chr18.11279_chr18_38132773_38141845_-_2.R 

fitVsDatCorrelation=0.953285325759426
cont.fitVsDatCorrelation=0.240312467629782

fstatistic=8196.19674689967,52,692
cont.fstatistic=781.697281678721,52,692

residuals=-0.805714746050245,-0.0944693467737148,0.00408418154904306,0.103816979805122,0.93054865098666
cont.residuals=-1.18549757971162,-0.469349976819594,-0.160689922559839,0.41806994328685,1.64584704644836

predictedValues:
Include	Exclude	Both
chr18.11279_chr18_38132773_38141845_-_2.R.tl.Lung	82.2971620690997	52.6989355716161	70.1608284736572
chr18.11279_chr18_38132773_38141845_-_2.R.tl.cerebhem	86.7812689014929	65.1846485165259	75.4786144106274
chr18.11279_chr18_38132773_38141845_-_2.R.tl.cortex	96.8975396834552	53.3187458429202	101.882211264009
chr18.11279_chr18_38132773_38141845_-_2.R.tl.heart	93.9293867304918	54.3895209208674	89.3945496834254
chr18.11279_chr18_38132773_38141845_-_2.R.tl.kidney	87.028913876005	56.2472892372533	72.0826001844775
chr18.11279_chr18_38132773_38141845_-_2.R.tl.liver	86.0509479781131	58.2495041657237	67.0901532461312
chr18.11279_chr18_38132773_38141845_-_2.R.tl.stomach	91.123424325009	51.9243980961634	77.0874588965213
chr18.11279_chr18_38132773_38141845_-_2.R.tl.testicle	138.207216055212	58.5943419332001	177.381056027386


diffExp=29.5982264974836,21.596620384967,43.578793840535,39.5398658096244,30.7816246387517,27.8014438123894,39.1990262288456,79.6128741220118
diffExpScore=0.996802133364214
diffExp1.5=1,0,1,1,1,0,1,1
diffExp1.5Score=0.857142857142857
diffExp1.4=1,0,1,1,1,1,1,1
diffExp1.4Score=0.875
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	91.6642925617301	88.3160892513828	81.7551817449872
cerebhem	83.4848960077994	94.6175134871267	82.1563507737869
cortex	101.562189712234	104.409191470479	109.558569949796
heart	91.2652220080848	83.929103654268	60.3878496832106
kidney	92.6861674839082	70.633597261938	81.1746111332418
liver	80.0529350900546	91.5214385450684	78.8519669592451
stomach	85.3506937761338	83.9003674565305	76.2463696656155
testicle	86.4701323740952	80.5567861213532	77.0064015295022
cont.diffExp=3.34820331034732,-11.1326174793272,-2.84700175824497,7.33611835381687,22.0525702219703,-11.4685034550139,1.45032631960326,5.91334625274197
cont.diffExpScore=4.18776112580052

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

tran.correlation=0.124454293875259
cont.tran.correlation=0.211551219138024

tran.covariance=0.00149166875840156
cont.tran.covariance=0.00116063270432934

tran.mean=75.8077027439468
cont.tran.mean=88.1512885163866

weightedLogRatios:
wLogRatio
Lung	1.86652679221757
cerebhem	1.23632859991163
cortex	2.55372301777841
heart	2.33265644399389
kidney	1.85417752674053
liver	1.66220548032667
stomach	2.37962595155152
testicle	3.86126118536251

cont.weightedLogRatios:
wLogRatio
Lung	0.167430153863702
cerebhem	-0.561700392795041
cortex	-0.128127066447689
heart	0.374730979181303
kidney	1.19373517672865
liver	-0.59573943210669
stomach	0.0760644541427973
testicle	0.313408575165529

varWeightedLogRatios=0.625824575094368
cont.varWeightedLogRatios=0.328020199999845

