chr4.17247_chr4_115992260_115993803_-_2.R 

fitVsDatCorrelation=0.859772224256799
cont.fitVsDatCorrelation=0.239830625600115

fstatistic=5916.02801913968,55,761
cont.fstatistic=1627.00212674688,55,761

residuals=-0.698997258993447,-0.138817580134457,0.00922478624461744,0.128131819575679,1.02312705485521
cont.residuals=-1.01599367477132,-0.317590907022972,-0.0163336478761994,0.264973514057016,1.52642811601628

predictedValues:
Include	Exclude	Both
chr4.17247_chr4_115992260_115993803_-_2.R.tl.Lung	159.645195513484	128.036893190451	153.075160455801
chr4.17247_chr4_115992260_115993803_-_2.R.tl.cerebhem	79.3500623367813	107.446007645078	66.673937738696
chr4.17247_chr4_115992260_115993803_-_2.R.tl.cortex	90.9135732141913	114.574988570855	99.2032471592781
chr4.17247_chr4_115992260_115993803_-_2.R.tl.heart	94.7154328938218	123.095027072288	84.1643989226037
chr4.17247_chr4_115992260_115993803_-_2.R.tl.kidney	114.495363765461	143.886640177883	102.169898207558
chr4.17247_chr4_115992260_115993803_-_2.R.tl.liver	119.211412764089	123.289467607618	111.539430839656
chr4.17247_chr4_115992260_115993803_-_2.R.tl.stomach	131.457353197126	118.070094111694	121.937501057840
chr4.17247_chr4_115992260_115993803_-_2.R.tl.testicle	203.505600131125	115.440763287786	205.778707496661


diffExp=31.6083023230333,-28.0959453082967,-23.661415356664,-28.3795941784665,-29.3912764124219,-4.07805484352954,13.3872590854317,88.0648368433383
diffExpScore=12.0595155885043
diffExp1.5=0,0,0,0,0,0,0,1
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,0,1
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,0,0,1
diffExp1.3Score=2
diffExp1.2=1,-1,-1,-1,-1,0,0,1
diffExp1.2Score=2

cont.predictedValues:
Include	Exclude	Both
Lung	117.571225212731	121.079168786014	96.5457599374902
cerebhem	117.807278917589	105.284160524440	102.913926048887
cortex	108.079060683022	123.962028197764	104.051114682723
heart	111.511315565834	117.402895276268	108.048886356883
kidney	112.291428311740	120.613607816393	96.9815093405033
liver	114.695217930603	116.265244611755	106.051814663175
stomach	112.030288999733	116.172814050653	118.752726209418
testicle	104.769768611230	136.433853561855	113.524744953878
cont.diffExp=-3.50794357328242,12.5231183931487,-15.8829675147424,-5.89157971043461,-8.32217950465315,-1.57002668115169,-4.14252505092017,-31.6640849506241
cont.diffExpScore=1.40442262631032

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

tran.correlation=0.0999354132780788
cont.tran.correlation=-0.809929027138149

tran.covariance=0.00588876614711107
cont.tran.covariance=-0.00235055795269746

tran.mean=122.945867217483
cont.tran.mean=115.998084816102

weightedLogRatios:
wLogRatio
Lung	1.09493308222201
cerebhem	-1.37174452811565
cortex	-1.06998773744367
heart	-1.22703526382948
kidney	-1.10927567244659
liver	-0.161378355691522
stomach	0.518222312849648
testicle	2.85294979587657

cont.weightedLogRatios:
wLogRatio
Lung	-0.140584708436089
cerebhem	0.529664156290821
cortex	-0.65147796888255
heart	-0.244034507021717
kidney	-0.340088631540419
liver	-0.0645676857240296
stomach	-0.171995754376404
testicle	-1.26328096976949

varWeightedLogRatios=2.19535137836519
cont.varWeightedLogRatios=0.263056565460138

