chr14.7198_chr14_45995867_45998693_-_2.R 

fitVsDatCorrelation=0.890145243133063
cont.fitVsDatCorrelation=0.220865608757175

fstatistic=6074.69308530747,65,991
cont.fstatistic=1314.12658875569,65,991

residuals=-1.05173719766544,-0.122735498867908,-0.0102053800623155,0.115619727983102,0.9039782015306
cont.residuals=-0.895110571331137,-0.318850780067152,-0.0983516001545575,0.204260278456202,2.23169696583691

predictedValues:
Include	Exclude	Both
chr14.7198_chr14_45995867_45998693_-_2.R.tl.Lung	64.6697531479709	70.7065853733913	76.6118184741715
chr14.7198_chr14_45995867_45998693_-_2.R.tl.cerebhem	89.099451911912	75.0684961144245	163.202938236562
chr14.7198_chr14_45995867_45998693_-_2.R.tl.cortex	102.584289312422	61.7094022213602	234.867773386418
chr14.7198_chr14_45995867_45998693_-_2.R.tl.heart	59.2294562945284	59.2676733281335	69.8721083835905
chr14.7198_chr14_45995867_45998693_-_2.R.tl.kidney	64.6903826442781	73.3357512757442	77.6834007728683
chr14.7198_chr14_45995867_45998693_-_2.R.tl.liver	62.2829742606287	68.9306595620727	70.7951601229141
chr14.7198_chr14_45995867_45998693_-_2.R.tl.stomach	57.9749623132718	63.6448139571737	75.0084625062534
chr14.7198_chr14_45995867_45998693_-_2.R.tl.testicle	62.9779889054746	65.504334764121	82.8941760914531


diffExp=-6.03683222542043,14.0309557974874,40.8748870910614,-0.0382170336051004,-8.64536863146606,-6.64768530144399,-5.66985164390189,-2.52634585864634
diffExpScore=3.20672734195740
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	92.9918002674701	76.9658435329131	91.0445011690928
cerebhem	93.1562342357122	77.5057998603198	84.0149646053384
cortex	94.6356012462858	103.636712485307	92.8054510315289
heart	88.4696799517643	79.281760146011	99.0199578390685
kidney	83.7605643842512	76.5444669807014	84.9418024109776
liver	86.7110865202175	85.2232991365248	78.901725599323
stomach	92.1702290074234	87.0567161323752	85.536739733532
testicle	87.3509246298922	79.9126966520438	87.4636216961886
cont.diffExp=16.0259567345570,15.6504343753925,-9.00111123902124,9.18791980575325,7.21609740354984,1.48778738369268,5.11351287504819,7.43822797784844
cont.diffExpScore=1.31416466226879

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

tran.correlation=0.0724612779493717
cont.tran.correlation=0.473099833478976

tran.covariance=0.00199266446708485
cont.tran.covariance=0.00202699892486314

tran.mean=68.8548109616817
cont.tran.mean=86.5858384480758

weightedLogRatios:
wLogRatio
Lung	-0.376071434168431
cerebhem	0.754648317892727
cortex	2.224380291203
heart	-0.00263284111900066
kidney	-0.530885257013491
liver	-0.424149028563233
stomach	-0.383178951003011
testicle	-0.163713507173786

cont.weightedLogRatios:
wLogRatio
Lung	0.839433861757222
cerebhem	0.817053975075221
cortex	-0.417534065769166
heart	0.485519978390424
kidney	0.394859036947888
liver	0.0770836353026851
stomach	0.256568385847101
testicle	0.393858663520292

varWeightedLogRatios=0.87762264803987
cont.varWeightedLogRatios=0.164576223240568

