chr11.4312_chr11_83834739_83856816_+_2.R 

fitVsDatCorrelation=0.725481626719077
cont.fitVsDatCorrelation=0.286212570690488

fstatistic=12629.1694261580,58,830
cont.fstatistic=6508.98030017061,58,830

residuals=-0.388832133984411,-0.0801090486627331,-0.0129981045841481,0.0706910645956189,0.98095108044973
cont.residuals=-0.492113010560955,-0.124622070642588,-0.022282736605953,0.0938908665644183,1.20939766176398

predictedValues:
Include	Exclude	Both
chr11.4312_chr11_83834739_83856816_+_2.R.tl.Lung	49.6026786173314	49.7661717108488	55.1048621094897
chr11.4312_chr11_83834739_83856816_+_2.R.tl.cerebhem	54.4206482818533	52.6077027259322	52.3242594144278
chr11.4312_chr11_83834739_83856816_+_2.R.tl.cortex	50.515021910868	63.9785961075329	50.5934029060749
chr11.4312_chr11_83834739_83856816_+_2.R.tl.heart	52.1247357439426	55.933588845757	55.0869240946078
chr11.4312_chr11_83834739_83856816_+_2.R.tl.kidney	49.145684149839	50.6634873328113	53.7156689178556
chr11.4312_chr11_83834739_83856816_+_2.R.tl.liver	50.678558338112	54.5766448922848	53.257558560452
chr11.4312_chr11_83834739_83856816_+_2.R.tl.stomach	54.9875177270991	65.1900290763307	52.8895387593208
chr11.4312_chr11_83834739_83856816_+_2.R.tl.testicle	52.9919391658386	50.8135094615451	51.0844754103832


diffExp=-0.163493093517381,1.81294555592109,-13.4635741966649,-3.80885310181445,-1.51780318297228,-3.89808655417288,-10.2025113492316,2.17842970429356
diffExpScore=1.23227099798394
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,-1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	54.7271859701555	53.3767971258537	55.2657412569563
cerebhem	52.7765148284797	55.6152885751708	52.1964385031606
cortex	54.860447741023	52.1627990190493	55.5603390397524
heart	51.6783681652725	50.6628011560987	49.9155305358778
kidney	55.471342947387	50.4498778897937	54.9684203854411
liver	52.1980585630763	52.027148162399	53.2673474859951
stomach	53.8402058701867	54.0180232026434	52.3311781165155
testicle	53.7147626076241	57.8436346205263	48.8352114874091
cont.diffExp=1.35038884430176,-2.83877374669109,2.69764872197373,1.01556700917381,5.02146505759322,0.170910400677279,-0.177817332456748,-4.12887201290219
cont.diffExpScore=4.23339530592009

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=0.363879142079357
cont.tran.correlation=-0.0431113706625658

tran.covariance=0.00161421965664196
cont.tran.covariance=-3.96547663620855e-05

tran.mean=53.6247821304954
cont.tran.mean=53.4639535277962

weightedLogRatios:
wLogRatio
Lung	-0.0128521954235273
cerebhem	0.134840095461576
cortex	-0.954659254132165
heart	-0.281319184805801
kidney	-0.118928225226765
liver	-0.293637702627343
stomach	-0.696494833109665
testicle	0.165775713587787

cont.weightedLogRatios:
wLogRatio
Lung	0.0996846525194686
cerebhem	-0.209161967261771
cortex	0.200662691615389
heart	0.078101604718467
kidney	0.376548690139678
liver	0.0129657448875928
stomach	-0.0131483213879574
testicle	-0.297756278117153

varWeightedLogRatios=0.156109659099895
cont.varWeightedLogRatios=0.046165462438669

