chr17.10034_chr17_24076272_24076691_-_0.R 

fitVsDatCorrelation=0.772165897491924
cont.fitVsDatCorrelation=0.229585214370185

fstatistic=9236.32850274857,58,830
cont.fstatistic=3928.55177449979,58,830

residuals=-0.490774935365798,-0.108050389502522,-0.0139509887568690,0.0920528516028519,1.17038129570264
cont.residuals=-0.597517180741994,-0.188149569724689,-0.0227740017263244,0.172963018023898,1.70484520999325

predictedValues:
Include	Exclude	Both
chr17.10034_chr17_24076272_24076691_-_0.R.tl.Lung	81.1050122574894	107.344147690465	78.41066445067
chr17.10034_chr17_24076272_24076691_-_0.R.tl.cerebhem	69.8409411947133	110.969846191997	75.1346750168222
chr17.10034_chr17_24076272_24076691_-_0.R.tl.cortex	78.3427779316049	95.1713212695643	71.5223456192621
chr17.10034_chr17_24076272_24076691_-_0.R.tl.heart	78.9510571716359	99.3405562290928	71.1536187824162
chr17.10034_chr17_24076272_24076691_-_0.R.tl.kidney	84.9780560936797	109.316057299970	83.5739748546064
chr17.10034_chr17_24076272_24076691_-_0.R.tl.liver	82.6614618631912	106.864352658410	80.2483983537862
chr17.10034_chr17_24076272_24076691_-_0.R.tl.stomach	86.01327846915	110.172619425949	76.8353857674878
chr17.10034_chr17_24076272_24076691_-_0.R.tl.testicle	75.7008679473145	107.694232864765	75.2594654728066


diffExp=-26.2391354329761,-41.1289049972837,-16.8285433379594,-20.3894990574569,-24.3380012062907,-24.2028907952184,-24.1593409567988,-31.9933649174505
diffExpScore=0.995244428769036
diffExp1.5=0,-1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,-1,0,0,0,0,0,-1
diffExp1.4Score=0.666666666666667
diffExp1.3=-1,-1,0,0,0,0,0,-1
diffExp1.3Score=0.75
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	75.7591602707865	83.1299319393254	80.0624919679658
cerebhem	74.4234496142963	82.3533622459259	77.2888879120984
cortex	79.3186758156604	83.5432130848422	79.8073902894958
heart	72.6706361057118	86.749488218385	79.4633520490739
kidney	75.7069934753537	79.767655985299	77.3034918818537
liver	79.9249352093118	76.8703029618862	77.979485395483
stomach	77.8255970099842	77.0855896983385	79.6964941788573
testicle	72.0197505391257	82.3643724469427	80.5666710718833
cont.diffExp=-7.37077166853884,-7.92991263162959,-4.22453726918184,-14.0788521126731,-4.06066250994539,3.05463224742563,0.740007311645684,-10.3446219078170
cont.diffExpScore=1.14573305619959

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=0.0590652507718893
cont.tran.correlation=-0.604886339333743

tran.covariance=0.000141508422950000
cont.tran.covariance=-0.000968542181264353

tran.mean=92.779161659937
cont.tran.mean=78.7195696638234

weightedLogRatios:
wLogRatio
Lung	-1.27138906293199
cerebhem	-2.07336405860706
cortex	-0.867534465698667
heart	-1.030019407437
kidney	-1.15053200726273
liver	-1.16671360193887
stomach	-1.13333696197584
testicle	-1.58735145582034

cont.weightedLogRatios:
wLogRatio
Lung	-0.40610439761568
cerebhem	-0.441482204031196
cortex	-0.228287648936492
heart	-0.774664240462076
kidney	-0.227433369429471
liver	0.169964107919478
stomach	0.0415570941289269
testicle	-0.583025694438262

varWeightedLogRatios=0.143926729591368
cont.varWeightedLogRatios=0.097759305985682