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.88753343127419	0.104952554344374	37.0408653277584	3.32975564532364e-189	***
df.mm.trans1	0.0710700176499956	0.0891127060819506	0.79752956424236	0.425334587314305	   
df.mm.trans2	0.357431182288033	0.0784087275519832	4.55856373961768	5.79340523995098e-06	***
df.mm.exp2	-0.375921244120965	0.0988616710996384	-3.80249736768145	0.000151998420493064	***
df.mm.exp3	-0.794982547763053	0.0988616710996384	-8.04136263245869	2.51981922111617e-15	***
df.mm.exp4	-0.172264496877452	0.0988616710996385	-1.74248012360457	0.0817346693793967	.  
df.mm.exp5	0.0229382126791061	0.0988616710996384	0.232023315244061	0.816567808022489	   
df.mm.exp6	0.0159175698070567	0.0988616710996384	0.161008504408287	0.872119518451404	   
df.mm.exp7	-0.193352822516139	0.0988616710996384	-1.95579156578556	0.0507703764864883	.  
df.mm.exp8	-0.181744163715609	0.0988616710996384	-1.83836831498061	0.0663072869047472	.  
df.mm.trans1:exp2	0.696380828848793	0.0887804014836112	7.8438576218575	1.12643645720157e-14	***
df.mm.trans2:exp2	0.435783508520698	0.0615088664343912	7.08488928154007	2.63755250424676e-12	***
df.mm.trans1:exp3	1.25637374481632	0.0887804014836112	14.1514762697738	1.51820492105914e-41	***
df.mm.trans2:exp3	0.658880139274041	0.0615088664343912	10.7119538607794	2.06689889617587e-25	***
df.mm.trans1:exp4	0.0843898892133985	0.0887804014836112	0.950546379641871	0.342066457290918	   
df.mm.trans2:exp4	-0.00421019744041823	0.0615088664343912	-0.0684486267505687	0.945442321952967	   
df.mm.trans1:exp5	-0.0226192659439972	0.0887804014836112	-0.254777693792844	0.798947664789548	   
df.mm.trans2:exp5	0.0135713025291406	0.0615088664343912	0.220639776277076	0.825418374311775	   
df.mm.trans1:exp6	-0.0535230660579702	0.0887804014836112	-0.60287028627428	0.546732918738933	   
df.mm.trans2:exp6	-0.0413552182518111	0.0615088664343912	-0.672345641354372	0.501520404926912	   
df.mm.trans1:exp7	0.0840704572575988	0.0887804014836112	0.946948378839199	0.343895923465655	   
df.mm.trans2:exp7	0.0881319528264394	0.0615088664343912	1.43283331225826	0.152220851643890	   
df.mm.trans1:exp8	0.155235848257534	0.0887804014836112	1.74853735355309	0.0806806468010063	.  
df.mm.trans2:exp8	0.105321769897534	0.0615088664343912	1.71230224198451	0.087153819440746	.  
df.mm.trans1:probe2	0.175026854715974	0.067027733229813	2.61126023933813	0.00915722380256953	** 
df.mm.trans1:probe3	0.29576760853536	0.067027733229813	4.41261540982273	1.13318096319315e-05	***
df.mm.trans1:probe4	0.615136192702233	0.067027733229813	9.17733843979419	2.5042764641716e-19	***
df.mm.trans1:probe5	0.115008186533172	0.067027733229813	1.71582986610709	0.0865057361845245	.  
df.mm.trans1:probe6	-0.0349406224935455	0.067027733229813	-0.521286052949862	0.60228398263096	   
df.mm.trans1:probe7	0.231017444975577	0.067027733229813	3.44659492188859	0.000591533421046601	***
df.mm.trans1:probe8	0.545050187945839	0.067027733229813	8.13171148242573	1.25689089871877e-15	***
df.mm.trans1:probe9	0.808981711505724	0.067027733229813	12.0693580481385	2.16154846303580e-31	***
df.mm.trans1:probe10	0.592318374129179	0.067027733229813	8.83691489459058	4.40888616247622e-18	***
