chr11.4264_chr11_102943307_102945142_-_2.R 

fitVsDatCorrelation=0.884354954346826
cont.fitVsDatCorrelation=0.289728528910758

fstatistic=11624.1190415646,55,761
cont.fstatistic=2754.65872134375,55,761

residuals=-0.744825484657907,-0.0799301056886948,-0.0036674257696063,0.0701675236290398,1.28917670528781
cont.residuals=-0.534587100879212,-0.174648371626298,-0.0680271956797363,0.0716981998546503,1.50906851304788

predictedValues:
Include	Exclude	Both
chr11.4264_chr11_102943307_102945142_-_2.R.tl.Lung	57.0527668604458	49.6386191926158	72.1613268155444
chr11.4264_chr11_102943307_102945142_-_2.R.tl.cerebhem	57.8698079183429	44.6119822815767	62.1275121217516
chr11.4264_chr11_102943307_102945142_-_2.R.tl.cortex	57.4509747357865	54.1604121103156	76.3120156657247
chr11.4264_chr11_102943307_102945142_-_2.R.tl.heart	59.6158358309162	54.6810933656325	82.2050762969599
chr11.4264_chr11_102943307_102945142_-_2.R.tl.kidney	56.8440720652382	48.9690633908892	64.5065896124028
chr11.4264_chr11_102943307_102945142_-_2.R.tl.liver	62.8857487606256	50.916740875117	65.5916992152807
chr11.4264_chr11_102943307_102945142_-_2.R.tl.stomach	57.7071505286574	50.9499675937723	74.1659050878765
chr11.4264_chr11_102943307_102945142_-_2.R.tl.testicle	57.0370789897871	49.0032057693205	70.8343024962729


diffExp=7.41414766782993,13.2578256367662,3.29056262547086,4.93474246528376,7.87500867434905,11.9690078855086,6.7571829348851,8.0338732204666
diffExpScore=0.984503896374599
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=0,1,0,0,0,1,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	51.8751181879023	51.1621930837704	56.5410190073568
cerebhem	51.2263047683973	46.8750501148078	50.9832577978135
cortex	46.7237759022423	47.0507681460313	49.7937205607055
heart	54.5532786127371	48.4473114790519	58.4542959703236
kidney	52.4339249004436	53.8542341050968	52.9634858023827
liver	52.8267085169733	54.7890678312058	57.6304205580731
stomach	50.5027854735691	61.7531893573974	57.7140418935903
testicle	49.2819112213114	59.4573923279319	58.7757466685772
cont.diffExp=0.712925104131955,4.35125465358956,-0.326992243788943,6.10596713368529,-1.42030920465324,-1.96235931423246,-11.2504038838283,-10.1754811066205
cont.diffExpScore=2.42597561080746

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,-1,-1
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.257983783313636
cont.tran.correlation=-0.0488265033090642

tran.covariance=0.000570639518014507
cont.tran.covariance=-6.6697219595895e-05

tran.mean=54.337157516815
cont.tran.mean=52.0508133768044

weightedLogRatios:
wLogRatio
Lung	0.553262233425564
cerebhem	1.02206512680827
cortex	0.237191566006520
heart	0.349476853185255
kidney	0.591385389696307
liver	0.852060221158721
stomach	0.497290137768231
testicle	0.602373945017314

cont.weightedLogRatios:
wLogRatio
Lung	0.0545499212903779
cerebhem	0.345471948129641
cortex	-0.0268342822707698
heart	0.467661314628184
kidney	-0.106185056376615
liver	-0.145357254104417
stomach	-0.809011168474292
testicle	-0.74919879403315

varWeightedLogRatios=0.0639647276527782
cont.varWeightedLogRatios=0.210372704019194

