chr8.22579_chr8_70755710_70757131_+_2.R 

fitVsDatCorrelation=0.945791995397126
cont.fitVsDatCorrelation=0.281353630649004

fstatistic=11397.4727392813,55,761
cont.fstatistic=1293.27040467686,55,761

residuals=-1.00886864250011,-0.0767922551774123,-0.00214714683381433,0.086426056159685,0.731033706530208
cont.residuals=-0.79785509496968,-0.286751843330246,-0.104473942601876,0.174246381506276,1.98161974099153

predictedValues:
Include	Exclude	Both
chr8.22579_chr8_70755710_70757131_+_2.R.tl.Lung	82.4705222899454	55.5120791694776	83.921912779332
chr8.22579_chr8_70755710_70757131_+_2.R.tl.cerebhem	60.8943666992406	64.9782676612056	65.0651884114639
chr8.22579_chr8_70755710_70757131_+_2.R.tl.cortex	65.2823609192415	51.7895270559652	72.7253353308152
chr8.22579_chr8_70755710_70757131_+_2.R.tl.heart	71.805123124952	51.2170647104468	76.856230646819
chr8.22579_chr8_70755710_70757131_+_2.R.tl.kidney	81.4311824866517	53.9858646400645	84.3431997485058
chr8.22579_chr8_70755710_70757131_+_2.R.tl.liver	74.3524673590026	53.0526141709984	85.3892723578551
chr8.22579_chr8_70755710_70757131_+_2.R.tl.stomach	70.5121451281386	51.6321628099256	76.9892259998756
chr8.22579_chr8_70755710_70757131_+_2.R.tl.testicle	72.2468917619858	53.9317216864145	74.2777637404537


diffExp=26.9584431204677,-4.08390096196494,13.4928338632762,20.5880584145052,27.4453178465872,21.2998531880042,18.879982318213,18.3151700755713
diffExpScore=1.04981246167570
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=1,0,0,1,1,1,0,0
diffExp1.4Score=0.8
diffExp1.3=1,0,0,1,1,1,1,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	71.150927451073	68.3535712749395	63.1766046324687
cerebhem	84.0037789091289	80.1006409252484	68.5522598096173
cortex	70.0008582522875	68.3295129988367	65.5973842998336
heart	72.7653980104525	70.6848849644213	73.0841526899434
kidney	71.8935095919898	59.6896597391092	68.0846186331859
liver	76.1938217238248	74.6053452855349	87.6003925938652
stomach	78.366868078692	65.8578107920577	89.510832878879
testicle	73.9696029699604	64.3008008710434	69.7427248959427
cont.diffExp=2.79735617613348,3.90313798388047,1.67134525345087,2.08051304603117,12.2038498528806,1.58847643828996,12.5090572866343,9.668802098917
cont.diffExpScore=0.978912980213594

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

tran.correlation=-0.398409824174206
cont.tran.correlation=0.649185614417901

tran.covariance=-0.00324608405445906
cont.tran.covariance=0.00335944475404169

tran.mean=63.4433976046035
cont.tran.mean=71.8916869899125

weightedLogRatios:
wLogRatio
Lung	1.66827710384147
cerebhem	-0.268840160802608
cortex	0.94071190852014
heart	1.38701477043021
kidney	1.72398386815299
liver	1.39740249263519
stomach	1.27771378306020
testicle	1.20863148465973

cont.weightedLogRatios:
wLogRatio
Lung	0.170254947065297
cerebhem	0.209679398791433
cortex	0.102376256565942
heart	0.123946868422802
kidney	0.777997774883851
liver	0.0910725797702624
stomach	0.743340474005673
testicle	0.593053641431956

varWeightedLogRatios=0.398547915639019
cont.varWeightedLogRatios=0.0897736888079236

