chr5.18272_chr5_120239756_120243700_+_2.R 

fitVsDatCorrelation=0.921089867533134
cont.fitVsDatCorrelation=0.252436944477564

fstatistic=8644.56811698289,59,853
cont.fstatistic=1387.53517590850,59,853

residuals=-0.842694675460826,-0.0948494033815818,0.00425469881516524,0.0959865513622657,1.09555814106670
cont.residuals=-0.79504901032878,-0.36562728281506,-0.0542573370688385,0.295409033840349,1.79773398944507

predictedValues:
Include	Exclude	Both
chr5.18272_chr5_120239756_120243700_+_2.R.tl.Lung	66.8323940793417	48.1782563222931	91.7604890758578
chr5.18272_chr5_120239756_120243700_+_2.R.tl.cerebhem	85.8569777494895	56.395722940609	125.002598687745
chr5.18272_chr5_120239756_120243700_+_2.R.tl.cortex	73.8242931796817	45.7954311499742	127.454557178453
chr5.18272_chr5_120239756_120243700_+_2.R.tl.heart	69.7914243138084	47.6350385419012	106.806821237022
chr5.18272_chr5_120239756_120243700_+_2.R.tl.kidney	68.5202894825967	44.9903752644875	99.1294462748264
chr5.18272_chr5_120239756_120243700_+_2.R.tl.liver	58.0285946343095	49.5824227895261	70.1973071452851
chr5.18272_chr5_120239756_120243700_+_2.R.tl.stomach	59.5251579293123	48.2121740378832	80.7975132703316
chr5.18272_chr5_120239756_120243700_+_2.R.tl.testicle	79.9794446005838	48.9756201108125	136.883030243248


diffExp=18.6541377570486,29.4612548088805,28.0288620297075,22.1563857719072,23.5299142181092,8.44617184478331,11.3129838914291,31.0038244897712
diffExpScore=0.994239416801524
diffExp1.5=0,1,1,0,1,0,0,1
diffExp1.5Score=0.8
diffExp1.4=0,1,1,1,1,0,0,1
diffExp1.4Score=0.833333333333333
diffExp1.3=1,1,1,1,1,0,0,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	73.0946291498312	65.9971963672606	69.3900101904208
cerebhem	71.4586235095789	64.0346354216499	72.3881621783644
cortex	66.7812832717018	78.0075952650848	85.5558279359202
heart	80.7550371312289	58.8708568976036	68.7080786845408
kidney	70.3531922083009	65.0583888325628	66.556164943949
liver	66.842366550181	56.2360579214961	77.0816973683568
stomach	74.4419709995601	82.9207698108827	72.849912616622
testicle	72.6004159313418	65.9070360589562	74.6764723079009
cont.diffExp=7.09743278257062,7.42398808792906,-11.2263119933830,21.8841802336253,5.29480337573808,10.6063086286849,-8.47879881132256,6.69337987238559
cont.diffExpScore=1.9532259237993

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=0,0,0,1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,1,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.502371092442905
cont.tran.correlation=-0.109991241207697

tran.covariance=0.00398716856657047
cont.tran.covariance=-0.0007931307394216

tran.mean=59.5077260704131
cont.tran.mean=69.5850034579513

weightedLogRatios:
wLogRatio
Lung	1.32173634047819
cerebhem	1.78309438807748
cortex	1.94006630033121
heart	1.54860128451472
kidney	1.68978817741239
liver	0.6264116526999
stomach	0.83914608770427
testicle	2.02875678499952

cont.weightedLogRatios:
wLogRatio
Lung	0.433154571706836
cerebhem	0.462282281777685
cortex	-0.664903134375055
heart	1.33806306094949
kidney	0.329747829458263
liver	0.711149143180667
stomach	-0.47072065206613
testicle	0.409787792870792

varWeightedLogRatios=0.259606559830852
cont.varWeightedLogRatios=0.403701728296516