df.mm.trans1:probe11	0.428061068499379	0.067027733229813	6.38632768665647	2.60793313484317e-10	***
df.mm.trans1:probe12	0.419913639186333	0.067027733229813	6.26477457840632	5.55789666819131e-10	***
df.mm.trans1:probe13	0.479191212622088	0.067027733229813	7.14914841262975	1.69298305875832e-12	***
df.mm.trans1:probe14	0.544413075443034	0.067027733229813	8.12220627507193	1.35273222361820e-15	***
df.mm.trans1:probe15	0.455090348429657	0.067027733229813	6.78958285623837	1.93470755460509e-11	***
df.mm.trans1:probe16	0.67546318466914	0.067027733229813	10.0773687565001	8.34946950006031e-23	***
df.mm.trans1:probe17	0.580698044014647	0.067027733229813	8.66354889286873	1.83515251418666e-17	***
df.mm.trans1:probe18	0.485686246285126	0.067027733229813	7.24604910358359	8.6188835841162e-13	***
df.mm.trans1:probe19	0.594342920427251	0.067027733229813	8.86711950096048	3.43069223907497e-18	***
df.mm.trans2:probe2	-0.0846838334718964	0.067027733229813	-1.26341484921692	0.206737271248624	   
df.mm.trans2:probe3	0.366876796641914	0.067027733229813	5.47350744182309	5.59022044007962e-08	***
df.mm.trans2:probe4	0.029633460775729	0.067027733229813	0.442107458328136	0.6585079106238	   
df.mm.trans2:probe5	-0.0192699133889388	0.067027733229813	-0.287491646522933	0.77379600273342	   
df.mm.trans2:probe6	0.046795997247934	0.067027733229813	0.698158732110006	0.485241714790449	   
df.mm.trans3:probe2	0.542092012594831	0.067027733229813	8.08757787968453	1.76689628847159e-15	***
df.mm.trans3:probe3	-0.236123811604093	0.067027733229813	-3.52277781488616	0.000446527762223868	***
df.mm.trans3:probe4	-0.366385568617547	0.067027733229813	-5.46617871383697	5.81914983571232e-08	***
df.mm.trans3:probe5	0.0291796192920809	0.067027733229813	0.43533650753241	0.663412984264385	   
df.mm.trans3:probe6	-0.478313149404593	0.067027733229813	-7.13604841393989	1.85365197222955e-12	***
df.mm.trans3:probe7	-0.496716578729679	0.067027733229813	-7.41061281345476	2.68973874239782e-13	***
df.mm.trans3:probe8	0.181272057711126	0.067027733229813	2.70443365717906	0.00695935230098278	** 
df.mm.trans3:probe9	0.33938643770136	0.067027733229813	5.06337334335522	4.90966176974702e-07	***
df.mm.trans3:probe10	-0.498868096095945	0.067027733229813	-7.44271172628668	2.13757978563554e-13	***
df.mm.trans3:probe11	0.351856588688793	0.067027733229813	5.24941799064588	1.86661151689363e-07	***
df.mm.trans3:probe12	-0.0170001958509835	0.067027733229813	-0.253629281967454	0.799834580824294	   
df.mm.trans3:probe13	0.49153028904155	0.067027733229813	7.33323753253413	4.66361120613513e-13	***
df.mm.trans3:probe14	-0.170943086984555	0.067027733229813	-2.55033370140170	0.0109114491308539	*  
df.mm.trans3:probe15	-0.232975128309277	0.067027733229813	-3.47580198647764	0.000531406051776415	***
df.mm.trans3:probe16	-0.416996539091178	0.067027733229813	-6.22125378552566	7.26470800394443e-10	***
df.mm.trans3:probe17	0.222077183103489	0.067027733229813	3.31321338798778	0.000955676673773204	***
df.mm.trans3:probe18	0.619606151660991	0.067027733229813	9.24402664098148	1.41295797584746e-19	***
