chr19.12100_chr19_37705183_37707751_+_1.R 

fitVsDatCorrelation=0.740242985161113
cont.fitVsDatCorrelation=0.314515420289289

fstatistic=8329.27336868527,50,646
cont.fstatistic=4172.06643107741,50,646

residuals=-0.669560392132144,-0.083552041629448,-0.0106292541157579,0.0747618498861182,0.873493216874926
cont.residuals=-0.462559604653671,-0.137191428219961,-0.0294350000009887,0.0881967314682717,1.37756484015217

predictedValues:
Include	Exclude	Both
chr19.12100_chr19_37705183_37707751_+_1.R.tl.Lung	44.0849325482277	53.665920123738	51.8586531854116
chr19.12100_chr19_37705183_37707751_+_1.R.tl.cerebhem	61.7440286987731	68.0698517610666	49.7944284398536
chr19.12100_chr19_37705183_37707751_+_1.R.tl.cortex	93.2600628734807	53.8940604783264	51.0274047257964
chr19.12100_chr19_37705183_37707751_+_1.R.tl.heart	46.9783299705725	52.588774302059	54.721213678647
chr19.12100_chr19_37705183_37707751_+_1.R.tl.kidney	43.915425478185	52.8776501225056	51.1737820905948
chr19.12100_chr19_37705183_37707751_+_1.R.tl.liver	49.0409531628854	51.9663968423512	53.7574223789497
chr19.12100_chr19_37705183_37707751_+_1.R.tl.stomach	48.2345361659549	56.2044935695641	54.4781059112088
chr19.12100_chr19_37705183_37707751_+_1.R.tl.testicle	48.3423415147248	55.8979130742767	52.2203783623669


diffExp=-9.58098757551026,-6.32582306229343,39.3660023951542,-5.61044433148651,-8.96222464432065,-2.92544367946579,-7.9699574036092,-7.55557155955193
diffExpScore=8.35788477511272
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=-1,0,1,0,-1,0,0,0
diffExp1.2Score=1.5

cont.predictedValues:
Include	Exclude	Both
Lung	49.8478727682568	46.9167503833702	54.0555182691022
cerebhem	52.7539193038225	52.2492537602305	50.2564780235091
cortex	54.6729477120085	46.5080033789558	51.3259465188911
heart	59.2002916098591	52.0868275764996	52.764366841899
kidney	53.1376333990136	50.4579262472665	54.2629859773171
liver	53.6830641982602	43.7087068885003	50.9171650963833
stomach	50.1595408728388	47.1917431965837	57.9807515701079
testicle	52.0147852819938	46.359960415446	56.4584314746702
cont.diffExp=2.9311223848866,0.504665543592019,8.16494433305268,7.11346403335948,2.67970715174712,9.97435730975994,2.96779767625514,5.65482486654783
cont.diffExpScore=0.975604331511941

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

tran.correlation=0.180245469905280
cont.tran.correlation=0.416581326285739

tran.covariance=0.00565680264932107
cont.tran.covariance=0.00136364932920214

tran.mean=55.0478544179182
cont.tran.mean=50.6843266870566

weightedLogRatios:
wLogRatio
Lung	-0.76391598127409
cerebhem	-0.406902109238669
cortex	2.33672479745415
heart	-0.44067093885685
kidney	-0.719668971871207
liver	-0.227225186238635
stomach	-0.604427448842662
testicle	-0.573749201564849

cont.weightedLogRatios:
wLogRatio
Lung	0.235051834620111
cerebhem	0.0380733398068868
cortex	0.634119237811006
heart	0.514223433034314
kidney	0.204240373565351
liver	0.797601246236982
stomach	0.236927912699380
testicle	0.448165586332124

varWeightedLogRatios=1.06039196319030
cont.varWeightedLogRatios=0.0643328391970498