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.15335922667902	0.0847441629607134	37.2103412967911	8.0726007326117e-181	***
df.mm.trans1	0.994887113679194	0.07318298622088	13.5945137668534	3.04841915183736e-38	***
df.mm.trans2	0.765002021639724	0.0646568375449498	11.8317265534042	5.14682352620092e-30	***
df.mm.exp2	0.098827749901256	0.0831693664696405	1.18827104373016	0.23505737334297	   
df.mm.exp3	-0.279801611051284	0.0831693664696405	-3.36423881686559	0.000801787076191202	***
df.mm.exp4	-0.119855965272961	0.0831693664696405	-1.44110710903049	0.149921404035633	   
df.mm.exp5	-0.120761959240935	0.0831693664696405	-1.45200047044986	0.146869176257736	   
df.mm.exp6	0.155348433951050	0.0831693664696405	1.86785640609342	0.0621247878865517	.  
df.mm.exp7	0.0121505122181211	0.0831693664696405	0.146093600731664	0.883882015221876	   
df.mm.exp8	-0.203948389774446	0.0831693664696405	-2.45220564291414	0.0143977561688986	*  
df.mm.trans1:exp2	0.151667208221844	0.0768751749334879	1.97290228416477	0.048829211986629	*  
df.mm.trans2:exp2	0.0586577657120092	0.0567760794111603	1.03314223737117	0.301830161126733	   
df.mm.trans1:exp3	0.37930156006359	0.0768751749334879	4.93399280576285	9.6870793968909e-07	***
df.mm.trans2:exp3	0.229078135015815	0.0567760794111603	4.03476494664028	5.95628882890372e-05	***
df.mm.trans1:exp4	0.163179202355511	0.0768751749334879	2.12265146058781	0.0340705698595846	*  
df.mm.trans2:exp4	0.10851675391144	0.0567760794111603	1.91131115492468	0.0562994840045063	.  
df.mm.trans1:exp5	0.145703953173649	0.0768751749334879	1.89533166330628	0.0583863214364058	.  
df.mm.trans2:exp5	0.0523027374948207	0.0567760794111603	0.921210799288471	0.357200841946413	   
df.mm.trans1:exp6	-0.296600438262297	0.0768751749334879	-3.8582083035117	0.000122829500653928	***
df.mm.trans2:exp6	-0.126619847899565	0.0567760794111603	-2.2301618782553	0.0259961706120273	*  
df.mm.trans1:exp7	-0.127939370993405	0.0768751749334879	-1.66424819330945	0.0964301081471712	.  
df.mm.trans2:exp7	-0.0114467552816024	0.0567760794111603	-0.201612288138240	0.840267906342028	   
df.mm.trans1:exp8	0.383530144636173	0.0768751749334879	4.98899865877381	7.3560122794299e-07	***
df.mm.trans2:exp8	0.220363209678879	0.0567760794111603	3.8812685194949	0.000111927957570359	***
df.mm.trans1:probe2	0.226222128227377	0.0526328342790338	4.29811792061315	1.92113485944122e-05	***
df.mm.trans1:probe3	0.00871298406386032	0.0526328342790338	0.165542748803310	0.868556030631536	   
df.mm.trans1:probe4	-0.0649068574277003	0.0526328342790338	-1.23320087768019	0.217840554738161	   
df.mm.trans1:probe5	0.0330532715383706	0.0526328342790338	0.627997180678855	0.530173876910148	   
df.mm.trans1:probe6	-0.0586454611581921	0.0526328342790338	-1.11423718599843	0.26549129744307	   
df.mm.trans1:probe7	0.168825671322708	0.0526328342790338	3.20761124942799	0.00138833499672951	** 
df.mm.trans1:probe8	0.258614066635923	0.0526328342790338	4.91355007151766	1.07232747645897e-06	***
df.mm.trans1:probe9	0.419166895577302	0.0526328342790338	7.96398106465409	5.31003559501691e-15	***
df.mm.trans1:probe10	0.243507525296744	0.0526328342790338	4.62653263181279	4.29539870675005e-06	***
df.mm.trans1:probe11	-0.353035256293671	0.0526328342790338	-6.70750988673817	3.60237818787775e-11	***
df.mm.trans1:probe12	-0.335523154923163	0.0526328342790338	-6.37478789655106	2.99723290711770e-10	***
df.mm.trans1:probe13	-0.388151345131351	0.0526328342790338	-7.37469966130952	3.90176219227487e-13	***
df.mm.trans1:probe14	-0.315603545829934	0.0526328342790338	-5.99632435062791	2.9780307958625e-09	***
df.mm.trans1:probe15	-0.346326840841214	0.0526328342790338	-6.58005303315336	8.20038783813257e-11	***
df.mm.trans1:probe16	-0.361568479239781	0.0526328342790338	-6.86963725576548	1.24070585913987e-11	***