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.00470335958519	0.0754866801272082	53.0517881146258	6.84691384120489e-258	***
df.mm.trans1	0.144710145274960	0.0660996144573379	2.18927366616275	0.0288799995919658	*  
df.mm.trans2	-0.00589681604522585	0.0592761280864657	-0.099480452512421	0.920783006802326	   
df.mm.exp2	0.108649066991790	0.0781710427974462	1.38988892950190	0.164969038214522	   
df.mm.exp3	-0.159934797793788	0.0781710427974462	-2.04595962993874	0.0411035752106053	*  
df.mm.exp4	-0.131062751225665	0.0781710427974462	-1.67661510625194	0.0940284728223473	.  
df.mm.exp5	-0.0455684699859269	0.0781710427974462	-0.582932865613706	0.560111187123787	   
df.mm.exp6	-0.166274284023530	0.0781710427974462	-2.12705725896959	0.0337363734477372	*  
df.mm.exp7	-0.142890542063558	0.0781710427974462	-1.82792165679317	0.0679526872434887	.  
df.mm.exp8	-0.0391582984069237	0.0781710427974462	-0.500930997023912	0.616564540178169	   
df.mm.trans1:exp2	-0.411949321303258	0.0733309382197947	-5.61767422187513	2.71501547204193e-08	***
df.mm.trans2:exp2	0.0488031635598354	0.0584978519429773	0.834272745731038	0.404389107057222	   
df.mm.trans1:exp3	-0.073784250077402	0.0733309382197947	-1.00618172723017	0.314647978429374	   
df.mm.trans2:exp3	0.0905221064663	0.0584978519429773	1.54744325577184	0.122172137726341	   
df.mm.trans1:exp4	-0.00742234646579917	0.0733309382197947	-0.101217121258591	0.919404766902966	   
df.mm.trans2:exp4	0.0505348831016139	0.0584978519429773	0.863875876175324	0.387928275321115	   
df.mm.trans1:exp5	0.0328858229552971	0.0733309382197947	0.448457687214207	0.653950574019499	   
df.mm.trans2:exp5	0.0176900765938451	0.0584978519429773	0.30240557569685	0.762425537929489	   
df.mm.trans1:exp6	0.0626502181933838	0.0733309382197947	0.8543490607689	0.393180473405994	   
df.mm.trans2:exp6	0.120957785597383	0.0584978519429773	2.06773037948968	0.0390028317949508	*  
df.mm.trans1:exp7	-0.0137654155494726	0.0733309382197947	-0.187716342974006	0.85114905762073	   
df.mm.trans2:exp7	0.0704346908788439	0.0584978519429773	1.20405602153567	0.228942296413845	   
df.mm.trans1:exp8	-0.0931933200366677	0.0733309382197947	-1.27085950758382	0.204166977222226	   
df.mm.trans2:exp8	0.0102764919468565	0.0584978519429773	0.175672979528785	0.860597597614815	   
df.mm.trans1:probe2	0.0844988835059709	0.0449058452495135	1.88169007924166	0.0602595548698533	.  
df.mm.trans1:probe3	0.133433543765753	0.0449058452495135	2.97140701893811	0.00305777035226191	** 
df.mm.trans1:probe4	0.161022606024980	0.0449058452495135	3.58578276681529	0.000357523607861162	***
df.mm.trans1:probe5	0.351041492315849	0.0449058452495135	7.8172783602075	1.79783142917332e-14	***
df.mm.trans1:probe6	0.0353996062931079	0.0449058452495135	0.788307314925586	0.430762457010675	   
df.mm.trans1:probe7	1.25124954049535	0.0449058452495135	27.8638456428767	2.53519904593336e-118	***
df.mm.trans1:probe8	0.0568996920871365	0.0449058452495135	1.26708876697412	0.205511175832855	   
df.mm.trans1:probe9	0.401605449430746	0.0449058452495135	8.94327781159173	2.83285573803311e-18	***
df.mm.trans1:probe10	1.92267735298899	0.0449058452495135	42.8157479790412	7.50862572958264e-205	***
df.mm.trans1:probe11	-0.107493781519216	0.0449058452495135	-2.39375922938185	0.0169178992274237	*  
df.mm.trans1:probe12	-0.155186456198788	0.0449058452495135	-3.45581862086138	0.000579043839291632	***
df.mm.trans1:probe13	-0.0928731187596247	0.0449058452495135	-2.06817438227890	0.0389609571288155	*  
df.mm.trans1:probe14	-0.00101157011779692	0.0449058452495135	-0.0225264687074981	0.982033903477519	   
