chr17.10725_chr17_78306919_78325794_+_2.R 

fitVsDatCorrelation=0.862732301627054
cont.fitVsDatCorrelation=0.237941848835650

fstatistic=8680.6242256967,55,761
cont.fstatistic=2342.69423537101,55,761

residuals=-0.737582827198624,-0.0967980595930792,-0.00110660061891884,0.0931270135873002,1.11925505677577
cont.residuals=-0.714070872351285,-0.229753760150121,-0.0655850844056644,0.188821032575374,1.63692787619050

predictedValues:
Include	Exclude	Both
chr17.10725_chr17_78306919_78325794_+_2.R.tl.Lung	65.418857766009	63.6694698354957	71.095571366474
chr17.10725_chr17_78306919_78325794_+_2.R.tl.cerebhem	52.693182770555	54.3350091076659	63.7146033702053
chr17.10725_chr17_78306919_78325794_+_2.R.tl.cortex	73.9592585411819	67.4523075397651	73.3423223783884
chr17.10725_chr17_78306919_78325794_+_2.R.tl.heart	68.5721206000217	71.6387989861611	68.6335795293965
chr17.10725_chr17_78306919_78325794_+_2.R.tl.kidney	58.7246693704739	57.8320117420405	65.2436560966727
chr17.10725_chr17_78306919_78325794_+_2.R.tl.liver	66.9914881899086	61.7112513020432	71.0443225061027
chr17.10725_chr17_78306919_78325794_+_2.R.tl.stomach	66.3148441912486	85.8324846880642	75.9807904592951
chr17.10725_chr17_78306919_78325794_+_2.R.tl.testicle	62.4787800506375	68.848460024443	65.9972762000059


diffExp=1.74938793051333,-1.64182633711097,6.50695100141671,-3.0666783861394,0.892657628433376,5.28023688786539,-19.5176404968155,-6.36967997380546
diffExpScore=2.62283039692657
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,-1,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	66.777346226773	66.0639025580859	73.2093620757198
cerebhem	63.0599103944235	70.6360947618558	75.7115327838427
cortex	65.0905535399737	63.8553034089035	71.0173941777563
heart	64.5658587147258	64.8585402169575	69.1221972096458
kidney	68.0967776437341	65.190119502117	68.6884622843125
liver	61.8038555080248	64.4335548279548	61.3289696048868
stomach	65.216463169858	65.1914904008301	62.805971595731
testicle	70.2379879163723	69.9016112885334	64.3867164372924
cont.diffExp=0.713443668687077,-7.57618436743226,1.23525013107025,-0.292681502231630,2.90665814161716,-2.62969931992998,0.0249727690278974,0.336376627838888
cont.diffExpScore=2.50168849559695

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=0.543922340918973
cont.tran.correlation=0.265419301736085

tran.covariance=0.00892441525551014
cont.tran.covariance=0.000407878771686493

tran.mean=65.4045621691072
cont.tran.mean=65.9362106299452

weightedLogRatios:
wLogRatio
Lung	0.112955169264227
cerebhem	-0.122111716087969
cortex	0.39208546654941
heart	-0.185930316174451
kidney	0.0622684819108894
liver	0.341821742996861
stomach	-1.11536852409422
testicle	-0.406124981647732

cont.weightedLogRatios:
wLogRatio
Lung	0.0450708434117022
cerebhem	-0.476607627726638
cortex	0.0798235178095455
heart	-0.0188599444269017
kidney	0.183173872321686
liver	-0.172708789060615
stomach	0.00159996766355978
testicle	0.0204001086592278

varWeightedLogRatios=0.234254579122756
cont.varWeightedLogRatios=0.0407748270119916