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.03287900572563	0.0824727684630687	61.0247370073354	0	***
df.mm.trans1	-0.570684788043832	0.0681310100664183	-8.37628544604717	2.31582381132213e-16	***
df.mm.trans2	-0.343503970761856	0.0627842295856126	-5.4711823817708	5.92116776679208e-08	***
df.mm.exp2	-0.0736280988350749	0.0805963302238166	-0.913541579754427	0.361223063498013	   
df.mm.exp3	-0.0630623152767607	0.0805963302238166	-0.782446484866447	0.434175391612008	   
df.mm.exp4	-0.0072839696915773	0.0805963302238166	-0.0903759472838238	0.928010274351366	   
df.mm.exp5	0.00107936317693528	0.0805963302238166	0.013392212448605	0.98931809816156	   
df.mm.exp6	-0.0086378364270422	0.0805963302238166	-0.107174066152328	0.914676788625016	   
df.mm.exp7	0.105059977153650	0.0805963302238166	1.30353301275501	0.192754300925475	   
df.mm.exp8	-0.0246808472050078	0.0805963302238166	-0.306227927952413	0.759507906235707	   
df.mm.trans1:exp2	-0.0758962759155127	0.0676449324435057	-1.12198021601837	0.262195325882258	   
df.mm.trans2:exp2	0.106846600633319	0.0545517544942697	1.95862812523367	0.050490881759502	.  
df.mm.trans1:exp3	0.0284113401913627	0.0676449324435057	0.420006926832113	0.674589094540125	   
df.mm.trans2:exp3	-0.0572990421593847	0.0545517544942697	-1.05036112386456	0.293857835894686	   
df.mm.trans1:exp4	-0.0196326619352330	0.0676449324435057	-0.290231082004988	0.771711954033821	   
df.mm.trans2:exp4	-0.0702021280799226	0.0545517544942697	-1.28689038016727	0.198491282465405	   
df.mm.trans1:exp5	0.0455689337464678	0.0676449324435057	0.67364889135672	0.500722105721268	   
df.mm.trans2:exp5	0.0171239248725578	0.0545517544942697	0.313902367234706	0.753674068080453	   
df.mm.trans1:exp6	0.0276465679762172	0.0676449324435057	0.408701243057734	0.682864346963072	   
df.mm.trans2:exp6	0.0041581277913623	0.0545517544942697	0.076223539094404	0.93925962442767	   
df.mm.trans1:exp7	-0.0463030546478342	0.0676449324435057	-0.684501454510353	0.493849654426246	   
df.mm.trans2:exp7	-0.079051580547199	0.0545517544942697	-1.44911160566802	0.147684116061389	   
df.mm.trans1:exp8	-0.0442742894320836	0.0676449324435057	-0.654510069458043	0.512964562148837	   
df.mm.trans2:exp8	0.0279368751074456	0.0545517544942697	0.51211689461574	0.608705436421175	   
df.mm.trans1:probe2	-0.119850688405630	0.05138001455194	-2.33263243404638	0.0199056256114637	*  
df.mm.trans1:probe3	-0.184720443665781	0.05138001455194	-3.59518083590746	0.000343299953629553	***
df.mm.trans1:probe4	-0.0350045668653234	0.05138001455194	-0.681287601231357	0.495879561448105	   
df.mm.trans1:probe5	-0.0878526766382992	0.05138001455194	-1.70986087498066	0.0876653510433278	.  
df.mm.trans1:probe6	-0.271848594080020	0.05138001455194	-5.29094038704892	1.55817957911217e-07	***
df.mm.trans1:probe7	-0.0219439521402552	0.05138001455194	-0.427091201347793	0.669423666733344	   
df.mm.trans1:probe8	-0.0971109306474426	0.05138001455194	-1.89005261081180	0.0590991183768412	.  
df.mm.trans1:probe9	-0.275630588168603	0.05138001455194	-5.36454865908939	1.05316072084442e-07	***
df.mm.trans1:probe10	-0.331397728134948	0.05138001455194	-6.44993449349724	1.89862150583123e-10	***
df.mm.trans1:probe11	-0.302325662517571	0.05138001455194	-5.8841100991116	5.80265094651608e-09	***
df.mm.trans2:probe2	-0.209878426808817	0.05138001455194	-4.08482614571179	4.83848206031431e-05	***
df.mm.trans2:probe3	0.026020328050842	0.05138001455194	0.506428973945464	0.612690031796906	   
df.mm.trans2:probe4	-0.138605696931045	0.05138001455194	-2.69765779826567	0.00712462123785794	** 
df.mm.trans2:probe5	-0.102356332983419	0.05138001455194	-1.99214293487494	0.04668258183413	*  
df.mm.trans2:probe6	0.078109393319866	0.05138001455194	1.52022910077040	0.128834240447111	   
