Both
chr17.10327_chr17_25304285_25305658_+_2.R.tl.Lung	56.8440287696596	47.5490166507144	59.0820914322872
chr17.10327_chr17_25304285_25305658_+_2.R.tl.cerebhem	56.7487621882258	54.403293925686	67.2246519695408
chr17.10327_chr17_25304285_25305658_+_2.R.tl.cortex	56.5322773873867	48.0643681611367	58.2927971986885
chr17.10327_chr17_25304285_25305658_+_2.R.tl.heart	57.9351949915777	51.8936100323013	56.3333119040844
chr17.10327_chr17_25304285_25305658_+_2.R.tl.kidney	79.1976263298421	50.7300962141956	83.2445708016562
chr17.10327_chr17_25304285_25305658_+_2.R.tl.liver	58.5310501810265	51.2475913343806	54.0375805090176
chr17.10327_chr17_25304285_25305658_+_2.R.tl.stomach	76.6767710693808	49.2880458081239	80.4301489588939
chr17.10327_chr17_25304285_25305658_+_2.R.tl.testicle	54.4647916251077	50.4435138673385	55.2778559843248


diffExp=9.29501211894512,2.34546826253977,8.46790922625002,6.04158495927636,28.4675301156465,7.28345884664585,27.3887252612569,4.02127775776921
diffExpScore=0.989396779223045
diffExp1.5=0,0,0,0,1,0,1,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,0,0,1,0,1,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,0,1,0,1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,0,0,0,1,0,1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	59.0104893387281	58.3424024140033	61.4752854076063
cerebhem	57.6012503783921	56.1104062099528	59.8218218598546
cortex	57.6579563175047	58.7675292879002	59.7536843769416
heart	62.0185376581649	61.2121104846368	56.5800667880633
kidney	58.6381701486262	61.877289086093	58.7677360436833
liver	59.2058797373446	56.7391967857587	57.6959794249011
stomach	58.8546276799684	61.4515341440973	54.813225952808
testicle	59.9680836038757	57.157331564583	64.8852075302892
cont.diffExp=0.66808692472479,1.49084416843922,-1.10957297039558,0.806427173528128,-3.23911893746681,2.46668295158597,-2.59690646412886,2.8107520392926
cont.diffExpScore=6.61171227783346

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=-0.0894556429017296
cont.tran.correlation=0.329453169189677

tran.covariance=-0.000501167738709255
cont.tran.covariance=0.000300570902069127

tran.mean=56.2843774085052
cont.tran.mean=59.0382996774769

weightedLogRatios:
wLogRatio
Lung	0.705457785600307
cerebhem	0.169576464907871
cortex	0.641565606833358
heart	0.440986776121249
kidney	1.84818025075303
liver	0.531968751789818
stomach	1.82009808507576
testicle	0.303672138171763

cont.weightedLogRatios:
wLogRatio
Lung	0.0463643311128242
cerebhem	0.105952212928189
cortex	-0.0774659667265772
heart	0.0539353564197774
kidney	-0.220353146833067
liver	0.172764735457664
stomach	-0.176886944670544
testicle	0.195370469129534

varWeightedLogRatios=0.431050740301038
cont.varWeightedLogRatios=0.0241293224214087