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98748636290754	0.0794408475646363	50.1944086090365	4.06657626856226e-225	***
df.mm.trans1	-0.200697711197828	0.0626962395728349	-3.20111242022219	0.00143610763673388	** 
df.mm.trans2	0.000413222155125507	0.0626962395728348	0.00659086028031175	0.994743327179417	   
df.mm.exp2	0.615254165851248	0.0830204433019247	7.4108754588761	3.94497145541589e-13	***
df.mm.exp3	0.769674991812768	0.0830204433019247	9.27090920261232	2.71973965295933e-19	***
df.mm.exp4	-0.0104367556879889	0.0830204433019247	-0.125713080693065	0.899998160108498	   
df.mm.exp5	-0.00535535412979313	0.0830204433019247	-0.0645064506620019	0.948586926584994	   
df.mm.exp6	0.0383968603481456	0.0830204433019247	0.462498859570115	0.643879357497744	   
df.mm.exp7	0.0868986839397918	0.0830204433019247	1.04671428486310	0.295622848781236	   
df.mm.exp8	0.125987635622813	0.0830204433019247	1.51754954095617	0.129617194100256	   
df.mm.trans1:exp2	-0.278374954752224	0.0629533680868439	-4.42192313472739	1.14763451397636e-05	***
df.mm.trans2:exp2	-0.377497921618078	0.0629533680868439	-5.99646902286977	3.35581317406703e-09	***
df.mm.trans1:exp3	-0.0204010847574291	0.0629533680868439	-0.32406661275511	0.745992498444342	   
df.mm.trans2:exp3	-0.765432880658853	0.0629533680868439	-12.1587280223505	8.93398638542843e-31	***
df.mm.trans1:exp4	0.0740051281204913	0.0629533680868439	1.17555470611837	0.240205932544043	   
df.mm.trans2:exp4	-0.00983872913731068	0.0629533680868439	-0.156285984949021	0.875856429792992	   
df.mm.trans1:exp5	0.00150293162869510	0.0629533680868439	0.0238737286720197	0.98096070216352	   
df.mm.trans2:exp5	-0.00944205472135185	0.0629533680868439	-0.149984901654929	0.880823342361484	   
df.mm.trans1:exp6	0.0681408090294755	0.0629533680868439	1.08240132498512	0.279478099094170	   
df.mm.trans2:exp6	-0.0705777307053159	0.0629533680868439	-1.12111127410934	0.262657079753486	   
df.mm.trans1:exp7	0.00305854000743472	0.0629533680868439	0.0485842155929685	0.961265665777734	   
df.mm.trans2:exp7	-0.0406801389645055	0.0629533680868439	-0.646194797844452	0.518382696507781	   
df.mm.trans1:exp8	-0.0337978815682617	0.0629533680868439	-0.536871697184457	0.591541090711478	   
df.mm.trans2:exp8	-0.0852387548376544	0.0629533680868439	-1.35399832333145	0.176210381297917	   
df.mm.trans1:probe2	-0.043747628962942	0.0468716395001053	-0.933349663666953	0.35098817731571	   
df.mm.trans1:probe3	-0.023186842829452	0.0468716395001053	-0.494688111547707	0.620988487053775	   
df.mm.trans1:probe4	-0.0655330063208976	0.0468716395001053	-1.39813770159993	0.162551547083148	   
df.mm.trans1:probe5	0.0796161735705159	0.0468716395001053	1.69860014327719	0.0898760619638916	.  
df.mm.trans1:probe6	0.0374276606907174	0.0468716395001053	0.798514007401711	0.424865724957077	   
df.mm.trans2:probe2	0.0164739276771184	0.0468716395001053	0.351468987490429	0.72535113930448	   
df.mm.trans2:probe3	-0.00597093272690544	0.0468716395001053	-0.127389030778239	0.898672140242153	   
df.mm.trans2:probe4	-0.0885364049953236	0.0468716395001053	-1.88891205726065	0.0593512913302679	.  
df.mm.trans2:probe5	0.0180162449244965	0.0468716395001053	0.384374114424908	0.700827677280102	   