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.42334096604238	0.06744604566842	65.5833996226928	0	***
df.mm.trans1	0.0695613476722951	0.0583949406301913	1.19122216619449	0.233937776686699	   
df.mm.trans2	-0.499480429369311	0.052162672562449	-9.57543785302286	1.38596710697670e-20	***
df.mm.exp2	0.0571679053808968	0.0679135248183804	0.841774970946944	0.400178369461642	   
df.mm.exp3	0.0382103485997331	0.0679135248183804	0.562632387317816	0.573850934778655	   
df.mm.exp4	0.0103807376112997	0.0679135248183804	0.152852287361916	0.878555281133368	   
df.mm.exp5	0.094891842928645	0.0679135248183804	1.39724514641836	0.162747108527527	   
df.mm.exp6	0.218220728687247	0.0679135248183805	3.21321458826986	0.00136787844614972	** 
df.mm.exp7	0.0100792162250866	0.0679135248183804	0.148412503283274	0.882056549435902	   
df.mm.exp8	0.00540244806409884	0.0679135248183804	0.0795489275302155	0.936616929682844	   
df.mm.trans1:exp2	-0.0429486829934514	0.0627477800334135	-0.684465378226625	0.493889736103874	   
df.mm.trans2:exp2	-0.163934564939462	0.0486042214161793	-3.37284622946128	0.000781572024384825	***
df.mm.trans1:exp3	-0.0312549517690591	0.0627477800334134	-0.498104502699789	0.618554301345302	   
df.mm.trans2:exp3	0.0489707455787161	0.0486042214161793	1.00754099442101	0.313995115023664	   
df.mm.trans1:exp4	0.0335639290280126	0.0627477800334134	0.534902254870844	0.592873702569731	   
df.mm.trans2:exp4	0.0863681262347989	0.0486042214161793	1.77696759084487	0.0759729510229912	.  
df.mm.trans1:exp5	-0.0985564754282306	0.0627477800334134	-1.57067668968924	0.116673430783223	   
df.mm.trans2:exp5	-0.108472247102455	0.0486042214161793	-2.23174539046000	0.0259222097988211	*  
df.mm.trans1:exp6	-0.120877734435554	0.0627477800334135	-1.92640654970082	0.0544257607310545	.  
df.mm.trans2:exp6	-0.19279810537918	0.0486042214161793	-3.96669465658803	7.97590364954718e-05	***
df.mm.trans1:exp7	0.00132530158352909	0.0627477800334134	0.0211210911816061	0.983154596840584	   
df.mm.trans2:exp7	0.015995763880076	0.0486042214161793	0.329102358067017	0.742168872355168	   
df.mm.trans1:exp8	-0.00567745712630975	0.0627477800334134	-0.0904806054220002	0.927929114424681	   
df.mm.trans2:exp8	-0.0182858717534083	0.0486042214161793	-0.376219826603814	0.706858337708772	   
df.mm.trans1:probe2	-0.690465790397602	0.0410780645276508	-16.8086251953967	3.707293148583e-54	***
df.mm.trans1:probe3	-0.724560607031777	0.0410780645276508	-17.6386257571618	1.22308775059202e-58	***
df.mm.trans1:probe4	-0.73548346191893	0.0410780645276508	-17.9045305657927	4.29106248149537e-60	***
df.mm.trans1:probe5	-0.6474949696054	0.0410780645276508	-15.7625481397633	1.18874888247296e-48	***
df.mm.trans1:probe6	-0.654514492491252	0.0410780645276508	-15.9334306525245	1.53930712252568e-49	***
df.mm.trans1:probe7	-0.726342370419854	0.0410780645276508	-17.6820008141068	7.09152366291022e-59	***
df.mm.trans1:probe8	-0.671665868196524	0.0410780645276508	-16.3509619043616	9.96831239517248e-52	***
df.mm.trans1:probe9	-0.677366496686922	0.0410780645276508	-16.4897373933226	1.84162582461518e-52	***
df.mm.trans1:probe10	-0.677068565784358	0.0410780645276508	-16.4824845953636	2.01188838230704e-52	***
df.mm.trans1:probe11	-0.699872798401855	0.0410780645276508	-17.0376283899829	2.197671083802e-55	***
df.mm.trans1:probe12	-0.60114425078778	0.0410780645276508	-14.6341912088661	6.48396206227439e-43	***
df.mm.trans1:probe13	-0.6502955905327	0.0410780645276508	-15.8307261554392	5.2657820146929e-49	***
df.mm.trans1:probe14	-0.63775407561883	0.0410780645276508	-15.5254168606103	1.99094103221732e-47	***
