chr17.10343_chr17_73679364_73682364_+_2.R 

fitVsDatCorrelation=0.9381298762113
cont.fitVsDatCorrelation=0.283140621064512

fstatistic=6909.47732303727,52,692
cont.fstatistic=889.170010213384,52,692

residuals=-0.675634171372679,-0.0844726707337152,6.57051915814728e-05,0.0815195195585436,1.81337372027526
cont.residuals=-1.13898003481019,-0.440699780991438,-0.0833213319405801,0.362359864961358,2.12536907478555

predictedValues:
Include	Exclude	Both
chr17.10343_chr17_73679364_73682364_+_2.R.tl.Lung	66.1832978756314	102.424157168562	85.095491131153
chr17.10343_chr17_73679364_73682364_+_2.R.tl.cerebhem	60.960201017611	71.3365792212722	81.0526607730767
chr17.10343_chr17_73679364_73682364_+_2.R.tl.cortex	60.5640087576131	97.111593211996	88.961876468011
chr17.10343_chr17_73679364_73682364_+_2.R.tl.heart	65.9705357494597	98.3570757214074	81.7925059668853
chr17.10343_chr17_73679364_73682364_+_2.R.tl.kidney	63.6282039269981	100.691373905624	83.5485414766508
chr17.10343_chr17_73679364_73682364_+_2.R.tl.liver	67.58144816018	91.7642013961462	73.0360360645803
chr17.10343_chr17_73679364_73682364_+_2.R.tl.stomach	68.9052840239062	91.216237960092	90.1691600838388
chr17.10343_chr17_73679364_73682364_+_2.R.tl.testicle	66.5358070925546	119.366255477862	97.060830282788


diffExp=-36.2408592929311,-10.3763782036612,-36.5475844543828,-32.3865399719477,-37.0631699786263,-24.1827532359661,-22.3109539361858,-52.8304483853073
diffExpScore=0.996046472724098
diffExp1.5=-1,0,-1,0,-1,0,0,-1
diffExp1.5Score=0.8
diffExp1.4=-1,0,-1,-1,-1,0,0,-1
diffExp1.4Score=0.833333333333333
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	74.4824395892173	80.6482095791841	57.48672747173
cerebhem	80.4969596145407	75.1579322131103	97.73764268977
cortex	80.9617234174169	59.7515671897053	63.4598540230177
heart	77.1980076029661	85.7978189216933	58.9464197095655
kidney	75.6702339078423	58.4958251690614	75.3514056286922
liver	73.676134388455	74.0049069927901	54.553323734252
stomach	74.7026460404824	85.7752219774827	61.9621087021058
testicle	78.953416610063	81.4105102907694	121.059552780848
cont.diffExp=-6.16576998996682,5.33902740143039,21.2101562277116,-8.59981131872725,17.1744087387809,-0.328772604335072,-11.0725759370004,-2.45709368070628
cont.diffExpScore=4.49376108331227

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=0,0,1,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,1,0,1,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.365327944653003
cont.tran.correlation=-0.275955058989390

tran.covariance=0.00272692949316046
cont.tran.covariance=-0.00149909734281692

tran.mean=80.7872662916823
cont.tran.mean=76.0739720940488

weightedLogRatios:
wLogRatio
Lung	-1.92616117221487
cerebhem	-0.658431568333909
cortex	-2.04907072482796
heart	-1.7529128355392
kidney	-2.01160882654771
liver	-1.33559527840287
stomach	-1.22662197723097
testicle	-2.62419026534843

cont.weightedLogRatios:
wLogRatio
Lung	-0.345995739799927
cerebhem	0.298798752068039
cortex	1.28866542349306
heart	-0.464641497505096
kidney	1.08060405878175
liver	-0.0191541295702457
stomach	-0.605742739189229
testicle	-0.134359231915226

varWeightedLogRatios=0.366962771479669
cont.varWeightedLogRatios=0.498263458578381