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.83100500885816	0.100370304791417	38.1687095283761	1.99158916304203e-172	***
df.mm.trans1	0.151263155992725	0.090147211757752	1.67795712194856	0.0938069984623005	.  
df.mm.trans2	0.208656476879334	0.0829026043253168	2.51688687680484	0.0120646099798270	*  
df.mm.exp2	0.192623702579034	0.113556037036434	1.69628764446255	0.0902811668379182	.  
df.mm.exp3	-0.198016977901493	0.113556037036434	-1.74378203985720	0.0816411652303677	.  
df.mm.exp4	-0.0784866401077341	0.113556037036434	-0.691171003815076	0.489689875315982	   
df.mm.exp5	0.0940438437365834	0.113556037036434	0.828171237663125	0.407859057406023	   
df.mm.exp6	0.189496108215666	0.113556037036434	1.66874534512741	0.0956201928676964	.  
df.mm.exp7	-0.00707865583941269	0.113556037036434	-0.0623362352557401	0.950313073168602	   
df.mm.exp8	-0.303049740423988	0.113556037036434	-2.66872416767023	0.00779240306009854	** 
df.mm.trans1:exp2	-0.139569524597291	0.108721628108065	-1.28373284162526	0.199665127316879	   
df.mm.trans2:exp2	0.0200050289685406	0.0946300308636951	0.211402540884255	0.832635416610084	   
df.mm.trans1:exp3	0.361334481880765	0.108721628108065	3.32348299200978	0.000935829816133559	***
df.mm.trans2:exp3	0.209709694148505	0.0946300308636951	2.21610087447367	0.0270091576682332	*  
df.mm.trans1:exp4	0.210693310755624	0.108721628108065	1.93791533866844	0.0530400108928504	.  
df.mm.trans2:exp4	0.110062887821662	0.0946300308636951	1.16308625091961	0.245195419717383	   
df.mm.trans1:exp5	-0.0381400612249674	0.108721628108065	-0.350804728448858	0.72584161590577	   
df.mm.trans2:exp5	-0.0288812526123001	0.0946300308636951	-0.305201766803824	0.760304262767125	   
df.mm.trans1:exp6	-0.144893193353971	0.108721628108065	-1.33269889234876	0.18306927371626	   
df.mm.trans2:exp6	-0.0893557854437892	0.0946300308636951	-0.944264570435331	0.345364028942435	   
df.mm.trans1:exp7	0.108956930329322	0.108721628108065	1.00216426322299	0.316614562401246	   
df.mm.trans2:exp7	-0.00772782371959739	0.0946300308636951	-0.0816635443216596	0.934937882487876	   
df.mm.trans1:exp8	0.821467240644786	0.108721628108065	7.55569296504907	1.32339132727464e-13	***
df.mm.trans2:exp8	0.409092620843071	0.0946300308636951	4.32307394501781	1.76467995222793e-05	***
df.mm.trans1:probe2	0.548162408214164	0.0543608140540324	10.0837785039296	2.10217222306872e-22	***
df.mm.trans1:probe3	0.537960539479545	0.0543608140540324	9.8961089682144	1.09958997724703e-21	***
df.mm.trans1:probe4	0.111533468400524	0.0543608140540324	2.05172550009395	0.0405721633412788	*  
df.mm.trans1:probe5	0.191526884040294	0.0543608140540324	3.5232526843679	0.000454316206364988	***
df.mm.trans1:probe6	0.869297199863841	0.0543608140540324	15.9912469117883	3.23539189776862e-49	***
df.mm.trans1:probe7	0.704491342244385	0.0543608140540324	12.9595436437753	1.52355011930553e-34	***
df.mm.trans1:probe8	0.393759812857138	0.0543608140540324	7.2434495271862	1.16918116860063e-12	***
df.mm.trans1:probe9	0.462003244744882	0.0543608140540324	8.49882866517912	1.17311371803669e-16	***
df.mm.trans1:probe10	0.546325470765941	0.0543608140540324	10.0499869303450	2.83646198543829e-22	***
df.mm.trans1:probe11	1.52285283116004	0.0543608140540324	28.0137973954251	5.1598153793843e-116	***
df.mm.trans1:probe12	1.27319758252768	0.0543608140540324	23.4212383438956	8.78575016733958e-90	***
