chr19.12243_chr19_57707674_57733076_+_2.R 

fitVsDatCorrelation=0.7388479891988
cont.fitVsDatCorrelation=0.268885410493177

fstatistic=12808.7557185147,54,738
cont.fstatistic=6262.82875242779,54,738

residuals=-0.463943445888869,-0.0823017427734629,-0.00604660951253931,0.0734866609166916,0.602899974659113
cont.residuals=-0.504947104284861,-0.125305191181634,-0.0326022588136424,0.0871520646312388,0.874999939993297

predictedValues:
Include	Exclude	Both
chr19.12243_chr19_57707674_57733076_+_2.R.tl.Lung	57.6344284458474	52.5385143006386	68.2392013723365
chr19.12243_chr19_57707674_57733076_+_2.R.tl.cerebhem	62.4425291404176	69.5496840661025	56.5327190538781
chr19.12243_chr19_57707674_57733076_+_2.R.tl.cortex	68.1541500723703	51.8012811623463	70.6811214662191
chr19.12243_chr19_57707674_57733076_+_2.R.tl.heart	54.2737196040265	46.435899268575	53.5914371549137
chr19.12243_chr19_57707674_57733076_+_2.R.tl.kidney	56.0535556739543	50.5216781909497	62.1622791373048
chr19.12243_chr19_57707674_57733076_+_2.R.tl.liver	53.8451491127695	50.8529288067092	54.1515516692351
chr19.12243_chr19_57707674_57733076_+_2.R.tl.stomach	58.5177382397137	54.4619748690861	55.1986629501168
chr19.12243_chr19_57707674_57733076_+_2.R.tl.testicle	54.6931231373639	53.5389056759943	54.0692404683677


diffExp=5.09591414520887,-7.10715492568493,16.352868910024,7.83782033545151,5.53187748300461,2.99222030606031,4.05576337062755,1.15421746136960
diffExpScore=1.35798014696785
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=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	57.9921848600494	58.4725187723827	52.7315838482761
cerebhem	58.3526971067231	57.1788749896285	50.6580472901939
cortex	55.9899813175297	60.882905994141	55.1202254985536
heart	58.5411478421104	59.3767581406101	54.0716314901985
kidney	59.6550204219589	54.6058124088109	53.614545236685
liver	56.9472334602176	58.1027013445346	57.024087599106
stomach	55.5630113977262	65.4852270246724	56.8136207406136
testicle	55.3822065230014	59.6018454541149	53.9770007346019
cont.diffExp=-0.4803339123333,1.17382211709464,-4.89292467661127,-0.835610298499624,5.04920801314795,-1.15546788431693,-9.9222156269462,-4.21963893111347
cont.diffExpScore=1.70293846017944

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=0.414738753611936
cont.tran.correlation=-0.77336987114856

tran.covariance=0.00421829383625938
cont.tran.covariance=-0.00112028181213594

tran.mean=55.9572037354291
cont.tran.mean=58.2581329411382

weightedLogRatios:
wLogRatio
Lung	0.371019552698339
cerebhem	-0.451460042431754
cortex	1.12063750673907
heart	0.610776652772655
kidney	0.412955638755648
liver	0.226269910888995
stomach	0.289708908058408
testicle	0.085127132401799

cont.weightedLogRatios:
wLogRatio
Lung	-0.0335259663322285
cerebhem	0.0824292666191605
cortex	-0.340737187042775
heart	-0.0577806525554516
kidney	0.357674961437991
liver	-0.081396102630093
stomach	-0.673604086354384
testicle	-0.297455523294807

varWeightedLogRatios=0.199350702094295
cont.varWeightedLogRatios=0.0953989679501107

