chr13.6343_chr13_22766889_22772299_-_2.R 

fitVsDatCorrelation=0.689674220787736
cont.fitVsDatCorrelation=0.205687967948632

fstatistic=12701.758613225,54,738
cont.fstatistic=6948.19925115157,54,738

residuals=-0.398456688839439,-0.0797946444693303,-0.000173681224259035,0.0669485494102347,0.824816179690085
cont.residuals=-0.382087508095826,-0.11712426507195,-0.0145581256789325,0.0918460005020635,1.26802627943691

predictedValues:
Include	Exclude	Both
chr13.6343_chr13_22766889_22772299_-_2.R.tl.Lung	58.3228357873866	54.0551088792763	47.7301462309878
chr13.6343_chr13_22766889_22772299_-_2.R.tl.cerebhem	67.0847152231063	77.9798302809219	50.345403794362
chr13.6343_chr13_22766889_22772299_-_2.R.tl.cortex	57.4737554114616	56.8057235556608	56.2445476118067
chr13.6343_chr13_22766889_22772299_-_2.R.tl.heart	58.1052938374714	52.8682373637322	51.1185476771485
chr13.6343_chr13_22766889_22772299_-_2.R.tl.kidney	57.7523099290515	53.736864221802	49.5599224682068
chr13.6343_chr13_22766889_22772299_-_2.R.tl.liver	56.8560377962133	54.0906789935529	50.0704068540881
chr13.6343_chr13_22766889_22772299_-_2.R.tl.stomach	56.6770908999012	52.606514252963	44.9138131704159
chr13.6343_chr13_22766889_22772299_-_2.R.tl.testicle	57.236487191936	56.3516611452242	46.7572989415459


diffExp=4.26772690811028,-10.8951150578156,0.668031855800784,5.23705647373921,4.01544570724953,2.76535880266037,4.07057664693814,0.884826046711801
diffExpScore=2.7305135999606
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	55.4794010472467	55.3547696552601	53.6619239673495
cerebhem	55.4438978775242	55.9524068919017	55.2685987493988
cortex	57.9145582266281	54.1910167994422	55.4757411013377
heart	56.9058759801958	55.4910190279344	59.4685638364256
kidney	56.5000458347985	58.752481660877	58.2279432142011
liver	56.2788717036206	55.4381497496352	57.574606206802
stomach	58.2504306853797	61.357989961798	55.2611360076505
testicle	54.7591558175443	58.0077352440877	53.1312740393659
cont.diffExp=0.12463139198654,-0.508509014377495,3.72354142718589,1.41485695226137,-2.25243582607852,0.840721953985422,-3.10755927641831,-3.2485794265434
cont.diffExpScore=3.79256835943053

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=0.969240786116906
cont.tran.correlation=0.214181429168314

tran.covariance=0.0069605360732522
cont.tran.covariance=0.000173072524795815

tran.mean=58.0001965481038
cont.tran.mean=56.6298628852421

weightedLogRatios:
wLogRatio
Lung	0.306086226181678
cerebhem	-0.64429531171381
cortex	0.0472970678169086
heart	0.379235801148763
kidney	0.289707131094051
liver	0.200219598850856
stomach	0.298128352696579
testicle	0.06293332787155

cont.weightedLogRatios:
wLogRatio
Lung	0.00902936706836275
cerebhem	-0.0367011846472346
cortex	0.267525197224235
heart	0.101435022759112
kidney	-0.158470429972346
liver	0.0605478857895492
stomach	-0.212611680813141
testicle	-0.232357547574865

varWeightedLogRatios=0.108776119335539
cont.varWeightedLogRatios=0.0294542131343956