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.64928051122459	0.0648882760107091	56.2394431718654	2.3799806740244e-285	***
df.mm.trans1	0.181655862986002	0.05617691594277	3.23363894114555	0.00127053369712911	** 
df.mm.trans2	0.271184616410029	0.0497694238831318	5.44881968187581	6.68667829708686e-08	***
df.mm.exp2	0.200003726732028	0.0643253791250913	3.10925064806983	0.00193964879392810	** 
df.mm.exp3	0.354855803654044	0.0643253791250913	5.51657539342238	4.62002968190286e-08	***
df.mm.exp4	0.166749955086173	0.0643253791250913	2.59228872575940	0.00970163919698621	** 
df.mm.exp5	0.0341474148982771	0.0643253791250913	0.530854467750152	0.595661643552593	   
df.mm.exp6	0.147826858246351	0.0643253791250913	2.29811095180454	0.0218033039355133	*  
df.mm.exp7	0.414064820598058	0.0643253791250913	6.4370366133846	2.05874427636629e-10	***
df.mm.exp8	0.162679068116604	0.0643253791250913	2.52900286526485	0.0116228960813614	*  
df.mm.trans1:exp2	-0.107304917536047	0.0596312224186696	-1.79947539533336	0.0723066077213495	.  
df.mm.trans2:exp2	-0.144476648351850	0.0447685691897767	-3.22718931979721	0.00129915623176048	** 
df.mm.trans1:exp3	-0.336629884679085	0.0596312224186696	-5.64519510124433	2.26511287527095e-08	***
df.mm.trans2:exp3	-0.103642682349053	0.0447685691897767	-2.31507694404315	0.0208518111527711	*  
df.mm.trans1:exp4	-0.117155181295011	0.0596312224186696	-1.96466174167061	0.0497866399505228	*  
df.mm.trans2:exp4	-0.0499203518702238	0.0447685691897767	-1.11507588412326	0.265140660267454	   
df.mm.trans1:exp5	-0.043403218584281	0.0596312224186696	-0.727860621061024	0.466904186309897	   
df.mm.trans2:exp5	-0.0162774049980293	0.0447685691897767	-0.363590020691267	0.716256768311489	   
df.mm.trans1:exp6	-0.126368786219564	0.0596312224186697	-2.11917148590937	0.0343723525957269	*  
df.mm.trans2:exp6	-0.0555562865305926	0.0447685691897767	-1.24096631936317	0.214968821022744	   
df.mm.trans1:exp7	-0.311003448193489	0.0596312224186696	-5.21544646544288	2.31720046107184e-07	***
df.mm.trans2:exp7	-0.144093761936059	0.0447685691897767	-3.21863674769761	0.00133803115714168	** 
df.mm.trans1:exp8	-0.0965840939964934	0.0596312224186696	-1.61968998922038	0.105678749262481	   
df.mm.trans2:exp8	-0.141852284902790	0.0447685691897767	-3.1685686514007	0.00158801830736927	** 
df.mm.trans1:probe2	0.0214851191081030	0.0400018400728664	0.537103269973735	0.59134023194431	   
df.mm.trans1:probe3	0.0313384379243082	0.0400018400728664	0.78342490913475	0.433601121047384	   
df.mm.trans1:probe4	0.0682271430503939	0.0400018400728664	1.70560011554751	0.0884567022610008	.  
df.mm.trans1:probe5	0.316056688609598	0.0400018400728664	7.9010537523743	8.76993106437536e-15	***
df.mm.trans1:probe6	0.0624530015520136	0.0400018400728664	1.56125321830822	0.118845201848611	   
df.mm.trans1:probe7	0.310306322933487	0.0400018400728664	7.75730122334974	2.54300600763190e-14	***
df.mm.trans1:probe8	0.129724939070529	0.0400018400728664	3.24297429403811	0.00123013654012029	** 
df.mm.trans1:probe9	-0.0329107365748789	0.0400018400728664	-0.822730567267143	0.410897530121296	   
df.mm.trans1:probe10	0.54095895133603	0.0400018400728664	13.5233516845883	8.36146404695382e-38	***
df.mm.trans1:probe11	0.104658818184932	0.0400018400728664	2.61635009775271	0.00904909412056773	** 
df.mm.trans1:probe12	-0.0747708265726821	0.0400018400728664	-1.86918467841683	0.0619490896759736	.  
df.mm.trans1:probe13	0.356224771122632	0.0400018400728664	8.9052096222009	3.31173781949394e-18	***
df.mm.trans1:probe14	0.257806719322542	0.0400018400728664	6.44487150723385	1.95996830858234e-10	***
df.mm.trans1:probe15	-0.00145323600498867	0.0400018400728664	-0.0363292289140071	0.971028579334557	   
df.mm.trans1:probe16	0.156746130642611	0.0400018400728664	3.91847300916871	9.64483917432582e-05	***