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.70218221542599	0.0676565201670821	54.7202576526728	5.8698898971281e-262	***
df.mm.trans1	0.446861927710533	0.0595993701312714	7.49776258920674	1.86536980250803e-13	***
df.mm.trans2	0.251117751888654	0.0537783316600122	4.66949687982562	3.58698516776576e-06	***
df.mm.exp2	0.548820995250391	0.0716167303245998	7.66330706195164	5.71007177345094e-14	***
df.mm.exp3	0.118361008435888	0.0716167303245997	1.65270053379179	0.0988171503562909	.  
df.mm.exp4	0.0580762633814317	0.0716167303245998	0.810931511648236	0.417666439639239	   
df.mm.exp5	0.0263143175912540	0.0716167303245998	0.367432546445299	0.713401740171947	   
df.mm.exp6	0.130615996154809	0.0716167303245997	1.82381959582345	0.0685836450706238	.  
df.mm.exp7	0.263246532625567	0.0716167303245997	3.67576865674282	0.000254317930949209	***
df.mm.exp8	0.199233712636532	0.0716167303245998	2.78194371250284	0.00554138161998224	** 
df.mm.trans1:exp2	-0.468694500506345	0.0675519235776081	-6.9382850359232	8.6909233289306e-12	***
df.mm.trans2:exp2	-0.268326125801593	0.0552697556544066	-4.85484552309939	1.47116957590457e-06	***
df.mm.trans1:exp3	0.0492909387843751	0.067551923577608	0.729674836390801	0.465820493478936	   
df.mm.trans2:exp3	-0.132492632979352	0.0552697556544066	-2.39719954269037	0.0167685911490442	*  
df.mm.trans1:exp4	-0.118156244007544	0.0675519235776081	-1.74911738629912	0.0806864257196206	.  
df.mm.trans2:exp4	-0.181549918527715	0.0552697556544066	-3.28479683649987	0.00106873639887535	** 
df.mm.trans1:exp5	-0.0541268377625458	0.067551923577608	-0.80126271608478	0.423237463808996	   
df.mm.trans2:exp5	-0.0654583086793847	0.0552697556544066	-1.18434228457035	0.236658882948094	   
df.mm.trans1:exp6	-0.198623783295792	0.067551923577608	-2.940312766484	0.00338129810545716	** 
df.mm.trans2:exp6	-0.163224784700329	0.0552697556544066	-2.95323876083239	0.00324447759556797	** 
df.mm.trans1:exp7	-0.248036711790574	0.067551923577608	-3.67179346870283	0.000258234731858525	***
df.mm.trans2:exp7	-0.227290289659428	0.0552697556544066	-4.11238093905535	4.35619937296079e-05	***
df.mm.trans1:exp8	-0.251615835935887	0.067551923577608	-3.72477677333370	0.000210397855047071	***
df.mm.trans2:exp8	-0.180371620492627	0.0552697556544066	-3.26347779824582	0.00115123348447131	** 
df.mm.trans1:probe2	-0.119637211455442	0.0394418258859358	-3.03325743086612	0.00250411526814853	** 
df.mm.trans1:probe3	-0.293363189376182	0.0394418258859358	-7.43787040246505	2.84764259491181e-13	***
df.mm.trans1:probe4	-0.270922778031203	0.0394418258859358	-6.86892079526696	1.37515108660426e-11	***
df.mm.trans1:probe5	-0.09988764128867	0.0394418258859358	-2.53253086146521	0.0115304153515270	*  
df.mm.trans1:probe6	-0.280739752204597	0.0394418258859358	-7.11781835395971	2.60301135832203e-12	***
df.mm.trans1:probe7	-0.00632704172949705	0.0394418258859358	-0.160414524109371	0.872598446592241	   
df.mm.trans1:probe8	0.249916715797269	0.0394418258859358	6.3363373825547	4.09138824454815e-10	***
df.mm.trans1:probe9	-0.187584765240551	0.0394418258859358	-4.75598583551985	2.37541263191445e-06	***
df.mm.trans1:probe10	0.111937465763310	0.0394418258859358	2.83803965077654	0.00466377315921871	** 
df.mm.trans1:probe11	-0.0492214233667154	0.0394418258859358	-1.24794991766004	0.212445200340982	   
df.mm.trans1:probe12	-0.141817086901178	0.0394418258859358	-3.59560146407287	0.000345186903543565	***
df.mm.trans1:probe13	-0.227556112623165	0.0394418258859358	-5.76941121542518	1.17046075166109e-08	***