df.mm.trans1:probe17	0.833458756806825	0.0526328342790338	15.8353386858901	1.04834344936103e-49	***
df.mm.trans1:probe18	0.448919485843092	0.0526328342790338	8.52926679690359	6.65916721407306e-17	***
df.mm.trans1:probe19	0.602572913390347	0.0526328342790338	11.4486122901115	2.48252323773147e-28	***
df.mm.trans1:probe20	0.0550815910878966	0.0526328342790338	1.04652526968015	0.295615037018233	   
df.mm.trans1:probe21	0.54913409617862	0.0526328342790338	10.4332989796327	4.5639126545897e-24	***
df.mm.trans1:probe22	0.102621601143366	0.0526328342790338	1.94976391731663	0.0515315880525301	.  
df.mm.trans2:probe2	-0.128138423496798	0.0526328342790338	-2.43457197872853	0.0151135415972128	*  
df.mm.trans2:probe3	-0.112019378186912	0.0526328342790338	-2.12831742241049	0.0335968436034787	*  
df.mm.trans2:probe4	-0.17293494191504	0.0526328342790338	-3.28568552850912	0.00105886111963964	** 
df.mm.trans2:probe5	-0.170308638656129	0.0526328342790338	-3.23578695673569	0.00125982131535316	** 
df.mm.trans2:probe6	-0.111853700784112	0.0526328342790338	-2.12516962683632	0.0338593265641154	*  
df.mm.trans3:probe2	-0.859955295998435	0.0526328342790338	-16.3387609232550	2.05026846116903e-52	***
df.mm.trans3:probe3	-0.313589869606679	0.0526328342790338	-5.95806541491149	3.73063314690822e-09	***
df.mm.trans3:probe4	-0.384787255332795	0.0526328342790338	-7.3107834796211	6.11496024558257e-13	***
df.mm.trans3:probe5	-0.0537493415076812	0.0526328342790338	-1.02121313138351	0.307443008378580	   
df.mm.trans3:probe6	-0.422335341203004	0.0526328342790338	-8.02418009571719	3.37068720623541e-15	***
df.mm.trans3:probe7	-0.302204453364929	0.0526328342790338	-5.74174766577812	1.30247391021938e-08	***
df.mm.trans3:probe8	-0.411672032108408	0.0526328342790338	-7.82158205514685	1.53835656275400e-14	***
df.mm.trans3:probe9	-0.768811132767914	0.0526328342790338	-14.6070631251217	2.70022150297287e-43	***
df.mm.trans3:probe10	-0.423560870559065	0.0526328342790338	-8.04746459811666	2.82515744721484e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14767007708268	0.210606328779317	19.6939479507703	1.84503810220178e-71	***
df.mm.trans1	0.0860384181906213	0.181874473929634	0.473064836046806	0.636287951329939	   
df.mm.trans2	0.0112550213439094	0.160685275658872	0.0700438873304316	0.9441751454952	   
df.mm.exp2	-0.0951243162269874	0.206692642031203	-0.460221105561306	0.645474813319659	   
df.mm.exp3	-0.132564314777744	0.206692642031203	-0.641359622069815	0.52146134742633	   
df.mm.exp4	-0.00472450927588319	0.206692642031202	-0.0228576558384211	0.981769163103748	   
df.mm.exp5	-0.0108571032761111	0.206692642031202	-0.0525277686201916	0.958120468254216	   
df.mm.exp6	-0.354594845169345	0.206692642031202	-1.71556588412961	0.0866046659588662	.  
df.mm.exp7	0.197879905791651	0.206692642031202	0.957363086789412	0.338655278317939	   
df.mm.exp8	-0.0815734661723254	0.206692642031202	-0.394660716369434	0.693191954746885	   
df.mm.trans1:exp2	0.072488014832326	0.191050307199511	0.379418467810303	0.704471572592747	   
df.mm.trans2:exp2	0.0649361698254585	0.141100002991484	0.460213808991753	0.645480048015995	   
df.mm.trans1:exp3	0.0422322741022948	0.191050307199511	0.221053159878944	0.825103942819876	   
df.mm.trans2:exp3	0.299758250079849	0.141100002991484	2.12443829712705	0.0339205602634196	*  
df.mm.trans1:exp4	0.104389957353285	0.191050307199510	0.546400363775767	0.584933646020388	   
df.mm.trans2:exp4	-0.109541573869896	0.141100002991484	-0.776339982618623	0.437763478311528	   
df.mm.trans1:exp5	-0.0273696295116638	0.191050307199511	-0.143258756883768	0.886119661070157	   
df.mm.trans2:exp5	-0.00347000215750919	0.141100002991484	-0.0245925023666982	0.98038575150353	   
df.mm.trans1:exp6	0.265177063052399	0.191050307199510	1.38799600450513	0.165500677373388	   
df.mm.trans2:exp6	0.194540734518085	0.141100002991484	1.37874365977035	0.168335249621636	   
