chr11.4208_chr11_120281821_120284396_+_2.R 

fitVsDatCorrelation=0.837524553326914
cont.fitVsDatCorrelation=0.255066811406391

fstatistic=7702.43536275376,77,1267
cont.fstatistic=2448.40154536970,77,1267

residuals=-0.724113692168163,-0.110300379530957,-0.0147050158462170,0.090014816348489,1.19628181956561
cont.residuals=-0.738039881618816,-0.272080907105183,-0.0596743995693027,0.240312892918851,1.62578916788203

predictedValues:
Include	Exclude	Both
chr11.4208_chr11_120281821_120284396_+_2.R.tl.Lung	86.5362465310709	86.6012870533558	64.0756042902671
chr11.4208_chr11_120281821_120284396_+_2.R.tl.cerebhem	85.9382488968878	85.7480957904882	65.0639180319025
chr11.4208_chr11_120281821_120284396_+_2.R.tl.cortex	74.4714877502333	77.8470197342467	59.0221749665618
chr11.4208_chr11_120281821_120284396_+_2.R.tl.heart	80.7526452638093	87.771378108618	60.3343181186725
chr11.4208_chr11_120281821_120284396_+_2.R.tl.kidney	88.2676972463381	89.83460061488	64.4716723217262
chr11.4208_chr11_120281821_120284396_+_2.R.tl.liver	89.2280613713566	86.4398649433182	60.5088207362394
chr11.4208_chr11_120281821_120284396_+_2.R.tl.stomach	92.4721833237946	89.7019542189348	64.4179139669642
chr11.4208_chr11_120281821_120284396_+_2.R.tl.testicle	83.3046269456497	89.61745486432	61.9472800791511


diffExp=-0.0650405222849457,0.190153106399606,-3.37553198401335,-7.01873284480872,-1.56690336854187,2.78819642803848,2.77022910485982,-6.31282791867031
diffExpScore=1.77239172361607
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	69.1820535048618	71.0510609024612	69.2966678017306
cerebhem	68.9180236221927	55.0604547377262	74.5468965250423
cortex	67.8953436346272	85.2946089439467	76.5527738497377
heart	68.5961609726021	66.3447627103838	79.999512606784
kidney	63.6563689740933	70.1592302565324	68.4177312781538
liver	70.557255275276	74.2037514756632	71.3151909421714
stomach	67.0818415320155	68.1951673989463	70.9936565292633
testicle	70.663460340796	75.6021394344426	73.3973079596123
cont.diffExp=-1.86900739759933,13.8575688844665,-17.3992653093195,2.2513982622183,-6.50286128243909,-3.64649620038715,-1.11332586693084,-4.93867909364667
cont.diffExpScore=2.53324705691345

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

tran.correlation=0.742893532005417
cont.tran.correlation=0.0629091831568458

tran.covariance=0.00240893202554642
cont.tran.covariance=0.000207727779105599

tran.mean=85.9083032910814
cont.tran.mean=69.5288552322854

weightedLogRatios:
wLogRatio
Lung	-0.0033515761873435
cerebhem	0.00986288258407312
cortex	-0.192060186658459
heart	-0.369472479887484
kidney	-0.0789914631318685
liver	0.142076312091009
stomach	0.13722482152428
testicle	-0.325714167520525

cont.weightedLogRatios:
wLogRatio
Lung	-0.113295393079382
cerebhem	0.925033693184291
cortex	-0.98832790237684
heart	0.140546805546251
kidney	-0.408733085662076
liver	-0.215751651698075
stomach	-0.069366124893259
testicle	-0.289930401037282

varWeightedLogRatios=0.0381398314413405
cont.varWeightedLogRatios=0.291078042123592

