chr16.9576_chr16_4378148_4381627_+_2.R 

fitVsDatCorrelation=0.853643460363887
cont.fitVsDatCorrelation=0.223953216394157

fstatistic=8750.65173403336,51,669
cont.fstatistic=2489.97287303740,51,669

residuals=-0.648308130371672,-0.0954613000967246,-0.00102265756521548,0.0869429817338958,1.1758045361737
cont.residuals=-0.60851072720676,-0.207995944457268,-0.065948945593585,0.140763263986035,1.22388855025397

predictedValues:
Include	Exclude	Both
chr16.9576_chr16_4378148_4381627_+_2.R.tl.Lung	66.411319733725	58.7529443367108	79.0949365291595
chr16.9576_chr16_4378148_4381627_+_2.R.tl.cerebhem	55.0543099354191	81.8123910289065	102.682335728019
chr16.9576_chr16_4378148_4381627_+_2.R.tl.cortex	62.787793165207	107.053309378355	109.736950529078
chr16.9576_chr16_4378148_4381627_+_2.R.tl.heart	87.3722415476267	84.2829145082548	118.898003521418
chr16.9576_chr16_4378148_4381627_+_2.R.tl.kidney	62.1105745087539	70.4150845309494	77.8297279164767
chr16.9576_chr16_4378148_4381627_+_2.R.tl.liver	60.2367325526272	56.400431684207	63.4669551154806
chr16.9576_chr16_4378148_4381627_+_2.R.tl.stomach	59.997949661045	59.6365249222747	72.3531338302699
chr16.9576_chr16_4378148_4381627_+_2.R.tl.testicle	55.5047061053179	55.8178082921292	67.1609455727597


diffExp=7.65837539701425,-26.7580810934874,-44.2655162131484,3.08932703937185,-8.30451002219556,3.83630086842026,0.361424738770204,-0.313102186811292
diffExpScore=1.43976729463881
diffExp1.5=0,0,-1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,-1,-1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,-1,-1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,-1,-1,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	65.9194509108552	71.4486640442664	63.2562797623069
cerebhem	67.095138286848	65.4475042188192	61.846466637959
cortex	65.7668352954817	64.9645391018214	68.4499283444584
heart	67.8919925059958	64.851064930195	61.071952381818
kidney	65.86057589356	61.2322491599413	66.8756787491708
liver	62.1366467265993	64.1786429066117	86.870257778651
stomach	63.5133550320754	60.1083629670493	56.2848396883444
testicle	65.9539966702056	68.9364396193689	63.9782476084142
cont.diffExp=-5.52921313341126,1.64763406802875,0.802296193660368,3.04092757580086,4.62832673361866,-2.04199618001238,3.4049920650261,-2.98244294916334
cont.diffExpScore=6.06414333057163

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=0.285402466155096
cont.tran.correlation=0.331508840010948

tran.covariance=0.0107350081774371
cont.tran.covariance=0.000549062631280139

tran.mean=67.7279397432194
cont.tran.mean=65.3315911418559

weightedLogRatios:
wLogRatio
Lung	0.506597578792788
cerebhem	-1.66618092022907
cortex	-2.35118305559352
heart	0.160271566885329
kidney	-0.526016900685556
liver	0.267524453959736
stomach	0.0247202601438301
testicle	-0.0226090616789004

cont.weightedLogRatios:
wLogRatio
Lung	-0.340603906214765
cerebhem	0.104268419732219
cortex	0.0513055578460656
heart	0.192235150424884
kidney	0.302474660196194
liver	-0.134042985278843
stomach	0.227220864045402
testicle	-0.186244883039006

varWeightedLogRatios=1.04424852791705
cont.varWeightedLogRatios=0.0509113306767373

