chr6.20278_chr6_92478496_92503156_+_2.R 

fitVsDatCorrelation=0.884181044778726
cont.fitVsDatCorrelation=0.247048451134531

fstatistic=6700.35515906312,52,692
cont.fstatistic=1547.00428758658,52,692

residuals=-0.53675345273904,-0.106830572117844,-0.00398427994451395,0.0934842555579995,2.58139373667983
cont.residuals=-0.667878768869544,-0.286430014518947,-0.106638324106861,0.199281893511729,2.38938482174407

predictedValues:
Include	Exclude	Both
chr6.20278_chr6_92478496_92503156_+_2.R.tl.Lung	61.0472424206719	99.6507949419904	143.884644703913
chr6.20278_chr6_92478496_92503156_+_2.R.tl.cerebhem	52.5589702202471	66.7448024558413	112.298513660988
chr6.20278_chr6_92478496_92503156_+_2.R.tl.cortex	53.0284970406705	111.434573510050	136.01016743379
chr6.20278_chr6_92478496_92503156_+_2.R.tl.heart	58.8082206960462	100.051737669547	138.655946275398
chr6.20278_chr6_92478496_92503156_+_2.R.tl.kidney	61.3446625514131	111.995244564711	153.089536127033
chr6.20278_chr6_92478496_92503156_+_2.R.tl.liver	58.1128937394525	60.5000402167078	103.876504638727
chr6.20278_chr6_92478496_92503156_+_2.R.tl.stomach	58.9271985623056	73.2934809471462	102.465680302423
chr6.20278_chr6_92478496_92503156_+_2.R.tl.testicle	56.7599241641492	74.4526705551043	118.300178199932


diffExp=-38.6035525213186,-14.1858322355941,-58.4060764693798,-41.2435169735009,-50.6505820132982,-2.38714647725525,-14.3662823848406,-17.6927463909552
diffExpScore=0.995807756024288
diffExp1.5=-1,0,-1,-1,-1,0,0,0
diffExp1.5Score=0.8
diffExp1.4=-1,0,-1,-1,-1,0,0,0
diffExp1.4Score=0.8
diffExp1.3=-1,0,-1,-1,-1,0,0,-1
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,-1,-1,-1,-1,0,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	67.4975059963615	69.6234499191619	54.2578838746829
cerebhem	63.268863879477	68.9300409934266	64.7025039610457
cortex	60.8071213350835	69.694713461198	68.5408601295623
heart	67.405702235956	70.5355700021389	60.0366607948934
kidney	67.9986668272433	60.111962383303	69.0643681744755
liver	66.0886400764587	68.9257249848258	66.1313078868947
stomach	70.4809308083904	59.0697785406278	59.0901728934684
testicle	69.5315154403226	70.9477257189017	59.8883395401681
cont.diffExp=-2.12594392280040,-5.66117711394964,-8.88759212611449,-3.12986776618295,7.88670444394022,-2.83708490836707,11.4111522677627,-1.41621027857909
cont.diffExpScore=7.52701159225263

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=0.254839315132001
cont.tran.correlation=-0.435982159024893

tran.covariance=0.00361705295218417
cont.tran.covariance=-0.00157859685385864

tran.mean=72.4194346410034
cont.tran.mean=66.9323695376798

weightedLogRatios:
wLogRatio
Lung	-2.13486794537536
cerebhem	-0.975213814963763
cortex	-3.22450390807018
heart	-2.30629285278846
kidney	-2.65909570584555
liver	-0.164347663699729
stomach	-0.913121362238155
testicle	-1.13267928476828

cont.weightedLogRatios:
wLogRatio
Lung	-0.131101074991148
cerebhem	-0.359098626517225
cortex	-0.569668222794971
heart	-0.192144521687110
kidney	0.512576386036877
liver	-0.177041877089036
stomach	0.735992466171246
testicle	-0.0857313043550288