df.mm.trans1:probe13	1.02031409225336	0.0543608140540324	18.7692938380065	7.61363417945635e-64	***
df.mm.trans1:probe14	1.12626378552841	0.0543608140540324	20.7183024229356	1.47619334517477e-74	***
df.mm.trans1:probe15	1.47966259722689	0.0543608140540324	27.2192869620415	1.78436178603375e-111	***
df.mm.trans1:probe16	1.21523985726642	0.0543608140540324	22.3550709902638	9.69010217883535e-84	***
df.mm.trans1:probe17	-0.202767051183182	0.0543608140540324	-3.73002234627391	0.000207147242317913	***
df.mm.trans1:probe18	-0.229439804366654	0.0543608140540324	-4.22068374727796	2.76041720634243e-05	***
df.mm.trans1:probe19	-0.234136523809149	0.0543608140540324	-4.30708273751065	1.89355135198526e-05	***
df.mm.trans1:probe20	-0.260651650715486	0.0543608140540324	-4.79484450796504	1.99423843224515e-06	***
df.mm.trans1:probe21	-0.193579035459569	0.0543608140540324	-3.56100324890572	0.000394746888496951	***
df.mm.trans1:probe22	-0.180305564024305	0.0543608140540324	-3.31682972674193	0.00095803990812088	***
df.mm.trans2:probe2	-0.110728584208719	0.0543608140540324	-2.03691916936085	0.0420382229815429	*  
df.mm.trans2:probe3	-0.0960154901689609	0.0543608140540324	-1.76626292007926	0.0777925978975994	.  
df.mm.trans2:probe4	-0.0809774164962205	0.0543608140540324	-1.48962847421182	0.136777563028251	   
df.mm.trans2:probe5	-0.22658282070831	0.0543608140540324	-4.16812780770899	3.46061361054119e-05	***
df.mm.trans2:probe6	-0.161291742903626	0.0543608140540324	-2.96705900583662	0.00311031439826316	** 
df.mm.trans3:probe2	0.179144435676774	0.0543608140540324	3.29547006964818	0.00103269328215112	** 
df.mm.trans3:probe3	0.000452108157554902	0.0543608140540324	0.0083168025612259	0.993366625133032	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.6729588583952	0.322536442188151	14.4881577619351	9.7061403109611e-42	***
df.mm.trans1	-0.140808578107575	0.28968489249834	-0.486074979241044	0.627067888508883	   
df.mm.trans2	-0.1901251622471	0.266404601468405	-0.713670714391353	0.475671441462072	   
df.mm.exp2	-0.0294418468046915	0.364908328721639	-0.080682857823041	0.935717498013273	   
df.mm.exp3	-0.0227962803331888	0.364908328721639	-0.0624712524733256	0.950205593708533	   
df.mm.exp4	0.247628294366384	0.364908328721639	0.678604117461187	0.497615604588529	   
df.mm.exp5	-0.205203389492736	0.364908328721639	-0.562342301727157	0.574064932643582	   
df.mm.exp6	-0.0636369268718023	0.364908328721639	-0.174391544020762	0.861608765972154	   
df.mm.exp7	-0.0528972509894805	0.364908328721639	-0.144960382720757	0.88478436101137	   
df.mm.exp8	-0.0904531564623486	0.364908328721639	-0.247879122899791	0.804301467102187	   
df.mm.trans1:exp2	-0.0640253331709944	0.349373125764158	-0.183257750666874	0.854649462580358	   
df.mm.trans2:exp2	0.098362135488032	0.304090273934699	0.323463602486525	0.746441894283015	   
df.mm.trans1:exp3	0.125334688375006	0.349373125764158	0.35874164076264	0.719897794954082	   
df.mm.trans2:exp3	0.190191690572419	0.304090273934699	0.625444832915836	0.531885347024881	   
df.mm.trans1:exp4	-0.251991408625644	0.349373125764158	-0.721267292882022	0.470988683908798	   
df.mm.trans2:exp4	-0.298578158249839	0.304090273934699	-0.981873423264949	0.326505362116354	   
df.mm.trans1:exp5	0.216289723513115	0.349373125764158	0.619079452777143	0.536067765480416	   
df.mm.trans2:exp5	-0.0182129995457117	0.304090273934699	-0.0598933971483177	0.952257830970404	   
