chr14.7522_chr14_58123834_58127870_-_2.R 

fitVsDatCorrelation=0.935275465873542
cont.fitVsDatCorrelation=0.229540082790716

fstatistic=10423.2806835857,59,853
cont.fstatistic=1365.68950544196,59,853

residuals=-0.617910866095309,-0.0958633363650938,-0.00462000369794142,0.092066088098251,0.949389156194782
cont.residuals=-0.91620610509131,-0.380914915697015,-0.0642842869137232,0.342044243364241,1.43219155279374

predictedValues:
Include	Exclude	Both
chr14.7522_chr14_58123834_58127870_-_2.R.tl.Lung	58.9222113343371	115.501584936218	83.2307279580535
chr14.7522_chr14_58123834_58127870_-_2.R.tl.cerebhem	73.2554713473067	89.2654037424135	79.0553388552186
chr14.7522_chr14_58123834_58127870_-_2.R.tl.cortex	58.2540550266423	107.936775785900	105.039571826085
chr14.7522_chr14_58123834_58127870_-_2.R.tl.heart	62.3196965434387	97.145593664102	74.5514232167788
chr14.7522_chr14_58123834_58127870_-_2.R.tl.kidney	60.8253890404644	127.394081132065	78.1176007864175
chr14.7522_chr14_58123834_58127870_-_2.R.tl.liver	62.013615440638	114.296485123745	73.3485219677794
chr14.7522_chr14_58123834_58127870_-_2.R.tl.stomach	60.9625804202217	97.7269588020846	70.469084166216
chr14.7522_chr14_58123834_58127870_-_2.R.tl.testicle	61.7576242911486	139.803049471842	137.853226854145


diffExp=-56.5793736018811,-16.0099323951068,-49.6827207592578,-34.8258971206633,-66.5686920916004,-52.2828696831066,-36.7643783818628,-78.0454251806936
diffExpScore=0.997447412154525
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	74.1173651387262	90.5247706315224	66.8431377366478
cerebhem	78.5729135182239	72.5388386647168	68.6026251903048
cortex	83.0985656534428	72.3586007702046	75.6626070973684
heart	77.7432233783182	73.2598340887756	74.3357862886633
kidney	75.7312327307888	78.1636881008545	71.6106412172818
liver	81.7462465664742	74.3235860752046	74.717779523136
stomach	84.7865084943111	79.8376475162445	70.3966183630409
testicle	77.7983529374746	65.0318103258808	70.1238980766229
cont.diffExp=-16.4074054927962,6.03407485350708,10.7399648832382,4.48338928954257,-2.43245537006561,7.42266049126961,4.94886097806666,12.7665426115938
cont.diffExpScore=2.28450042400910

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=-1,0,0,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.481412767758444
cont.tran.correlation=-0.29644754179533

tran.covariance=-0.00550529324952581
cont.tran.covariance=-0.00125498041245132

tran.mean=86.7112860064105
cont.tran.mean=77.4770740369477

weightedLogRatios:
wLogRatio
Lung	-2.97007336522494
cerebhem	-0.868282225433294
cortex	-2.69707959428393
heart	-1.93299392735066
kidney	-3.31022538238955
liver	-2.71056753166272
stomach	-2.05105549572060
testicle	-3.70249814100438

cont.weightedLogRatios:
wLogRatio
Lung	-0.881011335637448
cerebhem	0.34551476465342
cortex	0.602125051450961
heart	0.256823713641277
kidney	-0.137301847843715
liver	0.414656687847071
stomach	0.265226938571520
testicle	0.764384560069614

varWeightedLogRatios=0.797828202473325
cont.varWeightedLogRatios=0.262458914344288

