chr19.11829_chr19_5727732_5729061_-_2.R 

fitVsDatCorrelation=0.898096627141293
cont.fitVsDatCorrelation=0.274831128559957

fstatistic=6295.84476207668,51,669
cont.fstatistic=1306.87953239459,51,669

residuals=-0.625307171217508,-0.114075522326257,-0.00615888295815826,0.0926384268869376,1.00997450209660
cont.residuals=-0.813395990677714,-0.348556740616124,-0.0629662077226117,0.314028839213226,1.65372875135297

predictedValues:
Include	Exclude	Both
chr19.11829_chr19_5727732_5729061_-_2.R.tl.Lung	74.5918692552692	139.053752369095	58.008429600501
chr19.11829_chr19_5727732_5729061_-_2.R.tl.cerebhem	76.5651537333187	131.758396749749	53.8424898994497
chr19.11829_chr19_5727732_5729061_-_2.R.tl.cortex	65.1687278550089	104.266126561262	56.104217047785
chr19.11829_chr19_5727732_5729061_-_2.R.tl.heart	75.4901828073997	102.333829926676	63.61043998268
chr19.11829_chr19_5727732_5729061_-_2.R.tl.kidney	78.1082124627238	154.162313394438	58.4754410372186
chr19.11829_chr19_5727732_5729061_-_2.R.tl.liver	75.3379493042023	130.439998646008	60.3618775568819
chr19.11829_chr19_5727732_5729061_-_2.R.tl.stomach	71.4955836068378	111.773565085298	55.3831549540166
chr19.11829_chr19_5727732_5729061_-_2.R.tl.testicle	76.137336067696	106.218181799552	59.9725039871014


diffExp=-64.4618831138263,-55.1932430164303,-39.0973987062536,-26.8436471192762,-76.0541009317139,-55.1020493418059,-40.2779814784599,-30.0808457318559
diffExpScore=0.997423418519556
diffExp1.5=-1,-1,-1,0,-1,-1,-1,0
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,-1,-1,0,-1,-1,-1,0
diffExp1.4Score=0.857142857142857
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	75.2853017065758	73.8025753727407	87.4158099279682
cerebhem	75.2711510689513	87.1232777335515	80.2911513706715
cortex	76.5108233347546	67.1790186608837	63.0279601890079
heart	80.658137153777	77.2919638706622	80.202293538649
kidney	82.7873140983187	78.1016249789484	70.084837596079
liver	78.822232742575	76.1516368186383	75.2818291246488
stomach	80.1804013040208	78.6738829624353	66.551835941561
testicle	82.2857279422677	66.8982565819753	70.688139577398
cont.diffExp=1.48272633383509,-11.8521266646002,9.33180467387092,3.36617328311469,4.68568911937038,2.67059592393674,1.50651834158553,15.3874713602925
cont.diffExpScore=1.82324866254191

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

tran.correlation=0.545894564916355
cont.tran.correlation=-0.203559438575655

tran.covariance=0.00471971711402954
cont.tran.covariance=-0.00062335082023125

tran.mean=98.3063237265334
cont.tran.mean=77.3139578956923

weightedLogRatios:
wLogRatio
Lung	-2.87961653142858
cerebhem	-2.50219539807511
cortex	-2.07347628299578
heart	-1.36180486749137
kidney	-3.19425547569969
liver	-2.52312561227329
stomach	-2.00767419119725
testicle	-1.49797711901027

cont.weightedLogRatios:
wLogRatio
Lung	0.0857581068715762
cerebhem	-0.64255292347122
cortex	0.555715905098895
heart	0.186245121188477
kidney	0.255612382522901
liver	0.149936732518891
stomach	0.0829805353599128
testicle	0.89159053582574

varWeightedLogRatios=0.410049381342589
cont.varWeightedLogRatios=0.192444157675355

