chr12.5589_chr12_10450886_10451750_+_0.R 

fitVsDatCorrelation=0.972551051035282
cont.fitVsDatCorrelation=0.256876816310153

fstatistic=8825.10088536264,61,899
cont.fstatistic=497.704369676617,61,899

residuals=-1.18989594056818,-0.098181781285345,-0.000335349690748148,0.0874528692854591,0.876540957938246
cont.residuals=-1.10281648044036,-0.476562272596985,-0.236358160615763,0.0930324577968979,2.62360949599823

predictedValues:
Include	Exclude	Both
chr12.5589_chr12_10450886_10451750_+_0.R.tl.Lung	83.3326042551562	52.1354306395995	64.1019676151394
chr12.5589_chr12_10450886_10451750_+_0.R.tl.cerebhem	68.6913984603646	45.1627604553545	61.0299407108628
chr12.5589_chr12_10450886_10451750_+_0.R.tl.cortex	65.4072539399886	49.4730729004335	57.2049193847257
chr12.5589_chr12_10450886_10451750_+_0.R.tl.heart	68.5049026957866	58.6351430305005	60.5466755786197
chr12.5589_chr12_10450886_10451750_+_0.R.tl.kidney	79.8326826685282	53.585934263356	64.9337008817716
chr12.5589_chr12_10450886_10451750_+_0.R.tl.liver	81.7242788144346	51.2765399232795	63.5081712777804
chr12.5589_chr12_10450886_10451750_+_0.R.tl.stomach	74.2026999582706	54.0160274325065	61.5082979204123
chr12.5589_chr12_10450886_10451750_+_0.R.tl.testicle	81.5962303954314	51.2268538002432	65.4833837896949


diffExp=31.1971736155567,23.5286380050101,15.9341810395551,9.86975966528608,26.2467484051722,30.4477388911551,20.1866725257640,30.3693765951883
diffExpScore=0.994702836791594
diffExp1.5=1,1,0,0,0,1,0,1
diffExp1.5Score=0.8
diffExp1.4=1,1,0,0,1,1,0,1
diffExp1.4Score=0.833333333333333
diffExp1.3=1,1,1,0,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,0,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	95.1306625211019	96.0591012498083	73.1430627132739
cerebhem	79.4736008956806	74.2117659767797	71.9897101802615
cortex	108.031987408094	121.705329599861	75.9393654574111
heart	74.4648222127099	82.3339550319301	77.4633073855135
kidney	82.6273287386023	109.678630320578	63.2041419999086
liver	72.348199376084	78.2674105408758	82.6661505964176
stomach	76.5361716253026	82.7251868699153	81.7045480757204
testicle	82.1414693571639	78.7759494666257	73.1891798871863
cont.diffExp=-0.928438728706425,5.26183491890089,-13.673342191767,-7.86913281922021,-27.051301581976,-5.91921116479183,-6.18901524461268,3.36551989053814
cont.diffExpScore=1.30099593459288

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

tran.correlation=0.112506474487744
cont.tran.correlation=0.80555052424406

tran.covariance=0.00109240115940114
cont.tran.covariance=0.0190174147442674

tran.mean=63.6752383520771
cont.tran.mean=87.1569731994446

weightedLogRatios:
wLogRatio
Lung	1.96431207714032
cerebhem	1.68576988855134
cortex	1.12827452074257
heart	0.645483075624937
kidney	1.66658489713397
liver	1.94384740675596
stomach	1.31708475525530
testicle	1.94076039698386

cont.weightedLogRatios:
wLogRatio
Lung	-0.0442891250930749
cerebhem	0.29737992844004
cortex	-0.565131681137833
heart	-0.43804729271373
kidney	-1.29030841561715
liver	-0.339791762846765
stomach	-0.340330674203441
testicle	0.183553248554640

varWeightedLogRatios=0.222898197307204
cont.varWeightedLogRatios=0.246714598684605

