chr10.2485_chr10_68794433_68804307_+_2.R 

fitVsDatCorrelation=0.768303242842233
cont.fitVsDatCorrelation=0.282125740698515

fstatistic=13426.3745768193,52,692
cont.fstatistic=5969.24730509335,52,692

residuals=-0.305898101710627,-0.0723147418831569,-0.0087508637248349,0.0710573027418108,0.800145583270657
cont.residuals=-0.40109484224007,-0.133358280406307,-0.0272205714302426,0.101642571346271,1.17551418829718

predictedValues:
Include	Exclude	Both
chr10.2485_chr10_68794433_68804307_+_2.R.tl.Lung	50.9891444074601	50.9913255135106	50.2341551858876
chr10.2485_chr10_68794433_68804307_+_2.R.tl.cerebhem	53.9937383683945	52.9047002713639	46.9635596221821
chr10.2485_chr10_68794433_68804307_+_2.R.tl.cortex	49.3796330465357	45.0788503027786	46.7994399532638
chr10.2485_chr10_68794433_68804307_+_2.R.tl.heart	51.9021964278071	49.6891186169364	50.9623145856383
chr10.2485_chr10_68794433_68804307_+_2.R.tl.kidney	52.057840773323	54.4658747824325	46.6943401794087
chr10.2485_chr10_68794433_68804307_+_2.R.tl.liver	53.988654256724	53.2074564817159	49.4233650736598
chr10.2485_chr10_68794433_68804307_+_2.R.tl.stomach	51.5836788984852	48.2673843045339	53.5025437293755
chr10.2485_chr10_68794433_68804307_+_2.R.tl.testicle	52.2270493898298	49.2514152296357	44.4839442590222


diffExp=-0.00218110605049304,1.08903809703057,4.30078274375708,2.21307781087071,-2.40803400910955,0.781197775008081,3.3162945939513,2.97563416019406
diffExpScore=1.28799072287429
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	51.6873848045663	47.3222432516436	52.1791489111716
cerebhem	50.8651051854584	53.8768957160293	57.0441708337881
cortex	50.0167604297097	54.8473197177861	48.9936047127955
heart	51.934978964394	55.3982726052116	52.0237210450521
kidney	50.243893106265	53.7495396466835	57.9452443024144
liver	49.7319631273268	50.2510311654874	54.046676658998
stomach	49.6192291231885	58.173938965773	52.1013298761945
testicle	50.6030178513949	51.4267398604711	47.1969119395141
cont.diffExp=4.36514155292272,-3.01179053057089,-4.83055928807644,-3.46329364081765,-3.50564654041849,-0.519068038160647,-8.5547098425845,-0.8237220090762
cont.diffExpScore=1.36218189992025

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=0.765160164351023
cont.tran.correlation=-0.332394736411278

tran.covariance=0.00139422155778084
cont.tran.covariance=-0.000366964034756893

tran.mean=51.2486288169667
cont.tran.mean=51.8592695950868

weightedLogRatios:
wLogRatio
Lung	-0.000168175554106571
cerebhem	0.0810691757267223
cortex	0.351193016915391
heart	0.171144574020712
kidney	-0.179743666221036
liver	0.0580316436414666
stomach	0.259815333975135
testicle	0.23032455988752

cont.weightedLogRatios:
wLogRatio
Lung	0.344206670657631
cerebhem	-0.227678894348674
cortex	-0.364950468116835
heart	-0.25707897956561
kidney	-0.266453347837663
liver	-0.0406174888003368
stomach	-0.63367652648796
testicle	-0.0634915771481273

varWeightedLogRatios=0.0282015919260096
cont.varWeightedLogRatios=0.0804148650319908