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.15412495678799	0.105807892471094	48.7121030049442	3.44630417293819e-222	***
df.mm.trans1	-0.698206647018891	0.0937206192572021	-7.44987231788096	2.89421298902866e-13	***
df.mm.trans2	-0.235278062795890	0.0855838255072144	-2.74909495341566	0.00613728804604203	** 
df.mm.exp2	0.0467453102414912	0.115494990783314	0.404738854252064	0.685798852940713	   
df.mm.exp3	-0.389588458397266	0.115494990783314	-3.37320654129661	0.000785822441336477	***
df.mm.exp4	-0.386838415373337	0.115494990783314	-3.34939561230931	0.000855341262411652	***
df.mm.exp5	0.141190641972916	0.115494990783314	1.22248281951735	0.221955604412928	   
df.mm.exp6	-0.0937641543750014	0.115494990783314	-0.81184607002495	0.417168724905836	   
df.mm.exp7	-0.214468458947644	0.115494990783314	-1.85695031007898	0.0637575443628568	.  
df.mm.exp8	-0.282155867910395	0.115494990783314	-2.44301390040163	0.0148227358554205	*  
df.mm.trans1:exp2	-0.0206347591500220	0.109132508316552	-0.189079857764915	0.850087574546221	   
df.mm.trans2:exp2	-0.10063595847481	0.0926020040142375	-1.08675788981130	0.277535222189142	   
df.mm.trans1:exp3	0.254536668089492	0.109132508316552	2.33236339946644	0.0199773106620626	*  
df.mm.trans2:exp3	0.101674432277975	0.0926020040142375	1.09797226701857	0.272611610646695	   
df.mm.trans1:exp4	0.398809524079234	0.109132508316552	3.65436046720779	0.000278021722569197	***
df.mm.trans2:exp4	0.080218160914445	0.0926020040142375	0.866268087482334	0.386653726513547	   
df.mm.trans1:exp5	-0.095126947537113	0.109132508316552	-0.87166463049842	0.383704063771413	   
df.mm.trans2:exp5	-0.0380451775985931	0.0926020040142375	-0.410846158283396	0.68131690484842	   
df.mm.trans1:exp6	0.103716626998084	0.109132508316552	0.950373345192814	0.342265783931898	   
df.mm.trans2:exp6	0.0298169288460527	0.0926020040142375	0.321990103383382	0.747560777888164	   
df.mm.trans1:exp7	0.172072628949857	0.109132508316552	1.57673118307461	0.115330113605300	   
df.mm.trans2:exp7	-0.00391702261268067	0.0926020040142375	-0.0422995447493601	0.96627252926761	   
df.mm.trans1:exp8	0.302663120908597	0.109132508316552	2.77335438887454	0.0057027567787585	** 
df.mm.trans2:exp8	0.0127905993803399	0.0926020040142375	0.138124433876975	0.8901836384131	   
df.mm.trans1:probe2	-0.208851555712733	0.0597743365620957	-3.49400039757481	0.000507211649127421	***
df.mm.trans1:probe3	-0.360649907100682	0.0597743365620957	-6.03352421529641	2.65589550058142e-09	***
df.mm.trans1:probe4	-0.229292375424730	0.0597743365620957	-3.83596688164884	0.000136902364848236	***
df.mm.trans1:probe5	-0.0201450811958702	0.0597743365620957	-0.337018900660532	0.736208352684417	   
df.mm.trans1:probe6	-0.545893261415626	0.0597743365620957	-9.13256913940874	7.83126045577926e-19	***
df.mm.trans1:probe7	-0.389540708414707	0.0597743365620957	-6.51685540683565	1.4132668410184e-10	***
df.mm.trans1:probe8	-0.343123853001694	0.0597743365620957	-5.740320557891	1.43339120465493e-08	***
df.mm.trans1:probe9	-0.593510625062735	0.0597743365620957	-9.92918799602527	9.11892411422705e-22	***
df.mm.trans1:probe10	-0.504650779794434	0.0597743365620957	-8.44259943011136	1.92296708194359e-16	***
df.mm.trans1:probe11	-0.490672373221971	0.0597743365620957	-8.20874645278987	1.15043932260020e-15	***