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.40223386893553	0.0896880869714203	49.0838194635407	6.46897434427308e-224	***
df.mm.trans1	-0.0937871188949843	0.0804636635415397	-1.16558350399452	0.244198275893195	   
df.mm.trans2	-0.266259288486249	0.0741176853337601	-3.59238537047203	0.000351695448067907	***
df.mm.exp2	-0.117451169108807	0.101558837645320	-1.15648398339285	0.247896045715092	   
df.mm.exp3	0.216441535189856	0.101558837645320	2.13119350524420	0.0334371201150647	*  
df.mm.exp4	0.227530858243378	0.101558837645320	2.24038462352236	0.0253934356137278	*  
df.mm.exp5	0.130240371587748	0.101558837645320	1.28241297958327	0.200141984594408	   
df.mm.exp6	0.0816798465893482	0.101558837645320	0.804261337399348	0.42153175829668	   
df.mm.exp7	0.00245991413082393	0.101558837645320	0.0242215664127118	0.980683098408608	   
df.mm.exp8	-0.067091064995943	0.101558837645320	-0.660612769419923	0.509088080686103	   
df.mm.trans1:exp2	-0.0700961993690645	0.0970428295362497	-0.722322295259131	0.470348757946472	   
df.mm.trans2:exp2	0.448538612678591	0.0847059260957258	5.29524477628282	1.61419317848431e-07	***
df.mm.trans1:exp3	-0.272548376829666	0.0970428295362497	-2.80853699476948	0.00512189782961232	** 
df.mm.trans2:exp3	0.383544125553699	0.0847059260957258	4.52794914396257	7.05085596577264e-06	***
df.mm.trans1:exp4	0.0467792516716523	0.0970428295362497	0.482047482489968	0.629929878115443	   
df.mm.trans2:exp4	0.133307043212651	0.0847059260957258	1.57376289189025	0.116015045729483	   
df.mm.trans1:exp5	-0.197191635070358	0.0970428295362497	-2.03200623902561	0.0425476286885504	*  
df.mm.trans2:exp5	0.0508258693914467	0.0847059260957258	0.600027314901304	0.548691212785207	   
df.mm.trans1:exp6	-0.179265024933240	0.0970428295362497	-1.84727739071413	0.0651482854808138	.  
df.mm.trans2:exp6	-0.122544302277355	0.0847059260957258	-1.44670282146338	0.148448536727821	   
df.mm.trans1:exp7	-0.104017044678753	0.0970428295362497	-1.07186739273609	0.284166108578929	   
df.mm.trans2:exp7	0.0124670383625697	0.0847059260957258	0.147180237997525	0.883034101435228	   
df.mm.trans1:exp8	-0.112308643026347	0.0970428295362497	-1.15731006157848	0.247558740487198	   
df.mm.trans2:exp8	0.0158427603494757	0.0847059260957258	0.187032490874037	0.851691863734502	   
df.mm.trans1:probe2	-0.0929337701128987	0.0485214147681248	-1.91531451745611	0.0558784575070833	.  
df.mm.trans1:probe3	0.138403746111533	0.0485214147681248	2.85242602205521	0.00447263490355802	** 
df.mm.trans1:probe4	-0.0760871352780514	0.0485214147681248	-1.56811452513613	0.117327250028934	   
df.mm.trans1:probe5	0.359089001554604	0.0485214147681248	7.4006292535085	4.08132594310985e-13	***
df.mm.trans1:probe6	0.00579821950852426	0.0485214147681249	0.119498154293994	0.904916577062477	   
df.mm.trans1:probe7	-0.224614016392284	0.0485214147681248	-4.62917286038905	4.41083076139696e-06	***
df.mm.trans1:probe8	-0.190098574044622	0.0485214147681249	-3.91782834348645	9.85321931719878e-05	***
df.mm.trans1:probe9	0.267763261729404	0.0485214147681248	5.51845536674882	4.89382337604955e-08	***
df.mm.trans1:probe10	-0.334185209141982	0.0485214147681249	-6.88737562041406	1.31254413309445e-11	***
df.mm.trans1:probe11	-0.278315886403973	0.0485214147681248	-5.73593922877136	1.46916128875362e-08	***