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.91982695351662	0.085238407297001	45.986628303114	2.27325633781694e-238	***
df.mm.trans1	-0.00564534058110382	0.0719293045777134	-0.0784845705689328	0.937460068453936	   
df.mm.trans2	-0.00231147100487867	0.0641310108783158	-0.0360429529056469	0.971256111048223	   
df.mm.exp2	-0.287677313556157	0.0814184444586983	-3.53331871504975	0.000431277629513574	***
df.mm.exp3	-0.180787778369723	0.0814184444586983	-2.22047693949044	0.0266350418756190	*  
df.mm.exp4	-0.0213846116301505	0.0814184444586983	-0.262650702458439	0.79287999320889	   
df.mm.exp5	-0.0283567609146391	0.0814184444586983	-0.348284238334029	0.727708269557858	   
df.mm.exp6	-0.0267937040198606	0.0814184444586983	-0.329086415221951	0.742167020620208	   
df.mm.exp7	-0.0393003363565997	0.0814184444586983	-0.482695740724153	0.629429301320818	   
df.mm.exp8	-0.0599590692860896	0.0814184444586983	-0.736431034573566	0.461660542116855	   
df.mm.trans1:exp2	0.0944614203664963	0.0719293045777135	1.31325363036756	0.189432580159584	   
df.mm.trans2:exp2	0.144105408790771	0.0524270393527904	2.74868484983601	0.00610340355581118	** 
df.mm.trans1:exp3	-0.061418933022059	0.0719293045777134	-0.85387914401009	0.393399418055882	   
df.mm.trans2:exp3	0.128371549829412	0.0524270393527904	2.44857522786245	0.0145323470296203	*  
df.mm.trans1:exp4	-0.174549953679313	0.0719293045777135	-2.42668763036249	0.0154330377180447	*  
df.mm.trans2:exp4	0.138874070583040	0.0524270393527904	2.64890164116521	0.00821701557009811	** 
df.mm.trans1:exp5	-0.0145501414432882	0.0719293045777135	-0.202283916530404	0.839740539660576	   
df.mm.trans2:exp5	0.0557986057995982	0.0524270393527904	1.06430968615489	0.287474209835184	   
df.mm.trans1:exp6	0.00730495183304481	0.0719293045777135	0.101557381597544	0.91913065483397	   
df.mm.trans2:exp6	0.0101822718670254	0.0524270393527904	0.194217945409948	0.846049109018559	   
df.mm.trans1:exp7	-0.0767390067720466	0.0719293045777135	-1.06686707486705	0.286318279947416	   
df.mm.trans2:exp7	0.0747363748951193	0.0524270393527904	1.42553109650548	0.154350842586417	   
df.mm.trans1:exp8	0.0389022538369183	0.0719293045777135	0.540840121634816	0.588751761925194	   
df.mm.trans2:exp8	0.0423781838278974	0.0524270393527904	0.808326854826332	0.419116291635654	   
df.mm.trans1:probe2	0.147092417775916	0.0539469784332851	2.72661086955632	0.00652339764403875	** 
df.mm.trans1:probe3	0.110155445720104	0.0539469784332851	2.0419205842331	0.0414508160738639	*  
df.mm.trans1:probe4	0.00855546550646957	0.0539469784332851	0.158590263160891	0.874027317935521	   
df.mm.trans1:probe5	0.197291640280377	0.0539469784332851	3.65713976964182	0.000269862031349858	***
df.mm.trans1:probe6	0.187056646629508	0.0539469784332851	3.46741656459660	0.000550401239710646	***
df.mm.trans1:probe7	0.08748819382656	0.0539469784332851	1.62174409702583	0.105208784380649	   
df.mm.trans1:probe8	0.145668533022108	0.0539469784332851	2.70021671746181	0.00705965704406104	** 
df.mm.trans1:probe9	0.129544029268898	0.0539469784332851	2.40132131643114	0.0165381471431886	*  
df.mm.trans1:probe10	0.142013622905669	0.0539469784332851	2.63246667431604	0.00862212059250641	** 
df.mm.trans1:probe11	2.43777124455342	0.0539469784332851	45.1882814450518	1.35422695671553e-233	***
df.mm.trans1:probe12	2.43786539109967	0.0539469784332851	45.1900266131591	1.32195657290095e-233	***
df.mm.trans1:probe13	2.25531788661119	0.0539469784332851	41.8061947510237	5.24502206693041e-213	***
df.mm.trans1:probe14	2.13736315666248	0.0539469784332851	39.6197010237692	2.09808913556309e-199	***
df.mm.trans1:probe15	2.37168871116823	0.0539469784332851	43.9633280685264	3.32877712633616e-226	***
df.mm.trans1:probe16	1.9562173661537	0.0539469784332851	36.2618523403105	4.00191469619516e-178	***
df.mm.trans2:probe2	0.103272518784205	0.0539469784332851	1.91433369918797	0.0558947246522074	.  
df.mm.trans2:probe3	0.234232559865611	0.0539469784332851	4.34190322921016	1.57309185718883e-05	***