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.92102008382197	0.0772882117563264	50.7324467046039	8.29951719670828e-260	***
df.mm.trans1	0.179078538275544	0.0667442091394761	2.6830573106548	0.0074364898486021	** 
df.mm.trans2	-0.0676837495511567	0.0589682070962827	-1.14780070285406	0.251372650900463	   
df.mm.exp2	0.00387409029277171	0.0758519688291105	0.0510743538048403	0.95927822936505	   
df.mm.exp3	0.0187299023573413	0.0758519688291105	0.246927042850246	0.805024102219989	   
df.mm.exp4	0.154090242851806	0.0758519688291105	2.03146002971870	0.0425179058350754	*  
df.mm.exp5	0.0535383335605572	0.0758519688291105	0.705826551202323	0.480488724475608	   
df.mm.exp6	0.193401833187876	0.0758519688291105	2.54972726711416	0.0109545984437977	*  
df.mm.exp7	0.0267468570308491	0.0758519688291105	0.352619153381609	0.724461085754475	   
df.mm.exp8	0.0828918153354306	0.0758519688291105	1.09281033327138	0.274785615760782	   
df.mm.trans1:exp2	-0.00555142567587333	0.0701115521291832	-0.0791798998493802	0.936908102968354	   
df.mm.trans2:exp2	0.130789503384468	0.0517808129187382	2.52582947258324	0.0117225757404091	*  
df.mm.trans1:exp3	-0.0242293259767558	0.0701115521291832	-0.345582507317943	0.729741701042166	   
df.mm.trans2:exp3	-0.00794989489050311	0.0517808129187382	-0.15352974282152	0.878016849656018	   
df.mm.trans1:exp4	-0.135076364558308	0.0701115521291832	-1.92659213005903	0.0543624239573169	.  
df.mm.trans2:exp4	-0.0666556894809502	0.0517808129187382	-1.28726618459150	0.198350698428373	   
df.mm.trans1:exp5	0.278096814382378	0.0701115521291832	3.96649062725033	7.9061922011394e-05	***
df.mm.trans2:exp5	0.0112199061537929	0.0517808129187382	0.216680764965987	0.828508949562257	   
df.mm.trans1:exp6	-0.164155627280150	0.0701115521291832	-2.34134921129242	0.0194435596145273	*  
df.mm.trans2:exp6	-0.118494323083461	0.0517808129187382	-2.28838282762031	0.0223590126292418	*  
df.mm.trans1:exp7	0.272540771251458	0.0701115521291832	3.88724486870996	0.000109255634820206	***
df.mm.trans2:exp7	0.00917360762198484	0.0517808129187382	0.177162294388490	0.859422969315577	   
df.mm.trans1:exp8	-0.125648527503343	0.0701115521291832	-1.79212303375956	0.0734675707443506	.  
df.mm.trans2:exp8	-0.0237987508203301	0.0517808129187382	-0.459605585135917	0.645916461096398	   
df.mm.trans1:probe2	-0.193425118240873	0.0480020983035662	-4.02951381453471	6.08835284486189e-05	***
df.mm.trans1:probe3	-0.166021588726089	0.0480020983035662	-3.45863190555056	0.000569816898887011	***
df.mm.trans1:probe4	-0.0757936727076754	0.0480020983035662	-1.57896582412616	0.114714633232043	   
df.mm.trans1:probe5	-0.0571389953705358	0.0480020983035662	-1.19034370141879	0.234242499021195	   
df.mm.trans1:probe6	-0.0333321068695296	0.0480020983035662	-0.694388538157993	0.48762765478305	   
df.mm.trans1:probe7	-0.220170640591452	0.0480020983035662	-4.58668784016666	5.17794702733597e-06	***
df.mm.trans1:probe8	-0.201081372433745	0.0480020983035662	-4.18901213780494	3.09331254374548e-05	***
df.mm.trans1:probe9	0.112390895005886	0.0480020983035662	2.34137462689910	0.0194422445860808	*  
df.mm.trans1:probe10	0.208651380438806	0.0480020983035662	4.34671374403865	1.54856935225200e-05	***
df.mm.trans1:probe11	0.0375421349618242	0.0480020983035662	0.782093622749718	0.434376593548992	   
df.mm.trans1:probe12	-0.00535871190665139	0.0480020983035662	-0.111634951304895	0.911139120809582	   
df.mm.trans1:probe13	-0.0879032582802012	0.0480020983035662	-1.83123782890280	0.0674137028384814	.  
df.mm.trans1:probe14	0.0878664233009805	0.0480020983035662	1.83047046704732	0.0675283872544705	.  
df.mm.trans1:probe15	0.302848520242883	0.0480020983035662	6.30906837296285	4.50481355556424e-10	***
df.mm.trans1:probe16	0.165203863805152	0.0480020983035662	3.44159671438527	0.000606402381398676	***