df.mm.trans1:probe14	-0.140145126214997	0.0394418258859358	-3.55321091422823	0.000404765769542389	***
df.mm.trans1:probe15	-0.121750479215650	0.0394418258859358	-3.08683679015641	0.00209867480461172	** 
df.mm.trans1:probe16	-0.148081079408152	0.0394418258859358	-3.75441745106824	0.000187409733980963	***
df.mm.trans1:probe17	-0.0955135748979227	0.0394418258859358	-2.42163167532214	0.0156910468372717	*  
df.mm.trans1:probe18	-0.192105583079073	0.0394418258859358	-4.87060572790505	1.36190972967112e-06	***
df.mm.trans1:probe19	-0.135664811582243	0.0394418258859358	-3.43961793185184	0.000615269582320852	***
df.mm.trans1:probe20	-0.0886604031145604	0.0394418258859358	-2.24787775725603	0.0248784651617504	*  
df.mm.trans1:probe21	-0.154465884825992	0.0394418258859358	-3.91629650393726	9.82564440883878e-05	***
df.mm.trans1:probe22	-0.171359260899772	0.0394418258859358	-4.34460771150242	1.59064267055398e-05	***
df.mm.trans2:probe2	-0.0351664053033774	0.0394418258859358	-0.89160185953554	0.372896969839013	   
df.mm.trans2:probe3	-0.0591580849427152	0.0394418258859358	-1.49988200631984	0.134072626357305	   
df.mm.trans2:probe4	0.0508751361373366	0.0394418258859358	1.28987781357956	0.197497130091211	   
df.mm.trans2:probe5	0.00305061648920619	0.0394418258859358	0.0773447075708019	0.938370289781226	   
df.mm.trans2:probe6	0.131110667194151	0.0394418258859358	3.32415308493368	0.000930663656241096	***
df.mm.trans3:probe2	-0.245915066382504	0.0394418258859358	-6.23488038037795	7.60487414853487e-10	***
df.mm.trans3:probe3	-0.304065427118839	0.0394418258859358	-7.7092127529335	4.09676556712140e-14	***
df.mm.trans3:probe4	-0.114972485600882	0.0394418258859358	-2.91498892402643	0.00366471589182842	** 
df.mm.trans3:probe5	-0.220760496543857	0.0394418258859358	-5.59711655292752	3.07387109967689e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.19951572389091	0.0967021406533402	43.4273294832782	1.81337592813960e-205	***
df.mm.trans1	-0.110064812255909	0.0851859755578862	-1.29205319930999	0.196743164327695	   
df.mm.trans2	-0.0917102142105478	0.0768659070765915	-1.19311952071502	0.233205993778282	   
df.mm.exp2	0.0239414470218777	0.102362508622655	0.233888826524705	0.81513615247572	   
df.mm.exp3	-0.0390420494206507	0.102362508622655	-0.381409658145188	0.703009076831226	   
df.mm.exp4	-0.000327461928582218	0.102362508622655	-0.00319904165097555	0.997448402920538	   
df.mm.exp5	-0.0567523034238861	0.102362508622655	-0.55442470282841	0.57945619182909	   
df.mm.exp6	-0.102787047117586	0.102362508622655	-1.00414740221438	0.315636688878333	   
df.mm.exp7	-0.00408435284065611	0.102362508622655	-0.0399008669835603	0.968182951293702	   
df.mm.exp8	-0.0502636925857098	0.102362508622655	-0.491036154369758	0.623546859346933	   
df.mm.trans1:exp2	-0.0177441241242592	0.0965526397023277	-0.183776685743284	0.854239127961967	   
df.mm.trans2:exp2	-0.0463138145032981	0.0789976143019044	-0.586268521050536	0.557874344733924	   
df.mm.trans1:exp3	0.00390656143086321	0.0965526397023277	0.0404604311483058	0.96773699553549	   
df.mm.trans2:exp3	0.0794376156951696	0.0789976143019044	1.00556980609039	0.314952136947062	   
df.mm.trans1:exp4	0.00974909315027942	0.0965526397023277	0.100971793006757	0.919600276280896	   
df.mm.trans2:exp4	0.0156734553450073	0.0789976143019044	0.198404160473862	0.84278350355915	   
df.mm.trans1:exp5	0.0850223554105879	0.0965526397023276	0.880580330819668	0.378831676711282	   
df.mm.trans2:exp5	-0.0116642444065037	0.0789976143019044	-0.147653122307296	0.882656858590811	   
