chr1.1522_chr1_157935442_157939675_-_2.R 

fitVsDatCorrelation=0.960813720481253
cont.fitVsDatCorrelation=0.226193283572774

fstatistic=10480.2126544677,60,876
cont.fstatistic=835.272216017091,60,876

residuals=-0.965205294009849,-0.0857139952677112,-0.00056452467531362,0.089079221512465,0.639079032635879
cont.residuals=-0.87233688693272,-0.389512310149051,-0.192298905942932,0.184859585864913,1.98617358819829

predictedValues:
Include	Exclude	Both
chr1.1522_chr1_157935442_157939675_-_2.R.tl.Lung	68.9766996486314	54.0304800135698	64.5505181437276
chr1.1522_chr1_157935442_157939675_-_2.R.tl.cerebhem	62.2953943428285	62.5765286915093	66.3931676873865
chr1.1522_chr1_157935442_157939675_-_2.R.tl.cortex	66.3904287906446	52.2685027203359	66.615297601394
chr1.1522_chr1_157935442_157939675_-_2.R.tl.heart	69.9683206810044	51.8081536134397	64.0987248488648
chr1.1522_chr1_157935442_157939675_-_2.R.tl.kidney	80.201475761265	52.8436637571143	64.3993024099021
chr1.1522_chr1_157935442_157939675_-_2.R.tl.liver	83.4130995888347	53.6752081487458	63.2113332255187
chr1.1522_chr1_157935442_157939675_-_2.R.tl.stomach	76.5220263270847	50.8104810949175	65.0813118702017
chr1.1522_chr1_157935442_157939675_-_2.R.tl.testicle	81.4049294413493	52.9824352148034	65.9053434590437


diffExp=14.9462196350616,-0.281134348680794,14.1219260703087,18.1601670675647,27.3578120041507,29.7378914400889,25.7115452321671,28.4224942265459
diffExpScore=0.997250032862876
diffExp1.5=0,0,0,0,1,1,1,1
diffExp1.5Score=0.8
diffExp1.4=0,0,0,0,1,1,1,1
diffExp1.4Score=0.8
diffExp1.3=0,0,0,1,1,1,1,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	80.7642009149877	71.9039292779147	70.1075511515063
cerebhem	72.4208963485333	78.2448808757793	67.7004381714455
cortex	79.830620960896	73.8777793850268	79.4915552725339
heart	81.224302290197	70.2674121668721	78.0486131864135
kidney	78.3775355146437	63.944496292003	75.9944287137779
liver	71.5415184265907	82.2293047132257	82.604405825744
stomach	81.3139644821755	60.9266402732382	67.2403000744973
testicle	79.9980886647307	70.9029744635878	82.8708022260128
cont.diffExp=8.86027163707298,-5.823984527246,5.95284157586913,10.9568901233249,14.4330392226407,-10.687786286635,20.3873242089372,9.09511420114285
cont.diffExpScore=1.59112697904711

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

tran.correlation=-0.524920486135035
cont.tran.correlation=-0.753862242017072

tran.covariance=-0.00374407920978781
cont.tran.covariance=-0.00369977824325886

tran.mean=63.7604892397549
cont.tran.mean=74.8605340656501

weightedLogRatios:
wLogRatio
Lung	1.00415101538471
cerebhem	-0.0186150797911697
cortex	0.974805541354855
heart	1.23136707155869
kidney	1.74221923989476
liver	1.85307674284287
stomach	1.69229893459038
testicle	1.79722483092066

cont.weightedLogRatios:
wLogRatio
Lung	0.503557393966281
cerebhem	-0.334236063109745
cortex	0.33641840103934
heart	0.62668539466542
kidney	0.866957735865725
liver	-0.604259986995842
stomach	1.22790368862567
testicle	0.52158239467033

varWeightedLogRatios=0.405344476233481
cont.varWeightedLogRatios=0.361666648866404