df.mm.trans1:probe17	-0.258967001894137	0.0480020983035662	-5.39491003614934	8.88536138159494e-08	***
df.mm.trans1:probe18	-0.31705935264469	0.0480020983035662	-6.60511443978137	6.9832357420997e-11	***
df.mm.trans1:probe19	-0.268997048501552	0.0480020983035662	-5.60386020628535	2.82961673530906e-08	***
df.mm.trans1:probe20	-0.33908279962452	0.0480020983035662	-7.06391619549949	3.36122504443248e-12	***
df.mm.trans1:probe21	-0.301412851562517	0.0480020983035662	-6.2791599162265	5.4160462197848e-10	***
df.mm.trans1:probe22	-0.301956850465469	0.0480020983035662	-6.29049273129454	5.05132923081491e-10	***
df.mm.trans2:probe2	0.0229741695016220	0.0480020983035662	0.478607609116019	0.632340454088003	   
df.mm.trans2:probe3	0.00283577509823649	0.0480020983035662	0.0590760653899543	0.952905359135151	   
df.mm.trans2:probe4	0.0580281829613447	0.0480020983035662	1.20886763312665	0.227048681770700	   
df.mm.trans2:probe5	0.032442894764629	0.0480020983035662	0.67586409576222	0.499310125759805	   
df.mm.trans2:probe6	0.0185153646386326	0.0480020983035662	0.385719901691403	0.699800296618414	   
df.mm.trans3:probe2	0.108081503938583	0.0480020983035662	2.25159957081613	0.0246013077041712	*  
df.mm.trans3:probe3	0.200040092704322	0.0480020983035662	4.16731975838358	3.39626168170341e-05	***
df.mm.trans3:probe4	-0.04142479062979	0.0480020983035662	-0.862978746633507	0.388391718169354	   
df.mm.trans3:probe5	0.111475111758577	0.0480020983035662	2.32229664323434	0.0204515274452414	*  
df.mm.trans3:probe6	-0.0103424174557568	0.0480020983035662	-0.215457611672538	0.829462062401969	   
df.mm.trans3:probe7	-0.0541439343907525	0.0480020983035662	-1.1279493252221	0.259658478500053	   
df.mm.trans3:probe8	0.191946812270997	0.0480020983035662	3.99871711976259	6.92053925073421e-05	***
df.mm.trans3:probe9	-0.00257745209549654	0.0480020983035662	-0.0536945714163719	0.957191080451501	   
df.mm.trans3:probe10	-0.0379250811495563	0.0480020983035662	-0.790071319585185	0.429705695941063	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11035513509374	0.123446152663717	33.2967455558608	2.01532715652908e-156	***
df.mm.trans1	-0.0443003063633525	0.106605077845864	-0.415555311796726	0.677839945818996	   
df.mm.trans2	-0.0438860943453845	0.0941851044304638	-0.465955785798224	0.641366104536985	   
df.mm.exp2	-0.0359140696249396	0.121152159056849	-0.296437718522930	0.766967983323788	   
df.mm.exp3	0.0124777492805564	0.121152159056849	0.102992380636827	0.917993225727983	   
df.mm.exp4	0.180712516349842	0.121152159056849	1.49161614416665	0.136169519531645	   
df.mm.exp5	0.0975369443459004	0.121152159056849	0.805078053129308	0.420998983436341	   
df.mm.exp6	0.038889513789505	0.121152159056849	0.320997282196651	0.74829107893679	   
df.mm.exp7	0.163978627002245	0.121152159056849	1.35349322933073	0.176256654597378	   
df.mm.exp8	-0.0584086473814864	0.121152159056849	-0.482109834741607	0.629851601129656	   
df.mm.trans1:exp2	0.0117431315434280	0.111983459973388	0.104864875100472	0.91650768323244	   
df.mm.trans2:exp2	-0.00309378423527543	0.0827052663188926	-0.0374073426394216	0.970168970597952	   
df.mm.trans1:exp3	-0.0356647148827597	0.111983459973388	-0.318481987350945	0.750197317158653	   
df.mm.trans2:exp3	-0.00521741302768938	0.0827052663188926	-0.063084411185767	0.9497140668097	   
df.mm.trans1:exp4	-0.130994395014102	0.111983459973388	-1.16976556221099	0.242422068325838	   
df.mm.trans2:exp4	-0.132696605797617	0.0827052663188926	-1.60445170789934	0.108984676343229	   
df.mm.trans1:exp5	-0.103866305592755	0.111983459973388	-0.92751470277341	0.353921724368321	   
df.mm.trans2:exp5	-0.0387128719361048	0.0827052663188926	-0.46808230792507	0.639845305358617	   
df.mm.trans1:exp6	-0.035583870449799	0.111983459973388	-0.317760055442609	0.750744722503981	   
df.mm.trans2:exp6	-0.066753383683915	0.0827052663188925	-0.807123737762107	0.419820189725332	   