df.mm.trans1:probe12	-0.606908508025662	0.0597743365620957	-10.1533290527647	1.26556441005695e-22	***
df.mm.trans1:probe13	-0.604827265350284	0.0597743365620957	-10.1185107211013	1.72351611677267e-22	***
df.mm.trans1:probe14	-0.567819161781255	0.0597743365620957	-9.49938041037702	3.67670225216693e-20	***
df.mm.trans1:probe15	0.432709178311184	0.0597743365620957	7.23904610570909	1.24429814380125e-12	***
df.mm.trans1:probe16	0.224040630339775	0.0597743365620957	3.74810735217502	0.000193572159176522	***
df.mm.trans1:probe17	0.436285958519224	0.0597743365620957	7.29888416354056	8.2539394590577e-13	***
df.mm.trans1:probe18	0.209468707945594	0.0597743365620957	3.5043250999196	0.000488288446300853	***
df.mm.trans1:probe19	0.572060876565165	0.0597743365620957	9.57034254944659	2.01365953885032e-20	***
df.mm.trans1:probe20	0.138036910647389	0.0597743365620957	2.30930058929205	0.0212306306990123	*  
df.mm.trans2:probe2	0.207621039665077	0.0597743365620957	3.47341437155714	0.000547004177644456	***
df.mm.trans2:probe3	-0.0185734131913452	0.0597743365620957	-0.310725543094075	0.756106061833042	   
df.mm.trans2:probe4	0.146413916092182	0.0597743365620957	2.44944443574178	0.0145630461166939	*  
df.mm.trans2:probe5	-0.13618833901741	0.0597743365620957	-2.27837474826563	0.0230186782126121	*  
df.mm.trans2:probe6	-0.0391364827706826	0.0597743365620957	-0.654737217033371	0.512861994983718	   
df.mm.trans3:probe2	-0.0599535165370402	0.0597743365620957	-1.00299760708776	0.316224652682125	   
df.mm.trans3:probe3	-0.186552103057308	0.0597743365620957	-3.12093975084962	0.00188034398849816	** 
df.mm.trans3:probe4	-0.393702016596243	0.0597743365620957	-6.58647237660684	9.11883648932498e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98469521615341	0.231318587797480	17.2260052860171	2.59339185724521e-55	***
df.mm.trans1	0.349887585695828	0.204893234216947	1.70765807388914	0.0881637510901602	.  
df.mm.trans2	0.279052877425437	0.187104470113543	1.49142816981388	0.136320478360418	   
df.mm.exp2	0.25075923332652	0.252496647856188	0.993119059027438	0.321010996268303	   
df.mm.exp3	0.249212359321505	0.252496647856188	0.986992744012375	0.324003018991856	   
df.mm.exp4	0.201255182832660	0.252496647856188	0.79706081067376	0.425698497948379	   
df.mm.exp5	0.372576838414892	0.252496647856188	1.47557142472282	0.140529459210519	   
df.mm.exp6	0.226680490634058	0.252496647856188	0.897756435812827	0.369638382099131	   
df.mm.exp7	0.399606836089495	0.252496647856188	1.58262234165222	0.113980173666927	   
df.mm.exp8	0.203090393535599	0.252496647856188	0.804329068365576	0.421492678482085	   
df.mm.trans1:exp2	-0.250947211156832	0.238586906108901	-1.05180630089687	0.293268190495604	   
df.mm.trans2:exp2	-0.0848287598524873	0.202447703054307	-0.419015669591134	0.67533917824756	   
df.mm.trans1:exp3	-0.233065066289241	0.238586906108901	-0.976856065113901	0.328993510677004	   
df.mm.trans2:exp3	-0.343245010499368	0.202447703054307	-1.69547495635104	0.0904506527167768	.  
df.mm.trans1:exp4	-0.132320407990533	0.238586906108901	-0.554600460471777	0.579353329980598	   
df.mm.trans2:exp4	-0.155058820566722	0.202447703054307	-0.765920374631897	0.443993826543565	   
df.mm.trans1:exp5	-0.277586919427423	0.238586906108901	-1.16346250494032	0.245056696686589	   