df.mm.trans2:probe6	-0.0577754852393528	0.0468716395001053	-1.23263205331708	0.218161471744904	   
df.mm.trans3:probe2	0.08357485998823	0.0468716395001053	1.78305817504084	0.0750462794843346	.  
df.mm.trans3:probe3	0.0742630166866638	0.0468716395001053	1.58439127537873	0.113594092831568	   
df.mm.trans3:probe4	0.170311467230468	0.0468716395001053	3.63357179409279	0.000301727289471889	***
df.mm.trans3:probe5	-0.0186684016562660	0.0468716395001053	-0.398287788849887	0.690549658184711	   
df.mm.trans3:probe6	0.54431985739326	0.0468716395001053	11.6129895006561	1.89906037243842e-28	***
df.mm.trans3:probe7	-0.0283510544951576	0.0468716395001053	-0.604865859132021	0.54548052836083	   
df.mm.trans3:probe8	0.312797112279449	0.0468716395001053	6.67348348842685	5.3791167560189e-11	***
df.mm.trans3:probe9	0.169620986190182	0.0468716395001053	3.61884047580203	0.000319065583221157	***
df.mm.trans3:probe10	0.0511245346208844	0.0468716395001053	1.09073493409100	0.275796197086851	   
df.mm.trans3:probe11	-0.00508704612825275	0.0468716395001053	-0.108531431426488	0.91360785034744	   
df.mm.trans3:probe12	0.183850666808668	0.0468716395001053	3.92242876010888	9.70445281539289e-05	***
df.mm.trans3:probe13	0.279861330554409	0.0468716395001053	5.97080310266895	3.89641845580494e-09	***
df.mm.trans3:probe14	0.394425677194397	0.0468716395001053	8.41501772502561	2.52632912523082e-16	***
df.mm.trans3:probe15	0.340195779271902	0.0468716395001053	7.2580302908145	1.131153090072e-12	***
df.mm.trans3:probe16	0.150880375002243	0.0468716395001053	3.21901210649788	0.00135073897525766	** 
df.mm.trans3:probe17	0.039319746097174	0.0468716395001053	0.838881390037266	0.401846176689891	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.72249743281582	0.112159733354103	33.1892500231202	9.97052890193949e-142	***
df.mm.trans1	0.188935266218432	0.0885186113739864	2.13441290239169	0.0331848071178665	*  
df.mm.trans2	0.102100749774432	0.0885186113739863	1.15343822264746	0.249157139008432	   
df.mm.exp2	0.237185272260255	0.117213638438425	2.02352964569779	0.0434297981923368	*  
df.mm.exp3	0.135458087722473	0.117213638438425	1.15565124952275	0.248251077260503	   
df.mm.exp4	0.300663511365275	0.117213638438425	2.56508982547471	0.0105395752022072	*  
df.mm.exp5	0.132843961852407	0.117213638438425	1.13334901656682	0.257488149367691	   
df.mm.exp6	0.0631058487512806	0.117213638438425	0.538383157386856	0.590498003469795	   
df.mm.exp7	-0.0580223489968705	0.117213638438425	-0.495013632968581	0.620758816725393	   
df.mm.exp8	-0.0128794171841602	0.117213638438425	-0.109879851489518	0.912538786830306	   
df.mm.trans1:exp2	-0.180523025910147	0.088881642062267	-2.03104962646482	0.0426589168371774	*  
df.mm.trans2:exp2	-0.129534426338509	0.088881642062267	-1.45738111192593	0.145497129801026	   
df.mm.trans1:exp3	-0.0430648808401928	0.088881642062267	-0.484519410769022	0.628181525217385	   
df.mm.trans2:exp3	-0.14420843698323	0.088881642062267	-1.62247719143401	0.105189102139180	   
df.mm.trans1:exp4	-0.128712866489753	0.088881642062267	-1.44813781005061	0.148063670413465	   
df.mm.trans2:exp4	-0.196126186918812	0.088881642062267	-2.20659949983162	0.027693094777857	*  
df.mm.trans1:exp5	-0.0689343805656086	0.088881642062267	-0.775575011511554	0.438283782088754	   
