chr3.14691_chr3_90102070_90102971_-_0.R 

fitVsDatCorrelation=0.864902245920776
cont.fitVsDatCorrelation=0.275430701693727

fstatistic=8223.72375008461,42,462
cont.fstatistic=2234.00049291329,42,462

residuals=-0.686906682512336,-0.0882185479217633,-0.00325778682318253,0.0868643996658316,0.810713780010748
cont.residuals=-0.661795312976079,-0.210146251742454,-0.0448491604713927,0.168406878820133,1.00827581947692

predictedValues:
Include	Exclude	Both
chr3.14691_chr3_90102070_90102971_-_0.R.tl.Lung	55.7796230419821	78.1484109228177	97.887346650992
chr3.14691_chr3_90102070_90102971_-_0.R.tl.cerebhem	53.3113618284442	87.2805360049334	82.0357859731422
chr3.14691_chr3_90102070_90102971_-_0.R.tl.cortex	51.1985386331933	68.3156542585195	87.0104121759455
chr3.14691_chr3_90102070_90102971_-_0.R.tl.heart	50.3970071121175	66.9094564694196	88.5393232701058
chr3.14691_chr3_90102070_90102971_-_0.R.tl.kidney	51.6737269271023	63.3441937426444	76.9794581672685
chr3.14691_chr3_90102070_90102971_-_0.R.tl.liver	49.9386330417549	60.2724526229663	70.7744115986013
chr3.14691_chr3_90102070_90102971_-_0.R.tl.stomach	55.4372035413623	74.3807045557032	95.4614104169277
chr3.14691_chr3_90102070_90102971_-_0.R.tl.testicle	52.9651450311241	78.9770249474227	92.3158317945848


diffExp=-22.3687878808355,-33.9691741764892,-17.1171156253262,-16.5124493573021,-11.6704668155421,-10.3338195812115,-18.9435010143408,-26.0118799162985
diffExpScore=0.993667968306497
diffExp1.5=0,-1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=-1,-1,0,0,0,0,0,-1
diffExp1.4Score=0.75
diffExp1.3=-1,-1,-1,-1,0,0,-1,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	74.4855089983757	65.8966932138416	65.0333599680638
cerebhem	76.6243054517673	69.1987012440123	64.9441082309504
cortex	74.4663008907043	72.9579249646506	69.067077495916
heart	67.4631739724626	79.0971502484414	77.73999261947
kidney	68.1466457886947	66.6152770085756	68.1271879149664
liver	71.4644832627236	72.0973820428219	70.339340719506
stomach	71.2683707380107	63.2050815188033	69.7002190956039
testicle	74.6526271846968	62.4766942221245	82.6388016147897
cont.diffExp=8.58881578453415,7.42560420775496,1.50837592605366,-11.6339762759788,1.53136878011910,-0.632898780098301,8.06328921920743,12.1759329625723
cont.diffExpScore=1.83969600854371

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=0.685084886519928
cont.tran.correlation=-0.373849032894109

tran.covariance=0.00368203363655206
cont.tran.covariance=-0.00134004058344298

tran.mean=62.3956045425942
cont.tran.mean=70.6322700469192

weightedLogRatios:
wLogRatio
Lung	-1.41287569709325
cerebhem	-2.08166790077019
cortex	-1.17676439154761
heart	-1.15110225698775
kidney	-0.824058329741405
liver	-0.753230367382439
stomach	-1.22346755503760
testicle	-1.66576911687331

cont.weightedLogRatios:
wLogRatio
Lung	0.520614350513928
cerebhem	0.437080147686418
cortex	0.0879966090667858
heart	-0.682697091634218
kidney	0.0956916646082713
liver	-0.0376810309858132
stomach	0.50505593567047
testicle	0.752060052550001

varWeightedLogRatios=0.189240433942161
cont.varWeightedLogRatios=0.202162649417792