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81467858994804	0.105648486022344	36.1072717042178	2.59994672412933e-161	***
df.mm.trans1	0.0307336041475972	0.0948877903791782	0.323894191494855	0.746116008916098	   
df.mm.trans2	0.642554234855995	0.087262210197335	7.3634879680783	5.10510291953593e-13	***
df.mm.exp2	-0.395245315900692	0.119527617421587	-3.30672797155003	0.00099269886011557	***
df.mm.exp3	-0.186423035989166	0.119527617421587	-1.55966495451534	0.119296269225574	   
df.mm.exp4	-0.00414960126231456	0.119527617421587	-0.0347166734502743	0.972315676851338	   
df.mm.exp5	-0.0380875100352281	0.119527617421587	-0.318650290676248	0.750087864351122	   
df.mm.exp6	0.0638261275856577	0.119527617421587	0.533986445664151	0.593522401167148	   
df.mm.exp7	-0.133498344137930	0.119527617421587	-1.11688283442534	0.264432062117541	   
df.mm.exp8	0.0268222237800826	0.119527617421587	0.224401894379586	0.822510836854577	   
df.mm.trans1:exp2	0.313038391647344	0.114438981044956	2.73541750187701	0.00638972438806583	** 
df.mm.trans2:exp2	0.0335319496888496	0.0996063478513225	0.336644706007102	0.736486815190802	   
df.mm.trans1:exp3	0.0976957045769584	0.114438981044956	0.853692541517647	0.393570776823454	   
df.mm.trans2:exp3	0.133161204089451	0.0996063478513225	1.33687467678480	0.181702871119225	   
df.mm.trans1:exp4	0.000929682412293354	0.114438981044956	0.00812382637283479	0.993520537028343	   
df.mm.trans2:exp4	-0.0363685068278403	0.0996063478513225	-0.365122380374048	0.715131620004737	   
df.mm.trans1:exp5	-0.00128379310474549	0.114438981044956	-0.0112181451898909	0.991052636168698	   
df.mm.trans2:exp5	0.0210250501437542	0.0996063478513225	0.21108142801438	0.832885876037979	   
df.mm.trans1:exp6	-0.0429207508606399	0.114438981044956	-0.375053591605984	0.707735529585185	   
df.mm.trans2:exp6	-0.173726463651799	0.0996063478513225	-1.74413044348450	0.0815803611652113	.  
df.mm.trans1:exp7	0.173803077260913	0.114438981044956	1.51874016767622	0.129284632898349	   
df.mm.trans2:exp7	0.017608678545315	0.0996063478513225	0.176782694327862	0.859730809235	   
df.mm.trans1:exp8	-0.0215101016714888	0.114438981044956	-0.187961317682816	0.850962076850691	   
df.mm.trans2:exp8	0.126251725163946	0.0996063478513225	1.26750681946893	0.205400474577924	   
df.mm.trans1:probe2	0.00232853836336326	0.057219490522478	0.040694846145974	0.967550908363313	   
df.mm.trans1:probe3	0.31910104222188	0.057219490522478	5.57678929518823	3.51515948714775e-08	***
df.mm.trans1:probe4	1.25213591354513	0.057219490522478	21.8830315004858	4.44691631957401e-81	***
df.mm.trans1:probe5	-0.0322773069003032	0.057219490522478	-0.564096370058091	0.572871320265229	   
df.mm.trans1:probe6	0.458645329946662	0.057219490522478	8.01554375543572	4.67010849775365e-15	***
df.mm.trans1:probe7	-0.117910402632504	0.057219490522478	-2.06066851619701	0.0397078811457173	*  
df.mm.trans1:probe8	-0.0579281742359379	0.057219490522478	-1.01238535518210	0.311707681012788	   
df.mm.trans1:probe9	0.08717530599618	0.057219490522478	1.52352468014258	0.128084332720863	   
df.mm.trans1:probe10	0.0307568661557736	0.057219490522478	0.537524292420798	0.591078316257683	   
df.mm.trans1:probe11	0.665555481979502	0.057219490522478	11.6316219508813	1.09157539543153e-28	***
df.mm.trans1:probe12	0.77237379856664	0.057219490522478	13.4984389325036	4.98804121571834e-37	***
