chr17.10431_chr17_56298305_56298618_-_0.R 

fitVsDatCorrelation=0.928958536983709
cont.fitVsDatCorrelation=0.301819324696549

fstatistic=7594.22058618987,43,485
cont.fstatistic=1135.40558895883,43,485

residuals=-0.68613428298676,-0.0686576760771814,-0.0018037089630964,0.063550724022622,1.49716171730489
cont.residuals=-0.75397238742045,-0.31565054402493,-0.129538408180606,0.353503426874870,1.75302426920805

predictedValues:
Include	Exclude	Both
chr17.10431_chr17_56298305_56298618_-_0.R.tl.Lung	45.4665925313496	42.0271754440306	89.8175176620898
chr17.10431_chr17_56298305_56298618_-_0.R.tl.cerebhem	43.6809204644029	43.4189676628339	98.5809029181022
chr17.10431_chr17_56298305_56298618_-_0.R.tl.cortex	44.6803563597711	43.4229041748673	76.7503923174909
chr17.10431_chr17_56298305_56298618_-_0.R.tl.heart	47.4077375798635	42.965196412444	85.4114509147518
chr17.10431_chr17_56298305_56298618_-_0.R.tl.kidney	43.3899622567492	40.2200246299551	79.0551018188853
chr17.10431_chr17_56298305_56298618_-_0.R.tl.liver	45.2190923882372	46.2332481794928	77.955770614338
chr17.10431_chr17_56298305_56298618_-_0.R.tl.stomach	45.864133480917	41.8131899313065	97.8350763227804
chr17.10431_chr17_56298305_56298618_-_0.R.tl.testicle	45.0178589552455	44.24447035202	112.679898794640


diffExp=3.43941708731894,0.261952801569073,1.25745218490376,4.44254116741944,3.16993762679402,-1.01415579125565,4.05094354961052,0.773388603225477
diffExpScore=1.05916134566290
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	66.9633919827356	63.6049731262559	58.9940893617127
cerebhem	54.8839671829857	64.2138285791529	65.7570683474944
cortex	65.1668963346605	46.2906858344444	59.9381945605655
heart	60.485400195496	61.8688361213343	74.8597152527695
kidney	68.5286564285863	60.27588518203	63.3069338585353
liver	67.0742679053524	63.3641273586095	63.4185112743285
stomach	63.128850021152	52.9482642850088	61.1978152237421
testicle	55.1952078100364	51.6860975410779	61.6155984631052
cont.diffExp=3.35841885647972,-9.32986139616715,18.8762105002161,-1.38343592583828,8.25277124655632,3.71014054674297,10.1805857361432,3.5091102689585
cont.diffExpScore=1.5350926504658

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,1,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,1,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,1,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.184676143756056
cont.tran.correlation=0.0782287910370082

tran.covariance=0.000232333206813474
cont.tran.covariance=0.00068346310544228

tran.mean=44.0669894252179
cont.tran.mean=60.3549584930574

weightedLogRatios:
wLogRatio
Lung	0.29715494723852
cerebhem	0.0227000906744777
cortex	0.108057460999665
heart	0.374844607131031
kidney	0.283143711416644
liver	-0.0847847142555536
stomach	0.349491446335598
testicle	0.0658219164293752

cont.weightedLogRatios:
wLogRatio
Lung	0.214998122266059
cerebhem	-0.641133071207002
cortex	1.37007703887697
heart	-0.0930298117372752
kidney	0.534207515053938
liver	0.237702008416948
stomach	0.71351787188443
testicle	0.261306195418509

varWeightedLogRatios=0.0291504002828312
cont.varWeightedLogRatios=0.345548504791053