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.84435108649365	0.0822303400576475	46.7510055752971	2.8978969139229e-177	***
df.mm.trans1	0.160189553110119	0.0660640079871401	2.42476286242430	0.0157009024629688	*  
df.mm.trans2	0.539902493300386	0.0660640079871401	8.17241505246673	2.928217569792e-15	***
df.mm.exp2	0.241920338593189	0.0887034943314745	2.72729209166396	0.00662873789357034	** 
df.mm.exp3	-0.102378917252326	0.0887034943314745	-1.15417005861966	0.249027134741891	   
df.mm.exp4	-0.156375737714967	0.0887034943314745	-1.76290391819977	0.0785777825052457	.  
df.mm.exp5	-0.0462069448804291	0.0887034943314745	-0.520914595627532	0.60267584083923	   
df.mm.exp6	-0.0460284979363381	0.0887034943314745	-0.518902871676453	0.604076935686246	   
df.mm.exp7	-0.0304756668734673	0.0887034943314745	-0.343567827887178	0.731327618494409	   
df.mm.exp8	0.0173743370846171	0.0887034943314745	0.195869815677061	0.844798183156638	   
df.mm.trans1:exp2	-0.287179486859607	0.0701262696258236	-4.09517700559185	4.98055190398283e-05	***
df.mm.trans2:exp2	-0.131402578855402	0.0701262696258236	-1.87379964108362	0.0615890212812664	.  
df.mm.trans1:exp3	0.0166812823357310	0.0701262696258236	0.237874942225478	0.81208354930357	   
df.mm.trans2:exp3	-0.032091866898298	0.0701262696258236	-0.45762974516586	0.647433606480708	   
df.mm.trans1:exp4	0.0548989040987121	0.0701262696258236	0.782857898924884	0.434111653670294	   
df.mm.trans2:exp4	0.00110632418847088	0.0701262696258236	0.0157761733851515	0.987419767407695	   
df.mm.trans1:exp5	-0.0302522102365167	0.0701262696258236	-0.431396257036557	0.666381498647406	   
df.mm.trans2:exp5	-0.163819529160816	0.0701262696258236	-2.33606507283100	0.0199146752851743	*  
df.mm.trans1:exp6	-0.0645852138322453	0.0701262696258236	-0.92098459217717	0.357539126752992	   
df.mm.trans2:exp6	-0.213706064449513	0.0701262696258236	-3.04744663575855	0.00244020785875284	** 
df.mm.trans1:exp7	0.0243179550755246	0.0701262696258236	0.346773829625891	0.728919130783584	   
df.mm.trans2:exp7	-0.0189374952730095	0.0701262696258236	-0.270048519250421	0.787243510725515	   
df.mm.trans1:exp8	-0.0691489050296023	0.0701262696258236	-0.986062789288006	0.324618421874531	   
df.mm.trans2:exp8	-0.00682707334487988	0.0701262696258236	-0.0973540070120293	0.922487487647673	   
df.mm.trans1:probe2	-0.00258698490824994	0.0470421317675461	-0.0549929352911399	0.956167864969647	   
df.mm.trans1:probe3	0.0244842402494169	0.0470421317675461	0.520474717651047	0.602982074938623	   
df.mm.trans1:probe4	0.0490209636306635	0.0470421317675461	1.04206509757882	0.297926484440557	   
df.mm.trans1:probe5	0.171742624510958	0.0470421317675461	3.65082571851986	0.000291271389736538	***
df.mm.trans1:probe6	0.0103589259747555	0.0470421317675461	0.220205283764415	0.825808551721679	   
df.mm.trans2:probe2	0.0211530911795662	0.0470421317675461	0.449662682892265	0.653164572137685	   
df.mm.trans2:probe3	-0.0530528209163482	0.0470421317675461	-1.12777246529777	0.260001366750105	   
df.mm.trans2:probe4	-0.254603631701399	0.0470421317675461	-5.41224689730255	1.00169556194828e-07	***
df.mm.trans2:probe5	-0.134827643734147	0.0470421317675461	-2.86610403627931	0.00434527889403337	** 
df.mm.trans2:probe6	0.0366731529557145	0.0470421317675461	0.779581017648842	0.436036697772558	   
df.mm.trans3:probe2	-0.344190241986265	0.0470421317675461	-7.31663785321308	1.13365665229238e-12	***
df.mm.trans3:probe3	-0.158711862282559	0.0470421317675461	-3.37382376859148	0.000803735140331028	***
df.mm.trans3:probe4	0.294686302962131	0.0470421317675461	6.26430588686528	8.58155113134487e-10	***
df.mm.trans3:probe5	-0.245078243898703	0.0470421317675461	-5.20976058461236	2.85429272452981e-07	***
df.mm.trans3:probe6	0.165416768313342	0.0470421317675461	3.51635357706008	0.000480720144801196	***
df.mm.trans3:probe7	0.133524315070125	0.0470421317675461	2.83839847500793	0.00473377384252415	** 
df.mm.trans3:probe8	0.199103032538957	0.0470421317675461	4.23244068790939	2.78963436753447e-05	***