df.mm.trans2:exp5	-0.0600788791669457	0.088881642062267	-0.675942498056649	0.499319060820494	   
df.mm.trans1:exp6	0.0110159021184311	0.088881642062267	0.123939003182612	0.901402123681907	   
df.mm.trans2:exp6	-0.133933286916401	0.088881642062267	-1.50687232828769	0.132332218060041	   
df.mm.trans1:exp7	0.064255269182309	0.088881642062267	0.722930716528553	0.469984100713115	   
df.mm.trans2:exp7	0.0638665312898426	0.088881642062267	0.718557058667978	0.472673687471375	   
df.mm.trans1:exp8	0.0554316048523216	0.088881642062267	0.623656399298838	0.533073404462135	   
df.mm.trans2:exp8	0.00094081908354767	0.088881642062267	0.0105850776574151	0.991557755671827	   
df.mm.trans1:probe2	0.0258037072290132	0.0661764161557322	0.389923007741755	0.696722003944912	   
df.mm.trans1:probe3	0.0552059118977001	0.0661764161557322	0.834223354854766	0.404463502729558	   
df.mm.trans1:probe4	-0.077087487408135	0.0661764161557323	-1.16487854565478	0.244498131236802	   
df.mm.trans1:probe5	-0.0726613062588928	0.0661764161557322	-1.09799397549574	0.272616196485579	   
df.mm.trans1:probe6	0.0122310221764342	0.0661764161557322	0.184824487135282	0.853424709170804	   
df.mm.trans2:probe2	0.0432796589529153	0.0661764161557323	0.654004273230296	0.513341792862091	   
df.mm.trans2:probe3	0.0852307736682209	0.0661764161557323	1.28793275035699	0.198230528301209	   
df.mm.trans2:probe4	0.119063595115344	0.0661764161557323	1.79918469496978	0.0724560524608723	.  
df.mm.trans2:probe5	0.196557268268136	0.0661764161557322	2.97020116359248	0.00308648300587754	** 
df.mm.trans2:probe6	0.102730046987185	0.0661764161557323	1.55236643134967	0.121064318734729	   
df.mm.trans3:probe2	-0.0629836451155497	0.0661764161557323	-0.951753642375117	0.34157789271525	   
df.mm.trans3:probe3	-0.00610666991504496	0.0661764161557322	-0.0922786435075934	0.926505264631786	   
df.mm.trans3:probe4	-0.00625729245282684	0.0661764161557323	-0.0945547192840062	0.9246978378202	   
df.mm.trans3:probe5	-0.0876344997934803	0.0661764161557323	-1.32425575279343	0.185886451849028	   
df.mm.trans3:probe6	-0.072544786935093	0.0661764161557322	-1.09623323759894	0.273385211143256	   
df.mm.trans3:probe7	-0.0137916375232265	0.0661764161557322	-0.208407138439937	0.834976774137437	   
df.mm.trans3:probe8	-0.0190601258747356	0.0661764161557323	-0.288019916791528	0.773423872863441	   
df.mm.trans3:probe9	0.0892695058600534	0.0661764161557322	1.34896253145496	0.177821647884044	   
df.mm.trans3:probe10	-0.0253155543709993	0.0661764161557323	-0.382546469597636	0.702181894191359	   
df.mm.trans3:probe11	0.00784376003793076	0.0661764161557323	0.118528026955587	0.905686135440392	   
df.mm.trans3:probe12	-0.00419731692232379	0.0661764161557323	-0.0634261745520683	0.9494467650513	   
df.mm.trans3:probe13	-0.0327057316915182	0.0661764161557322	-0.494220352074555	0.621318577699117	   
df.mm.trans3:probe14	0.0514288818986151	0.0661764161557323	0.777148187922236	0.437355830840497	   
df.mm.trans3:probe15	-0.0526988641031466	0.0661764161557323	-0.796339045909208	0.426127541591181	   
df.mm.trans3:probe16	-0.0898603181504252	0.0661764161557323	-1.35789036896404	0.174972564441061	   
df.mm.trans3:probe17	-0.0752565480365048	0.0661764161557323	-1.13721099461482	0.255871725175464