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.85455679195003	0.0831605536954853	34.3258512010726	6.96014149025513e-132	***
df.mm.trans1	0.96603942762544	0.0665743721596197	14.5106802555982	6.9590464814885e-40	***
df.mm.trans2	0.878835756199684	0.0665743721596196	13.2008117792320	3.28256037347245e-34	***
df.mm.exp2	-0.100584006961147	0.0891476198222639	-1.12828595044584	0.259756988836382	   
df.mm.exp3	0.172448260616939	0.0891476198222639	1.93441239329501	0.0536437196235676	.  
df.mm.exp4	0.114181441054019	0.0891476198222639	1.28081311965105	0.200871444737640	   
df.mm.exp5	0.0369337868085705	0.0891476198222639	0.414299191410903	0.678838172786036	   
df.mm.exp6	0.231562730837937	0.0891476198222639	2.59752006054240	0.00967534624178514	** 
df.mm.exp7	-0.0819021611846379	0.0891476198222639	-0.918725158876126	0.358696017433039	   
df.mm.exp8	-0.185275607434965	0.089147619822264	-2.07830122446739	0.0382068846483164	*  
df.mm.trans1:exp2	0.0605175848810051	0.0699331466252422	0.865363390629436	0.387267204514633	   
df.mm.trans2:exp2	0.133163952085949	0.0699331466252422	1.90416073796233	0.0574807213254585	.  
df.mm.trans1:exp3	-0.189892136822516	0.0699331466252422	-2.71533809053538	0.00685700690004707	** 
df.mm.trans2:exp3	-0.139777656183376	0.0699331466252422	-1.99873254570421	0.0461949390213439	*  
df.mm.trans1:exp4	-0.0723738117914513	0.0699331466252422	-1.03489997639157	0.301231356479453	   
df.mm.trans2:exp4	-0.0921074822756315	0.0699331466252422	-1.31707904935577	0.188434058879539	   
df.mm.trans1:exp5	-0.083683483057034	0.0699331466252422	-1.19662116028437	0.232038859256735	   
df.mm.trans2:exp5	-0.0808852335120232	0.0699331466252422	-1.15660795224146	0.248001941116951	   
df.mm.trans1:exp6	-0.237021161412570	0.0699331466252422	-3.38925349209179	0.000757964759916294	***
df.mm.trans2:exp6	-0.136179977443353	0.0699331466252422	-1.94728800311410	0.0520769665812136	.  
df.mm.trans1:exp7	0.0906077409523446	0.0699331466252422	1.29563369195860	0.195718006279313	   
df.mm.trans2:exp7	0.0767975561076545	0.0699331466252422	1.09815673702196	0.272680924786688	   
df.mm.trans1:exp8	0.175357057949296	0.0699331466252422	2.50749560704024	0.0124847318042795	*  
df.mm.trans2:exp8	0.236689563855563	0.0699331466252422	3.38451185564315	0.00077086751147904	***
df.mm.trans1:probe2	-0.00453205453548527	0.0478799524049518	-0.094654533011118	0.924628325802895	   
df.mm.trans1:probe3	-0.0608951266139532	0.0478799524049518	-1.27182930548726	0.204043279328423	   
df.mm.trans1:probe4	-0.0277420640259189	0.0478799524049518	-0.579408763636319	0.562582414209412	   
df.mm.trans1:probe5	-0.00381262759599489	0.0478799524049518	-0.0796288927722615	0.936565259704089	   
df.mm.trans1:probe6	0.0390875787279125	0.0478799524049518	0.81636628201556	0.414691455616995	   
df.mm.trans2:probe2	-0.0424803798808193	0.0478799524049518	-0.887226861078206	0.375396519760759	   
df.mm.trans2:probe3	0.0373820736251843	0.0478799524049518	0.780745839281959	0.435332890192223	   
df.mm.trans2:probe4	0.00898377392667104	0.0478799524049518	0.187631220906183	0.851244221326621	   
df.mm.trans2:probe5	0.00771663520011365	0.0478799524049518	0.161166308914618	0.872029526917514	   
df.mm.trans2:probe6	0.0671802215902765	0.0478799524049518	1.40309708376670	0.161227744816688	   
df.mm.trans3:probe2	-0.0848881712465699	0.0478799524049518	-1.77293766979164	0.076866609328183	.  
df.mm.trans3:probe3	-0.27225578074468	0.0478799524049518	-5.68621661195559	2.24655851360760e-08	***
df.mm.trans3:probe4	-0.0605408697325382	0.0478799524049518	-1.26443044931425	0.206682855300302	   
df.mm.trans3:probe5	0.135636483384305	0.0478799524049518	2.83284499193188	0.00480567894172873	** 
df.mm.trans3:probe6	-0.767459124194308	0.0478799524049518	-16.0288196968829	1.04184064618846e-46	***
df.mm.trans3:probe7	-0.333513016924332	0.0478799524049518	-6.96560878138718	1.06990778136675e-11	***
df.mm.trans3:probe8	-0.442295203500712	0.0478799524049518	-9.23758653224913	7.90007380989348e-19	***
df.mm.trans3:probe9	-0.138768676349793	0.0478799524049518	-2.89826262098458	0.00392228917803211	** 