df.mm.trans1:probe13	1.72723090978192	0.057219490522478	30.1860588762739	2.26506011295135e-128	***
df.mm.trans1:probe14	1.22430127628107	0.057219490522478	21.3965777238109	2.40093716120328e-78	***
df.mm.trans1:probe15	1.09581463944284	0.057219490522478	19.1510729899344	6.41236389400846e-66	***
df.mm.trans1:probe16	1.18681891794945	0.057219490522478	20.7415149473101	1.09628865509035e-74	***
df.mm.trans1:probe17	0.121590769001998	0.057219490522478	2.12498866892623	0.0339412995273622	*  
df.mm.trans1:probe18	-0.00591536140852781	0.057219490522478	-0.103380183124910	0.917691191608088	   
df.mm.trans1:probe19	0.0216117309440876	0.057219490522478	0.37769876569589	0.70577020110355	   
df.mm.trans1:probe20	-0.0125124543006658	0.057219490522478	-0.218674689103538	0.826967942305186	   
df.mm.trans1:probe21	-0.0574581594055828	0.057219490522478	-1.00417111164265	0.315647140780300	   
df.mm.trans1:probe22	-0.00604018470158353	0.057219490522478	-0.105561665202362	0.915960732511101	   
df.mm.trans2:probe2	0.127103221227519	0.057219490522478	2.22132738454894	0.0266516439459524	*  
df.mm.trans2:probe3	0.71372226875117	0.057219490522478	12.4734118083556	2.35623618793541e-32	***
df.mm.trans2:probe4	0.0535291749698155	0.057219490522478	0.935505969749715	0.349853948622408	   
df.mm.trans2:probe5	0.465629683270005	0.057219490522478	8.13760624252829	1.8717675641843e-15	***
df.mm.trans2:probe6	0.187023580235853	0.057219490522478	3.26852928133610	0.00113454671676668	** 
df.mm.trans3:probe2	0.268390080480338	0.057219490522478	4.6905360049458	3.28403032818792e-06	***
df.mm.trans3:probe3	-0.400967214356296	0.057219490522478	-7.00752856579141	5.76658455187235e-12	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.66251104575986	0.292607504328762	15.9343522527066	6.29972585270385e-49	***
df.mm.trans1	-0.189343237004511	0.262804329522051	-0.720472289588453	0.471477550274052	   
df.mm.trans2	-0.39203882418425	0.241684273096481	-1.62211143969532	0.105234970365289	   
df.mm.exp2	-0.523581593311754	0.331047600859103	-1.58159005518543	0.114200108179084	   
df.mm.exp3	-0.315341470934362	0.331047600859103	-0.952556279266239	0.341147532502744	   
df.mm.exp4	0.0726324460950453	0.331047600859103	0.219401819878943	0.82640175158212	   
df.mm.exp5	-0.575928217889144	0.331047600859103	-1.739714217516	0.0823538196343364	.  
df.mm.exp6	-0.0444742103006944	0.331047600859103	-0.134343853226180	0.893169740286674	   
df.mm.exp7	-0.0103832551135078	0.331047600859103	-0.0313648402421953	0.974987624587411	   
df.mm.exp8	-0.677026094402542	0.331047600859103	-2.04510195103541	0.0412225407757661	*  
df.mm.trans1:exp2	0.601237620869897	0.316953947020205	1.8969242267602	0.0582540575769895	.  
df.mm.trans2:exp2	0.453076651239867	0.275873000715919	1.64233777884782	0.100974331332826	   
df.mm.trans1:exp3	0.398754576127764	0.316953947020206	1.25808364236065	0.20878584180554	   
df.mm.trans2:exp3	0.0154402860797374	0.275873000715919	0.0559688191293393	0.955382802405054	   
df.mm.trans1:exp4	-0.0368221850387353	0.316953947020206	-0.116175190070714	0.907547404457212	   
df.mm.trans2:exp4	-0.0107354648559919	0.275873000715919	-0.0389145180142032	0.968969765315508	   
df.mm.trans1:exp5	0.591749702358045	0.316953947020206	1.86698953561326	0.0623256854255844	.  
df.mm.trans2:exp5	0.254787000510277	0.275873000715919	0.923566278139138	0.356034093611625	   