df.mm.trans1:exp6	0.0846038997571149	0.0965526397023276	0.87624636693465	0.381181232609413	   
df.mm.trans2:exp6	0.0964423253478128	0.0789976143019044	1.22082579581758	0.222541882969657	   
df.mm.trans1:exp7	-0.0387061875944151	0.0965526397023276	-0.400881712957269	0.688623247207095	   
df.mm.trans2:exp7	0.117352049146262	0.0789976143019044	1.48551383713664	0.137834786524190	   
df.mm.trans1:exp8	0.00421379517830689	0.0965526397023276	0.0436424647870638	0.965201207084858	   
df.mm.trans2:exp8	0.0693933508836445	0.0789976143019044	0.878423373881199	0.37999990142152	   
df.mm.trans1:probe2	0.00359219069612377	0.0563745960482025	0.0637200254712655	0.949210413687064	   
df.mm.trans1:probe3	-0.0733608139720842	0.0563745960482025	-1.3013097940313	0.193558536307220	   
df.mm.trans1:probe4	-0.0196501149685936	0.0563745960482025	-0.34856329527917	0.727516599725492	   
df.mm.trans1:probe5	-0.0335169672867292	0.0563745960482025	-0.594540265229942	0.552333041043507	   
df.mm.trans1:probe6	-0.0701582090348565	0.0563745960482025	-1.24450043020918	0.213710516555626	   
df.mm.trans1:probe7	-0.0166998785671533	0.0563745960482025	-0.296230567273143	0.767137345808723	   
df.mm.trans1:probe8	-0.019297889163658	0.0563745960482025	-0.342315342661747	0.732211055133368	   
df.mm.trans1:probe9	0.00824328234793813	0.0563745960482025	0.146223351044321	0.883785003255583	   
df.mm.trans1:probe10	0.0205689507091160	0.0563745960482025	0.364862050479772	0.71531891347502	   
df.mm.trans1:probe11	0.0242596693388153	0.0563745960482025	0.430329812351513	0.667081379349148	   
df.mm.trans1:probe12	-0.100679851356189	0.0563745960482025	-1.78590816455881	0.074524615356449	.  
df.mm.trans1:probe13	-0.0434093598386935	0.0563745960482025	-0.770016335045253	0.441536606429548	   
df.mm.trans1:probe14	-0.0623616589626359	0.0563745960482025	-1.10620143352006	0.268999801546600	   
df.mm.trans1:probe15	-0.0605103817768836	0.0563745960482025	-1.07336257851222	0.283459303397276	   
df.mm.trans1:probe16	-0.0716913402384885	0.0563745960482025	-1.27169585706990	0.203881808085850	   
df.mm.trans1:probe17	-0.0665542684340916	0.0563745960482025	-1.18057197921534	0.238153154324091	   
df.mm.trans1:probe18	0.000285256386587344	0.0563745960482025	0.00506001650714159	0.995964075615948	   
df.mm.trans1:probe19	-0.0227288328756351	0.0563745960482025	-0.403175090712863	0.686936197306935	   
df.mm.trans1:probe20	-0.0429323060465349	0.0563745960482025	-0.761554122885884	0.446569449587806	   
df.mm.trans1:probe21	-0.0668080773335664	0.0563745960482025	-1.18507416490298	0.236369590935524	   
df.mm.trans1:probe22	-0.0734410565318535	0.0563745960482025	-1.30273317557892	0.193072221826202	   
df.mm.trans2:probe2	-0.0763747477991402	0.0563745960482025	-1.35477241794933	0.175904651754861	   
df.mm.trans2:probe3	-0.101165687404550	0.0563745960482025	-1.79452616064955	0.0731383777952996	.  
df.mm.trans2:probe4	-0.0445989856128662	0.0563745960482025	-0.791118495549526	0.429129015930059	   
df.mm.trans2:probe5	-0.165113614008288	0.0563745960482025	-2.92886558099875	0.00350683900395926	** 
df.mm.trans2:probe6	-0.0444818994778465	0.0563745960482025	-0.789041564746872	0.430341119106464	   
df.mm.trans3:probe2	-0.0504052586400986	0.0563745960482025	-0.894112990131231	0.371552928425463	   
df.mm.trans3:probe3	-0.0204594417809501	0.0563745960482025	-0.36291952785713	0.716768915840393	   
df.mm.trans3:probe4	-0.0307940930433217	0.0563745960482025	-0.546240597750652	0.585065653284467	   
df.mm.trans3:probe5	-0.060971743460749	0.0563745960482025	-1.08154643642352	0.279807394314786