df.mm.trans3:probe9	0.309617374570470	0.0470421317675461	6.58170373954166	1.26483118807941e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.24031529937158	0.157488331949345	26.924631475146	1.03624286155201e-96	***
df.mm.trans1	0.072706956084723	0.126526418503061	0.574638537507992	0.565815482949384	   
df.mm.trans2	-0.056304113132591	0.126526418503061	-0.444998869000855	0.65652898369176	   
df.mm.exp2	0.0785769083777627	0.169885778783702	0.462527875731178	0.643920575118323	   
df.mm.exp3	0.04135893138771	0.169885778783702	0.243451404136469	0.807763786199823	   
df.mm.exp4	-0.09490359460049	0.169885778783702	-0.558631777656449	0.576683693896642	   
df.mm.exp5	-0.124572983293532	0.169885778783702	-0.733274934402473	0.463762663980879	   
df.mm.exp6	-0.029905421248781	0.169885778783702	-0.176032517041090	0.860345528502033	   
df.mm.exp7	-0.155158627898306	0.169885778783702	-0.913311455550692	0.361555122257834	   
df.mm.exp8	-0.290632501208977	0.169885778783702	-1.71075238486565	0.0877981163350554	.  
df.mm.trans1:exp2	-0.0502671747905227	0.134306500757002	-0.374272090384292	0.70837361036777	   
df.mm.trans2:exp2	-0.0296830754806928	0.134306500757002	-0.221009968343957	0.825182312386945	   
df.mm.trans1:exp3	-0.0416168417520383	0.134306500757002	-0.309864686500430	0.756803575463633	   
df.mm.trans2:exp3	0.06043571184787	0.134306500757002	0.449983519094246	0.652933384182393	   
df.mm.trans1:exp4	-0.00411912355430215	0.134306500757002	-0.0306695769086768	0.975546298245932	   
df.mm.trans2:exp4	0.277492180124295	0.134306500757002	2.0661113092832	0.0393748282381807	*  
df.mm.trans1:exp5	0.0356303257801934	0.134306500757002	0.265291148078219	0.790903512234693	   
df.mm.trans2:exp5	0.135418657648100	0.134306500757002	1.00828073760264	0.313847485200449	   
df.mm.trans1:exp6	-0.0114985864275845	0.134306500757002	-0.0856145187520639	0.931809945715765	   
df.mm.trans2:exp6	0.119834893394450	0.134306500757002	0.892249390156213	0.37272375551669	   
df.mm.trans1:exp7	0.111006652355747	0.134306500757002	0.826517344507314	0.408937326356268	   
df.mm.trans2:exp7	0.113455068197120	0.134306500757002	0.844747406548787	0.398689116026869	   
df.mm.trans1:exp8	0.292873621562842	0.134306500757002	2.18063623065224	0.0297141072614806	*  
df.mm.trans2:exp8	0.237337834563328	0.134306500757002	1.76713586628795	0.0778656192733363	.  
df.mm.trans1:probe2	0.0869485830169108	0.0900955396538352	0.965070894197252	0.335014184876049	   
df.mm.trans1:probe3	0.0228717895375321	0.0900955396538352	0.253861507744001	0.799715491300187	   
df.mm.trans1:probe4	-0.0575460093809711	0.0900955396538353	-0.638722067730258	0.523320263278468	   
df.mm.trans1:probe5	-0.0382431612150605	0.0900955396538352	-0.424473413023533	0.671418185218487	   
df.mm.trans1:probe6	-0.0502960881471486	0.0900955396538352	-0.55825280963293	0.576942196472994	   
df.mm.trans2:probe2	0.0625304093678179	0.0900955396538353	0.694045561057429	0.488002463169737	   
df.mm.trans2:probe3	0.0692996877518234	0.0900955396538352	0.769180006225463	0.442179506268686	   
df.mm.trans2:probe4	0.00836314424909648	0.0900955396538353	0.0928252861487851	0.92608260071255	   
df.mm.trans2:probe5	-0.0343591388370703	0.0900955396538352	-0.381363372360995	0.703108957006827	   
df.mm.trans2:probe6	-0.0446779751117858	0.0900955396538353	-0.495895526942258	0.620203937226247	   
df.mm.trans3:probe2	-0.123355448963014	0.0900955396538353	-1.36916266262425	0.171613663403701	   
df.mm.trans3:probe3	-0.0632902444118352	0.0900955396538353	-0.702479219892669	0.482734137072837	   
df.mm.trans3:probe4	-0.0842562304252421	0.0900955396538353	-0.935187588075626	0.350180178198037	   
df.mm.trans3:probe5	-0.0640147766650244	0.0900955396538353	-0.710521041452016	0.477739596673987	   
df.mm.trans3:probe6	-0.095462382160264	0.0900955396538353	-1.05956834852257	0.289894651391420	   
df.mm.trans3:probe7	-0.118250383355044	0.0900955396538353	-1.31249986191753	0.190003183049616	   
df.mm.trans3:probe8	-0.169121905995934	0.0900955396538352	-1.87713960808420	0.0611288676210004	.  
df.mm.trans3:probe9	-0.0186085922937295	0.0900955396538353	-0.206542880649002	0.836457865426471