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.18585504771251	0.0678810624909863	61.6645481686198	2.22395097000778e-293	***
df.mm.trans1	-0.0408034194779592	0.0597971719253846	-0.682363699889922	0.495223116380485	   
df.mm.trans2	-0.225321706244428	0.0539568142591291	-4.17596385810166	3.32181772878364e-05	***
df.mm.exp2	0.453064526865828	0.0718544160201956	6.3053122126619	4.94983843790392e-10	***
df.mm.exp3	-0.129178178962667	0.0718544160201956	-1.79777647801576	0.072621072315883	.  
df.mm.exp4	-0.0945224706745662	0.0718544160201956	-1.31547197667015	0.188759858337524	   
df.mm.exp5	-0.0533544717934804	0.0718544160201956	-0.74253573751799	0.457999078545549	   
df.mm.exp6	-0.0726804302610291	0.0718544160201956	-1.01149566424145	0.312110762784457	   
df.mm.exp7	0.00503007790140494	0.0718544160201956	0.0700037406189645	0.944209646360907	   
df.mm.exp8	0.0433983540881191	0.0718544160201956	0.60397615751162	0.546045101163306	   
df.mm.trans1:exp2	-0.313102010978851	0.0677761187603762	-4.61965094351049	4.53515188610893e-06	***
df.mm.trans2:exp2	-0.0866183807314919	0.0554531880766714	-1.56200903384906	0.118714550388903	   
df.mm.trans1:exp3	0.114512883813903	0.0677761187603762	1.68957570761415	0.0915315101066408	.  
df.mm.trans2:exp3	0.178811205464082	0.0554531880766714	3.22454328896026	0.00131729768538705	** 
df.mm.trans1:exp4	0.0907855352144701	0.0677761187603762	1.33949150342238	0.180823065272873	   
df.mm.trans2:exp4	0.072321140328965	0.0554531880766714	1.30418363375198	0.192577582232153	   
df.mm.trans1:exp5	0.0435241080363914	0.0677761187603762	0.642174689735063	0.52095918860443	   
df.mm.trans2:exp5	0.0474496613887933	0.0554531880766714	0.855670576111654	0.392457736201499	   
df.mm.trans1:exp6	0.0472091394257678	0.0677761187603762	0.696545336162972	0.486306699133087	   
df.mm.trans2:exp6	0.0733382481244692	0.0554531880766714	1.32252537082393	0.186402956377876	   
df.mm.trans1:exp7	-0.0336537005107153	0.0677761187603762	-0.496542161549536	0.619659812317397	   
df.mm.trans2:exp7	-0.0321941816866169	0.0554531880766714	-0.580565027967448	0.561710877081797	   
df.mm.trans1:exp8	-0.0622004822260303	0.0677761187603762	-0.9177344965111	0.359057701470988	   
df.mm.trans2:exp8	-0.00179069594747352	0.0554531880766714	-0.0322920288189318	0.974247897123337	   
df.mm.trans1:probe2	-0.0432678243513892	0.0395727276707391	-1.09337482903364	0.274586017474543	   
df.mm.trans1:probe3	0.0227320360435514	0.0395727276707391	0.574436926175295	0.565847219696289	   
df.mm.trans1:probe4	-0.0936292168656199	0.0395727276707391	-2.36600361856914	0.0182387146138216	*  
df.mm.trans1:probe5	-0.0989346341253918	0.0395727276707391	-2.50007113354852	0.0126327697143727	*  
df.mm.trans1:probe6	-0.140844744456969	0.0395727276707391	-3.55913662633653	0.000395894417438065	***
df.mm.trans1:probe7	0.0452058645603608	0.0395727276707391	1.14234896660376	0.253679471100189	   
df.mm.trans1:probe8	-0.0599260171644102	0.0395727276707391	-1.51432617086744	0.130371276217838	   
df.mm.trans1:probe9	0.0393799087269892	0.0395727276707391	0.995127479072095	0.320000429399814	   
df.mm.trans1:probe10	0.124963681209158	0.0395727276707391	3.15782329307459	0.00165424511738291	** 
df.mm.trans1:probe11	-0.136478293131559	0.0395727276707391	-3.44879671341114	0.00059505374318255	***
df.mm.trans1:probe12	-0.0599974237579048	0.0395727276707391	-1.51613061038166	0.129914521874117	   
df.mm.trans1:probe13	-0.150854303957723	0.0395727276707391	-3.81207747954326	0.000149302709427918	***