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.60991219210061	0.114786982565431	48.8723726917628	6.40394783372425e-237	***
df.mm.trans1	-0.170494401174837	0.100512769663603	-1.69624617593814	0.0902482441169972	.  
df.mm.trans2	-0.767935726027942	0.0901368012176616	-8.5196691656889	8.52818848819499e-17	***
df.mm.exp2	-0.043299668213791	0.118868893314567	-0.364264081261406	0.715762007233175	   
df.mm.exp3	-0.240375135945314	0.118868893314567	-2.02218704357918	0.0435064162388814	*  
df.mm.exp4	0.0367183420822867	0.118868893314567	0.30889782060238	0.757483800771382	   
df.mm.exp5	0.188579773616046	0.118868893314567	1.58645183241506	0.113052252875279	   
df.mm.exp6	-0.0132879175265805	0.118868893314567	-0.111786331613404	0.911022302969827	   
df.mm.exp7	-0.0478909020229811	0.118868893314567	-0.402888431847729	0.687143457660498	   
df.mm.exp8	-0.15669353562215	0.118868893314567	-1.31820471489951	0.187831753489481	   
df.mm.trans1:exp2	-0.655784924058286	0.111508906111083	-5.88100939134856	6.10405110482115e-09	***
df.mm.trans2:exp2	-0.132030315295194	0.0889533345456189	-1.48426493475052	0.138152712244562	   
df.mm.trans1:exp3	-0.322669378567178	0.111508906111083	-2.89366463917904	0.00391656199254922	** 
df.mm.trans2:exp3	0.129286216147691	0.088953334545619	1.45341618510532	0.146520476391464	   
df.mm.trans1:exp4	-0.558795213860847	0.111508906111083	-5.01121599474919	6.73097082953208e-07	***
df.mm.trans2:exp4	-0.0760801576113012	0.088953334545619	-0.855281682242999	0.392664414819252	   
df.mm.trans1:exp5	-0.520999267409774	0.111508906111083	-4.67226597031418	3.52223551306885e-06	***
df.mm.trans2:exp5	-0.0718724554884101	0.088953334545619	-0.807979328212265	0.419354971381222	   
df.mm.trans1:exp6	-0.278767412597119	0.111508906111083	-2.49995648167704	0.0126302755971145	*  
df.mm.trans2:exp6	-0.0244955472547683	0.088953334545619	-0.275375255800062	0.783102704391936	   
df.mm.trans1:exp7	-0.146380434084063	0.111508906111083	-1.31272415082470	0.189671655487171	   
df.mm.trans2:exp7	-0.0331490824147227	0.088953334545619	-0.372656995761327	0.709507510327373	   
df.mm.trans1:exp8	0.399433234361129	0.111508906111083	3.58207472650857	0.000362552003657135	***
df.mm.trans2:exp8	0.053132611612679	0.088953334545619	0.597308823599305	0.550478892066037	   
df.mm.trans1:probe2	-0.727770105961023	0.0682849804370178	-10.6578357539734	8.14616490590001e-25	***
df.mm.trans1:probe3	-0.920953438235331	0.0682849804370178	-13.4869107721978	2.50801407962303e-37	***
df.mm.trans1:probe4	-0.118253185663796	0.0682849804370178	-1.73175982341923	0.0837215721735887	.  
df.mm.trans1:probe5	-0.978638739282734	0.0682849804370178	-14.3316836736210	2.04381337741352e-41	***
df.mm.trans1:probe6	-0.487376015565634	0.0682849804370178	-7.13738237085918	2.22206115510488e-12	***
df.mm.trans1:probe7	-0.562924449379202	0.0682849804370178	-8.24375207807831	7.29520552158154e-16	***
df.mm.trans1:probe8	-0.400487395853823	0.0682849804370178	-5.8649412109477	6.69731719841723e-09	***
df.mm.trans1:probe9	-0.235876321426825	0.0682849804370178	-3.45429287549382	0.000582278623228453	***
df.mm.trans1:probe10	-0.198001353464787	0.0682849804370178	-2.89963257216442	0.00384357823054201	** 
df.mm.trans1:probe11	-0.544134801528763	0.0682849804370178	-7.96858691393553	5.8598509572235e-15	***
df.mm.trans1:probe12	-0.588025923070286	0.0682849804370178	-8.61135083157339	4.12737750350906e-17	***
df.mm.trans1:probe13	-0.449919293388914	0.0682849804370178	-6.5888470716323	8.27302427722757e-11	***
df.mm.trans1:probe14	-0.444145010642582	0.0682849804370178	-6.50428553687932	1.41364900427249e-10	***