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.79578149125812	0.077840923530884	48.763315221358	7.66534088572063e-252	***
df.mm.trans1	0.0160763569235209	0.0670789862379285	0.239663087132805	0.81064751860074	   
df.mm.trans2	0.197336929072505	0.0591244257224066	3.33765489747023	0.00088048876228401	***
df.mm.exp2	0.0168146587695534	0.0757398810857199	0.222005349473987	0.824361463763651	   
df.mm.exp3	-0.102856345366705	0.0757398810857198	-1.35802095134393	0.174806702009391	   
df.mm.exp4	-0.0207032991832168	0.0757398810857199	-0.273347394878869	0.784650617194124	   
df.mm.exp5	0.130907979807335	0.0757398810857199	1.72838903271022	0.0842708767807028	.  
df.mm.exp6	0.204404030815691	0.0757398810857198	2.69876355607626	0.00709374369101045	** 
df.mm.exp7	0.0341748902831295	0.0757398810857199	0.451213941628077	0.651947037213612	   
df.mm.exp8	0.125307805363612	0.0757398810857198	1.65444946001161	0.098394450669519	.  
df.mm.trans1:exp2	-0.118695924106992	0.0698287200859871	-1.6998152616979	0.0895206722773956	.  
df.mm.trans2:exp2	0.130027275977311	0.050807856814626	2.55919623714338	0.0106583958237648	*  
df.mm.trans1:exp3	0.0646404854233379	0.0698287200859871	0.925700561942702	0.354856559671910	   
df.mm.trans2:exp3	0.0697019605547413	0.0508078568146261	1.37187366137192	0.170453997368399	   
df.mm.trans1:exp4	0.0349771157789853	0.0698287200859871	0.50089870952689	0.616568242255458	   
df.mm.trans2:exp4	-0.0212974902944811	0.0508078568146261	-0.419177104284985	0.675189469110895	   
df.mm.trans1:exp5	0.0198651745924169	0.0698287200859871	0.284484300556489	0.7761064981109	   
df.mm.trans2:exp5	-0.153118497766520	0.050807856814626	-3.01367755631136	0.00265534770301939	** 
df.mm.trans1:exp6	-0.0143674256913097	0.0698287200859871	-0.205752384887159	0.837032141419407	   
df.mm.trans2:exp6	-0.211001141133494	0.050807856814626	-4.15292347211845	3.60371568222075e-05	***
df.mm.trans1:exp7	0.0696349736342128	0.0698287200859871	0.997225404510699	0.318930488747942	   
df.mm.trans2:exp7	-0.0956205682348252	0.050807856814626	-1.88200357640945	0.0601668347797374	.  
df.mm.trans1:exp8	0.0403592627421386	0.0698287200859871	0.577975118152533	0.563429455916175	   
df.mm.trans2:exp8	-0.144895689802733	0.0508078568146261	-2.85183628845809	0.00444897250375161	** 
df.mm.trans1:probe2	0.47131743006727	0.0486448194013256	9.68895425798265	3.70062997505525e-21	***
df.mm.trans1:probe3	0.297992811047673	0.0486448194013256	6.12588996557262	1.36087603927495e-09	***
df.mm.trans1:probe4	-0.0634683430452941	0.0486448194013256	-1.30472974977402	0.192327536387703	   
df.mm.trans1:probe5	0.538504638843474	0.0486448194013256	11.0701333763981	9.45925645803155e-27	***
df.mm.trans1:probe6	0.538288107596828	0.0486448194013256	11.0656821059584	9.88049339235658e-27	***
df.mm.trans1:probe7	1.13345921391495	0.0486448194013256	23.300717894824	7.89133530347015e-94	***
df.mm.trans1:probe8	0.207604692505456	0.0486448194013256	4.26776571607127	2.18965364040157e-05	***
df.mm.trans1:probe9	0.25401262265692	0.0486448194013256	5.22178159530793	2.21477550961407e-07	***
df.mm.trans1:probe10	0.636840845086122	0.0486448194013256	13.0916478450071	6.76417924548368e-36	***
df.mm.trans1:probe11	1.70462345888836	0.0486448194013256	35.0422404660405	7.47560688836912e-169	***
df.mm.trans1:probe12	1.56781698140226	0.0486448194013256	32.2298859508057	6.50295217409415e-151	***
df.mm.trans1:probe13	1.40058319429550	0.0486448194013256	28.7920319477500	8.13312058458063e-129	***
df.mm.trans1:probe14	1.56295443257166	0.0486448194013256	32.129925690074	2.84397689649156e-150	***
df.mm.trans1:probe15	1.67156686235479	0.0486448194013256	34.3626902705541	1.54842817543726e-164	***
df.mm.trans1:probe16	1.88524941300748	0.0486448194013256	38.7553995720273	3.69467529701202e-192	***
df.mm.trans1:probe17	-0.0599870098777116	0.0486448194013256	-1.23316337928633	0.217845617938947	   