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87712820420583	0.0685364305032032	56.5703258214568	9.8451690178674e-262	***
df.mm.trans1	0.0754988818962275	0.0615557372923417	1.22651251072937	0.220423034998166	   
df.mm.trans2	0.0573531259164315	0.0566088604760571	1.01314750790098	0.311343815056253	   
df.mm.exp2	0.161415188759387	0.0775401196274168	2.08169899059988	0.0377369423747687	*  
df.mm.exp3	-0.0844931060771434	0.0775401196274168	-1.08966953472778	0.276238048647645	   
df.mm.exp4	-0.0225124604750856	0.0775401196274168	-0.290333063493567	0.771648428630747	   
df.mm.exp5	0.159733707650910	0.0775401196274168	2.06001368605615	0.0397706303809426	*  
df.mm.exp6	0.115976066446253	0.0775401196274168	1.4956910951843	0.135190068275529	   
df.mm.exp7	-0.106340925784814	0.0775401196274168	-1.37143102558761	0.170685152760796	   
df.mm.exp8	0.110837222086203	0.0775401196274168	1.42941773392639	0.153335478904351	   
df.mm.trans1:exp2	-0.104159860371243	0.0742390124699581	-1.40303402356534	0.161055205309801	   
df.mm.trans2:exp2	-0.124578532118926	0.0646167663561807	-1.92795986466150	0.0542689976439968	.  
df.mm.trans1:exp3	0.052418403438342	0.0742390124699582	0.706076248785689	0.480378360222408	   
df.mm.trans2:exp3	-0.0387492388514561	0.0646167663561807	-0.599677777712713	0.548917296925344	   
df.mm.trans1:exp4	0.0402608148212332	0.0742390124699581	0.542313447899449	0.587777186704895	   
df.mm.trans2:exp4	-0.00335710198995177	0.0646167663561807	-0.0519540388549737	0.958580308965246	   
df.mm.trans1:exp5	-0.138991039989813	0.0742390124699582	-1.87221024856786	0.0615990553564029	.  
df.mm.trans2:exp5	-0.093814883164383	0.0646167663561807	-1.45186595453038	0.146992093640562	   
df.mm.trans1:exp6	-0.0588149036262287	0.0742390124699582	-0.79223714957185	0.428493908689956	   
df.mm.trans2:exp6	-0.0734330507766663	0.0646167663561807	-1.13643957934831	0.256166032609345	   
df.mm.trans1:exp7	0.117933492509998	0.0742390124699581	1.58856494161640	0.112615409803170	   
df.mm.trans2:exp7	0.0514414548583015	0.0646167663561807	0.796100729874748	0.426246501016838	   
df.mm.trans1:exp8	-0.086849429177634	0.0742390124699582	-1.16986239832836	0.242459106646674	   
df.mm.trans2:exp8	-0.145554649541190	0.0646167663561807	-2.25258331156442	0.024597963704829	*  
df.mm.trans1:probe2	0.198459644555243	0.0371195062349791	5.34650550842259	1.21923054847716e-07	***
df.mm.trans1:probe3	0.180800591825325	0.0371195062349791	4.87077038904548	1.37882427999066e-06	***
df.mm.trans1:probe4	0.182406785970230	0.0371195062349791	4.91404128103248	1.11480490480713e-06	***
df.mm.trans1:probe5	-0.071547306528564	0.0371195062349791	-1.92748540553436	0.0543281584299087	.  
df.mm.trans1:probe6	0.206949470996899	0.0371195062349791	5.57522154758306	3.54558857314336e-08	***
df.mm.trans1:probe7	-0.168402373022513	0.0371195062349791	-4.5367622068157	6.73366295093584e-06	***
df.mm.trans1:probe8	-0.0825687237106249	0.0371195062349791	-2.22440253348029	0.0264432116404441	*  
df.mm.trans1:probe9	-0.141371065416748	0.0371195062349791	-3.80853841432644	0.000152208611721756	***
df.mm.trans1:probe10	-0.132915518693661	0.0371195062349791	-3.58074587124787	0.00036658714503679	***
df.mm.trans1:probe11	-0.119102871557084	0.0371195062349791	-3.20863297057678	0.00139520741357532	** 
df.mm.trans1:probe12	0.00583997014730957	0.0371195062349791	0.157328874752282	0.875031557864677	   