df.mm.trans1:probe12	-0.322788996931836	0.0485214147681248	-6.65250587754679	5.99635266443064e-11	***
df.mm.trans1:probe13	-0.111839430779298	0.0485214147681248	-2.30494991363625	0.0214746093306114	*  
df.mm.trans1:probe14	-0.062546899745001	0.0485214147681248	-1.28905762628525	0.197823588506626	   
df.mm.trans1:probe15	-0.0617434317046676	0.0485214147681248	-1.27249858644329	0.203638104861304	   
df.mm.trans1:probe16	-0.277184563186559	0.0485214147681248	-5.71262327183109	1.67463113042398e-08	***
df.mm.trans1:probe17	-0.291255042581203	0.0485214147681248	-6.00260820862414	3.18325557371368e-09	***
df.mm.trans1:probe18	-0.298527017281659	0.0485214147681248	-6.1524796568333	1.31328145778990e-09	***
df.mm.trans1:probe19	-0.349844594645609	0.0485214147681248	-7.21010704896908	1.51602002754840e-12	***
df.mm.trans1:probe20	-0.303262235884022	0.0485214147681248	-6.25006993990711	7.30583330960262e-10	***
df.mm.trans1:probe21	-0.197728948878080	0.0485214147681248	-4.07508622374248	5.15200831006709e-05	***
df.mm.trans2:probe2	-0.113360149425766	0.0485214147681248	-2.33629109883738	0.0197704418217074	*  
df.mm.trans2:probe3	-0.188134398992381	0.0485214147681249	-3.87734776266194	0.000116019482129099	***
df.mm.trans2:probe4	0.0383950149246937	0.0485214147681248	0.791300400208374	0.429049164040472	   
df.mm.trans2:probe5	-0.184084939515812	0.0485214147681248	-3.79389060264464	0.000161742411983185	***
df.mm.trans2:probe6	-0.116515337791995	0.0485214147681248	-2.40131781706697	0.0166080043185070	*  
df.mm.trans3:probe2	0.374990495978138	0.0485214147681248	7.72835041538154	4.00221916881089e-14	***
df.mm.trans3:probe3	0.610393696919516	0.0485214147681248	12.5798825083003	1.0010207277528e-32	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.37254942503347	0.167819318555356	26.0551017765643	7.50444009809574e-104	***
df.mm.trans1	-0.135058289569260	0.150559094747016	-0.89704504265384	0.370017571867807	   
df.mm.trans2	-0.107734449795127	0.138684856212570	-0.776829228059305	0.437534107459095	   
df.mm.exp2	-0.0475133785907584	0.190031201494382	-0.250029354217197	0.802641405364319	   
df.mm.exp3	-0.176363602660155	0.190031201494382	-0.928077080359717	0.353702300191337	   
df.mm.exp4	-0.0322596422230752	0.190031201494382	-0.169759712980760	0.865250431339564	   
df.mm.exp5	-0.210839690687231	0.190031201494382	-1.10950038219626	0.267613038229831	   
df.mm.exp6	-0.483627640932379	0.190031201494382	-2.54499070220675	0.0111515033501749	*  
df.mm.exp7	-0.0932443945209799	0.190031201494382	-0.490679392582467	0.623814125463999	   
df.mm.exp8	-0.0466191054033573	0.190031201494382	-0.245323426030832	0.806281249578388	   
df.mm.trans1:exp2	0.0651914092644543	0.181581100382335	0.359020895496218	0.719692736278202	   
df.mm.trans2:exp2	-0.0402174688488943	0.158496978528652	-0.253742810886606	0.79977221936809	   
df.mm.trans1:exp3	0.174045735126679	0.181581100382335	0.95850137905438	0.338156257128259	   
df.mm.trans2:exp3	0.081225964978198	0.158496978528652	0.512476425306206	0.608486765792994	   
df.mm.trans1:exp4	0.0617441832035922	0.181581100382334	0.340036397365059	0.73393585170014	   
df.mm.trans2:exp4	-0.0646262321423493	0.158496978528652	-0.407744253185663	0.683591893596876	   
df.mm.trans1:exp5	0.209946155868032	0.181581100382335	1.15621149682413	0.248007378427116	   