df.mm.trans1:probe15	-0.0829877218782646	0.0449058452495135	-1.84803829918253	0.0649846358229547	.  
df.mm.trans1:probe16	-0.0634416304214444	0.0449058452495135	-1.41276998726868	0.158132205699941	   
df.mm.trans1:probe17	0.566782573004891	0.0449058452495135	12.6215767647984	2.69303465194662e-33	***
df.mm.trans1:probe18	0.594524735256092	0.0449058452495135	13.2393618682087	3.70984091537803e-36	***
df.mm.trans1:probe19	0.355373878836555	0.0449058452495135	7.91375547797767	8.81453139840113e-15	***
df.mm.trans1:probe20	0.519009472400678	0.0449058452495135	11.5577263832107	1.40096839737980e-28	***
df.mm.trans1:probe21	0.775060415638072	0.0449058452495135	17.2596776952209	1.39688716485469e-56	***
df.mm.trans1:probe22	0.659182770397124	0.0449058452495135	14.6792197482190	3.86623514668240e-43	***
df.mm.trans2:probe2	0.0522450441173291	0.0449058452495135	1.16343526832724	0.245017606459793	   
df.mm.trans2:probe3	-0.0166873746095843	0.0449058452495135	-0.371608072776785	0.710288118472616	   
df.mm.trans2:probe4	0.0187724608984507	0.0449058452495135	0.41804047544688	0.67603545627359	   
df.mm.trans2:probe5	0.0416523756757347	0.0449058452495135	0.927549085075645	0.353935601388509	   
df.mm.trans2:probe6	0.117546648617866	0.0449058452495135	2.61762467591319	0.0090303287171843	** 
df.mm.trans3:probe2	0.178701348494046	0.0449058452495135	3.97946742792871	7.56760032507095e-05	***
df.mm.trans3:probe3	0.147748527334539	0.0449058452495135	3.29018475242128	0.00104731612258551	** 
df.mm.trans3:probe4	0.58100624047077	0.0449058452495135	12.9383209967986	9.42241122383781e-35	***
df.mm.trans3:probe5	0.140271769955636	0.0449058452495135	3.12368621893728	0.00185371379549697	** 
df.mm.trans3:probe6	0.670492521165435	0.0449058452495135	14.9310745057783	2.11050077097226e-44	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.42620577832476	0.223039883043827	19.8449071884378	5.93941051146249e-71	***
df.mm.trans1	-0.0849704487306768	0.195303996055495	-0.435067640431344	0.663636684501986	   
df.mm.trans2	-0.253690776785642	0.175142696081171	-1.44848048169857	0.147894609329097	   
df.mm.exp2	0.242986150656202	0.230971347707634	1.05201858614851	0.293124953574598	   
df.mm.exp3	-0.0542496558533449	0.230971347707635	-0.23487612810752	0.814368061539438	   
df.mm.exp4	-0.0897023732226336	0.230971347707635	-0.388370133840928	0.697850765241769	   
df.mm.exp5	-0.199969649279049	0.230971347707635	-0.865776864809102	0.38688539718027	   
df.mm.exp6	-0.170856061138808	0.230971347707635	-0.739728381180334	0.459692951144163	   
df.mm.exp7	-0.289023501113135	0.230971347707635	-1.25133919848354	0.211195278874945	   
df.mm.exp8	-0.121149906634598	0.230971347707635	-0.52452353002655	0.600067207936658	   
df.mm.trans1:exp2	-0.0769277254143538	0.216670329871109	-0.355045037592899	0.722654226758337	   
df.mm.trans2:exp2	-0.0843961066714672	0.172843129856681	-0.488281522912987	0.625491108301447	   
df.mm.trans1:exp3	0.0379537989714875	0.216670329871109	0.17516841827889	0.860993894342766	   
df.mm.trans2:exp3	0.0538976258054558	0.172843129856681	0.311829725891605	0.755255344219529	   
df.mm.trans1:exp4	0.112139553776205	0.216670329871109	0.517558420864146	0.60491680662618	   
df.mm.trans2:exp4	0.123240320479339	0.172843129856681	0.713018333916591	0.47605298942207	   
df.mm.trans1:exp5	0.210352280397854	0.216670329871109	0.970840264668387	0.331936291594576	   
df.mm.trans2:exp5	0.0644346393548814	0.172843129856681	0.372792597590137	0.709406617768486	   