df.mm.trans2:probe4	0.197693860783086	0.0539469784332851	3.66459561822484	0.000262234268838674	***
df.mm.trans2:probe5	0.168905557651479	0.0539469784332851	3.13095492197697	0.00179869673596410	** 
df.mm.trans2:probe6	0.167798361683645	0.0539469784332851	3.11043114103522	0.00192705900856084	** 
df.mm.trans3:probe2	0.181716254732515	0.0539469784332851	3.36842321868386	0.000788118149191887	***
df.mm.trans3:probe3	0.147572425522242	0.0539469784332851	2.73550863844472	0.00635106647639093	** 
df.mm.trans3:probe4	0.211864185426843	0.0539469784332851	3.92726694950766	9.24905466549032e-05	***
df.mm.trans3:probe5	1.43696034522707e-05	0.0539469784332851	0.000266365306632349	0.999787530353583	   
df.mm.trans3:probe6	0.114514703090135	0.0539469784332851	2.12272691475672	0.0340495008495519	*  
df.mm.trans3:probe7	0.656170164503462	0.0539469784332851	12.1632421974278	1.27724527515284e-31	***
df.mm.trans3:probe8	0.622905697715341	0.0539469784332851	11.5466281116314	7.42611956666229e-29	***
df.mm.trans3:probe9	0.401336313315884	0.0539469784332851	7.43945861235226	2.36068081171742e-13	***
df.mm.trans3:probe10	0.134080486133225	0.0539469784332851	2.48541234425277	0.0131209603448598	*  
df.mm.trans3:probe11	-0.0719943371942293	0.0539469784332851	-1.33453882469549	0.182365232695560	   
df.mm.trans3:probe12	0.0381611085258747	0.0539469784332851	0.707381759537611	0.479512596666144	   
df.mm.trans3:probe13	0.125297303086147	0.0539469784332851	2.32260094494633	0.0204230582204742	*  
df.mm.trans3:probe14	0.0154938566323305	0.0539469784332851	0.287205272330338	0.774021334522442	   
df.mm.trans3:probe15	0.0700192857114402	0.0539469784332851	1.29792784962056	0.194645030959128	   
df.mm.trans3:probe16	0.259112978381724	0.0539469784332851	4.80310456501587	1.82961165164090e-06	***
df.mm.trans3:probe17	0.896023920072345	0.0539469784332851	16.6093439539053	3.10837936615915e-54	***
df.mm.trans3:probe18	0.672599800621207	0.0539469784332851	12.4677937514702	5.0521771284065e-33	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.81739965953118	0.354025955346079	13.6074759118210	1.76317096152446e-38	***
df.mm.trans1	-0.240168381493242	0.298748434866639	-0.803915112058922	0.421658519072743	   
df.mm.trans2	-0.218312425515742	0.266359298741899	-0.819616309799965	0.41265210841219	   
df.mm.exp2	-0.421973278728736	0.338160267141647	-1.24784996858304	0.212410928065583	   
df.mm.exp3	0.326297260899414	0.338160267141647	0.964918982521197	0.334844877697688	   
df.mm.exp4	-0.456491638537939	0.338160267141647	-1.34992689234754	0.177379218964638	   
df.mm.exp5	0.137727497327111	0.338160267141647	0.407284683358204	0.683895868760996	   
df.mm.exp6	-0.600986041128347	0.338160267141647	-1.77722251702802	0.075869673898546	.  
df.mm.exp7	-0.477619789913923	0.338160267141647	-1.41240659037527	0.158176345149040	   
df.mm.exp8	-0.345794552128506	0.338160267141647	-1.02257593729562	0.306783475124784	   
df.mm.trans1:exp2	0.242146839467969	0.298748434866639	0.810537600225631	0.417845773216400	   
df.mm.trans2:exp2	0.1639323473873	0.217748469045962	0.752851894231676	0.451736009451858	   
df.mm.trans1:exp3	-0.199121239493419	0.298748434866639	-0.666518101031415	0.505251006529882	   
df.mm.trans2:exp3	-0.0896581089918425	0.217748469045962	-0.411750812231509	0.680620226233964	   
df.mm.trans1:exp4	0.211567125774432	0.298748434866639	0.708178189682819	0.479018176869758	   
df.mm.trans2:exp4	0.302311597405071	0.217748469045962	1.38835234401239	0.165373691378554	   
df.mm.trans1:exp5	-0.278638356691795	0.298748434866639	-0.932685578139276	0.351232749663711	   
df.mm.trans2:exp5	-0.0051365901700727	0.217748469045962	-0.0235895581382411	0.981185236038443	   
df.mm.trans1:exp6	0.327225264642181	0.298748434866639	1.09532043168110	0.273669629185099	   
df.mm.trans2:exp6	0.396153704664996	0.217748469045962	1.81931797913755	0.0691954510442185	.  
df.mm.trans1:exp7	0.260131909129778	0.298748434866639	0.870738985614771	0.384129134892139	   