df.mm.trans1:exp7	-0.166623374719846	0.111983459973388	-1.48792843835547	0.137139336388348	   
df.mm.trans2:exp7	-0.112058968697568	0.0827052663188926	-1.35491938645714	0.175801959304201	   
df.mm.trans1:exp8	0.0745059146320021	0.111983459973388	0.665329635731097	0.506019531991373	   
df.mm.trans2:exp8	0.0378871726131811	0.0827052663188925	0.458098671336077	0.646998228469883	   
df.mm.trans1:probe2	0.082659801989637	0.0766698338686269	1.07812679144804	0.281281961213403	   
df.mm.trans1:probe3	-0.00441714282341916	0.0766698338686269	-0.0576125263423408	0.954070764480496	   
df.mm.trans1:probe4	-0.0106154948009468	0.0766698338686269	-0.138457255811149	0.889911727909048	   
df.mm.trans1:probe5	0.0831839977158093	0.0766698338686269	1.08496384455905	0.278244208406839	   
df.mm.trans1:probe6	0.0261202091777277	0.0766698338686269	0.340684306457276	0.733425142273496	   
df.mm.trans1:probe7	-0.0190235993663108	0.0766698338686269	-0.248123654459817	0.804098452880594	   
df.mm.trans1:probe8	-0.0294794702899899	0.0766698338686269	-0.384498945706636	0.700704514021453	   
df.mm.trans1:probe9	0.0102698919476330	0.0766698338686269	0.133949578725192	0.89347404156107	   
df.mm.trans1:probe10	0.0177203660795070	0.0766698338686269	0.231125661624241	0.817272605782392	   
df.mm.trans1:probe11	-0.00240238682652715	0.0766698338686269	-0.0313341858891154	0.975010357299276	   
df.mm.trans1:probe12	-0.0412135645872418	0.0766698338686269	-0.537546027005366	0.591030703589231	   
df.mm.trans1:probe13	0.0673869761684302	0.0766698338686269	0.878924249189026	0.379689867805607	   
df.mm.trans1:probe14	-0.0346017116196859	0.0766698338686268	-0.451308029165365	0.651882271335802	   
df.mm.trans1:probe15	-0.0147596868764605	0.0766698338686269	-0.192509702078540	0.847388790947752	   
df.mm.trans1:probe16	0.0668392857676086	0.0766698338686268	0.871780756459407	0.383573298389286	   
df.mm.trans1:probe17	0.071700154328866	0.0766698338686269	0.935180770728205	0.349959811520511	   
df.mm.trans1:probe18	0.0267981531140736	0.0766698338686269	0.349526688162544	0.726780210630356	   
df.mm.trans1:probe19	0.00598439865613036	0.0766698338686269	0.0780541492549023	0.937803284806942	   
df.mm.trans1:probe20	0.0647461142088133	0.0766698338686269	0.844479646581147	0.398638212155438	   
df.mm.trans1:probe21	0.0124510614697232	0.0766698338686269	0.162398440709524	0.871030560191352	   
df.mm.trans1:probe22	-0.00621504061584652	0.0766698338686269	-0.0810623983677328	0.935411344331871	   
df.mm.trans2:probe2	-0.0715381106775799	0.0766698338686269	-0.933067245197895	0.351049281057384	   
df.mm.trans2:probe3	-0.0439623944309166	0.0766698338686269	-0.573398848186443	0.566525804423801	   
df.mm.trans2:probe4	0.109320417208714	0.0766698338686269	1.42585958117548	0.154274722572518	   
df.mm.trans2:probe5	-0.00490670300458486	0.0766698338686269	-0.0639978301373713	0.948986948588576	   
df.mm.trans2:probe6	0.008848428939363	0.0766698338686269	0.115409522792559	0.908147693345926	   
df.mm.trans3:probe2	0.119609210121860	0.0766698338686269	1.56005568404921	0.119117636368656	   
df.mm.trans3:probe3	0.195866053741710	0.0766698338686269	2.55466907724524	0.0108014942277261	*  
df.mm.trans3:probe4	0.182527659540306	0.0766698338686269	2.38069720945354	0.0174985853786236	*  
df.mm.trans3:probe5	0.00993085362335591	0.0766698338686269	0.129527522393910	0.896970784605313	   
df.mm.trans3:probe6	0.0640707058876884	0.0766698338686269	0.835670336751649	0.403574398843393	   
df.mm.trans3:probe7	0.00894423365514128	0.0766698338686269	0.116659097898492	0.907157664046718	   
df.mm.trans3:probe8	0.135677022057442	0.0766698338686269	1.76962718205343	0.0771464905432579	.  
df.mm.trans3:probe9	0.0370971190554158	0.0766698338686269	0.483855477226955	0.628612630068204	   
df.mm.trans3:probe10	0.210942105995295	0.0766698338686269	2.75130511377842	0.00606167246414656	**