df.mm.trans1:probe13	0.0163808642948639	0.0371195062349791	0.441300705649679	0.659133164216127	   
df.mm.trans1:probe14	0.425070928143531	0.0371195062349791	11.4514165531375	6.32549118434311e-28	***
df.mm.trans1:probe15	-0.0315592589681329	0.0371195062349791	-0.850206863430567	0.395504150928954	   
df.mm.trans1:probe16	-0.0334050063390318	0.0371195062349791	-0.899931322565735	0.368469857773034	   
df.mm.trans1:probe17	-0.0685652244299386	0.0371195062349791	-1.84714807346567	0.0651523737486231	.  
df.mm.trans1:probe18	-0.147560791431692	0.0371195062349791	-3.97528971688314	7.76805402381903e-05	***
df.mm.trans1:probe19	-0.166073549965862	0.0371195062349791	-4.47402368217293	8.97225090183951e-06	***
df.mm.trans1:probe20	-0.255366856598398	0.0371195062349791	-6.87958657051738	1.34518653732364e-11	***
df.mm.trans1:probe21	-0.160158144585843	0.0371195062349791	-4.31466258123122	1.83138464984743e-05	***
df.mm.trans1:probe22	-0.162669834440492	0.0371195062349791	-4.38232753988526	1.35640190853034e-05	***
df.mm.trans2:probe2	0.108625255762374	0.0371195062349791	2.92636585936082	0.0035418727115747	** 
df.mm.trans2:probe3	-0.113326092066281	0.0371195062349791	-3.05300645296542	0.00235244376493444	** 
df.mm.trans2:probe4	0.0588207874066346	0.0371195062349791	1.58463280826741	0.113506642857854	   
df.mm.trans2:probe5	-0.0528735327405985	0.0371195062349791	-1.42441368712965	0.154777545116638	   
df.mm.trans2:probe6	-0.0266786168763848	0.0371195062349791	-0.71872229946972	0.472554648004665	   
df.mm.trans3:probe2	-0.120241226003145	0.0371195062349791	-3.23930025474955	0.00125552565236628	** 
df.mm.trans3:probe3	-0.159613877457373	0.0371195062349791	-4.30000001742918	1.95345737695549e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81572590454236	0.102724168079831	37.1453570845861	6.18973232779002e-167	***
df.mm.trans1	0.098956111686157	0.0922613252174097	1.07256330269451	0.283840989603467	   
df.mm.trans2	0.0749539155326543	0.0848468187744093	0.88340277944824	0.377325482627388	   
df.mm.exp2	0.0245417535153665	0.116219129345592	0.211167934689896	0.832818401476762	   
df.mm.exp3	0.177710485049415	0.116219129345592	1.52909840273343	0.126697011572655	   
df.mm.exp4	0.165329936860077	0.116219129345592	1.42257077463080	0.155311228879753	   
df.mm.exp5	-0.00578560842683346	0.116219129345592	-0.0497818944214358	0.960310559879003	   
df.mm.exp6	-0.0136803598062525	0.116219129345592	-0.117711773296565	0.906330195079968	   
df.mm.exp7	0.167114219877526	0.116219129345592	1.43792352273257	0.150907808477366	   
df.mm.exp8	0.16232979921326	0.116219129345592	1.39675628381755	0.162934650452899	   
df.mm.trans1:exp2	-0.0405783654289335	0.11127134487531	-0.364679383307584	0.715462165529541	   
df.mm.trans2:exp2	0.105179537613260	0.0968492744546601	1.08601265425586	0.277851555281636	   
df.mm.trans1:exp3	-0.210566071287977	0.11127134487531	-1.89236565374433	0.0588594198293358	.  
df.mm.trans2:exp3	-0.0301376091767644	0.0968492744546602	-0.311180536420780	0.755757095019838	   
df.mm.trans1:exp4	-0.160551149308618	0.11127134487531	-1.44287956156664	0.149506884293286	   
df.mm.trans2:exp4	-0.00776196798501484	0.0968492744546602	-0.0801448232701897	0.93614524518318	   
df.mm.trans1:exp5	-0.0225391263340119	0.11127134487531	-0.202560024409421	0.839538476962814	   