df.mm.trans1:exp6	0.0335897486128094	0.316953947020206	0.105976748132018	0.915631513209443	   
df.mm.trans2:exp6	-0.0414909924188874	0.275873000715919	-0.150398887572230	0.880493745815683	   
df.mm.trans1:exp7	0.0133353814639600	0.316953947020206	0.0420735617566230	0.966452192519602	   
df.mm.trans2:exp7	0.0720168273201997	0.275873000715919	0.26105065422607	0.794131113978436	   
df.mm.trans1:exp8	0.73532072239646	0.316953947020206	2.31996076814776	0.0206330350910383	*  
df.mm.trans2:exp8	0.686433873688436	0.275873000715919	2.48822418977960	0.0130724500579421	*  
df.mm.trans1:probe2	-0.145767000097334	0.158476973510103	-0.91979924192609	0.357998192088725	   
df.mm.trans1:probe3	-0.0537062557058421	0.158476973510103	-0.338889963105072	0.734795432906752	   
df.mm.trans1:probe4	-0.323914582797294	0.158476973510103	-2.04392206402556	0.0413393197153706	*  
df.mm.trans1:probe5	-0.257904512866392	0.158476973510103	-1.62739423371150	0.104108599371351	   
df.mm.trans1:probe6	-0.132382116889573	0.158476973510103	-0.835339759193052	0.403814748346946	   
df.mm.trans1:probe7	-0.152702418889307	0.158476973510103	-0.96356218513709	0.335602061326275	   
df.mm.trans1:probe8	-0.49091461179422	0.158476973510103	-3.09770309793886	0.00202916249271426	** 
df.mm.trans1:probe9	-0.146295904325933	0.158476973510103	-0.923136661974471	0.356257746964052	   
df.mm.trans1:probe10	-0.316460591552459	0.158476973510103	-1.99688689494241	0.0462286902691158	*  
df.mm.trans1:probe11	-0.159697494999726	0.158476973510103	-1.00770156990375	0.313949974345141	   
df.mm.trans1:probe12	-0.0505620594443427	0.158476973510103	-0.319049880398678	0.749784970235953	   
df.mm.trans1:probe13	-0.328445853611624	0.158476973510103	-2.07251467728645	0.0385871783704368	*  
df.mm.trans1:probe14	-0.168388764166620	0.158476973510103	-1.06254404306809	0.288359433629502	   
df.mm.trans1:probe15	-0.194932623154922	0.158476973510103	-1.23003751798993	0.219101052402326	   
df.mm.trans1:probe16	-0.150176608786606	0.158476973510103	-0.947624159272779	0.34365161600941	   
df.mm.trans1:probe17	-0.191112111079722	0.158476973510103	-1.20592983855499	0.228256697710129	   
df.mm.trans1:probe18	-0.186043597831561	0.158476973510103	-1.17394719062894	0.240820039497408	   
df.mm.trans1:probe19	-0.188746900493102	0.158476973510103	-1.19100520607222	0.234059899738741	   
df.mm.trans1:probe20	0.0410093670355363	0.158476973510103	0.258771770606296	0.795888291388883	   
df.mm.trans1:probe21	-0.247045121397721	0.158476973510103	-1.55887076794770	0.119484164476307	   
df.mm.trans1:probe22	-0.220920771293729	0.158476973510103	-1.39402442134374	0.163757679622540	   
df.mm.trans2:probe2	0.0740123118675005	0.158476973510103	0.467022496885216	0.64063079094382	   
df.mm.trans2:probe3	0.229593544574963	0.158476973510103	1.44875018426779	0.1478603268841	   
df.mm.trans2:probe4	0.269297849328315	0.158476973510103	1.69928692707618	0.0897145685009062	.  
df.mm.trans2:probe5	0.206833283798594	0.158476973510103	1.30513145990517	0.192281885577587	   
df.mm.trans2:probe6	0.296882456198192	0.158476973510103	1.87334758875406	0.0614416938036837	.  
df.mm.trans3:probe2	-0.123452799150819	0.158476973510103	-0.778995184072904	0.436248855902649	   
df.mm.trans3:probe3	0.0347281291779451	0.158476973510103	0.219136751597109	0.826608140310133