df.mm.trans2:exp7	0.328180262588884	0.217748469045962	1.50715301938408	0.132122740382458	   
df.mm.trans1:exp8	0.19898620737522	0.298748434866639	0.666066108309646	0.505539719204688	   
df.mm.trans2:exp8	0.14743865253262	0.217748469045962	0.677105346267664	0.498513309528097	   
df.mm.trans1:probe2	0.234438144655278	0.224061326149979	1.04631240332101	0.295698054529935	   
df.mm.trans1:probe3	-0.175897199074836	0.224061326149979	-0.785040426642376	0.432636692668137	   
df.mm.trans1:probe4	0.208651062492876	0.224061326149979	0.931223009691607	0.351988217602186	   
df.mm.trans1:probe5	-0.0836689125148926	0.224061326149979	-0.373419696975673	0.708924138438763	   
df.mm.trans1:probe6	-0.259356266752648	0.224061326149979	-1.15752357271618	0.247365956463345	   
df.mm.trans1:probe7	-0.377888905361159	0.224061326149979	-1.68654230453056	0.0920383420585682	.  
df.mm.trans1:probe8	-0.0471492618145117	0.224061326149979	-0.210430164922578	0.833379634937969	   
df.mm.trans1:probe9	0.229314098695807	0.224061326149979	1.02344345914614	0.306373531578409	   
df.mm.trans1:probe10	0.0408925271594169	0.224061326149979	0.182505958801854	0.855226824921183	   
df.mm.trans1:probe11	-0.243777599996548	0.224061326149979	-1.08799498862812	0.276888911912398	   
df.mm.trans1:probe12	-0.183544083304644	0.224061326149979	-0.819168959045548	0.412907123676209	   
df.mm.trans1:probe13	0.0129583213838279	0.224061326149979	0.0578338154401266	0.953893854266656	   
df.mm.trans1:probe14	-0.0232858025868087	0.224061326149979	-0.103926023231791	0.917251234866696	   
df.mm.trans1:probe15	0.155180901153274	0.224061326149979	0.692582266737995	0.488750599485891	   
df.mm.trans1:probe16	-0.124285181697406	0.224061326149979	-0.554692698793606	0.579242840854836	   
df.mm.trans2:probe2	-0.101828326794087	0.224061326149979	-0.454466321983322	0.649602935418704	   
df.mm.trans2:probe3	-0.297522290584416	0.224061326149979	-1.3278609731394	0.184561082745957	   
df.mm.trans2:probe4	0.00415927480274721	0.224061326149979	0.0185631089229702	0.985193751516041	   
df.mm.trans2:probe5	-0.270722818034079	0.224061326149979	-1.20825321658976	0.227267604216189	   
df.mm.trans2:probe6	-0.153052094336768	0.224061326149979	-0.683081266038387	0.494731484804487	   
df.mm.trans3:probe2	0.146794620070895	0.224061326149979	0.655153758987555	0.512536361947806	   
df.mm.trans3:probe3	0.0776472006450504	0.224061326149979	0.346544412546572	0.729014707108493	   
df.mm.trans3:probe4	-0.296844921996909	0.224061326149979	-1.32483783389825	0.185561590164618	   
df.mm.trans3:probe5	-0.286638175071569	0.224061326149979	-1.27928447089393	0.201126981238715	   
df.mm.trans3:probe6	-0.259309447194090	0.224061326149979	-1.15731461403793	0.247451242577295	   
df.mm.trans3:probe7	-0.140465296076512	0.224061326149979	-0.626905582012353	0.530880294294342	   
df.mm.trans3:probe8	-0.173712471351238	0.224061326149979	-0.775289847365093	0.438372287483331	   
df.mm.trans3:probe9	-0.133380780990512	0.224061326149979	-0.595286938992904	0.55180149851444	   
df.mm.trans3:probe10	0.131352057112387	0.224061326149979	0.58623261483539	0.557866361975293	   
df.mm.trans3:probe11	-0.0619981694452612	0.224061326149979	-0.276701787455108	0.782072702961236	   
df.mm.trans3:probe12	-0.0914012391914906	0.224061326149979	-0.407929564472495	0.683422516417938	   
df.mm.trans3:probe13	-0.0118383440569174	0.224061326149979	-0.0528352851441804	0.957874885117218	   
df.mm.trans3:probe14	0.0400303523782851	0.224061326149979	0.178658017722747	0.85824648846528	   
df.mm.trans3:probe15	-0.286834805980288	0.224061326149979	-1.28016204719011	0.20081836961921	   
df.mm.trans3:probe16	0.0423503011968853	0.224061326149979	0.189012097377918	0.850125996738158	   
df.mm.trans3:probe17	0.146527687227692	0.224061326149979	0.65396242067054	0.513303259151222	   
df.mm.trans3:probe18	-0.0393067554368033	0.224061326149979	-0.175428558387147	0.860782406085303