df.mm.trans3:probe19	0.568939529239854	0.067027733229813	8.48812128688842	7.58478149055395e-17	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.25799578489589	0.224634319292923	18.9552326567851	1.30690879084105e-68	***
df.mm.trans1	0.308674557610053	0.190731632937549	1.61837107382770	0.105900852326838	   
df.mm.trans2	0.071258982807378	0.16782146228162	0.42461185737852	0.671211814615443	   
df.mm.exp2	0.0891112168446925	0.211597748433634	0.421134995548601	0.673747827103498	   
df.mm.exp3	0.295895354592983	0.211597748433634	1.39838612075680	0.162309993164882	   
df.mm.exp4	-0.104178081073791	0.211597748433634	-0.492340215550384	0.622587905901823	   
df.mm.exp5	-0.0406568423747823	0.211597748433634	-0.192142131358898	0.847670237776164	   
df.mm.exp6	0.175128867749076	0.211597748433634	0.827649958685659	0.408068023036403	   
df.mm.exp7	0.176726368075942	0.211597748433634	0.835199662492491	0.403806604001358	   
df.mm.exp8	0.0151207716938943	0.211597748433634	0.0714599838884239	0.94304608974935	   
df.mm.trans1:exp2	-0.0873445152896565	0.190020387578042	-0.459658652436881	0.645862071503635	   
df.mm.trans2:exp2	-0.0821201793348538	0.131649986303639	-0.623776588517455	0.532917865717016	   
df.mm.trans1:exp3	-0.278372934903032	0.190020387578042	-1.46496351497390	0.143248040604852	   
df.mm.trans2:exp3	0.00163454712114563	0.131649986303639	0.0124158548514824	0.990096334590991	   
df.mm.trans1:exp4	0.0543266548728566	0.190020387578042	0.285899084647138	0.775015164274846	   
df.mm.trans2:exp4	0.133824439532296	0.131649986303639	1.01651692711643	0.309631394815113	   
df.mm.trans1:exp5	-0.0638921730728939	0.190020387578042	-0.336238515704812	0.736762191144543	   
df.mm.trans2:exp5	0.0351669491015403	0.131649986303639	0.267124593696734	0.789428810163569	   
df.mm.trans1:exp6	-0.245058440031165	0.190020387578042	-1.28964288071731	0.197475568706798	   
df.mm.trans2:exp6	-0.0732157402112362	0.131649986303639	-0.556139368236396	0.578241127287614	   
df.mm.trans1:exp7	-0.185600505552617	0.190020387578042	-0.976739958897254	0.328936272986294	   
df.mm.trans2:exp7	-0.0535282852112366	0.131649986303639	-0.406595448386741	0.684392932292232	   
df.mm.trans1:exp8	-0.0776984699239171	0.190020387578042	-0.408895439664368	0.682704743140871	   
df.mm.trans2:exp8	0.0224522422813436	0.131649986303639	0.170544964809638	0.864616387151351	   
df.mm.trans1:probe2	-0.0157200276954193	0.143462246554019	-0.109576059716171	0.912767782045606	   
df.mm.trans1:probe3	-0.101715474250994	0.143462246554020	-0.709005168218206	0.478488026408829	   
df.mm.trans1:probe4	-0.124702948260800	0.143462246554020	-0.869238780628216	0.384927012306365	   
df.mm.trans1:probe5	-0.0382667754695238	0.143462246554020	-0.266737600927745	0.789726693406054	   
df.mm.trans1:probe6	-0.154083034732430	0.143462246554020	-1.07403193825221	0.283069808540231	   
df.mm.trans1:probe7	0.0618189989389273	0.143462246554020	0.430907785315141	0.666629129968988	   
df.mm.trans1:probe8	-0.155036051761756	0.143462246554020	-1.08067491960945	0.280104661407775	   
df.mm.trans1:probe9	-0.0488094632765009	0.143462246554020	-0.340225142495047	0.733759087064055	   
df.mm.trans1:probe10	0.174838357973173	0.143462246554020	1.21870639957767	0.223245676177539	   