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22551203319613	0.0848532240863802	49.797896057486	1.20554467953349e-241	***
df.mm.trans1	-0.324153817171605	0.0743013918233003	-4.36268835908876	1.46189924344579e-05	***
df.mm.trans2	-0.0247470352552643	0.0666312330999034	-0.371402930787127	0.710440820718287	   
df.mm.exp2	-0.265249628167627	0.0878706680487186	-3.0186367539685	0.00262393026803306	** 
df.mm.exp3	0.149306664754171	0.0878706680487187	1.69916387424516	0.0896970086997261	.  
df.mm.exp4	0.200250265173751	0.0878706680487186	2.27892048189192	0.0229479671858190	*  
df.mm.exp5	-0.118217139479238	0.0878706680487187	-1.34535382630406	0.178911734139402	   
df.mm.exp6	-0.00676278598603085	0.0878706680487186	-0.0769629517586155	0.938673247177078	   
df.mm.exp7	0.245840077745781	0.0878706680487186	2.79774904646769	0.00527603473317954	** 
df.mm.exp8	0.106630684532085	0.0878706680487187	1.21349577623520	0.225316827521638	   
df.mm.trans1:exp2	0.0489251542090979	0.0824299932483846	0.593535826961343	0.552998980072425	   
df.mm.trans2:exp2	0.106713215063985	0.0657563868370498	1.62285703635801	0.105034124121993	   
df.mm.trans1:exp3	-0.0266028453639689	0.0824299932483846	-0.322732591810448	0.746986416161958	   
df.mm.trans2:exp3	-0.0915910388087817	0.0657563868370498	-1.39288429937236	0.164061546962588	   
df.mm.trans1:exp4	-0.153174780240122	0.0824299932483846	-1.85824084418597	0.0635207768256787	.  
df.mm.trans2:exp4	-0.0823186199314374	0.0657563868370498	-1.25187261482950	0.211000921112927	   
df.mm.trans1:exp5	0.0102664779302299	0.0824299932483846	0.124547843881220	0.90091441223473	   
df.mm.trans2:exp5	0.0220544309752391	0.0657563868370498	0.33539602821992	0.737418800385513	   
df.mm.trans1:exp6	0.0305177934191037	0.0824299932483846	0.370226809641304	0.711316518699822	   
df.mm.trans2:exp6	-0.0244761119899398	0.0657563868370498	-0.372224101220674	0.709829633333239	   
df.mm.trans1:exp7	-0.232236873272959	0.0824299932483846	-2.81738314078426	0.00496716570011675	** 
df.mm.trans2:exp7	0.0528522992726173	0.0657563868370498	0.803759175570123	0.421787060262346	   
df.mm.trans1:exp8	-0.152614266574938	0.0824299932483846	-1.85144096900589	0.0644933529989677	.  
df.mm.trans2:exp8	-0.0284279939571108	0.0657563868370498	-0.432322932030282	0.665629198129045	   
df.mm.trans1:probe2	0.193807925605714	0.0504778557399012	3.83946431093179	0.000133554469174611	***
df.mm.trans1:probe3	0.0280841274989777	0.0504778557399012	0.556365302910007	0.578124708957802	   
df.mm.trans1:probe4	-0.00377401787252665	0.0504778557399012	-0.07476581200226	0.940420692425006	   
df.mm.trans1:probe5	0.561097205960708	0.0504778557399012	11.1157100026572	1.05294136190714e-26	***
df.mm.trans1:probe6	0.181536022388734	0.0504778557399012	3.59634972064067	0.000343550100434403	***
df.mm.trans1:probe7	-0.0737238085212439	0.0504778557399012	-1.46051783382248	0.144560528615832	   
df.mm.trans1:probe8	0.318544879881447	0.0504778557399012	6.3105865970778	4.7167180727664e-10	***
df.mm.trans1:probe9	0.57161347545357	0.0504778557399012	11.3240443175507	1.39492784984654e-27	***
df.mm.trans1:probe10	0.223643861546851	0.0504778557399012	4.43053410785172	1.07796496312997e-05	***
df.mm.trans1:probe11	0.684178274914633	0.0504778557399012	13.5540280958054	1.20183277957534e-37	***
df.mm.trans1:probe12	0.813069398191059	0.0504778557399012	16.1074472414317	1.89868188676994e-50	***
df.mm.trans1:probe13	0.80206495947567	0.0504778557399012	15.8894419685435	2.60799775368812e-49	***
df.mm.trans1:probe14	0.692332982886316	0.0504778557399012	13.7155783013788	2.02748999119867e-38	***