df.mm.trans1:exp7	-0.179614888087835	0.191050307199510	-0.940144461009773	0.347409614479985	   
df.mm.trans2:exp7	0.0303934036707405	0.141100002991484	0.215403281547591	0.829504403691174	   
df.mm.trans1:exp8	0.0747892256545485	0.191050307199510	0.39146351948258	0.695552416326277	   
df.mm.trans2:exp8	0.0802064088658636	0.141100002991484	0.568436620591033	0.569888141202937	   
df.mm.trans1:probe2	0.200953142964691	0.130803203589329	1.53630138597832	0.124835304734207	   
df.mm.trans1:probe3	0.0213705297639347	0.130803203589329	0.163379253546646	0.870258536340482	   
df.mm.trans1:probe4	0.117570644810950	0.130803203589329	0.898836126216566	0.368993604521456	   
df.mm.trans1:probe5	0.0335709916874098	0.130803203589329	0.256652671847469	0.797508798881297	   
df.mm.trans1:probe6	-0.0607224478504121	0.130803203589329	-0.464227527951507	0.642603193676025	   
df.mm.trans1:probe7	0.130297583536962	0.130803203589329	0.996134497944294	0.31946728906862	   
df.mm.trans1:probe8	0.0195053958985095	0.130803203589329	0.149120169562122	0.881494058097412	   
df.mm.trans1:probe9	-0.0345971214998214	0.130803203589329	-0.264497508856455	0.791460478290888	   
df.mm.trans1:probe10	0.248126813834014	0.130803203589329	1.89694752899963	0.0581724138301553	.  
df.mm.trans1:probe11	0.209925682082923	0.130803203589329	1.60489709978364	0.108886593502127	   
df.mm.trans1:probe12	0.124741281233362	0.130803203589329	0.95365616292549	0.340527878294001	   
df.mm.trans1:probe13	0.230416120474166	0.130803203589329	1.76154799080902	0.078503903330759	.  
df.mm.trans1:probe14	0.0690313677079953	0.130803203589329	0.52774982426827	0.597810234686868	   
df.mm.trans1:probe15	0.099840391919295	0.130803203589329	0.763287053983437	0.445503267203218	   
df.mm.trans1:probe16	-0.0492951922879564	0.130803203589329	-0.376865328487855	0.706367419347058	   
df.mm.trans1:probe17	0.0785552093161352	0.130803203589329	0.600560285685109	0.548292423718649	   
df.mm.trans1:probe18	-0.00215458079486040	0.130803203589329	-0.0164719268010051	0.986861750077847	   
df.mm.trans1:probe19	0.212791055010866	0.130803203589329	1.62680308411212	0.104148249830743	   
df.mm.trans1:probe20	0.0255750016741486	0.130803203589329	0.195522746938554	0.845030296305457	   
df.mm.trans1:probe21	0.0710526661765184	0.130803203589329	0.54320279799565	0.587132096037368	   
df.mm.trans1:probe22	0.110930140842213	0.130803203589329	0.84806899065324	0.396637455582572	   
df.mm.trans2:probe2	0.088849677267319	0.130803203589329	0.679262241514145	0.497155984807168	   
df.mm.trans2:probe3	0.0214377526003949	0.130803203589329	0.163893177018057	0.86985406291838	   
df.mm.trans2:probe4	0.079026370930597	0.130803203589329	0.604162350478117	0.545896356316028	   
df.mm.trans2:probe5	0.097775640638779	0.130803203589329	0.747501880349629	0.454966677958729	   
df.mm.trans2:probe6	0.203905173181077	0.130803203589329	1.55886987157638	0.119398099875844	   
df.mm.trans3:probe2	-0.137982535786389	0.130803203589329	-1.05488651653823	0.291775920774881	   
df.mm.trans3:probe3	-0.00472460504312967	0.130803203589329	-0.0361199490034134	0.971195166490904	   
df.mm.trans3:probe4	0.00862247459631032	0.130803203589329	0.0659194450877633	0.94745740545841	   
df.mm.trans3:probe5	0.145319552962959	0.130803203589329	1.11097854620751	0.266890637868082	   
df.mm.trans3:probe6	-0.0597088571607561	0.130803203589329	-0.456478553447502	0.648162095621045	   
df.mm.trans3:probe7	-0.0134279289802386	0.130803203589329	-0.102657493178814	0.91825893890394	   
df.mm.trans3:probe8	-0.0156476441323354	0.130803203589329	-0.119627376875744	0.90480650014949	   
df.mm.trans3:probe9	0.101733264228663	0.130803203589329	0.777758200388313	0.436927232875899	   
df.mm.trans3:probe10	-0.0763897691707941	0.130803203589329	-0.584005338360274	0.559371121598705