df.mm.trans3:probe2	-0.0523422518161087	0.05138001455194	-1.01872785114134	0.308629034281268	   
df.mm.trans3:probe3	0.180638459754936	0.05138001455194	3.51573391580745	0.000462307199360441	***
df.mm.trans3:probe4	0.0383989399478713	0.05138001455194	0.747351675213207	0.455062975262325	   
df.mm.trans3:probe5	0.175905142076085	0.05138001455194	3.42361020350164	0.000648228065024745	***
df.mm.trans3:probe6	0.821916072299585	0.05138001455194	15.9968049730447	2.08666307653092e-50	***
df.mm.trans3:probe7	0.290282670991187	0.05138001455194	5.64971951687053	2.20844990661767e-08	***
df.mm.trans3:probe8	0.364724379844365	0.05138001455194	7.09856513325167	2.70735329498119e-12	***
df.mm.trans3:probe9	0.202708120688397	0.05138001455194	3.94527176483143	8.64491882238054e-05	***
df.mm.trans3:probe10	0.492073779479774	0.05138001455194	9.57714363786987	1.11486538598345e-20	***
df.mm.trans3:probe11	0.349814873559584	0.05138001455194	6.80838408883588	1.89252141892883e-11	***
df.mm.trans3:probe12	0.229964185153954	0.05138001455194	4.47575165478950	8.67720630096753e-06	***
df.mm.trans3:probe13	0.165094429893803	0.05138001455194	3.21320325292842	0.00136328417733101	** 
df.mm.trans3:probe14	0.314810306694261	0.05138001455194	6.12709648760452	1.38130632842040e-09	***
df.mm.trans3:probe15	0.261962196921285	0.05138001455194	5.09852321385522	4.24240206374823e-07	***
df.mm.trans3:probe16	-0.0142782482461463	0.05138001455194	-0.277894982526960	0.78116218271612	   
df.mm.trans3:probe17	0.149974093477022	0.05138001455194	2.9189188594996	0.00360743176246751	** 
df.mm.trans3:probe18	0.0733120135844888	0.05138001455194	1.42685856015821	0.153996727593250	   
df.mm.trans3:probe19	0.272458536968619	0.05138001455194	5.30281159599888	1.46325409819500e-07	***
df.mm.trans3:probe20	0.547976245571264	0.05138001455194	10.6651633003590	5.63104217999847e-25	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.32862321166539	0.126325388121845	34.2656632686557	4.73870897177728e-161	***
df.mm.trans1	-0.0171579934289295	0.104357795308250	-0.164415062413388	0.869444437329414	   
df.mm.trans2	0.096339402166851	0.0961680117951316	1.00178219730782	0.316740759275285	   
df.mm.exp2	0.00808345295430312	0.123451205609513	0.065478930840674	0.94780844865841	   
df.mm.exp3	0.0540648047307032	0.123451205609513	0.437944728557086	0.661540296315025	   
df.mm.exp4	0.00850926815795851	0.123451205609513	0.068928190016014	0.945063381470492	   
df.mm.exp5	-0.00690719282692004	0.123451205609513	-0.0559507928077115	0.9553944727921	   
df.mm.exp6	0.00160501030502084	0.123451205609513	0.0130011715729827	0.989629982481646	   
df.mm.exp7	-0.0439955647962583	0.123451205609513	-0.35638019555208	0.721646429985736	   
df.mm.exp8	-0.0661484033056938	0.123451205609513	-0.535826304644825	0.592222153812343	   
df.mm.trans1:exp2	-0.0258717425325941	0.103613259317570	-0.249695287099293	0.802884786528896	   
df.mm.trans2:exp2	-0.0174689972508733	0.0835581451628139	-0.209063966377362	0.834449601891499	   
df.mm.trans1:exp3	-0.00815056016891153	0.103613259317570	-0.0786632929278908	0.93731940320537	   
df.mm.trans2:exp3	-0.0491056135849224	0.0835581451628139	-0.587681948770399	0.556905640039764	   
df.mm.trans1:exp4	-0.0501312349663929	0.103613259317570	-0.483830306049372	0.628633928474529	   
df.mm.trans2:exp4	0.0341104223348478	0.083558145162814	0.408223785585274	0.683214674873027	   
df.mm.trans1:exp5	0.00621836834428389	0.103613259317570	0.0600151793818672	0.952157992052378	   
df.mm.trans2:exp5	-0.0343795271633277	0.083558145162814	-0.411444355261104	0.680852953648355	   
df.mm.trans1:exp6	0.0519235092515088	0.103613259317570	0.501128037024349	0.616413890041662	   
df.mm.trans2:exp6	-0.0798902145446563	0.083558145162814	-0.95610325467361	0.339298458709576	   