df.mm.trans1:exp6	-0.0718078779845188	0.349373125764158	-0.205533490383551	0.837215781627483	   
df.mm.trans2:exp6	0.0992878705091724	0.304090273934699	0.326507879467706	0.744138853530798	   
df.mm.trans1:exp7	-0.0184670805381444	0.349373125764158	-0.0528577591586438	0.957860495512314	   
df.mm.trans2:exp7	0.00160494191057729	0.304090273934699	0.00527784690319277	0.99579042809209	   
df.mm.trans1:exp8	0.0321193109643260	0.349373125764158	0.0919341202734864	0.92677698891717	   
df.mm.trans2:exp8	-0.00150679238556227	0.304090273934699	-0.00495508246964136	0.996047860454895	   
df.mm.trans1:probe2	-0.109953551144957	0.174686562882079	-0.629433365285115	0.529273122937674	   
df.mm.trans1:probe3	0.173176217920857	0.174686562882079	0.991353971729115	0.321859275303711	   
df.mm.trans1:probe4	0.0570315947263783	0.174686562882079	0.326479574533029	0.744160256112323	   
df.mm.trans1:probe5	-0.0812831080064362	0.174686562882079	-0.465308302283707	0.641857083708858	   
df.mm.trans1:probe6	0.00717774561016389	0.174686562882079	0.0410892829519417	0.967236570305721	   
df.mm.trans1:probe7	-0.196414162642459	0.174686562882079	-1.1243804869814	0.261241513743351	   
df.mm.trans1:probe8	0.269265015799458	0.174686562882079	1.54141801954867	0.123672219723599	   
df.mm.trans1:probe9	0.112825799962501	0.174686562882079	0.645875664968364	0.518573994027331	   
df.mm.trans1:probe10	-0.255721656539889	0.174686562882079	-1.46388853453206	0.143678501003526	   
df.mm.trans1:probe11	-0.0282705558419116	0.174686562882079	-0.161835892672497	0.871482327309112	   
df.mm.trans1:probe12	-0.102776155285294	0.174686562882079	-0.588346084493476	0.556491986871583	   
df.mm.trans1:probe13	-0.00752493163433487	0.174686562882079	-0.0430767628041003	0.965652768959962	   
df.mm.trans1:probe14	0.00212415324293801	0.174686562882079	0.0121597975705316	0.990301629067548	   
df.mm.trans1:probe15	-0.122729570560944	0.174686562882079	-0.702570183625356	0.482559912005819	   
df.mm.trans1:probe16	-0.0159058651238213	0.174686562882079	-0.0910537414063066	0.927476238752573	   
df.mm.trans1:probe17	0.00126087664535917	0.174686562882079	0.0072179372274347	0.99424304960255	   
df.mm.trans1:probe18	-0.0354069338965328	0.174686562882079	-0.202688365449345	0.839438195472656	   
df.mm.trans1:probe19	-0.0669939512364939	0.174686562882079	-0.383509470512152	0.701459841985027	   
df.mm.trans1:probe20	0.0119901565039576	0.174686562882079	0.0686381156405918	0.945297506491668	   
df.mm.trans1:probe21	-0.0281126261263096	0.174686562882079	-0.160931817894241	0.872194071433837	   
df.mm.trans1:probe22	0.0658072338493057	0.174686562882079	0.376716060832501	0.706500108721564	   
df.mm.trans2:probe2	0.0604174650738938	0.174686562882079	0.345862120572366	0.729551457396495	   
df.mm.trans2:probe3	-0.186904073331112	0.174686562882079	-1.06993961211132	0.285019541266995	   
df.mm.trans2:probe4	-0.0203533069974988	0.174686562882079	-0.116513294793247	0.907279554933922	   
df.mm.trans2:probe5	0.00567162238349907	0.174686562882079	0.0324674221641631	0.974108657740552	   
df.mm.trans2:probe6	0.123965747688423	0.174686562882079	0.70964672750534	0.478162276390597	   
df.mm.trans3:probe2	-0.0471453486797797	0.174686562882079	-0.269885375852319	0.78732889968767	   
df.mm.trans3:probe3	0.232257573643347	0.174686562882079	1.32956748253231	0.184098931946256