chr8.23730_chr8_122973855_122982683_-_2.R 

fitVsDatCorrelation=0.881878455135069
cont.fitVsDatCorrelation=0.268058586189290

fstatistic=11146.2985108488,56,784
cont.fstatistic=2658.88860771731,56,784

residuals=-0.552354348808415,-0.0948741812875964,-0.00065365003764505,0.0919147216970603,0.562543594821978
cont.residuals=-0.710351287021795,-0.237165689601854,-0.03894559009593,0.204281651748742,1.09626605207785

predictedValues:
Include	Exclude	Both
chr8.23730_chr8_122973855_122982683_-_2.R.tl.Lung	65.7184626370832	81.438112219495	98.6456270693248
chr8.23730_chr8_122973855_122982683_-_2.R.tl.cerebhem	59.2964520746576	73.1303880011877	96.2037866470704
chr8.23730_chr8_122973855_122982683_-_2.R.tl.cortex	77.6526127803957	94.9815155503605	135.120712519601
chr8.23730_chr8_122973855_122982683_-_2.R.tl.heart	69.9103086132892	93.1768554667528	124.193981970006
chr8.23730_chr8_122973855_122982683_-_2.R.tl.kidney	59.5532296195386	71.298871520383	80.4815247829307
chr8.23730_chr8_122973855_122982683_-_2.R.tl.liver	58.2109882229546	70.7622746603312	82.4779951520014
chr8.23730_chr8_122973855_122982683_-_2.R.tl.stomach	67.7171820302581	86.4192151951102	95.0291879655166
chr8.23730_chr8_122973855_122982683_-_2.R.tl.testicle	61.5796699757357	77.6356137283518	91.161872150373


diffExp=-15.7196495824118,-13.8339359265301,-17.3289027699649,-23.2665468534636,-11.7456419008444,-12.5512864373767,-18.7020331648521,-16.0559437526161
diffExpScore=0.992319740884803
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,-1,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=-1,-1,-1,-1,0,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	77.1996621454702	79.5219993408666	78.1797987428937
cerebhem	75.9907355182308	78.315266839232	80.1072768033763
cortex	76.1946698958045	75.140207837953	84.7200720628505
heart	72.4960500181732	75.2302514383203	70.7359534646877
kidney	74.8578893359136	78.1966671003986	86.1326778216216
liver	77.1980553941225	69.7133032862133	67.4597754493562
stomach	82.428688169045	83.8809975970497	86.4561487277246
testicle	71.0622916424128	78.129190235987	75.5967032427883
cont.diffExp=-2.32233719539636,-2.32453132100119,1.05446205785154,-2.7342014201471,-3.33877776448503,7.48475210790922,-1.45230942800468,-7.06689859357418
cont.diffExpScore=2.37424325392774

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=0.959269323495401
cont.tran.correlation=0.382732689778128

tran.covariance=0.0112552471137977
cont.tran.covariance=0.000821940883738769

tran.mean=73.0301095184928
cont.tran.mean=76.5972453621996

weightedLogRatios:
wLogRatio
Lung	-0.92060839652388
cerebhem	-0.878074108712251
cortex	-0.896992114187511
heart	-1.26143259625381
kidney	-0.751879244401532
liver	-0.812579751059507
stomach	-1.0577317082248
testicle	-0.981499289447588

cont.weightedLogRatios:
wLogRatio
Lung	-0.129260393487298
cerebhem	-0.130940162891621
cortex	0.060290371197505
heart	-0.159267438934743
kidney	-0.189265105267539
liver	0.438056458060724
stomach	-0.0772094366366768
testicle	-0.408708790505368

varWeightedLogRatios=0.0251970965165738
cont.varWeightedLogRatios=0.0598751646782989