df.mm.trans2:exp5	-0.315959603044273	0.202447703054307	-1.56069739630248	0.119068066667485	   
df.mm.trans1:exp6	-0.180770311454111	0.238586906108901	-0.757670713796842	0.448914925505189	   
df.mm.trans2:exp6	-0.195347544507382	0.202447703054307	-0.964928431195785	0.33492932580868	   
df.mm.trans1:exp7	-0.336612643141459	0.238586906108901	-1.41085966799793	0.158750826433551	   
df.mm.trans2:exp7	-0.335689219012997	0.202447703054307	-1.65815276710227	0.0977554007216482	.  
df.mm.trans1:exp8	-0.114177635222697	0.238586906108901	-0.478557843281565	0.632409571931113	   
df.mm.trans2:exp8	-0.301311114448301	0.202447703054307	-1.48834049437189	0.137132325891340	   
df.mm.trans1:probe2	-0.125669914164879	0.130679430401212	-0.961665610104412	0.336565048906033	   
df.mm.trans1:probe3	0.198339119687148	0.130679430401212	1.51775316955551	0.129549025539509	   
df.mm.trans1:probe4	-0.0562231287540658	0.130679430401212	-0.430237020328673	0.667161791978295	   
df.mm.trans1:probe5	0.0530195393902483	0.130679430401212	0.405722149442093	0.685076491392463	   
df.mm.trans1:probe6	-0.126276689447245	0.130679430401212	-0.966308844931067	0.334238842352866	   
df.mm.trans1:probe7	0.0335832145988805	0.130679430401212	0.256989294304185	0.797266053926811	   
df.mm.trans1:probe8	0.0127584933720770	0.130679430401212	0.097631994055268	0.922253760781452	   
df.mm.trans1:probe9	0.0560954727962581	0.130679430401212	0.429260156889525	0.667872111285712	   
df.mm.trans1:probe10	-0.148049242621648	0.130679430401212	-1.13291925261005	0.257654020402656	   
df.mm.trans1:probe11	0.0975311199279214	0.130679430401212	0.746338728509002	0.455725001902370	   
df.mm.trans1:probe12	-0.0591655348067861	0.130679430401212	-0.452753234576673	0.650873229185716	   
df.mm.trans1:probe13	-0.106319864130968	0.130679430401212	-0.81359295647788	0.416167641363983	   
df.mm.trans1:probe14	0.197116167761138	0.130679430401212	1.50839475773617	0.131925611284996	   
df.mm.trans1:probe15	-0.153818667117528	0.130679430401212	-1.17706869891668	0.239586691338576	   
df.mm.trans1:probe16	-0.217345572069378	0.130679430401212	-1.66319650615314	0.0967414626399578	.  
df.mm.trans1:probe17	-0.0701048218104953	0.130679430401212	-0.536464090754446	0.591816184899185	   
df.mm.trans1:probe18	0.0124665685631744	0.130679430401212	0.0953980938308311	0.924027197441902	   
df.mm.trans1:probe19	0.149658561456280	0.130679430401212	1.14523426523055	0.252521650672073	   
df.mm.trans1:probe20	-0.066744003986556	0.130679430401212	-0.510746058363113	0.60969737696454	   
df.mm.trans2:probe2	0.0945341088594938	0.130679430401212	0.723404659549364	0.46968415897133	   
df.mm.trans2:probe3	0.141813019066912	0.130679430401212	1.08519771345434	0.278224989091769	   
df.mm.trans2:probe4	0.0759123952720401	0.130679430401212	0.580905464914973	0.561499806444747	   
df.mm.trans2:probe5	0.064929079503105	0.130679430401212	0.496857686812375	0.619452641712686	   
df.mm.trans2:probe6	-0.000733262018468991	0.130679430401212	-0.00561115101449194	0.995524645481418	   
df.mm.trans3:probe2	-0.316482797925844	0.130679430401212	-2.42182566111727	0.0157075671045412	*  
df.mm.trans3:probe3	-0.0401917890903431	0.130679430401212	-0.307560179646836	0.758512745664193	   
df.mm.trans3:probe4	-0.215163498374829	0.130679430401212	-1.64649859365191	0.100130788177817