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.1951634527765	0.0911041828468483	57.024422923697	0	***
df.mm.trans1	-0.651415749575783	0.078389359282658	-8.310002218884	2.43637548126117e-16	***
df.mm.trans2	-0.6961277754296	0.0671864564591927	-10.3611324680058	3.3408822747016e-24	***
df.mm.exp2	-0.0321415950476969	0.0836029318659502	-0.38445535736992	0.700705494751158	   
df.mm.exp3	-0.174565602947124	0.0836029318659502	-2.08803207077742	0.0369942390365175	*  
df.mm.exp4	0.00441077769609654	0.0836029318659502	0.0527586485025289	0.957932526113179	   
df.mm.exp5	0.0503041456449273	0.0836029318659502	0.601703128373362	0.547479414175463	   
df.mm.exp6	0.0860410614550109	0.0836029318659502	1.02916320677569	0.30359941361839	   
df.mm.exp7	0.0961943363175558	0.0836029318659502	1.15060960388082	0.250109988178812	   
df.mm.exp8	0.0299561654198372	0.0836029318659502	0.358314771399037	0.720167454841139	   
df.mm.trans1:exp2	0.0252072360986772	0.0778300939292659	0.323875185369637	0.746085985185112	   
df.mm.trans2:exp2	0.0222407968163191	0.0482681752178483	0.460775587971577	0.645038695450358	   
df.mm.trans1:exp3	0.0244185791082320	0.0778300939292659	0.313742125641326	0.753768548488746	   
df.mm.trans2:exp3	0.067996540833584	0.0482681752178483	1.40872408220729	0.159162123556498	   
df.mm.trans1:exp4	-0.0735834186533917	0.0778300939292659	-0.945436590636346	0.344616290036342	   
df.mm.trans2:exp4	0.00901000264158332	0.0482681752178483	0.186665491308063	0.851952797969372	   
df.mm.trans1:exp5	-0.0304932957752309	0.0778300939292659	-0.391793125714894	0.695276961376387	   
df.mm.trans2:exp5	-0.0136486145392227	0.0482681752178483	-0.282766325381527	0.777402157737877	   
df.mm.trans1:exp6	-0.0554088431853718	0.0778300939292659	-0.711920548826895	0.476645038693103	   
df.mm.trans2:exp6	-0.0879067697561768	0.0482681752178483	-1.8212159328466	0.0688097787689187	.  
df.mm.trans1:exp7	-0.0298498190570434	0.0778300939292659	-0.383525414785851	0.701394573764888	   
df.mm.trans2:exp7	-0.0610164588317914	0.0482681752178483	-1.26411364333552	0.206421747789151	   
df.mm.trans1:exp8	-0.0680154334320663	0.0778300939292659	-0.873896329790897	0.382340319267324	   
df.mm.trans2:exp8	0.0042792668359193	0.0482681752178483	0.0886560723832158	0.929369254942706	   
df.mm.trans1:probe2	-0.525203296374791	0.0591162000494903	-8.8842533169437	2.15915418623046e-18	***
df.mm.trans1:probe3	-0.280252070065197	0.0591162000494903	-4.74069831671485	2.37070567620146e-06	***
df.mm.trans1:probe4	-0.0236873532890050	0.0591162000494903	-0.400691405556762	0.688714895513894	   
df.mm.trans1:probe5	-0.485535195086976	0.0591162000494903	-8.2132341842084	5.25877965881261e-16	***
df.mm.trans1:probe6	-0.6209740987933	0.0591162000494903	-10.5042965933778	8.33942619729626e-25	***
df.mm.trans1:probe7	-0.339706210575368	0.0591162000494903	-5.74641486244001	1.14064123442961e-08	***
df.mm.trans1:probe8	0.141513746260469	0.0591162000494903	2.39382345519499	0.0168184956594652	*  
df.mm.trans1:probe9	-0.133992028394604	0.0591162000494903	-2.26658730233726	0.0235834581302129	*  
df.mm.trans1:probe10	-0.525015270001311	0.0591162000494903	-8.88107269347123	2.21807843897144e-18	***
df.mm.trans1:probe11	0.131209542206931	0.0591162000494903	2.21951921972465	0.0266279459787673	*  
df.mm.trans1:probe12	0.166607231381805	0.0591162000494903	2.81830075753053	0.00490291954047279	** 
df.mm.trans1:probe13	0.0063402630118946	0.0591162000494903	0.107250855206978	0.914606951147156	   
df.mm.trans1:probe14	0.0113757542602536	0.0591162000494903	0.192430404030202	0.8474359162375	   
df.mm.trans1:probe15	-0.0347242649863772	0.0591162000494903	-0.58739000404808	0.557046465871217	   
df.mm.trans1:probe16	0.239037971704354	0.0591162000494903	4.04352734959687	5.58294672691794e-05	***
df.mm.trans1:probe17	0.0139173649947481	0.0591162000494903	0.235423876756234	0.81391780113371	   