df.mm.trans1:probe14	-0.151740743891573	0.0395727276707391	-3.83447775331832	0.000136573740971471	***
df.mm.trans1:probe15	-0.184387380300263	0.0395727276707391	-4.65945592212999	3.76118688375624e-06	***
df.mm.trans1:probe16	-0.142311851287593	0.0395727276707391	-3.59621031109314	0.000344394378338085	***
df.mm.trans1:probe17	-0.0865524469438451	0.0395727276707391	-2.18717414841848	0.0290429526041464	*  
df.mm.trans1:probe18	-0.234938922151040	0.0395727276707391	-5.93688977181015	4.46969688172074e-09	***
df.mm.trans1:probe19	-0.130453300011064	0.0395727276707391	-3.29654556785894	0.00102564942077660	** 
df.mm.trans1:probe20	-0.246356380552557	0.0395727276707391	-6.22540812961746	8.05448381855601e-10	***
df.mm.trans1:probe21	-0.226885679396585	0.0395727276707391	-5.7333849029656	1.43530113120727e-08	***
df.mm.trans1:probe22	-0.179286090003459	0.0395727276707391	-4.53054668091598	6.85988352410907e-06	***
df.mm.trans2:probe2	-0.0253616757085882	0.0395727276707391	-0.640887732571972	0.521794582402562	   
df.mm.trans2:probe3	0.0313996142763723	0.0395727276707391	0.793466008651958	0.427761397144574	   
df.mm.trans2:probe4	0.0111959892527985	0.0395727276707391	0.282921848247448	0.777316082397734	   
df.mm.trans2:probe5	0.117506486512900	0.0395727276707391	2.96938051606149	0.00308072122387746	** 
df.mm.trans2:probe6	0.189437500232363	0.0395727276707391	4.78707209188507	2.04507877210468e-06	***
df.mm.trans3:probe2	-0.0676987443746026	0.0395727276707391	-1.71074243195676	0.087548915689267	.  
df.mm.trans3:probe3	-0.0258585645508018	0.0395727276707391	-0.653444078102358	0.513673559576865	   
df.mm.trans3:probe4	-0.165040850677042	0.0395727276707391	-4.17057050123629	3.3995603407487e-05	***
df.mm.trans3:probe5	-0.0122354767211415	0.0395727276707391	-0.309189622280919	0.757264556978	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.08683608749527	0.0917384185301178	44.5487959458725	2.18899207387433e-211	***
df.mm.trans1	-0.0817146290581451	0.0808133783370991	-1.01115224656599	0.312274963707249	   
df.mm.trans2	-0.0576612702301582	0.0729203790779365	-0.790742875438572	0.429348081556717	   
df.mm.exp2	-0.0194027282983521	0.0971082397387815	-0.199805169474237	0.841687999212135	   
df.mm.exp3	-0.0115328130289675	0.0971082397387815	-0.118762455791500	0.905495872343405	   
df.mm.exp4	-0.0748989698607669	0.0971082397387815	-0.771293662229313	0.440779762132908	   
df.mm.exp5	-0.00386144941420999	0.0971082397387815	-0.0397643848204558	0.968291724977392	   
df.mm.exp6	-0.0545653699916353	0.0971082397387815	-0.561902575295512	0.574353005427023	   
df.mm.exp7	0.122336079274532	0.0971082397387815	1.25979092612133	0.208143034260817	   
df.mm.exp8	0.0436843038550016	0.0971082397387815	0.449851670388745	0.652949496730636	   
df.mm.trans1:exp2	0.0187625891881913	0.0915965914648432	0.204839381991554	0.837754097734461	   
df.mm.trans2:exp2	0.0301413526776017	0.0749426657439626	0.402192160879064	0.687659067454971	   
df.mm.trans1:exp3	0.0544898038665369	0.0915965914648432	0.594888990901494	0.552100023087881	   
df.mm.trans2:exp3	-0.00971486204523855	0.0749426657439626	-0.129630590916379	0.89689402137328	   
df.mm.trans1:exp4	0.100285774649499	0.0915965914648432	1.09486360841267	0.273933584851844	   
df.mm.trans2:exp4	0.077357330127076	0.0749426657439626	1.03222015602385	0.302307138141065	   
df.mm.trans1:exp5	0.0220910992451842	0.0915965914648432	0.241178180234614	0.80948401119306	   
df.mm.trans2:exp5	0.0634320145071325	0.0749426657439626	0.846407235150194	0.397599968121672	   