df.mm.trans1:probe18	0.0232159269590846	0.0486448194013256	0.477253842131686	0.633300419080958	   
df.mm.trans1:probe19	0.131585674251492	0.0486448194013256	2.7050295565062	0.00696241801505186	** 
df.mm.trans1:probe20	0.0265219123219712	0.0486448194013256	0.545215557347685	0.585744057705897	   
df.mm.trans1:probe21	-0.0545877539318652	0.0486448194013256	-1.12216993718303	0.262097677333944	   
df.mm.trans1:probe22	0.0489650225406475	0.0486448194013256	1.00658247154091	0.314413416074262	   
df.mm.trans2:probe2	-0.00997081931890353	0.0486448194013256	-0.204971864252246	0.837641723767746	   
df.mm.trans2:probe3	-0.0874201663174888	0.0486448194013256	-1.79711154020867	0.0726622587290487	.  
df.mm.trans2:probe4	0.0156160953236053	0.0486448194013256	0.321022783428810	0.748269696169905	   
df.mm.trans2:probe5	-0.0201587888420435	0.0486448194013256	-0.414407722962873	0.678676988059882	   
df.mm.trans2:probe6	0.0412421778916746	0.0486448194013256	0.84782261295744	0.396768353224843	   
df.mm.trans3:probe2	0.0980558567537837	0.0486448194013256	2.01575127548139	0.0441309009021257	*  
df.mm.trans3:probe3	0.0362825280970174	0.0486448194013256	0.745866230845307	0.455948320037416	   
df.mm.trans3:probe4	0.222028956178125	0.0486448194013256	4.56428780928057	5.72760304441789e-06	***
df.mm.trans3:probe5	-0.196311048358989	0.0486448194013256	-4.03560031211955	5.92230025828324e-05	***
df.mm.trans3:probe6	0.186193640236591	0.0486448194013256	3.82761499637754	0.000138591518744860	***
df.mm.trans3:probe7	0.0699256994322974	0.0486448194013256	1.43747474639390	0.150940238648339	   
df.mm.trans3:probe8	0.0528130493494875	0.0486448194013256	1.08568702689126	0.277916155448502	   
df.mm.trans3:probe9	0.225688347226848	0.0486448194013256	4.63951454655206	4.02525721408361e-06	***
df.mm.trans3:probe10	0.63736986956169	0.0486448194013256	13.1025230930207	6.00051598840021e-36	***
df.mm.trans3:probe11	0.408740138741930	0.0486448194013256	8.40254201315407	1.75238282259956e-16	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.34570647626274	0.273538530374679	15.8869994304284	3.9135530163653e-50	***
df.mm.trans1	0.0710268604385068	0.235720318853441	0.301318362303200	0.763243249304967	   
df.mm.trans2	-0.087009390372795	0.207767428593486	-0.418782630953362	0.675477656617682	   
df.mm.exp2	0.0104114225017670	0.266155318092042	0.0391178450853518	0.968805346468113	   
df.mm.exp3	-0.110165756893499	0.266155318092042	-0.413915294585252	0.679037461362472	   
df.mm.exp4	-0.124643463184209	0.266155318092042	-0.46831100004962	0.639678703739249	   
df.mm.exp5	-0.227941433918486	0.266155318092042	-0.85642261651018	0.391998265620214	   
df.mm.exp6	-0.151107530233682	0.266155318092042	-0.567741915949342	0.570355701430416	   
df.mm.exp7	-0.117118696498205	0.266155318092042	-0.440038911631677	0.660017472151275	   
df.mm.exp8	-0.190801880910367	0.266155318092042	-0.716881715075796	0.473638173625337	   
df.mm.trans1:exp2	-0.119450350520384	0.245383078769451	-0.486791310629096	0.626527999872926	   
df.mm.trans2:exp2	0.074101072220836	0.178542415148066	0.41503343706527	0.678219051568864	   
df.mm.trans1:exp3	0.0985390996965167	0.245383078769451	0.401572513437647	0.688096606784296	   
df.mm.trans2:exp3	0.137246942281369	0.178542415148066	0.768707772702356	0.442273998963616	   
df.mm.trans1:exp4	0.130324145496842	0.245383078769451	0.53110485918749	0.595480746122291	   
df.mm.trans2:exp4	0.101620688272659	0.178542415148066	0.569168330048548	0.569387812900987	   
df.mm.trans1:exp5	0.197944974047285	0.245383078769451	0.806677359498221	0.420071340127238	   
df.mm.trans2:exp5	0.110625983125982	0.178542415148066	0.619606176124813	0.535678219188085	   
df.mm.trans1:exp6	0.0298516793357604	0.245383078769451	0.121653373514836	0.903201441399547	   
df.mm.trans2:exp6	0.285288361350243	0.178542415148066	1.59787443848372	0.110431544739297	   