df.mm.trans1:exp6	0.239333081201418	0.216670329871109	1.10459554542512	0.26968400514438	   
df.mm.trans2:exp6	0.258374406781491	0.172843129856681	1.49484915597011	0.135368350951589	   
df.mm.trans1:exp7	0.385621378508402	0.216670329871109	1.77976088714037	0.0755140787524125	.  
df.mm.trans2:exp7	0.251827725560573	0.172843129856681	1.45697272300834	0.145536394484683	   
df.mm.trans1:exp8	0.160000785170153	0.216670329871109	0.738452677232424	0.460467085403892	   
df.mm.trans2:exp8	0.06002818140557	0.172843129856681	0.347298625379815	0.728463008602216	   
df.mm.trans1:probe2	0.0358433268994862	0.132682937646182	0.270142699094193	0.787123715125491	   
df.mm.trans1:probe3	-0.123024957427791	0.132682937646182	-0.927210081494079	0.354111437506805	   
df.mm.trans1:probe4	-0.216863915189266	0.132682937646182	-1.63445216872997	0.102577512217429	   
df.mm.trans1:probe5	-0.00118775545132445	0.132682937646182	-0.00895183263496745	0.992859912478893	   
df.mm.trans1:probe6	0.0275926248667226	0.132682937646182	0.207959104284397	0.835316540388427	   
df.mm.trans1:probe7	-0.263066708389001	0.132682937646182	-1.98267172144247	0.0477632998489838	*  
df.mm.trans1:probe8	0.103474608237033	0.132682937646182	0.779863711737845	0.435713575017017	   
df.mm.trans1:probe9	-0.06286174510587	0.132682937646182	-0.473774143239877	0.635796864023508	   
df.mm.trans1:probe10	-0.0365460478882966	0.132682937646182	-0.275438941408968	0.783053800045759	   
df.mm.trans1:probe11	-0.0988793693477122	0.132682937646182	-0.745230480285172	0.456362495562192	   
df.mm.trans1:probe12	-0.00250553019683617	0.132682937646182	-0.0188835900175613	0.984938920257823	   
df.mm.trans1:probe13	-0.2131574525235	0.132682937646182	-1.60651743400432	0.108575092467298	   
df.mm.trans1:probe14	-0.220800596023897	0.132682937646182	-1.66412200348392	0.0964997232370617	.  
df.mm.trans1:probe15	0.047254104931591	0.132682937646182	0.35614304122208	0.72183215504426	   
df.mm.trans1:probe16	-0.162455173020022	0.132682937646182	-1.22438631448779	0.221185423517238	   
df.mm.trans1:probe17	-0.233798263520962	0.132682937646182	-1.76208235714842	0.078456824355262	.  
df.mm.trans1:probe18	-0.212263327371447	0.132682937646182	-1.59977862366506	0.110062751849200	   
df.mm.trans1:probe19	-0.113928458643563	0.132682937646182	-0.858651916099184	0.390802955286088	   
df.mm.trans1:probe20	-0.279787574457036	0.132682937646182	-2.10869294440200	0.0352973095698946	*  
df.mm.trans1:probe21	-0.109381595923458	0.132682937646182	-0.824383284421536	0.409980115567386	   
df.mm.trans1:probe22	-0.00375135420465573	0.132682937646182	-0.0282730716639636	0.977451770306995	   
df.mm.trans2:probe2	0.130949175065383	0.132682937646182	0.98693304043793	0.323989145895859	   
df.mm.trans2:probe3	0.107411204935358	0.132682937646182	0.80953291237631	0.418461720334299	   
df.mm.trans2:probe4	0.107117265253500	0.132682937646182	0.807317558337025	0.419735804512113	   
df.mm.trans2:probe5	-0.00582756881965781	0.132682937646182	-0.0439210114204573	0.964978888408491	   
df.mm.trans2:probe6	0.28649564455057	0.132682937646182	2.15925008620589	0.0311425888573513	*  
df.mm.trans3:probe2	0.133965630397924	0.132682937646182	1.00966735267169	0.312975605708181	   
df.mm.trans3:probe3	-0.0588624473229071	0.132682937646182	-0.443632379318221	0.65743449407673	   
df.mm.trans3:probe4	0.161013459943799	0.132682937646182	1.21352046314474	0.225307400340224	   
df.mm.trans3:probe5	0.117645568401987	0.132682937646182	0.886666895450455	0.375538379763034	   
df.mm.trans3:probe6	-0.00342521175147824	0.132682937646182	-0.0258150129341577	0.979411654946541