df.mm.trans1:probe15	-0.745382825222907	0.0682849804370178	-10.915765376991	7.14610417601025e-26	***
df.mm.trans1:probe16	-0.820298941769198	0.0682849804370178	-12.0128751083966	1.45391615354400e-30	***
df.mm.trans1:probe17	-0.317774064000062	0.0682849804370178	-4.65364509100444	3.84622459830404e-06	***
df.mm.trans1:probe18	-0.220644473959436	0.0682849804370178	-3.23122995052984	0.00128561344240659	** 
df.mm.trans1:probe19	-0.309332391471431	0.0682849804370178	-4.53002094299114	6.8452227337813e-06	***
df.mm.trans1:probe20	-0.373111808975245	0.0682849804370178	-5.46403918676351	6.31003012427995e-08	***
df.mm.trans1:probe21	-0.274186083243400	0.0682849804370178	-4.01532052127179	6.5246971652125e-05	***
df.mm.trans1:probe22	-0.543754428383177	0.0682849804370178	-7.96301653604053	6.10865489234768e-15	***
df.mm.trans2:probe2	-0.0106269073555589	0.0682849804370178	-0.155625838764948	0.876369227062797	   
df.mm.trans2:probe3	-0.00230874666943625	0.0682849804370178	-0.0338104610217426	0.973037159929777	   
df.mm.trans2:probe4	-0.0385985706730355	0.0682849804370178	-0.565257109630962	0.57206549902257	   
df.mm.trans2:probe5	0.049544020625248	0.0682849804370178	0.725547848270157	0.468339057812388	   
df.mm.trans2:probe6	0.126094016484242	0.0682849804370178	1.84658493972249	0.0651954148799816	.  
df.mm.trans3:probe2	0.173795082278740	0.0682849804370178	2.54514361967254	0.0111195672136288	*  
df.mm.trans3:probe3	-0.126389569598325	0.0682849804370178	-1.85091316991589	0.0645693555889985	.  
df.mm.trans3:probe4	0.341740631282103	0.0682849804370178	5.00462369755389	6.95781687124632e-07	***
df.mm.trans3:probe5	0.725214680623072	0.0682849804370178	10.6204128050087	1.15553057174871e-24	***
df.mm.trans3:probe6	1.04232105950963	0.0682849804370178	15.2642799754627	4.3243080725876e-46	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.10274872144953	0.218213744938242	23.3841764774886	1.47932169923208e-91	***
df.mm.trans1	-0.224019050554053	0.191078007211378	-1.17239578653463	0.241405066294649	   
df.mm.trans2	-0.343828574080299	0.171352957546803	-2.00655173393425	0.0451507441240939	*  
df.mm.exp2	-0.201651773097486	0.225973588529958	-0.892368769329661	0.372477385865845	   
df.mm.exp3	-0.135515817463450	0.225973588529959	-0.599697594506643	0.548886300276474	   
df.mm.exp4	-0.196317985685429	0.225973588529958	-0.86876518164158	0.385249481642883	   
df.mm.exp5	-0.0543025412968473	0.225973588529959	-0.240304814602916	0.81015868088973	   
df.mm.exp6	-0.159247137547662	0.225973588529959	-0.704715708519846	0.481202785551098	   
df.mm.exp7	-0.296667118082106	0.225973588529959	-1.31283978810105	0.189632697513991	   
df.mm.exp8	-0.157887508566863	0.225973588529959	-0.69869894793449	0.484953656211039	   
df.mm.trans1:exp2	0.203657510918505	0.211982016188954	0.960730134470328	0.336993023240495	   
df.mm.trans2:exp2	0.0618701394211313	0.169103149347787	0.365872189014563	0.714562130147405	   
df.mm.trans1:exp3	0.0513344981765261	0.211982016188955	0.242164401959306	0.808718022219862	   
df.mm.trans2:exp3	0.159046492828983	0.169103149347787	0.940529454610445	0.347244491761554	   
df.mm.trans1:exp4	0.143399734372321	0.211982016188954	0.676471225957672	0.498947056251455	   
df.mm.trans2:exp4	0.165484935281334	0.169103149347787	0.978603508684445	0.328086914384562	   
df.mm.trans1:exp5	0.00835574968906263	0.211982016188955	0.0394172573659011	0.968568059465705	   
df.mm.trans2:exp5	0.0504500343913548	0.169103149347787	0.298338822109081	0.76552601567423	   