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.6247965247123	0.0762811334031909	47.5189127769393	4.89801871810587e-233	***
df.mm.trans1	0.360232099787895	0.0664858459400281	5.41817727810568	8.0166309416768e-08	***
df.mm.trans2	0.734839112486302	0.0593314427929037	12.3853234961984	2.64799934585721e-32	***
df.mm.exp2	-0.185364629159705	0.0776239515586395	-2.38798238736500	0.0171769705472534	*  
df.mm.exp3	0.006069581952842	0.0776239515586395	0.0781921279575268	0.937695175390765	   
df.mm.exp4	-0.033821609115288	0.0776239515586394	-0.435710994302295	0.663166363277654	   
df.mm.exp5	-0.0279659931534633	0.0776239515586395	-0.360275309256022	0.718738298621132	   
df.mm.exp6	-0.0828207484660888	0.0776239515586394	-1.06694836842368	0.286323588712233	   
df.mm.exp7	0.126676538305779	0.0776239515586395	1.63192591670734	0.103096663789203	   
df.mm.exp8	-0.0339680652656312	0.0776239515586394	-0.437597733477542	0.661798436661647	   
df.mm.trans1:exp2	0.0825342029900222	0.0724839483217834	1.13865490085642	0.255194930279276	   
df.mm.trans2:exp2	0.0777652415473311	0.0565370897813715	1.37547301865110	0.169377706436215	   
df.mm.trans1:exp3	0.160795714969191	0.0724839483217835	2.21836308164889	0.0268158540702454	*  
df.mm.trans2:exp3	0.147769345044260	0.0565370897813716	2.61367087721853	0.00912931616469388	** 
df.mm.trans1:exp4	0.0956548237899028	0.0724839483217834	1.31966905783409	0.187330756049980	   
df.mm.trans2:exp4	0.168477595548039	0.0565370897813715	2.97994814023044	0.00297204310024147	** 
df.mm.trans1:exp5	-0.0705433790469389	0.0724839483217834	-0.973227599768301	0.330740369028379	   
df.mm.trans2:exp5	-0.10499687927267	0.0565370897813715	-1.85713271904677	0.0636671058503608	.  
df.mm.trans1:exp6	-0.0384850138120216	0.0724839483217834	-0.530945329318599	0.595607036467726	   
df.mm.trans2:exp6	-0.0576966091440929	0.0565370897813715	-1.02050900333224	0.307801919008909	   
df.mm.trans1:exp7	-0.0967164942585034	0.0724839483217834	-1.33431603131141	0.182487722761873	   
df.mm.trans2:exp7	-0.067309861648532	0.0565370897813716	-1.19054344517588	0.234193214809546	   
df.mm.trans1:exp8	-0.0310800518056722	0.0724839483217834	-0.428785303853706	0.668197262051868	   
df.mm.trans2:exp8	-0.0138490455450344	0.0565370897813715	-0.244955048068242	0.806555337181091	   
df.mm.trans1:probe2	0.286816164152780	0.0460627452609303	6.22664069473197	7.76287341558947e-10	***
df.mm.trans1:probe3	-0.0472331142709006	0.0460627452609303	-1.02540814715538	0.305486918053218	   
df.mm.trans1:probe4	0.07677331471016	0.0460627452609303	1.66671166200070	0.0959711621479633	.  
df.mm.trans1:probe5	0.483483789161543	0.0460627452609303	10.4962000510991	3.31629304545557e-24	***
df.mm.trans1:probe6	0.45561830045682	0.0460627452609303	9.8912537208951	8.08093331343037e-22	***
df.mm.trans1:probe7	0.224825385511650	0.0460627452609303	4.88085076645102	1.28002375195901e-06	***
df.mm.trans1:probe8	0.0392641823720885	0.0460627452609303	0.85240647620262	0.394248842688611	   
df.mm.trans1:probe9	0.249758871341709	0.0460627452609303	5.42214472730419	7.84697495900642e-08	***
df.mm.trans1:probe10	0.174911613193912	0.0460627452609303	3.79724682502302	0.000157613849044114	***
df.mm.trans1:probe11	0.393522423496599	0.0460627452609303	8.5431821587581	6.76033857810853e-17	***
df.mm.trans1:probe12	0.318300040337977	0.0460627452609303	6.91014047328078	1.00280803592044e-11	***
df.mm.trans1:probe13	0.278180714054168	0.0460627452609303	6.03916923488529	2.39072567438116e-09	***
df.mm.trans1:probe14	0.547662864620108	0.0460627452609303	11.8894968486524	4.34201746779275e-30	***
df.mm.trans1:probe15	0.249596099354499	0.0460627452609303	5.41861102590866	7.99791115045897e-08	***