df.mm.trans1:probe11	-0.153695254255801	0.143462246554020	-1.07132892414262	0.284282395171681	   
df.mm.trans1:probe12	-0.103498242050159	0.143462246554019	-0.721431906555201	0.470813988779562	   
df.mm.trans1:probe13	0.0153546549051413	0.143462246554020	0.107029237823623	0.914787447254999	   
df.mm.trans1:probe14	-0.106872427206559	0.143462246554020	-0.74495157976156	0.456477619291532	   
df.mm.trans1:probe15	-0.0952719403692226	0.143462246554020	-0.664090676520591	0.506786751742161	   
df.mm.trans1:probe16	-0.258880404789702	0.143462246554020	-1.80451938407519	0.0714534154498647	.  
df.mm.trans1:probe17	-0.133436415370631	0.143462246554020	-0.93011519459502	0.352537954412784	   
df.mm.trans1:probe18	-0.111545934950699	0.143462246554020	-0.777528148555077	0.43703280446829	   
df.mm.trans1:probe19	0.0514795311643874	0.143462246554020	0.358836783899123	0.719793626913713	   
df.mm.trans2:probe2	0.112515529056692	0.143462246554019	0.784286680010448	0.433059336486864	   
df.mm.trans2:probe3	0.122949629472581	0.143462246554019	0.857017315885163	0.391642509053399	   
df.mm.trans2:probe4	0.0212934208429751	0.143462246554019	0.148425257197943	0.882037363702726	   
df.mm.trans2:probe5	-0.0142254603355288	0.143462246554019	-0.0991582153299986	0.921032704315717	   
df.mm.trans2:probe6	0.110141012298393	0.143462246554019	0.767735170360099	0.442827421058692	   
df.mm.trans3:probe2	-0.20190105443257	0.143462246554019	-1.40734624810539	0.159638303473638	   
df.mm.trans3:probe3	-0.0325864345847988	0.143462246554019	-0.227142926920001	0.820359453662396	   
df.mm.trans3:probe4	-0.144044538201425	0.143462246554019	-1.00405884935858	0.315595343936285	   
df.mm.trans3:probe5	-0.214303285175182	0.143462246554019	-1.49379568717745	0.135547342737211	   
df.mm.trans3:probe6	-0.273908325024017	0.143462246554019	-1.90927112605112	0.0565156879859992	.  
df.mm.trans3:probe7	-0.0281262824266312	0.143462246554019	-0.196053547900078	0.844608447743862	   
df.mm.trans3:probe8	-0.0552614327835382	0.143462246554019	-0.385198434507506	0.700173062853696	   
df.mm.trans3:probe9	-0.0939395171882685	0.143462246554019	-0.654803054076644	0.512746511612689	   
df.mm.trans3:probe10	-0.167342690661348	0.143462246554019	-1.16645803813156	0.243709931460957	   
df.mm.trans3:probe11	-0.061035509154058	0.143462246554019	-0.425446489373604	0.670603592916347	   
df.mm.trans3:probe12	-0.181141607396367	0.143462246554019	-1.26264304196686	0.20701451781656	   
df.mm.trans3:probe13	-0.209368914863896	0.143462246554020	-1.45940078238674	0.144771727383858	   
df.mm.trans3:probe14	-0.165473489827633	0.143462246554019	-1.15342882048990	0.249012528392379	   
df.mm.trans3:probe15	-0.100725665378457	0.143462246554019	-0.702105730238442	0.482778093991616	   
df.mm.trans3:probe16	-0.210244561223809	0.143462246554019	-1.46550445342875	0.143100532127258	   
df.mm.trans3:probe17	-0.160912426451127	0.143462246554019	-1.12163604234747	0.262288922539701	   
df.mm.trans3:probe18	-0.200157988834200	0.143462246554019	-1.39519625296564	0.163269245655619	   
df.mm.trans3:probe19	0.0954363482313135	0.143462246554019	0.665236677409605	0.506053911671889