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.77041453532574	0.0776992158918477	48.5257732918941	1.44040996855594e-247	***
df.mm.trans1	0.313949270424661	0.0670991422574128	4.67888649336613	3.35304308630266e-06	***
df.mm.trans2	0.909703370519093	0.0592817889017107	15.3454102410334	4.09744188857677e-47	***
df.mm.exp2	0.0115330686924455	0.0762553352955839	0.151242777279000	0.879819966882724	   
df.mm.exp3	-0.311863571382896	0.0762553352955839	-4.08972788820662	4.72763338758822e-05	***
df.mm.exp4	-0.00688654900531226	0.0762553352955839	-0.0903090777664061	0.928062807549192	   
df.mm.exp5	0.193191405980244	0.0762553352955839	2.53348051295543	0.011471650970753	*  
df.mm.exp6	0.167041661620618	0.0762553352955839	2.19055704067296	0.0287538459709733	*  
df.mm.exp7	0.0333778363167834	0.0762553352955839	0.437711488480157	0.661706247575433	   
df.mm.exp8	-0.266623233007634	0.0762553352955839	-3.49645348714472	0.000495824136071797	***
df.mm.trans1:exp2	0.206201748553617	0.0704843921421436	2.92549516689846	0.00353028284068293	** 
df.mm.trans2:exp2	-0.269203324232587	0.0520561735172905	-5.17140054758675	2.89678764487051e-07	***
df.mm.trans1:exp3	0.300459153574206	0.0704843921421435	4.26277569320993	2.24426349384384e-05	***
df.mm.trans2:exp3	0.244124965439125	0.0520561735172905	4.68964483834063	3.18569631279298e-06	***
df.mm.trans1:exp4	0.062945959204475	0.0704843921421435	0.893048195372586	0.372083237793055	   
df.mm.trans2:exp4	-0.166186884464176	0.0520561735172905	-3.19245294525878	0.00146238392652152	** 
df.mm.trans1:exp5	-0.161402243292051	0.0704843921421436	-2.28990047848545	0.0222704506172121	*  
df.mm.trans2:exp5	-0.0951903751126143	0.0520561735172905	-1.82860876397296	0.067807292524031	.  
df.mm.trans1:exp6	-0.115905818826499	0.0704843921421435	-1.64441822230311	0.100458302292533	   
df.mm.trans2:exp6	-0.177530094875201	0.0520561735172905	-3.41035621483462	0.000679247239285944	***
df.mm.trans1:exp7	0.000664281870949244	0.0704843921421435	0.00942452436292008	0.992482632502158	   
df.mm.trans2:exp7	-0.200484633075487	0.0520561735172905	-3.85131329349237	0.000126279036277013	***
df.mm.trans1:exp8	0.313622549173065	0.0704843921421435	4.4495318699861	9.74575056223602e-06	***
df.mm.trans2:exp8	0.457573623413551	0.0520561735172905	8.7899972759536	8.13609030762412e-18	***
df.mm.trans1:probe2	-0.185643689918078	0.0482573644103649	-3.84695045380898	0.000128508777214247	***
df.mm.trans1:probe3	-0.249835390477998	0.0482573644103649	-5.17714536487072	2.81166211341362e-07	***
df.mm.trans1:probe4	-0.176642941063607	0.0482573644103649	-3.66043490401783	0.000267272817791389	***
df.mm.trans1:probe5	-0.12503248971894	0.0482573644103649	-2.59095147956495	0.00973456220755076	** 
df.mm.trans1:probe6	-0.258613847444653	0.0482573644103649	-5.35905453197745	1.07718087738472e-07	***
df.mm.trans1:probe7	-0.174703079584731	0.0482573644103649	-3.62023665650517	0.000311684820607924	***
df.mm.trans1:probe8	-0.0177766189187175	0.0482573644103649	-0.368371110522136	0.712687934827023	   
df.mm.trans1:probe9	-0.260526501724203	0.0482573644103649	-5.3986889857633	8.70631408539723e-08	***
df.mm.trans1:probe10	-0.211733510806838	0.0482573644103649	-4.38758961236104	1.28963638500531e-05	***
df.mm.trans1:probe11	-0.397676652134486	0.0482573644103649	-8.24074536588393	6.41907608757507e-16	***
df.mm.trans1:probe12	-0.296322858749594	0.0482573644103649	-6.14046917750752	1.25999702388430e-09	***
df.mm.trans1:probe13	-0.292760893133766	0.0482573644103649	-6.06665732185917	1.96164652672982e-09	***
df.mm.trans1:probe14	-0.234396931230468	0.0482573644103649	-4.85722612692298	1.41610963282470e-06	***
df.mm.trans1:probe15	-0.299107359240609	0.0482573644103649	-6.19817022531727	8.8853724328238e-10	***
df.mm.trans1:probe16	-0.272744441162535	0.0482573644103649	-5.65187188515324	2.16377524113173e-08	***