df.mm.trans1:probe16	0.243173824024384	0.0460627452609303	5.27918652366213	1.68150210507263e-07	***
df.mm.trans1:probe17	0.358531511351387	0.0460627452609303	7.78354631970856	2.22787404112912e-14	***
df.mm.trans1:probe18	0.252456130843374	0.0460627452609303	5.48070093116018	5.71358091004653e-08	***
df.mm.trans1:probe19	0.202688676679916	0.0460627452609303	4.40027348634457	1.23073413265617e-05	***
df.mm.trans1:probe20	0.771333142063439	0.0460627452609303	16.7452707756364	4.885484849432e-54	***
df.mm.trans1:probe21	0.126203934214928	0.0460627452609303	2.7398265887112	0.00628687814205377	** 
df.mm.trans1:probe22	0.124319129842779	0.0460627452609303	2.69890839416001	0.00710647673179159	** 
df.mm.trans2:probe2	0.0290235300553691	0.0460627452609303	0.630086849816709	0.528821356146035	   
df.mm.trans2:probe3	-0.0507295233089789	0.0460627452609303	-1.10131350230241	0.271098233659719	   
df.mm.trans2:probe4	-0.0464107837932751	0.0460627452609303	-1.00755574880249	0.313978641191166	   
df.mm.trans2:probe5	0.485774627359906	0.0460627452609303	10.5459330443323	2.08815265145056e-24	***
df.mm.trans2:probe6	0.105042708833048	0.0460627452609303	2.28042658417372	0.0228500151790128	*  
df.mm.trans3:probe2	-0.238700026889176	0.0460627452609303	-5.18206254397168	2.79367954897528e-07	***
df.mm.trans3:probe3	-0.322101986699485	0.0460627452609303	-6.99267889646793	5.77884764576965e-12	***
df.mm.trans3:probe4	-0.0366260716262042	0.0460627452609303	-0.795134363328317	0.426775996005528	   
df.mm.trans3:probe5	0.220037483435198	0.0460627452609303	4.77690771986685	2.12479202433037e-06	***
df.mm.trans3:probe6	-0.753875624155614	0.0460627452609303	-16.3662764753850	5.19112473812233e-52	***
df.mm.trans3:probe7	0.232929393314816	0.0460627452609303	5.05678487018843	5.31247141976948e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.39423456511054	0.155870745596685	28.1915284891279	2.72191112896449e-121	***
df.mm.trans1	-0.0769762095201027	0.135855327732519	-0.566604275333674	0.571145234020636	   
df.mm.trans2	0.00217415530116894	0.121236219401403	0.0179332159308803	0.985696693496727	   
df.mm.exp2	-0.0554302011047895	0.158614622853781	-0.349464633887431	0.726834362619322	   
df.mm.exp3	-0.150122733602508	0.158614622853781	-0.946462128784296	0.344204541981101	   
df.mm.exp4	-0.0182859707473274	0.158614622853781	-0.115285529280515	0.908248327877829	   
df.mm.exp5	-0.144487838310234	0.158614622853781	-0.910936430138794	0.362608856188304	   
df.mm.exp6	0.0158164317984324	0.158614622853781	0.099716101289178	0.920595208887543	   
df.mm.exp7	0.0182777993193960	0.158614622853781	0.115234011786199	0.908289147397418	   
df.mm.exp8	-0.0669095924556452	0.158614622853781	-0.421837477855531	0.673259289676393	   
df.mm.trans1:exp2	0.039646552217528	0.148111683251882	0.267680113729477	0.789015963050343	   
df.mm.trans2:exp2	0.0401390592365533	0.115526316206000	0.347445158425892	0.728350164038058	   
df.mm.trans1:exp3	0.137019164307474	0.148111683251882	0.925107063123822	0.355194876494276	   
df.mm.trans2:exp3	0.0934448351052145	0.115526316206000	0.808861895488722	0.418839977047996	   
df.mm.trans1:exp4	-0.0445770320513676	0.148111683251882	-0.300969046280833	0.763517897795586	   
df.mm.trans2:exp4	-0.0371943040735725	0.115526316206000	-0.321955250501104	0.747572419947853	   
df.mm.trans1:exp5	0.113684264903748	0.148111683251882	0.76755771325895	0.442981090318269	   
df.mm.trans2:exp5	0.127681160104947	0.115526316206000	1.10521277141109	0.269406454825434	   
df.mm.trans1:exp6	-0.015837244947401	0.148111683251882	-0.106927722376012	0.914873675137773	   