df.mm.trans1:exp7	0.0709065875341887	0.103613259317570	0.684338935008917	0.493952196573202	   
df.mm.trans2:exp7	-0.0314929049623728	0.083558145162814	-0.376898085770197	0.70634568083051	   
df.mm.trans1:exp8	0.0155294330027509	0.103613259317570	0.149878819612786	0.880896634538979	   
df.mm.trans2:exp8	0.0568965446872789	0.083558145162814	0.680921585518866	0.496111023834882	   
df.mm.trans1:probe2	-0.0135348441772067	0.0786999199970638	-0.171980405795987	0.863494856191986	   
df.mm.trans1:probe3	0.0290364632164764	0.0786999199970638	0.36895162304561	0.712257878659421	   
df.mm.trans1:probe4	0.0492722221073582	0.0786999199970638	0.626077156230864	0.531436463463689	   
df.mm.trans1:probe5	0.147691384895481	0.0786999199970639	1.87663958109476	0.0609181483105749	.  
df.mm.trans1:probe6	0.0165568503744371	0.0786999199970638	0.210379507057374	0.833423119706497	   
df.mm.trans1:probe7	0.0221479487946546	0.0786999199970638	0.281422761236363	0.77845630709506	   
df.mm.trans1:probe8	0.0704775046789692	0.0786999199970638	0.89552193549369	0.370767777891696	   
df.mm.trans1:probe9	-0.0077919177951245	0.0786999199970638	-0.0990079506486818	0.92115586380762	   
df.mm.trans1:probe10	0.0949370373537204	0.0786999199970638	1.20631682163416	0.228039083462165	   
df.mm.trans1:probe11	0.009655159785469	0.0786999199970638	0.122683222369594	0.902387649016078	   
df.mm.trans2:probe2	0.00653182478895321	0.0786999199970639	0.082996587406911	0.933874264714198	   
df.mm.trans2:probe3	-0.0263579598773961	0.0786999199970638	-0.334917238522981	0.7377721395668	   
df.mm.trans2:probe4	-0.00298321797477516	0.0786999199970639	-0.0379062389756744	0.969771553684216	   
df.mm.trans2:probe5	-0.0536921643345298	0.0786999199970638	-0.682239122181229	0.495278105310127	   
df.mm.trans2:probe6	-0.0420008930016765	0.0786999199970639	-0.53368406223594	0.593703026068049	   
df.mm.trans3:probe2	-0.0354424918195064	0.0786999199970638	-0.450349782068758	0.652575855494313	   
df.mm.trans3:probe3	-0.0626476592551553	0.0786999199970638	-0.796032057688148	0.426241010474202	   
df.mm.trans3:probe4	-0.0397581073672219	0.0786999199970638	-0.505186121773761	0.613562229852865	   
df.mm.trans3:probe5	-0.083741129229068	0.0786999199970638	-1.06405609093621	0.287612750340726	   
df.mm.trans3:probe6	0.0274721130523449	0.0786999199970638	0.34907421828853	0.727122128155825	   
df.mm.trans3:probe7	-0.121086227561863	0.0786999199970638	-1.53858132976985	0.124287662271688	   
df.mm.trans3:probe8	-0.0902255538249537	0.0786999199970638	-1.14645038811119	0.251939242999997	   
df.mm.trans3:probe9	-0.0265467235438235	0.0786999199970638	-0.337315762770965	0.735964160792232	   
df.mm.trans3:probe10	-0.0161297517411324	0.0786999199970638	-0.204952581168242	0.837659423300329	   
df.mm.trans3:probe11	0.132191929439664	0.0786999199970638	1.67969585540362	0.0933928347864687	.  
df.mm.trans3:probe12	0.0101462630408655	0.0786999199970638	0.128923422555500	0.89744947608653	   
df.mm.trans3:probe13	-0.0928469107318972	0.0786999199970638	-1.17975864188123	0.238434251056461	   
df.mm.trans3:probe14	-0.0307089861324922	0.0786999199970638	-0.390203524141294	0.696486172574071	   
df.mm.trans3:probe15	0.00263353069424100	0.0786999199970638	0.0334629399158125	0.97331346425374	   
df.mm.trans3:probe16	-0.0111142464307631	0.0786999199970638	-0.141223096938063	0.887727968060366	   
df.mm.trans3:probe17	0.101437859548831	0.0786999199970639	1.28891947479255	0.197785199773498	   
df.mm.trans3:probe18	0.0256998627322076	0.0786999199970638	0.326555131608348	0.744086710221418	   
df.mm.trans3:probe19	-0.119373664061660	0.0786999199970638	-1.51682065326260	0.129692728536159	   
df.mm.trans3:probe20	-0.0699029281768767	0.0786999199970638	-0.888221083064438	0.374679165578964