df.mm.trans1:probe15	-0.742680390145122	0.0410780645276508	-18.0797318151444	4.66855862852741e-61	***
df.mm.trans1:probe16	-0.379762915686519	0.0410780645276508	-9.24490771542777	2.31775139666272e-19	***
df.mm.trans1:probe17	-0.698597420627686	0.0410780645276508	-17.0065807301471	3.22672207691942e-55	***
df.mm.trans1:probe18	-0.724930077496262	0.0410780645276508	-17.6476201065484	1.09242326176515e-58	***
df.mm.trans1:probe19	-0.721080309683977	0.0410780645276508	-17.5539017715549	3.54112482087043e-58	***
df.mm.trans1:probe20	-0.508840260483307	0.0410780645276508	-12.3871527622922	3.11139247514497e-32	***
df.mm.trans2:probe2	-0.0611592872576453	0.0410780645276508	-1.48885513377772	0.136939798437931	   
df.mm.trans2:probe3	-0.0478252530843816	0.0410780645276508	-1.16425283504263	0.244686427459229	   
df.mm.trans2:probe4	-0.00845401816114916	0.0410780645276508	-0.205803711989851	0.83699928341588	   
df.mm.trans2:probe5	-0.107607211525286	0.0410780645276508	-2.61957842373154	0.00897923211056857	** 
df.mm.trans2:probe6	-0.0422337340645483	0.0410780645276508	-1.02813349533836	0.304213706282122	   
df.mm.trans3:probe2	0.400948937158532	0.0410780645276508	9.7606579513658	2.76625293448998e-21	***
df.mm.trans3:probe3	0.530091487395988	0.0410780645276508	12.9044903524889	1.35135823634302e-34	***
df.mm.trans3:probe4	-0.0339948836659953	0.0410780645276508	-0.827567804298869	0.408174740258405	   
df.mm.trans3:probe5	0.039720157095284	0.0410780645276508	0.966943247010755	0.333879594190800	   
df.mm.trans3:probe6	0.939774599226065	0.0410780645276508	22.8777721159057	1.454072007586e-88	***
df.mm.trans3:probe7	0.334909846396024	0.0410780645276508	8.15300940409658	1.45945477825632e-15	***
df.mm.trans3:probe8	0.0724088833807881	0.0410780645276508	1.76271409603653	0.0783500778708887	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.85026099941647	0.138284326294912	27.8430759477774	3.37739583865076e-118	***
df.mm.trans1	0.0506039701310383	0.119726886047203	0.422661707839686	0.672661495104719	   
df.mm.trans2	0.0617111044350832	0.106948894654291	0.577014887667258	0.56410004660594	   
df.mm.exp2	0.00336769322565125	0.139242796708834	0.0241857626049649	0.980710774917271	   
df.mm.exp3	-0.061282260726129	0.139242796708834	-0.440110814883117	0.65998181973576	   
df.mm.exp4	-0.0374642488396083	0.139242796708834	-0.269056997741497	0.787958750004134	   
df.mm.exp5	0.127358377938977	0.139242796708834	0.914649669133639	0.360665285926864	   
df.mm.exp6	0.0675832999396061	0.139242796708834	0.485362988513707	0.627558579543126	   
df.mm.exp7	0.140799887112021	0.139242796708834	1.01118255622547	0.31225045632386	   
df.mm.exp8	0.0602140389387561	0.139242796708834	0.432439166419989	0.665544769980381	   
df.mm.trans1:exp2	-0.0159537849737601	0.128651493240689	-0.124007771475399	0.901341867987084	   
df.mm.trans2:exp2	-0.0908829827689772	0.0996530181571806	-0.911994282256755	0.362060534492498	   
df.mm.trans1:exp3	-0.0433038410635292	0.128651493240689	-0.336598044629874	0.736512728307565	   
df.mm.trans2:exp3	-0.0224913901606612	0.0996530181571805	-0.225697029318129	0.821497667447326	   
df.mm.trans1:exp4	0.0878028053349123	0.128651493240689	0.682485707108318	0.49513956441964	   
df.mm.trans2:exp4	-0.0170597479409516	0.0996530181571806	-0.171191482771185	0.864118712166944	   
df.mm.trans1:exp5	-0.116643831204124	0.128651493240689	-0.906665194984563	0.364870862166325	   
df.mm.trans2:exp5	-0.0760781926127088	0.0996530181571806	-0.763430892707256	0.445443066773262	   