df.mm.trans1:probe15	0.816304073891675	0.0504778557399012	16.1715283251704	8.76105731263788e-51	***
df.mm.trans1:probe16	0.669010815332787	0.0504778557399012	13.2535506020703	3.18163986407482e-36	***
df.mm.trans1:probe17	0.161133946967236	0.0504778557399012	3.19217099469351	0.00147012014840045	** 
df.mm.trans1:probe18	0.23724070583382	0.0504778557399012	4.69989666471289	3.08934757062597e-06	***
df.mm.trans1:probe19	0.24334020293203	0.0504778557399012	4.8207317716881	1.72735256726277e-06	***
df.mm.trans1:probe20	0.280138888268602	0.0504778557399012	5.54973827953553	3.95196575636251e-08	***
df.mm.trans1:probe21	0.19304818266952	0.0504778557399012	3.82441329648085	0.000141816795709302	***
df.mm.trans1:probe22	0.231973582487993	0.0504778557399012	4.59555143711513	5.05137712687041e-06	***
df.mm.trans2:probe2	-0.0742846017227823	0.0504778557399012	-1.47162752129470	0.141534885769650	   
df.mm.trans2:probe3	-0.230977160478351	0.0504778557399012	-4.57581165231173	5.53781828624525e-06	***
df.mm.trans2:probe4	-0.121155988649595	0.0504778557399012	-2.40018096794522	0.0166262275130058	*  
df.mm.trans2:probe5	-0.00899102384037656	0.0504778557399012	-0.178118180905007	0.858677567036972	   
df.mm.trans2:probe6	-0.129309193730574	0.0504778557399012	-2.56170140025103	0.0106076356896291	*  
df.mm.trans3:probe2	1.00730682678687	0.0504778557399012	19.9554202931530	1.39305150196552e-71	***
df.mm.trans3:probe3	0.30958251962886	0.0504778557399012	6.13303626097066	1.38482788870757e-09	***
df.mm.trans3:probe4	0.198684818467115	0.0504778557399012	3.93607881227929	9.0411877437712e-05	***
df.mm.trans3:probe5	0.220749099360013	0.0504778557399012	4.37318693760436	1.39495077519325e-05	***
df.mm.trans3:probe6	0.58815993600895	0.0504778557399012	11.6518407406127	5.50147557662501e-29	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93301770810491	0.162987060924404	24.130858522132	5.48419209049654e-96	***
df.mm.trans1	0.157088646515784	0.142718978639447	1.10068505263508	0.271381787263006	   
df.mm.trans2	0.208250111370955	0.127986048446040	1.62713134673234	0.104123154272254	   
df.mm.exp2	-0.0239670719766060	0.168783002424812	-0.141999322397898	0.88711812470535	   
df.mm.exp3	-0.0291888430824992	0.168783002424812	-0.17293710067459	0.8627468536841	   
df.mm.exp4	0.00535537097269813	0.168783002424812	0.031729326388087	0.974696227194177	   
df.mm.exp5	0.0699934895694073	0.168783002424812	0.414695132589477	0.678481969505168	   
df.mm.exp6	0.0746848869682898	0.168783002424812	0.44249057011271	0.658259987548981	   
df.mm.exp7	0.116327647831223	0.168783002424812	0.689214234608987	0.490898543334746	   
df.mm.exp8	0.235407579192717	0.168783002424812	1.39473510845729	0.163502703925150	   
df.mm.trans1:exp2	-0.0333115895487922	0.158332490912730	-0.210390106015277	0.833419531876045	   
df.mm.trans2:exp2	0.0908858488981482	0.126305633556936	0.719570824663008	0.472010199639784	   
df.mm.trans1:exp3	0.00360438017129175	0.158332490912731	0.0227646274653660	0.981843991463322	   
df.mm.trans2:exp3	-0.00481401163153884	0.126305633556936	-0.0381139898195339	0.969606793084061	   
df.mm.trans1:exp4	-0.0390334974428886	0.158332490912731	-0.246528663939248	0.805339501982769	   
df.mm.trans2:exp4	-0.0237692712036546	0.126305633556936	-0.188188527576166	0.850779034948567	   
df.mm.trans1:exp5	-0.0504274902241728	0.158332490912731	-0.318491106490375	0.750199826994293	   
df.mm.trans2:exp5	-0.0833080676792053	0.126305633556936	-0.659575232973687	0.509726002524834	   