varWeightedLogRatios=1.08917743481046
cont.varWeightedLogRatios=0.19169070569339

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.73947998442441	0.103429046358819	36.1550272004955	1.42941663529764e-161	***
df.mm.trans1	0.297114617192183	0.09289440899265	3.19841226629357	0.00144486222580070	** 
df.mm.trans2	1.12402212121937	0.0854290252864041	13.1573796780549	1.89454940210293e-35	***
df.mm.exp2	-0.302656570496308	0.117016608083166	-2.58644115099632	0.00990031928908831	** 
df.mm.exp3	0.0272292853805009	0.117016608083166	0.232695903825451	0.816066355874255	   
df.mm.exp4	0.00366526947465498	0.117016608083166	0.0313226432956423	0.975021264165872	   
df.mm.exp5	0.0596334706595079	0.117016608083166	0.509615443793459	0.61048337963904	   
df.mm.exp6	-0.222479300135687	0.117016608083166	-1.90126259665266	0.0576827605670488	.  
df.mm.exp7	-0.00306164274717546	0.117016608083166	-0.0261641727386210	0.979133935223823	   
df.mm.exp8	-0.168539286385979	0.117016608083166	-1.44030227116304	0.150234161144090	   
df.mm.trans1:exp2	0.152944321677686	0.112034872636522	1.36514924396698	0.172649788045782	   
df.mm.trans2:exp2	-0.0981390250527085	0.097513840069305	-1.00641124360357	0.314569562790382	   
df.mm.trans1:exp3	-0.168047866655824	0.112034872636523	-1.49996034896229	0.134080753537826	   
df.mm.trans2:exp3	0.0845363246633688	0.097513840069305	0.866916169061614	0.386288437296498	   
df.mm.trans1:exp4	-0.0410316469283704	0.112034872636522	-0.366239956923863	0.714297970008593	   
df.mm.trans2:exp4	0.000350135448148357	0.097513840069305	0.00359062311462157	0.997136138241006	   
df.mm.trans1:exp5	-0.054773333606263	0.112034872636523	-0.48889539763182	0.62507066058953	   
df.mm.trans2:exp5	0.0571509165229366	0.097513840069305	0.586080052660405	0.558012825190995	   
df.mm.trans1:exp6	0.173218832256623	0.112034872636523	1.54611531374343	0.122533897175739	   
df.mm.trans2:exp6	-0.276548694056348	0.097513840069305	-2.83599429434632	0.00470178985769376	** 
df.mm.trans1:exp7	-0.0322836282712873	0.112034872636522	-0.288156959628328	0.773312877399412	   
df.mm.trans2:exp7	-0.304138712883901	0.097513840069305	-3.11892868404879	0.00189041781701195	** 
df.mm.trans1:exp8	0.0957217722558183	0.112034872636523	0.854392654743946	0.393183143290165	   
df.mm.trans2:exp8	-0.122969108643546	0.097513840069305	-1.26104262283333	0.207718463301566	   
df.mm.trans1:probe2	0.699756161381995	0.0560174363182613	12.4917562704290	1.95224891243281e-32	***
df.mm.trans1:probe3	0.6777319431191	0.0560174363182613	12.0985890762403	1.05852572720000e-30	***
df.mm.trans1:probe4	0.0917036550319729	0.0560174363182613	1.63705555018551	0.102073507254444	   
df.mm.trans1:probe5	0.000887376536170895	0.0560174363182613	0.0158410772518988	0.98736574361943	   
df.mm.trans1:probe6	-0.0786282549580131	0.0560174363182613	-1.40363894040579	0.160874974319332	   
df.mm.trans1:probe7	-0.0620652089393709	0.0560174363182612	-1.10796232420830	0.268263034142709	   
df.mm.trans1:probe8	0.271973006388045	0.0560174363182613	4.85514911540826	1.48819783143871e-06	***
df.mm.trans1:probe9	0.407858336522781	0.0560174363182613	7.28091757369164	9.03804500955288e-13	***
df.mm.trans1:probe10	-0.110558465460794	0.0560174363182613	-1.97364379249096	0.0488196629371723	*  
df.mm.trans1:probe11	0.215163261537208	0.0560174363182613	3.84100515265933	0.000133783086557480	***