df.mm.trans1:exp6	0.0688727545703158	0.0915965914648432	0.751913946456738	0.452342540724962	   
df.mm.trans2:exp6	0.0560705223166079	0.0749426657439626	0.748178914640822	0.454590600023087	   
df.mm.trans1:exp7	-0.0735963926720961	0.0915965914648431	-0.803483966980846	0.421953768125625	   
df.mm.trans2:exp7	-0.019373508946843	0.0749426657439626	-0.258511073158667	0.796084583071032	   
df.mm.trans1:exp8	-0.0567515191519697	0.0915965914648432	-0.619581124628991	0.535724828326553	   
df.mm.trans2:exp8	0.00312923600087603	0.0749426657439626	0.0417550666207537	0.966705249111694	   
df.mm.trans1:probe2	-0.0256644700633732	0.0534808873081311	-0.47988115671131	0.631454090403536	   
df.mm.trans1:probe3	0.0145430627189807	0.0534808873081311	0.271930094113633	0.78575182618407	   
df.mm.trans1:probe4	-0.0118778327943585	0.0534808873081311	-0.222094908895660	0.824301445847923	   
df.mm.trans1:probe5	0.057491538640397	0.0534808873081311	1.07499223618259	0.282729528577654	   
df.mm.trans1:probe6	0.0471288882312249	0.0534808873081311	0.881228614620602	0.378480994480373	   
df.mm.trans1:probe7	0.0141794003682799	0.0534808873081311	0.265130237772329	0.790983170451196	   
df.mm.trans1:probe8	0.0369046666681258	0.0534808873081311	0.690053372815206	0.490377560741788	   
df.mm.trans1:probe9	0.0204535206827199	0.0534808873081311	0.382445425126861	0.702241113951381	   
df.mm.trans1:probe10	-0.0171305216854340	0.0534808873081311	-0.320311097060454	0.748823179810919	   
df.mm.trans1:probe11	0.00716227842806707	0.0534808873081311	0.133922206391257	0.893500601597221	   
df.mm.trans1:probe12	0.0158103524004885	0.0534808873081311	0.295626217070724	0.767598706514124	   
df.mm.trans1:probe13	0.0606849214950019	0.0534808873081311	1.13470296678820	0.256868203992318	   
df.mm.trans1:probe14	0.0174709833360911	0.0534808873081311	0.326677140478835	0.744004716210061	   
df.mm.trans1:probe15	-0.0106639492698254	0.0534808873081311	-0.199397388610717	0.842006827612733	   
df.mm.trans1:probe16	0.00857586504819194	0.0534808873081311	0.160353828813309	0.872646238548643	   
df.mm.trans1:probe17	0.0272406849991313	0.0534808873081311	0.509353647073648	0.610656622985798	   
df.mm.trans1:probe18	-0.0315018802906392	0.0534808873081311	-0.589030621521676	0.556020982186389	   
df.mm.trans1:probe19	0.0548601158877898	0.0534808873081311	1.02578918655018	0.305327064857459	   
df.mm.trans1:probe20	-0.0164382491329403	0.0534808873081311	-0.30736679887585	0.758650911059035	   
df.mm.trans1:probe21	0.00546409150587366	0.0534808873081311	0.102169051055421	0.918650245991227	   
df.mm.trans1:probe22	0.0193457430483968	0.0534808873081311	0.361731901285332	0.717655928186078	   
df.mm.trans2:probe2	-0.0523665750736052	0.0534808873081311	-0.979164290448179	0.327819675419774	   
df.mm.trans2:probe3	0.0105909198356414	0.0534808873081311	0.198031864628978	0.843074667774714	   
df.mm.trans2:probe4	-0.0265731754495385	0.0534808873081311	-0.496872374170545	0.619427030623428	   
df.mm.trans2:probe5	-0.065287602659955	0.0534808873081311	-1.22076513584749	0.2225648423325	   
df.mm.trans2:probe6	-0.0358954477391169	0.0534808873081311	-0.671182726126152	0.502314145407323	   
df.mm.trans3:probe2	0.0300606875711687	0.0534808873081311	0.562082812836922	0.574230267979463	   
df.mm.trans3:probe3	0.0388338117452028	0.0534808873081311	0.726125045784321	0.46799233474083	   
df.mm.trans3:probe4	0.0639357284181203	0.0534808873081311	1.19548742805544	0.232280641656244	   
df.mm.trans3:probe5	0.0433873268906948	0.0534808873081311	0.81126789540192	0.417473402583834