df.mm.trans1:exp6	-0.0494056508676763	0.128651493240689	-0.384027030104076	0.70106568806193	   
df.mm.trans2:exp6	0.000906539032651758	0.0996530181571806	0.00909695510899522	0.99274416425229	   
df.mm.trans1:exp7	-0.167610651328666	0.128651493240689	-1.30282709595209	0.193027886987107	   
df.mm.trans2:exp7	0.0473448936255553	0.0996530181571805	0.475097438101466	0.634853847126175	   
df.mm.trans1:exp8	-0.111496194434817	0.128651493240689	-0.8666529367539	0.386405363485835	   
df.mm.trans2:exp8	0.0900450788159943	0.0996530181571805	0.90358606774928	0.36650086605094	   
df.mm.trans1:probe2	0.00364570044388275	0.0842221722920793	0.0432867063941263	0.965484342982614	   
df.mm.trans1:probe3	0.153268092608392	0.0842221722920793	1.81980692776321	0.069181158609469	.  
df.mm.trans1:probe4	0.0291880511824115	0.0842221722920793	0.346560179915432	0.729017567514184	   
df.mm.trans1:probe5	0.146046936773705	0.0842221722920793	1.73406755963524	0.083311064934458	.  
df.mm.trans1:probe6	0.0966438743442724	0.0842221722920793	1.14748731496874	0.251540886195304	   
df.mm.trans1:probe7	0.0245407557580125	0.0842221722920793	0.291381177784231	0.770839210943195	   
df.mm.trans1:probe8	0.135700353638426	0.0842221722920793	1.61121887438170	0.107546675752353	   
df.mm.trans1:probe9	0.102066941440752	0.0842221722920793	1.21187733185969	0.225935478217851	   
df.mm.trans1:probe10	0.000211050184482153	0.0842221722920793	0.00250587438840023	0.998001260333174	   
df.mm.trans1:probe11	0.0369152851638444	0.0842221722920793	0.438308395036684	0.661287133050695	   
df.mm.trans1:probe12	0.0921388645508995	0.0842221722920793	1.09399772106762	0.274302132043976	   
df.mm.trans1:probe13	0.118833184055528	0.0842221722920793	1.41094893211041	0.158668333814884	   
df.mm.trans1:probe14	0.0550951812761044	0.0842221722920793	0.654164809297917	0.513203265402407	   
df.mm.trans1:probe15	0.0524150230828803	0.0842221722920793	0.622342331673743	0.533903212715631	   
df.mm.trans1:probe16	0.0921024208241636	0.0842221722920793	1.09356501165460	0.274491833579722	   
df.mm.trans1:probe17	0.00147674130777551	0.0842221722920793	0.0175338781651728	0.986015302008784	   
df.mm.trans1:probe18	0.113934578581525	0.0842221722920793	1.35278603580070	0.176525772030727	   
df.mm.trans1:probe19	0.0311050997654824	0.0842221722920793	0.369321983973663	0.711990479638467	   
df.mm.trans1:probe20	0.0579519100268526	0.0842221722920793	0.688083772357208	0.491609709167435	   
df.mm.trans2:probe2	0.0429650561981014	0.0842221722920793	0.510139492117352	0.61010166324551	   
df.mm.trans2:probe3	0.0512268860059433	0.0842221722920793	0.608235154850797	0.543213006312664	   
df.mm.trans2:probe4	0.0704772481684668	0.0842221722920793	0.836801595713471	0.402966793157135	   
df.mm.trans2:probe5	0.0819610567124615	0.0842221722920793	0.973152965328699	0.330786500810728	   
df.mm.trans2:probe6	0.0757721002824373	0.0842221722920793	0.89966927022094	0.368580873056919	   
df.mm.trans3:probe2	0.236456953338888	0.0842221722920793	2.80753804970577	0.0051199322875512	** 
df.mm.trans3:probe3	-0.00544502233150811	0.0842221722920793	-0.0646506992555949	0.948469095501284	   
df.mm.trans3:probe4	0.0440236491180170	0.0842221722920793	0.522708544792037	0.601329229775375	   
df.mm.trans3:probe5	0.0579167070539535	0.0842221722920793	0.687665794858633	0.491872796120932	   
df.mm.trans3:probe6	0.0418087093104001	0.0842221722920793	0.496409771590895	0.619748684005463	   
df.mm.trans3:probe7	0.0724321208529604	0.0842221722920793	0.860012498867502	0.390052997693896	   
df.mm.trans3:probe8	0.131929289772471	0.0842221722920793	1.56644368320192	0.117660488890781