df.mm.trans1:probe12	-0.00542412389392665	0.0560174363182613	-0.0968292062333889	0.9228900658293	   
df.mm.trans1:probe13	0.100264682100390	0.0560174363182612	1.78988344862374	0.0739097749892857	.  
df.mm.trans1:probe14	-0.0843293756081761	0.0560174363182613	-1.5054129776497	0.132674228240994	   
df.mm.trans1:probe15	-0.15190685893497	0.0560174363182613	-2.71177813407804	0.00685847825820916	** 
df.mm.trans1:probe16	0.0348032336402355	0.0560174363182612	0.621292867501147	0.534611545749346	   
df.mm.trans1:probe17	0.143842343344828	0.0560174363182613	2.567813752268	0.0104430332475523	*  
df.mm.trans1:probe18	0.0719451379698692	0.0560174363182613	1.28433471251907	0.199454664370489	   
df.mm.trans1:probe19	0.0272215252972502	0.0560174363182613	0.485947360078958	0.62715832465111	   
df.mm.trans1:probe20	-0.158565739821971	0.0560174363182613	-2.83064970915636	0.00478013895027153	** 
df.mm.trans1:probe21	-0.127842766863720	0.0560174363182612	-2.28219596015401	0.0227804462342451	*  
df.mm.trans1:probe22	-0.087494150253962	0.0560174363182613	-1.56190921978055	0.118766556507574	   
df.mm.trans2:probe2	-0.326096994828047	0.0560174363182613	-5.82134807054249	8.93282277590543e-09	***
df.mm.trans2:probe3	-0.084907956850207	0.0560174363182613	-1.51574156960353	0.130041348724014	   
df.mm.trans2:probe4	-0.734125296373676	0.0560174363182613	-13.1052997892079	3.28567946889014e-35	***
df.mm.trans2:probe5	-0.733518830895247	0.0560174363182613	-13.0944734194507	3.68351969564128e-35	***
df.mm.trans2:probe6	-0.477821656138656	0.0560174363182612	-8.52987368832675	9.2052807558833e-17	***
df.mm.trans3:probe2	-0.207770683478479	0.0560174363182613	-3.70903592049513	0.000224724407957232	***
df.mm.trans3:probe3	-0.367043784740972	0.0560174363182613	-6.55231315220541	1.10643536348927e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.45223052028383	0.214544093590049	20.7520535559051	9.57741245173523e-75	***
df.mm.trans1	-0.146971753428362	0.192691970761965	-0.762728996165171	0.445884948136855	   
df.mm.trans2	-0.206683029465886	0.177206437085071	-1.16634041553843	0.243878639790276	   
df.mm.exp2	-0.250758260924977	0.242728933505672	-1.03307939973756	0.301927645532917	   
df.mm.exp3	-0.337042465465286	0.242728933505672	-1.38855496375099	0.165414895396955	   
df.mm.exp4	-0.0895524269851333	0.242728933505672	-0.36894005873857	0.71228525540401	   
df.mm.exp5	-0.380785756252597	0.242728933505672	-1.56876953543611	0.117158772863056	   
df.mm.exp6	-0.229059708234464	0.242728933505672	-0.94368522502123	0.345659876739258	   
df.mm.exp7	-0.206446787806099	0.242728933505672	-0.850524017983518	0.395327999775575	   
df.mm.exp8	-0.0502021399526351	0.242728933505672	-0.206823880563303	0.836208246318454	   
df.mm.trans1:exp2	0.186060938226914	0.232395260775116	0.800622773486596	0.423624843656481	   
df.mm.trans2:exp2	0.240748917813248	0.202274111254727	1.19021122535087	0.234371535283166	   
df.mm.trans1:exp3	0.232658725806488	0.232395260775116	1.00113369364975	0.317112115785726	   
df.mm.trans2:exp3	0.338065498593521	0.202274111254727	1.67132361376583	0.0951098895962479	.  
df.mm.trans1:exp4	0.088191395238298	0.232395260775116	0.379488785374324	0.704441354808326	   
df.mm.trans2:exp4	0.102568113883687	0.202274111254727	0.507074846343146	0.612263848066613	   
df.mm.trans1:exp5	0.388183206760967	0.232395260775117	1.67035767195185	0.0953008167219655	.  