df.mm.trans2:exp6	-0.147458971954632	0.115526316206000	-1.27641023099612	0.202188507980409	   
df.mm.trans1:exp7	0.0472606537274905	0.148111683251882	0.319087952346866	0.749744815372046	   
df.mm.trans2:exp7	0.0350875951403825	0.115526316206000	0.303719501259058	0.76142218408806	   
df.mm.trans1:exp8	-0.0159286487379301	0.148111683251882	-0.107544849860639	0.914384261373935	   
df.mm.trans2:exp8	0.0492396294457575	0.115526316206000	0.426220025556396	0.670064520640317	   
df.mm.trans1:probe2	0.106068634023832	0.0941233320446575	1.12691116771670	0.260124832988063	   
df.mm.trans1:probe3	-0.0534935095033098	0.0941233320446575	-0.568334209395917	0.569970795665626	   
df.mm.trans1:probe4	0.186972959205537	0.0941233320446575	1.98646770300084	0.0473291709734882	*  
df.mm.trans1:probe5	0.00461672185532682	0.0941233320446575	0.0490497069646491	0.960892175733222	   
df.mm.trans1:probe6	-0.00936067020422629	0.0941233320446575	-0.0994511137768163	0.92080552416516	   
df.mm.trans1:probe7	0.0303376401861159	0.0941233320446575	0.322317958014087	0.747297758899511	   
df.mm.trans1:probe8	0.0497343726631726	0.0941233320446575	0.528395792868614	0.597374193190231	   
df.mm.trans1:probe9	0.164602017488941	0.0941233320446575	1.74879080365371	0.0807185314808005	.  
df.mm.trans1:probe10	0.0689232382061446	0.0941233320446575	0.732265174945607	0.464225576450667	   
df.mm.trans1:probe11	-0.060448217465897	0.0941233320446575	-0.642223518364361	0.520915807136825	   
df.mm.trans1:probe12	-0.0183293737719054	0.0941233320446575	-0.194737833582102	0.845648643372894	   
df.mm.trans1:probe13	0.0125810242350980	0.0941233320446575	0.133665308715684	0.89370154090486	   
df.mm.trans1:probe14	-0.0441952170063306	0.0941233320446575	-0.469545818728153	0.638810102729641	   
df.mm.trans1:probe15	0.150874332269322	0.0941233320446575	1.60294295783896	0.109350024920176	   
df.mm.trans1:probe16	0.068629705297769	0.0941233320446575	0.729146576166758	0.466129769454248	   
df.mm.trans1:probe17	0.0781836145591109	0.0941233320446575	0.830650730915646	0.406423848164943	   
df.mm.trans1:probe18	0.0980774376618043	0.0941233320446575	1.04200983466321	0.297728328569461	   
df.mm.trans1:probe19	0.0495767400267851	0.0941233320446575	0.526721047266612	0.598536305428277	   
df.mm.trans1:probe20	0.0189846608195259	0.0941233320446575	0.201699837937298	0.840203711065894	   
df.mm.trans1:probe21	-0.0161369795802145	0.0941233320446575	-0.171445052248662	0.863918072755496	   
df.mm.trans1:probe22	-0.0412341037421309	0.0941233320446575	-0.43808589056927	0.661444696099217	   
df.mm.trans2:probe2	0.0416796350892373	0.0941233320446575	0.442819375215724	0.658018537504825	   
df.mm.trans2:probe3	-0.0649125170564948	0.0941233320446575	-0.689653836582162	0.490615976212344	   
df.mm.trans2:probe4	-0.142316912886685	0.0941233320446575	-1.51202586856106	0.130930246149071	   
df.mm.trans2:probe5	-0.0885780881683766	0.0941233320446575	-0.941085342435073	0.346950997379471	   
df.mm.trans2:probe6	-0.0107473218957308	0.0941233320446575	-0.114183398125256	0.909121646427049	   
df.mm.trans3:probe2	0.0260544184756006	0.0941233320446575	0.27681147606673	0.78199783131732	   
df.mm.trans3:probe3	0.129188281494294	0.0941233320446575	1.37254258522212	0.170287101057475	   
df.mm.trans3:probe4	0.055874715864329	0.0941233320446575	0.593632998859611	0.552928831368045	   
df.mm.trans3:probe5	0.0830823065120197	0.0941233320446575	0.882696189214813	0.377671006507504	   
df.mm.trans3:probe6	0.0717109159838098	0.0941233320446575	0.761882462361044	0.446359286056367	   
df.mm.trans3:probe7	-0.0888592119914805	0.0941233320446575	-0.944072102646351	0.345423643290633