df.mm.trans1:probe17	-0.0214246573903340	0.0400018400728664	-0.535591796560041	0.592384179900609	   
df.mm.trans1:probe18	0.0295800909340158	0.0400018400728664	0.739468256463537	0.459831790523052	   
df.mm.trans1:probe19	-0.011341966270627	0.0400018400728664	-0.283536113587943	0.776836609787165	   
df.mm.trans1:probe20	0.0142386851498766	0.0400018400728664	0.355950754363794	0.721967896617506	   
df.mm.trans1:probe21	0.0544035013131764	0.0400018400728664	1.36002496920332	0.174191307032219	   
df.mm.trans1:probe22	-0.0459455616834671	0.0400018400728664	-1.14858620502891	0.251057551723729	   
df.mm.trans2:probe2	0.0114648800372222	0.0400018400728664	0.286608816402896	0.774483389955104	   
df.mm.trans2:probe3	-0.0577214276902501	0.0400018400728664	-1.44296931303925	0.149406393367575	   
df.mm.trans2:probe4	-0.0546807096816583	0.0400018400728664	-1.36695485962779	0.172009569492111	   
df.mm.trans2:probe5	-0.081575580318883	0.0400018400728664	-2.03929569665513	0.0417368014552791	*  
df.mm.trans2:probe6	-0.0144320223844913	0.0400018400728664	-0.360783962892762	0.718352754534937	   
df.mm.trans3:probe2	-0.240724704434197	0.0400018400728664	-6.01784077921661	2.6501641190541e-09	***
df.mm.trans3:probe3	-0.029299548456001	0.0400018400728664	-0.732455017134952	0.464097670908353	   
df.mm.trans3:probe4	-0.0841689638405848	0.0400018400728664	-2.10412730232571	0.0356675825987001	*  
df.mm.trans3:probe5	-0.108629703647727	0.0400018400728664	-2.71561766783351	0.00675272331534092	** 
df.mm.trans3:probe6	0.125458336899230	0.0400018400728664	3.13631414631673	0.00177117056488279	** 
df.mm.trans3:probe7	-0.178109512082324	0.0400018400728664	-4.45253297743014	9.64631542883393e-06	***
df.mm.trans3:probe8	-0.218323138151057	0.0400018400728664	-5.45782738377446	6.3674280107457e-08	***
df.mm.trans3:probe9	-0.102150097516818	0.0400018400728664	-2.55363496606015	0.0108381802968329	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03038780911822	0.090337107393667	44.6149752344270	1.12218559386396e-222	***
df.mm.trans1	-0.0156438652544238	0.0782091989580594	-0.200025898012496	0.841509323810533	   
df.mm.trans2	-0.0670072437570701	0.0692887231201734	-0.967072861782337	0.333789308621691	   
df.mm.exp2	0.0619266514394229	0.0895534453897755	0.691504957401597	0.489441637140354	   
df.mm.exp3	-0.0258909133308843	0.0895534453897755	-0.289111303514855	0.772568399322607	   
df.mm.exp4	-0.00768448658243054	0.0895534453897755	-0.0858089440220231	0.931638990949483	   
df.mm.exp5	-0.0374954936199796	0.0895534453897755	-0.418694037474303	0.675548073334119	   
df.mm.exp6	-0.0360960801980236	0.0895534453897755	-0.403067464807387	0.687002396431673	   
df.mm.exp7	0.0501624110276634	0.0895534453897754	0.560139376093626	0.575535519834939	   
df.mm.exp8	0.185396169596616	0.0895534453897755	2.07022933388762	0.0387396302131705	*  
df.mm.trans1:exp2	-0.098220941839219	0.0830182657767321	-1.1831244717081	0.237098590292172	   
df.mm.trans2:exp2	-0.0208446543950313	0.0623265602262296	-0.334442560593277	0.738130118792168	   
df.mm.trans1:exp3	0.0283229730814360	0.0830182657767321	0.341165559367468	0.733065294393929	   
df.mm.trans2:exp3	0.00288435089734921	0.0623265602262295	0.0462780375955252	0.963099776066681	   
df.mm.trans1:exp4	-0.0496368174373722	0.0830182657767321	-0.597902364894789	0.550068180896805	   
df.mm.trans2:exp4	-0.0444997179087906	0.0623265602262295	-0.71397679813017	0.475442251158858	   
df.mm.trans1:exp5	0.0510014504846873	0.0830182657767321	0.614340109462774	0.539158959676033	   
df.mm.trans2:exp5	-0.0189003207908688	0.0623265602262295	-0.3032466531486	0.761777889256564	   
df.mm.trans1:exp6	-0.0112192051295182	0.0830182657767321	-0.135141405623805	0.892532839099961	   
df.mm.trans2:exp6	0.0104856018020193	0.0623265602262296	0.168236491215931	0.866438219755307	   