df.mm.trans2:exp5	0.233893184587106	0.202274111254727	1.15631794467539	0.247950168454351	   
df.mm.trans1:exp6	0.207965931668479	0.232395260775116	0.894880261218938	0.371162248534116	   
df.mm.trans2:exp6	0.218987748828499	0.202274111254727	1.08262865410752	0.279350378605538	   
df.mm.trans1:exp7	0.249698327055459	0.232395260775116	1.07445533193160	0.282993152301534	   
df.mm.trans2:exp7	0.0420647852975426	0.202274111254727	0.207959313411936	0.835321924154687	   
df.mm.trans1:exp8	0.0798916003208162	0.232395260775116	0.343774653813296	0.731120189653292	   
df.mm.trans2:exp8	0.0690440536229687	0.202274111254727	0.341339053202021	0.732951969588927	   
df.mm.trans1:probe2	-0.114820889467481	0.116197630387558	-0.988151729811658	0.323423722029143	   
df.mm.trans1:probe3	-0.112732485931964	0.116197630387558	-0.970178871599731	0.332296312249889	   
df.mm.trans1:probe4	-0.232533322336308	0.116197630387558	-2.00118816158928	0.04576216741741	*  
df.mm.trans1:probe5	-0.15410650534183	0.116197630387558	-1.32624481951855	0.185196169689404	   
df.mm.trans1:probe6	-0.174434315859104	0.116197630387558	-1.50118651539887	0.133763456471618	   
df.mm.trans1:probe7	0.00838972898832506	0.116197630387558	0.0722022382069452	0.942461826760185	   
df.mm.trans1:probe8	-0.0189358345427720	0.116197630387558	-0.162962312394965	0.870595685320687	   
df.mm.trans1:probe9	-0.089562132737202	0.116197630387558	-0.770774175329412	0.441103872682159	   
df.mm.trans1:probe10	-0.267257398856057	0.116197630387558	-2.30002451826826	0.0217435441141156	*  
df.mm.trans1:probe11	0.0399958890617482	0.116197630387558	0.344205720274574	0.730796150422043	   
df.mm.trans1:probe12	-0.0227243059747862	0.116197630387558	-0.195566001638700	0.845007282384525	   
df.mm.trans1:probe13	-0.0369702060403319	0.116197630387558	-0.318166609052386	0.750454552864	   
df.mm.trans1:probe14	-0.117709148607473	0.116197630387558	-1.01300816733416	0.311410317868280	   
df.mm.trans1:probe15	-0.165081688374208	0.116197630387558	-1.42069754627185	0.155855125354557	   
df.mm.trans1:probe16	-0.095945823489154	0.116197630387558	-0.825712393352105	0.409251831257593	   
df.mm.trans1:probe17	-0.138398895372149	0.116197630387558	-1.19106469650493	0.234036561741107	   
df.mm.trans1:probe18	-0.229225432445466	0.116197630387558	-1.97272037029432	0.0489250733052563	*  
df.mm.trans1:probe19	-0.104563811622570	0.116197630387558	-0.899879036033821	0.368497665765638	   
df.mm.trans1:probe20	-0.0764382030118448	0.116197630387558	-0.657829275492948	0.510866602679731	   
df.mm.trans1:probe21	-0.215602360003721	0.116197630387558	-1.85547983452515	0.0639527510855191	.  
df.mm.trans1:probe22	-0.0105458045765344	0.116197630387558	-0.0907574839638346	0.92771155687836	   
df.mm.trans2:probe2	-0.0123476911549905	0.116197630387558	-0.106264569370363	0.915403238887732	   
df.mm.trans2:probe3	-0.0278377181827164	0.116197630387558	-0.239572167606759	0.810732885343635	   
df.mm.trans2:probe4	0.117441281916175	0.116197630387558	1.0107028992284	0.3125119112534	   
df.mm.trans2:probe5	-0.0667272241656467	0.116197630387558	-0.574256324703773	0.565980973075929	   
df.mm.trans2:probe6	-0.0325431532935952	0.116197630387558	-0.280067271467179	0.779509591931402	   
df.mm.trans3:probe2	-0.219464103456775	0.116197630387558	-1.88871410479532	0.0593481000898481	.  
df.mm.trans3:probe3	-0.0950181884893164	0.116197630387558	-0.817729141053899	0.413793329012321