df.mm.trans1:probe17	0.450540231487517	0.0482573644103649	9.33619639183503	8.43804538423571e-20	***
df.mm.trans1:probe18	0.854809295497668	0.0482573644103649	17.7135512049239	5.0202177256524e-60	***
df.mm.trans1:probe19	0.352883360067071	0.0482573644103649	7.3125286550311	6.04066598981861e-13	***
df.mm.trans1:probe20	0.460230796218011	0.0482573644103649	9.53700646194347	1.48900961158725e-20	***
df.mm.trans1:probe21	0.557467659883846	0.0482573644103649	11.5519707032345	8.80324384857818e-29	***
df.mm.trans1:probe22	0.516923982872965	0.0482573644103649	10.7118154749856	3.30190646974424e-25	***
df.mm.trans2:probe2	0.507963386270302	0.0482573644103649	10.5261319692213	1.91284995838028e-24	***
df.mm.trans2:probe3	0.194924010240306	0.0482573644103649	4.03925934667164	5.8454190062723e-05	***
df.mm.trans2:probe4	0.115282394131844	0.0482573644103649	2.38890779760619	0.0171149923262668	*  
df.mm.trans2:probe5	0.199266660101203	0.0482573644103649	4.12924871749532	3.99736106887261e-05	***
df.mm.trans2:probe6	0.089225091886265	0.0482573644103649	1.8489424977196	0.0648119653147565	.  
df.mm.trans3:probe2	-0.870503106858937	0.0482573644103649	-18.0387619070213	7.21687224254935e-62	***
df.mm.trans3:probe3	-0.6304138459928	0.0482573644103649	-13.0635780402752	1.1008342378775e-35	***
df.mm.trans3:probe4	-0.322565126951689	0.0482573644103649	-6.68426738370335	4.18961970098923e-11	***
df.mm.trans3:probe5	-0.889962668188795	0.0482573644103649	-18.4420073301319	3.5769016988701e-64	***
df.mm.trans3:probe6	-0.884469481440404	0.0482573644103649	-18.3281762741778	1.60827573390750e-63	***
df.mm.trans3:probe7	-0.723095695280539	0.0482573644103649	-14.9841522452733	3.12274779365515e-45	***
df.mm.trans3:probe8	-0.147177225694912	0.0482573644103649	-3.0498396979032	0.0023604163562062	** 
df.mm.trans3:probe9	-0.640557897898266	0.0482573644103649	-13.2737853740037	1.08884248636466e-36	***
df.mm.trans3:probe10	-0.61576065376994	0.0482573644103649	-12.7599312828963	2.98158450724654e-34	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.78441760999486	0.213676688506416	22.3909198679444	1.04075817015518e-87	***
df.mm.trans1	-0.41143487004018	0.184525961486429	-2.22968555061797	0.0260279257392382	*  
df.mm.trans2	-0.281982143934377	0.163027852930791	-1.72965624502265	0.0840534587029215	.  
df.mm.exp2	-0.189106377090287	0.209705945418895	-0.901769268928166	0.36743399573865	   
df.mm.exp3	-0.233547039643449	0.209705945418895	-1.11368821316406	0.265726684708524	   
df.mm.exp4	-0.270093328317161	0.209705945418895	-1.28796218808980	0.198108410014215	   
df.mm.exp5	-0.194172556606953	0.209705945418895	-0.925927761461827	0.35474540760546	   
df.mm.exp6	-0.210594563319804	0.209705945418895	-1.00423744734149	0.315548962247037	   
df.mm.exp7	-0.0429383560738452	0.209705945418895	-0.204755072575908	0.83781233653029	   
df.mm.exp8	-0.330191577181404	0.209705945418895	-1.57454561682472	0.115732162004356	   
df.mm.trans1:exp2	0.247483553207528	0.193835566182349	1.27677060552815	0.202030707769900	   
df.mm.trans2:exp2	-0.0323950206580486	0.143157053077251	-0.226290077657351	0.821030027609442	   
df.mm.trans1:exp3	0.347924628669614	0.193835566182349	1.79494731293176	0.0730160218636233	.  
df.mm.trans2:exp3	0.00955784103161823	0.143157053077251	0.0667647232613864	0.94678465256059	   
df.mm.trans1:exp4	0.317854864258397	0.193835566182349	1.63981703935272	0.101411916428587	   
df.mm.trans2:exp4	0.0584822990846747	0.143157053077251	0.40851846156065	0.68299561155831	   
df.mm.trans1:exp5	0.215713365373573	0.193835566182349	1.11286782721105	0.266078716083057	   
df.mm.trans2:exp5	0.0473542278894803	0.143157053077251	0.330785154287347	0.740888010379835	   
df.mm.trans1:exp6	0.308564606284937	0.193835566182349	1.59188848755784	0.111780253889189	   
df.mm.trans2:exp6	0.0133993865904152	0.143157053077251	0.0935992066222863	0.925449519731685	   