df.mm.trans1:exp7	-0.0665024892042892	0.0830182657767321	-0.80105852106258	0.42332701595847	   
df.mm.trans2:exp7	-0.0382207979251525	0.0623265602262295	-0.613234514890935	0.539889288123855	   
df.mm.trans1:exp8	-0.204068884214273	0.0830182657767321	-2.45812029804492	0.0141699638934101	*  
df.mm.trans2:exp8	-0.105028895286886	0.0623265602262296	-1.68513864563772	0.0923377267712313	.  
df.mm.trans1:probe2	-0.0117429855058859	0.0556903456952752	-0.210862140632792	0.833046604490632	   
df.mm.trans1:probe3	-0.0544656233993935	0.0556903456952752	-0.978008355297648	0.328355158427852	   
df.mm.trans1:probe4	-0.094387025080175	0.0556903456952752	-1.69485435764107	0.0904781502105643	.  
df.mm.trans1:probe5	-0.00388426510581980	0.0556903456952752	-0.0697475488314187	0.944411395687302	   
df.mm.trans1:probe6	0.0447622820803194	0.0556903456952752	0.80377095026934	0.421759402563686	   
df.mm.trans1:probe7	-0.000645883715615889	0.0556903456952752	-0.0115977681149623	0.990749314347718	   
df.mm.trans1:probe8	-0.0966794701725259	0.0556903456952752	-1.73601849594423	0.0829316550225273	.  
df.mm.trans1:probe9	-0.0388761583380455	0.0556903456952752	-0.698077159563113	0.485324460648567	   
df.mm.trans1:probe10	0.022665925026357	0.0556903456952752	0.406999179900583	0.684113524942787	   
df.mm.trans1:probe11	-0.00869302023218064	0.0556903456952752	-0.156095641419553	0.875995590751766	   
df.mm.trans1:probe12	0.0046215271018731	0.0556903456952752	0.0829861449803353	0.933882565386749	   
df.mm.trans1:probe13	-0.0516403167310254	0.0556903456952752	-0.927275923435443	0.354052848767664	   
df.mm.trans1:probe14	0.0148550330179938	0.0556903456952752	0.266743415443623	0.789732980228125	   
df.mm.trans1:probe15	-0.0537393272739515	0.0556903456952752	-0.964966667077284	0.3348425721423	   
df.mm.trans1:probe16	-0.0222770483231892	0.0556903456952752	-0.400016341164124	0.689247406052	   
df.mm.trans1:probe17	-0.0280135431351116	0.0556903456952752	-0.503023329903449	0.615081321893875	   
df.mm.trans1:probe18	0.0809817201301116	0.0556903456952752	1.45414288812688	0.146284720927093	   
df.mm.trans1:probe19	-0.0575774230198342	0.0556903456952752	-1.03388517885819	0.301490986574065	   
df.mm.trans1:probe20	0.0428674487535796	0.0556903456952752	0.769746501272239	0.441669289742382	   
df.mm.trans1:probe21	0.000615997468376649	0.0556903456952752	0.0110611176979803	0.991177342966535	   
df.mm.trans1:probe22	-0.0726318986896147	0.0556903456952752	-1.30420987305483	0.192523587059780	   
df.mm.trans2:probe2	0.0595409211603808	0.0556903456952752	1.06914260303168	0.285316247472441	   
df.mm.trans2:probe3	0.0196419473274333	0.0556903456952752	0.352699324850837	0.724403410593365	   
df.mm.trans2:probe4	0.00633244447474235	0.0556903456952752	0.113708119346072	0.909496673829879	   
df.mm.trans2:probe5	0.109605702382719	0.0556903456952752	1.96812752756926	0.0493858589242798	*  
df.mm.trans2:probe6	0.0148126160454257	0.0556903456952752	0.265981757888108	0.790319313725815	   
df.mm.trans3:probe2	0.116474917649833	0.0556903456952752	2.09147413605864	0.0367890323862075	*  
df.mm.trans3:probe3	0.101212940443777	0.0556903456952752	1.81742345428399	0.0695127534352019	.  
df.mm.trans3:probe4	0.0803272514771324	0.0556903456952752	1.44239096515336	0.149569347767741	   
df.mm.trans3:probe5	0.0296153804369708	0.0556903456952752	0.53178661520651	0.595016094214376	   
df.mm.trans3:probe6	0.143035265535802	0.0556903456952752	2.56840326182313	0.0103906520133536	*  
df.mm.trans3:probe7	0.0395619223449682	0.0556903456952752	0.710391035484713	0.477661230648152	   
df.mm.trans3:probe8	0.0261348950030606	0.0556903456952752	0.469289509281639	0.638985961923126	   
df.mm.trans3:probe9	0.0433860111708422	0.0556903456952752	0.779058032935196	0.436167588831242