df.mm.trans2:exp5	0.133140266704347	0.0968492744546601	1.37471620158266	0.169664416850040	   
df.mm.trans1:exp6	-0.0248855366218998	0.11127134487531	-0.223647306948490	0.823097757510807	   
df.mm.trans2:exp6	0.0737309832603926	0.0968492744546602	0.761296186012314	0.446739528927829	   
df.mm.trans1:exp7	-0.207949521432444	0.11127134487531	-1.8688506161714	0.0620658453745042	.  
df.mm.trans2:exp7	0.0393428065006685	0.0968492744546601	0.406227168166209	0.684701283040098	   
df.mm.trans1:exp8	-0.183532327457451	0.11127134487531	-1.64941232320969	0.0995170212545168	.  
df.mm.trans2:exp8	-0.0791519752574937	0.0968492744546601	-0.817269677064525	0.414055614591148	   
df.mm.trans1:probe2	0.0255161129946714	0.055635672437655	0.458628643758455	0.646644867446004	   
df.mm.trans1:probe3	0.0608048447342261	0.055635672437655	1.09291111386069	0.274813146958716	   
df.mm.trans1:probe4	9.8760537153263e-05	0.055635672437655	0.00177512974726662	0.998584163714823	   
df.mm.trans1:probe5	0.0134073860351379	0.055635672437655	0.240985422620031	0.809637796602405	   
df.mm.trans1:probe6	-0.0385309166886957	0.055635672437655	-0.692557760164995	0.488819461363351	   
df.mm.trans1:probe7	0.0288351942871333	0.055635672437655	0.51828607481011	0.604424358067009	   
df.mm.trans1:probe8	0.166011084054772	0.055635672437655	2.98389642438138	0.00294624563853210	** 
df.mm.trans1:probe9	0.0244895901169883	0.055635672437655	0.440177839935901	0.659945760527284	   
df.mm.trans1:probe10	0.0515864451276033	0.055635672437655	0.9272188663741	0.354136182322263	   
df.mm.trans1:probe11	0.0180395680066414	0.055635672437655	0.324244629681728	0.745850818181243	   
df.mm.trans1:probe12	0.000286004874171797	0.055635672437655	0.00514067434867965	0.995899834936817	   
df.mm.trans1:probe13	0.0317670006765741	0.055635672437655	0.570982595962546	0.568196814361974	   
df.mm.trans1:probe14	0.0567204435329359	0.055635672437655	1.01949776191699	0.308323002980471	   
df.mm.trans1:probe15	0.0110458743438164	0.055635672437655	0.19853942371586	0.842681356641462	   
df.mm.trans1:probe16	0.0212955523314557	0.055635672437655	0.382767950820031	0.702009366380148	   
df.mm.trans1:probe17	0.044898405918509	0.055635672437655	0.807007517862247	0.41993942941373	   
df.mm.trans1:probe18	0.0053944874559361	0.055635672437655	0.09696094644998	0.922785483121363	   
df.mm.trans1:probe19	0.0433806513439023	0.055635672437655	0.779727276461238	0.435818011655467	   
df.mm.trans1:probe20	0.122108068914765	0.055635672437655	2.19478013951567	0.0285108926904161	*  
df.mm.trans1:probe21	0.0637471219430463	0.055635672437655	1.14579583835319	0.252275714093078	   
df.mm.trans1:probe22	0.0123915160227488	0.055635672437655	0.222726094245282	0.823814414303215	   
df.mm.trans2:probe2	-0.0925018788355428	0.055635672437655	-1.6626361250365	0.0968381310151935	.  
df.mm.trans2:probe3	-0.0399772978282314	0.055635672437655	-0.718555129769119	0.47265760993765	   
df.mm.trans2:probe4	-0.0768298382552469	0.055635672437655	-1.38094562155858	0.167741481436139	   
df.mm.trans2:probe5	-0.0494667392866334	0.055635672437655	-0.889119105050191	0.374248037987824	   
df.mm.trans2:probe6	-0.0445186306429951	0.055635672437655	-0.800181406864137	0.423880309061149	   
df.mm.trans3:probe2	-0.0317683826309718	0.055635672437655	-0.57100743532077	0.568179986061611	   
df.mm.trans3:probe3	-0.0730905058247533	0.055635672437655	-1.31373456313767	0.189370910328884