df.mm.trans1:exp7	0.177424936071617	0.193835566182349	0.91533736334387	0.360273231844023	   
df.mm.trans2:exp7	-0.0826899992240572	0.143157053077251	-0.577617361119022	0.56367491507936	   
df.mm.trans1:exp8	0.378661985473953	0.193835566182349	1.95352170363684	0.0510843641417504	.  
df.mm.trans2:exp8	-0.000555404896185007	0.143157053077251	-0.0038796893638576	0.99690537064486	   
df.mm.trans1:probe2	-0.0689901060455594	0.132710140056072	-0.519855574083562	0.603299118787616	   
df.mm.trans1:probe3	-0.143348067283873	0.132710140056072	-1.08015911386504	0.28037663959566	   
df.mm.trans1:probe4	0.0611696673869761	0.132710140056072	0.460926854279039	0.644968579092559	   
df.mm.trans1:probe5	-0.180100830491085	0.132710140056072	-1.35709924211511	0.175108659941227	   
df.mm.trans1:probe6	-0.305214232988169	0.132710140056072	-2.29985615913909	0.0216970317844298	*  
df.mm.trans1:probe7	-0.129060486167549	0.132710140056072	-0.972499057818936	0.331078019806253	   
df.mm.trans1:probe8	-0.181762872861653	0.132710140056072	-1.36962309575481	0.171165027509892	   
df.mm.trans1:probe9	-0.139317970294371	0.132710140056072	-1.04979144951175	0.294111337270835	   
df.mm.trans1:probe10	-0.133943879829075	0.132710140056072	-1.00929649966823	0.313118668884292	   
df.mm.trans1:probe11	0.00146877026499528	0.132710140056072	0.0110675059522557	0.991172176024641	   
df.mm.trans1:probe12	-0.0587253356490673	0.132710140056072	-0.442508278751381	0.658233622181038	   
df.mm.trans1:probe13	-0.0952118729278704	0.132710140056072	-0.717442336264898	0.473297623147321	   
df.mm.trans1:probe14	-0.0572407890084154	0.132710140056072	-0.431321894349822	0.666343315917494	   
df.mm.trans1:probe15	0.00579401934214369	0.132710140056072	0.0436592059935708	0.965186274862195	   
df.mm.trans1:probe16	-0.00713763463618478	0.132710140056072	-0.053783641801365	0.957120136026212	   
df.mm.trans1:probe17	-0.0791713682684916	0.132710140056072	-0.596573617020073	0.550950380088666	   
df.mm.trans1:probe18	-0.134264410620128	0.132710140056072	-1.01171176945031	0.311962774479687	   
df.mm.trans1:probe19	-0.159821359463033	0.132710140056072	-1.20428898195350	0.228811956364737	   
df.mm.trans1:probe20	-0.0850248284715405	0.132710140056072	-0.640680722932068	0.52190221447858	   
df.mm.trans1:probe21	-0.129400288376112	0.132710140056072	-0.975059541956914	0.329807156217426	   
df.mm.trans1:probe22	-0.135348531818650	0.132710140056072	-1.01988086035824	0.308074134249201	   
df.mm.trans2:probe2	0.0785097677115202	0.132710140056072	0.591588311777445	0.554283047790254	   
df.mm.trans2:probe3	-0.156774874804751	0.132710140056072	-1.18133305215796	0.237799696708141	   
df.mm.trans2:probe4	0.145230540679885	0.132710140056072	1.09434396360762	0.274113069208628	   
df.mm.trans2:probe5	0.0476265407994868	0.132710140056072	0.358876426318017	0.71977634365236	   
df.mm.trans2:probe6	-0.063583081112068	0.132710140056072	-0.479112455801817	0.631981426980022	   
df.mm.trans3:probe2	0.205044657579703	0.132710140056072	1.54505644778212	0.122703508214302	   
df.mm.trans3:probe3	0.0950102075651523	0.132710140056072	0.715922743544757	0.474234987936399	   
df.mm.trans3:probe4	0.0989187714226295	0.132710140056072	0.74537462910396	0.45625060778986	   
df.mm.trans3:probe5	0.0142561339848030	0.132710140056072	0.107423095015796	0.914478593823028	   
df.mm.trans3:probe6	0.046119176059205	0.132710140056072	0.347518102533226	0.728287850491708	   
df.mm.trans3:probe7	0.230172009687625	0.132710140056072	1.73439655470466	0.0832090019528145	.  
df.mm.trans3:probe8	0.250248614765460	0.132710140056072	1.88567817545611	0.0596779231386835	.  
df.mm.trans3:probe9	0.0168674097798445	0.132710140056072	0.127099630613890	0.898891498360848	   
df.mm.trans3:probe10	0.156843525159408	0.132710140056072	1.18185034763085	0.237594451935096