df.mm.trans3:probe10	-0.0524350937255338	0.0478799524049518	-1.09513671363029	0.274000284858710	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.35431629411397	0.214170300818033	20.3310929549171	6.42387040697856e-67	***
df.mm.trans1	-0.107864025298655	0.171454525957196	-0.629111565859646	0.529572217603095	   
df.mm.trans2	-0.211694513328761	0.171454525957196	-1.23469772609917	0.217540874779820	   
df.mm.exp2	-0.297927797878768	0.229589290908995	-1.29765546423880	0.195022595361029	   
df.mm.exp3	-0.360822038886179	0.229589290908995	-1.57159786267733	0.116695957519002	   
df.mm.exp4	-0.367597768491363	0.229589290908995	-1.60111025664987	0.110003612635502	   
df.mm.exp5	-0.101211237555909	0.229589290908995	-0.440836056225406	0.659528119763115	   
df.mm.exp6	-0.0744579105712543	0.229589290908995	-0.324309162141052	0.745843803614654	   
df.mm.exp7	-0.279018806776794	0.229589290908995	-1.21529538974617	0.22484471323622	   
df.mm.exp8	-0.444250571088479	0.229589290908995	-1.93497949895482	0.0535738901922378	.  
df.mm.trans1:exp2	0.0990029852543393	0.180104657608753	0.549696973797349	0.58278026060186	   
df.mm.trans2:exp2	0.307454722710791	0.180104657608753	1.70708923796232	0.0884455033318705	.  
df.mm.trans1:exp3	0.333627572205226	0.180104657608753	1.85240946366848	0.0645744122898857	.  
df.mm.trans2:exp3	0.0430711487604284	0.180104657608753	0.239145113359551	0.811094083350915	   
df.mm.trans1:exp4	0.265853703574219	0.180104657608753	1.47610676538827	0.140564169696125	   
df.mm.trans2:exp4	0.339922705078775	0.180104657608753	1.88736210152432	0.0597084871941508	.  
df.mm.trans1:exp5	0.124317155617523	0.180104657608753	0.690249531955918	0.490367447214954	   
df.mm.trans2:exp5	0.0474516861414168	0.180104657608753	0.263467290471175	0.792302328953482	   
df.mm.trans1:exp6	0.076112310506298	0.180104657608753	0.422600456405959	0.672774086265875	   
df.mm.trans2:exp6	0.0706641362498569	0.180104657608753	0.392350409967535	0.694971683265138	   
df.mm.trans1:exp7	0.220050601197472	0.180104657608753	1.22179295149321	0.222379442035454	   
df.mm.trans2:exp7	0.095642436924535	0.180104657608753	0.531038109698984	0.595635464429461	   
df.mm.trans1:exp8	0.250980623818217	0.180104657608753	1.39352655922664	0.164098860154520	   
df.mm.trans2:exp8	0.236747748980779	0.180104657608753	1.31450098028599	0.189298944614969	   
df.mm.trans1:probe2	-0.0539761998014331	0.123309229605073	-0.437730411375569	0.661776589000291	   
df.mm.trans1:probe3	-0.0664476486695272	0.123309229605073	-0.538870033349016	0.590223788401451	   
df.mm.trans1:probe4	-0.235019485518189	0.123309229605073	-1.90593588388228	0.0572494122157294	.  
df.mm.trans1:probe5	-0.253810011258945	0.123309229605073	-2.05832127953302	0.0400929780893132	*  
df.mm.trans1:probe6	-0.067645647811452	0.123309229605073	-0.54858543864156	0.583542414534507	   
df.mm.trans2:probe2	0.155649116910739	0.123309229605073	1.26226655870970	0.207459512571865	   
df.mm.trans2:probe3	-0.147162330234248	0.123309229605073	-1.19344132394282	0.233280026634826	   
df.mm.trans2:probe4	0.012703279546645	0.123309229605073	0.103019697611689	0.917989919678499	   
df.mm.trans2:probe5	0.0590297688015597	0.123309229605073	0.478713304678136	0.632358336882545	   
df.mm.trans2:probe6	0.0808982499824107	0.123309229605073	0.656059974111479	0.512096649589912	   
df.mm.trans3:probe2	0.111433215929394	0.123309229605073	0.903689174656956	0.366608870064617	   
df.mm.trans3:probe3	-0.0719532213533932	0.123309229605073	-0.583518537775642	0.559815531629402	   
df.mm.trans3:probe4	0.00236769744191067	0.123309229605073	0.0192012994444437	0.984688417525734	   
df.mm.trans3:probe5	0.152455227748007	0.123309229605073	1.23636509802454	0.216921286198057	   
df.mm.trans3:probe6	-0.00306523816217719	0.123309229605073	-0.0248581405625056	0.980178340086027	   
df.mm.trans3:probe7	-0.0633186013006145	0.123309229605073	-0.513494419707326	0.607839350056377	   
df.mm.trans3:probe8	0.124961881879826	0.123309229605073	1.01340250263541	0.311373231282789	   
df.mm.trans3:probe9	0.0878282390747274	0.123309229605073	0.712260058359119	0.476646241603524	   
df.mm.trans3:probe10	0.0860858296505943	0.123309229605073	0.698129652794883	0.485430690596607