df.mm.trans2:exp5	0.0565344820453962	0.158496978528652	0.356691228881542	0.721435443671387	   
df.mm.trans1:exp6	0.424530024301249	0.181581100382335	2.33796371652867	0.0196829170827610	*  
df.mm.trans2:exp6	0.376318924846836	0.158496978528652	2.37429715279277	0.0178635901471323	*  
df.mm.trans1:exp7	0.0560610378346758	0.181581100382335	0.308738286730472	0.757616733851542	   
df.mm.trans2:exp7	-0.0795858291825297	0.158496978528652	-0.502128368132537	0.615742448684392	   
df.mm.trans1:exp8	0.0471430283123922	0.181581100382335	0.259625193443197	0.795232775415688	   
df.mm.trans2:exp8	0.0108248141006509	0.158496978528652	0.0682966590350118	0.945569890664563	   
df.mm.trans1:probe2	-0.109616759587184	0.0907905501911672	-1.20735868828173	0.227720723866212	   
df.mm.trans1:probe3	-0.00904124633685377	0.0907905501911672	-0.0995835614809763	0.92070477764145	   
df.mm.trans1:probe4	-0.112064146661974	0.0907905501911672	-1.23431509585539	0.217518907656654	   
df.mm.trans1:probe5	-0.0412852719954659	0.0907905501911672	-0.454730937399721	0.649450336568704	   
df.mm.trans1:probe6	-0.0306850443770350	0.0907905501911673	-0.33797619149157	0.73548715765926	   
df.mm.trans1:probe7	-0.0334188134596766	0.0907905501911672	-0.368086914214204	0.71292486074307	   
df.mm.trans1:probe8	-0.0628575979939768	0.0907905501911673	-0.692336348459446	0.488966359333366	   
df.mm.trans1:probe9	-0.151090211491220	0.0907905501911672	-1.66416230734461	0.096548273632972	.  
df.mm.trans1:probe10	-0.0508056890680059	0.0907905501911673	-0.559592258897321	0.575944884231333	   
df.mm.trans1:probe11	-0.0440149510497016	0.0907905501911672	-0.484796611068271	0.627979319052104	   
df.mm.trans1:probe12	-0.131255652219057	0.0907905501911672	-1.44569728834870	0.148730402618683	   
df.mm.trans1:probe13	-0.0318458821029528	0.0907905501911672	-0.350762078607284	0.725877270119604	   
df.mm.trans1:probe14	0.0616475163906008	0.0907905501911672	0.679008071443523	0.49736758013112	   
df.mm.trans1:probe15	-0.0572297732398008	0.0907905501911672	-0.630349448475624	0.528681226475012	   
df.mm.trans1:probe16	-0.153383087281234	0.0907905501911672	-1.68941687167081	0.091605497525301	.  
df.mm.trans1:probe17	-0.0444210610463378	0.0907905501911672	-0.489269653645732	0.624811173877241	   
df.mm.trans1:probe18	-0.098378774284658	0.0907905501911672	-1.08357944827422	0.278941672141745	   
df.mm.trans1:probe19	-0.0324405793131927	0.0907905501911672	-0.35731228905306	0.720970717381096	   
df.mm.trans1:probe20	-0.0521922185454337	0.0907905501911672	-0.57486399669942	0.565576546900106	   
df.mm.trans1:probe21	0.00699733502222119	0.0907905501911672	0.0770711820501991	0.93858996158492	   
df.mm.trans2:probe2	0.0578464679156712	0.0907905501911672	0.637141946974333	0.524250356199832	   
df.mm.trans2:probe3	0.00939553374512047	0.0907905501911673	0.103485811302359	0.917608423675716	   
df.mm.trans2:probe4	0.00148300991993148	0.0907905501911672	0.0163344083366482	0.986972477372142	   
df.mm.trans2:probe5	-0.0249014100101839	0.0907905501911672	-0.274273148006613	0.78395938566022	   
df.mm.trans2:probe6	-0.00634552084447228	0.0907905501911672	-0.0698918646391198	0.944300614339984	   
df.mm.trans3:probe2	0.0900080876550857	0.0907905501911672	0.991381674255371	0.321857671678873	   
df.mm.trans3:probe3	-0.0376949222860151	0.0907905501911672	-0.415185525438994	0.678139212743144