df.mm.trans1:exp7	0.123902653785449	0.245383078769451	0.50493560683482	0.613731063302735	   
df.mm.trans2:exp7	-0.0485416939657173	0.178542415148066	-0.271877659577202	0.785780151984338	   
df.mm.trans1:exp8	0.181270814242936	0.245383078769451	0.738725812521279	0.460271383076569	   
df.mm.trans2:exp8	0.176783354070616	0.178542415148066	0.990147657205202	0.322375362380517	   
df.mm.trans1:probe2	-0.0639345174294872	0.170941348147044	-0.37401435125275	0.708484145681508	   
df.mm.trans1:probe3	-0.126929060836292	0.170941348147044	-0.742529892341248	0.457965404455047	   
df.mm.trans1:probe4	0.10601288454191	0.170941348147044	0.620171103662512	0.535306435349218	   
df.mm.trans1:probe5	-0.167007227610375	0.170941348147044	-0.976985553353159	0.328846023012390	   
df.mm.trans1:probe6	0.0452614837986797	0.170941348147044	0.264777856787146	0.791242882770014	   
df.mm.trans1:probe7	0.0375420081559035	0.170941348147044	0.219619235268987	0.82621887374781	   
df.mm.trans1:probe8	0.168414841628714	0.170941348147044	0.985220038652338	0.324788032953378	   
df.mm.trans1:probe9	-0.0624397281578386	0.170941348147044	-0.36526989423371	0.71499812296314	   
df.mm.trans1:probe10	-0.089288454907716	0.170941348147044	-0.522333864074302	0.601569955212673	   
df.mm.trans1:probe11	0.0476695078521525	0.170941348147044	0.278864700488656	0.78041447673175	   
df.mm.trans1:probe12	-0.131312412655567	0.170941348147044	-0.768172323893294	0.442591836712404	   
df.mm.trans1:probe13	0.0460434787194004	0.170941348147044	0.269352495569379	0.787721868725191	   
df.mm.trans1:probe14	-0.149390413647102	0.170941348147044	-0.873927901391044	0.382397032532267	   
df.mm.trans1:probe15	-0.0870154913981296	0.170941348147044	-0.509037119113386	0.610854394181586	   
df.mm.trans1:probe16	0.258897806272229	0.170941348147044	1.51454173655823	0.130249211630326	   
df.mm.trans1:probe17	-0.00413306626853901	0.170941348147044	-0.0241782711634153	0.980715916541378	   
df.mm.trans1:probe18	-0.106745733723522	0.170941348147044	-0.624458241850877	0.532489283962779	   
df.mm.trans1:probe19	-0.301790381240504	0.170941348147044	-1.76546157212299	0.0778345639110419	.  
df.mm.trans1:probe20	-0.0209096319194935	0.170941348147044	-0.122320504349287	0.902673249013624	   
df.mm.trans1:probe21	-0.24452848725733	0.170941348147044	-1.43048179921330	0.152935376589323	   
df.mm.trans1:probe22	0.0139981939879626	0.170941348147044	0.0818888708887527	0.934753790631853	   
df.mm.trans2:probe2	0.22018959553836	0.170941348147044	1.28810026319058	0.198051201442926	   
df.mm.trans2:probe3	0.0326722288838248	0.170941348147044	0.191131222714589	0.848467075116367	   
df.mm.trans2:probe4	0.090490168683706	0.170941348147044	0.529363841250779	0.596687194725368	   
df.mm.trans2:probe5	0.026262614254547	0.170941348147044	0.153635235355438	0.87793273722062	   
df.mm.trans2:probe6	-0.0868395560123002	0.170941348147044	-0.508007904194138	0.611575689560426	   
df.mm.trans3:probe2	-0.0153739684661853	0.170941348147044	-0.0899370961609628	0.9283577607709	   
df.mm.trans3:probe3	-0.0821714982186184	0.170941348147044	-0.480699954161672	0.630849791420246	   
df.mm.trans3:probe4	-0.190681813375990	0.170941348147044	-1.11548092631143	0.264950295872265	   
df.mm.trans3:probe5	-0.166666810430149	0.170941348147044	-0.974994126563117	0.329832329524749	   
df.mm.trans3:probe6	-0.00630585628159768	0.170941348147044	-0.0368890052053022	0.97058191074101	   
df.mm.trans3:probe7	0.0472640544039849	0.170941348147044	0.276492814151251	0.782234798983362	   
df.mm.trans3:probe8	-0.241931080804621	0.170941348147044	-1.41528707610584	0.157339736838126	   
df.mm.trans3:probe9	0.0393086451387739	0.170941348147044	0.229953990446832	0.818181206383343	   
df.mm.trans3:probe10	-0.160449830748836	0.170941348147044	-0.938625045888936	0.348182010073396	   
df.mm.trans3:probe11	-0.0996196634144269	0.170941348147044	-0.58277101762842	0.560197435027306