df.mm.trans1:exp6	0.134481146880626	0.211982016188954	0.634398848064328	0.526011218893278	   
df.mm.trans2:exp6	0.118676690730979	0.169103149347787	0.701800594422415	0.483018099410067	   
df.mm.trans1:exp7	0.248392068353778	0.211982016188955	1.17176009936790	0.241660106397819	   
df.mm.trans2:exp7	0.255301357739698	0.169103149347787	1.50973745151624	0.131525502482385	   
df.mm.trans1:exp8	0.042608449429567	0.211982016188955	0.201000302740715	0.84075204626167	   
df.mm.trans2:exp8	0.277282797318381	0.169103149347787	1.6397258027886	0.101475466800908	   
df.mm.trans1:probe2	-0.212131516859364	0.129811943577335	-1.63414483300596	0.102642030030922	   
df.mm.trans1:probe3	-0.0577811618049884	0.129811943577335	-0.44511437247348	0.656363682117727	   
df.mm.trans1:probe4	-0.11828876250504	0.129811943577335	-0.911231734501914	0.362461833627532	   
df.mm.trans1:probe5	-0.169210491389105	0.129811943577335	-1.30350479875758	0.192796683148430	   
df.mm.trans1:probe6	-0.201392570936428	0.129811943577335	-1.55141788487628	0.121217325418923	   
df.mm.trans1:probe7	-0.225437994809139	0.129811943577335	-1.73665063935226	0.0828535179978006	.  
df.mm.trans1:probe8	-0.134478980849856	0.129811943577335	-1.03595229486523	0.300553468600811	   
df.mm.trans1:probe9	0.0224408295591968	0.129811943577335	0.172871840146417	0.862798133652232	   
df.mm.trans1:probe10	-0.227753028435619	0.129811943577335	-1.75448438841018	0.0797499860315118	.  
df.mm.trans1:probe11	-0.156100626910994	0.129811943577335	-1.20251359473712	0.229538619571031	   
df.mm.trans1:probe12	-0.158447724483010	0.129811943577335	-1.22059434684155	0.222617715566784	   
df.mm.trans1:probe13	-0.151867815316058	0.129811943577335	-1.16990633628086	0.242404927950069	   
df.mm.trans1:probe14	-0.214712237840837	0.129811943577335	-1.65402529169377	0.0985347570410454	.  
df.mm.trans1:probe15	-0.233721867275654	0.129811943577335	-1.80046504839837	0.0721831043958118	.  
df.mm.trans1:probe16	-0.070197646958861	0.129811943577335	-0.540764162559825	0.5888283876901	   
df.mm.trans1:probe17	-0.203965024917113	0.129811943577335	-1.57123465912519	0.116543810260102	   
df.mm.trans1:probe18	-0.203075288445479	0.129811943577335	-1.56438061744679	0.118143931309949	   
df.mm.trans1:probe19	-0.184569445998647	0.129811943577335	-1.42182175932594	0.155487696473994	   
df.mm.trans1:probe20	-0.213259854440735	0.129811943577335	-1.64283692673999	0.100829770419966	   
df.mm.trans1:probe21	-0.0187213843313327	0.129811943577335	-0.144219274555268	0.885365504160531	   
df.mm.trans1:probe22	0.00548282473895678	0.129811943577335	0.0422366739751523	0.966321106021498	   
df.mm.trans2:probe2	0.163480794044597	0.129811943577335	1.25936635366070	0.208284161542802	   
df.mm.trans2:probe3	0.102109894135988	0.129811943577335	0.78659860812542	0.431761752088747	   
df.mm.trans2:probe4	-0.00276868946864632	0.129811943577335	-0.0213284647956671	0.982989228284536	   
df.mm.trans2:probe5	0.102195580684509	0.129811943577335	0.787258690288589	0.431375559840347	   
df.mm.trans2:probe6	0.0852760817533472	0.129811943577335	0.656920152362899	0.511430869382467	   
df.mm.trans3:probe2	0.0882365932504053	0.129811943577335	0.67972630883412	0.496884479462434	   
df.mm.trans3:probe3	0.173953825589101	0.129811943577335	1.34004484329647	0.180630735584164	   
df.mm.trans3:probe4	-0.02896655967933	0.129811943577335	-0.223142485052411	0.823484495337395	   
df.mm.trans3:probe5	-0.075368524252708	0.129811943577335	-0.580597764548586	0.561683464318595	   
df.mm.trans3:probe6	0.0528406373585566	0.129811943577335	0.407055282452318	0.684081850431008