df.mm.trans1:exp6	-0.152083032273239	0.158332490912731	-0.960529524903793	0.337093860653245	   
df.mm.trans2:exp6	-0.0996728460301076	0.126305633556936	-0.789140145401171	0.430275884385224	   
df.mm.trans1:exp7	-0.139979602850708	0.158332490912731	-0.884086406041966	0.376928791885703	   
df.mm.trans2:exp7	-0.129621196921450	0.126305633556936	-1.02625032052129	0.305099696156944	   
df.mm.trans1:exp8	-0.184882170797538	0.158332490912731	-1.16768308091257	0.243300339136253	   
df.mm.trans2:exp8	-0.178941373060804	0.126305633556936	-1.41673311016757	0.156970194187701	   
df.mm.trans1:probe2	0.110044612690147	0.096958453110011	1.13496667036641	0.256746632499287	   
df.mm.trans1:probe3	0.147962247055104	0.096958453110011	1.52603762033231	0.127415976855254	   
df.mm.trans1:probe4	0.084335840523428	0.096958453110011	0.869814212358966	0.384676209273775	   
df.mm.trans1:probe5	0.292068928870970	0.096958453110011	3.01231011327689	0.00267857689744949	** 
df.mm.trans1:probe6	0.148032033588615	0.096958453110011	1.52675737741664	0.127236848883461	   
df.mm.trans1:probe7	0.163544315550969	0.096958453110011	1.68674633624165	0.092061979533272	.  
df.mm.trans1:probe8	0.266813921878821	0.096958453110011	2.75183765128852	0.0060671945677336	** 
df.mm.trans1:probe9	0.164110365937178	0.096958453110011	1.69258440778727	0.0909439168267167	.  
df.mm.trans1:probe10	0.114764729520984	0.096958453110011	1.18364851995699	0.236921825836165	   
df.mm.trans1:probe11	0.156020644161990	0.096958453110011	1.60914947750833	0.107998389675603	   
df.mm.trans1:probe12	0.118149353196786	0.096958453110011	1.21855649927430	0.223390188561475	   
df.mm.trans1:probe13	0.142620886208345	0.096958453110011	1.47094844888381	0.141718416105861	   
df.mm.trans1:probe14	0.171298989961909	0.096958453110011	1.76672568989472	0.0776749900663999	.  
df.mm.trans1:probe15	0.137635252433653	0.096958453110011	1.41952813827887	0.156154583283686	   
df.mm.trans1:probe16	0.0615389299177468	0.096958453110011	0.634693808985623	0.525818893778611	   
df.mm.trans1:probe17	0.0916956076534358	0.096958453110011	0.94572061240907	0.344591282398491	   
df.mm.trans1:probe18	0.137063254682326	0.096958453110011	1.41362872741804	0.157879865576613	   
df.mm.trans1:probe19	0.133331362752286	0.096958453110011	1.37513912893191	0.169493005611088	   
df.mm.trans1:probe20	0.118086085090374	0.096958453110011	1.2179039712648	0.223637943530934	   
df.mm.trans1:probe21	0.19436631735397	0.096958453110011	2.00463508976817	0.0453558794695712	*  
df.mm.trans1:probe22	0.161727442739621	0.096958453110011	1.66800766258226	0.095725587114463	.  
df.mm.trans2:probe2	0.147934142711019	0.096958453110011	1.52574776067405	0.127488170543271	   
df.mm.trans2:probe3	0.0730648765319582	0.096958453110011	0.753568917287256	0.4513411928205	   
df.mm.trans2:probe4	0.00737286031280285	0.096958453110011	0.0760414391557738	0.939406115777797	   
df.mm.trans2:probe5	0.179749597745426	0.096958453110011	1.85388268871697	0.0641427097762594	.  
df.mm.trans2:probe6	0.184134619151754	0.096958453110011	1.89910846600276	0.0579281431374809	.  
df.mm.trans3:probe2	-0.103057077947264	0.096958453110011	-1.06289936196005	0.288164760101359	   
df.mm.trans3:probe3	0.0504979449035523	0.096958453110011	0.520820447148185	0.602643361574924	   
df.mm.trans3:probe4	0.0618558586651353	0.096958453110011	0.637962515707139	0.523689993842647	   
df.mm.trans3:probe5	0.0498214908176996	0.096958453110011	0.513843705418558	0.607510431652184	   
df.mm.trans3:probe6	-0.0893171523985849	0.096958453110011	-0.921189948206412	0.357243197331636