df.mm.trans1:probe18	-0.0208789049450627	0.0591162000494903	-0.353184151342331	0.724009140609167	   
df.mm.trans1:probe19	-0.0233288631656774	0.0591162000494903	-0.394627245089286	0.693184420851441	   
df.mm.trans1:probe20	0.238063355113502	0.0591162000494903	4.02704089427606	5.98371207866849e-05	***
df.mm.trans1:probe21	0.195380898041456	0.0591162000494903	3.30503141064359	0.000976223584762101	***
df.mm.trans1:probe22	0.221204330772442	0.0591162000494903	3.74185638771194	0.000190838963713943	***
df.mm.trans1:probe23	0.00236887026584832	0.0591162000494903	0.0400714231270815	0.96804249764806	   
df.mm.trans1:probe24	-0.492608306878422	0.0591162000494903	-8.33288178986514	2.02881921809070e-16	***
df.mm.trans1:probe25	-0.478741687482639	0.0591162000494903	-8.09831631738594	1.29796691665533e-15	***
df.mm.trans1:probe26	-0.55698207791261	0.0591162000494903	-9.42181800329387	2.02618504297909e-20	***
df.mm.trans1:probe27	-0.300185312109851	0.0591162000494903	-5.07788578864245	4.38729648888155e-07	***
df.mm.trans1:probe28	-0.357743957978912	0.0591162000494903	-6.05153845611558	1.88639121125976e-09	***
df.mm.trans1:probe29	0.200300280155167	0.0591162000494903	3.38824687627895	0.000725009547253665	***
df.mm.trans1:probe30	-0.526977809742169	0.0591162000494903	-8.91427069569761	1.67398174286823e-18	***
df.mm.trans2:probe2	-0.393547213536453	0.0591162000494903	-6.65718048871522	4.14653362442233e-11	***
df.mm.trans2:probe3	0.0385309615680468	0.0591162000494903	0.651783462668268	0.514659082085327	   
df.mm.trans2:probe4	-0.324416855182007	0.0591162000494903	-5.48778261983034	4.91076935808619e-08	***
df.mm.trans2:probe5	-0.275896114448769	0.0591162000494903	-4.66701368182998	3.38064143211089e-06	***
df.mm.trans2:probe6	-0.0254167742658235	0.0591162000494903	-0.429946008785161	0.667308028822466	   
df.mm.trans3:probe2	0.447437658450881	0.0591162000494903	7.56878246701072	7.22090380167125e-14	***
df.mm.trans3:probe3	0.140565290156136	0.0591162000494903	2.37777952639816	0.0175648909715915	*  
df.mm.trans3:probe4	0.137835358369675	0.0591162000494903	2.33160044546645	0.0198778370704294	*  
df.mm.trans3:probe5	0.0412463975372450	0.0591162000494903	0.697717334718991	0.485482001110075	   
df.mm.trans3:probe6	0.607104554815458	0.0591162000494903	10.2696816491454	8.04071114355012e-24	***
df.mm.trans3:probe7	0.181450518934704	0.0591162000494903	3.06938738929091	0.00219056592150803	** 
df.mm.trans3:probe8	0.192324739233155	0.0591162000494903	3.25333392660805	0.00117067607492921	** 
df.mm.trans3:probe9	0.0191849073252525	0.0591162000494903	0.324528763844622	0.74559131998641	   
df.mm.trans3:probe10	0.128125226671123	0.0591162000494903	2.16734544107809	0.0303943078320934	*  
df.mm.trans3:probe11	0.680805551300544	0.0591162000494903	11.5163956873174	2.94482104923496e-29	***
df.mm.trans3:probe12	0.36014300210624	0.0591162000494903	6.09212029536301	1.47558289737435e-09	***
df.mm.trans3:probe13	0.133274734422536	0.0591162000494903	2.25445367447438	0.0243378575410996	*  
df.mm.trans3:probe14	0.184061205186176	0.0591162000494903	3.11354933219803	0.00188992892505397	** 
df.mm.trans3:probe15	0.247382055879752	0.0591162000494903	4.18467451684397	3.0526783914238e-05	***
df.mm.trans3:probe16	0.280798269765377	0.0591162000494903	4.7499377417747	2.26673305898028e-06	***
df.mm.trans3:probe17	0.169883980127748	0.0591162000494903	2.87372970497978	0.00412426245725034	** 
df.mm.trans3:probe18	0.493727829115085	0.0591162000494903	8.35181944546082	1.74299431382533e-16	***
df.mm.trans3:probe19	0.535941887552023	0.0591162000494903	9.06590557416324	4.5761867588124e-19	***
df.mm.trans3:probe20	1.26758234141162	0.0591162000494903	21.4422161835577	2.87095225157819e-87	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.1833443801254	0.161220385891706	25.9479863975478	2.10856286610303e-119	***
df.mm.trans1	-0.0385088130618325	0.138719895820798	-0.277601225361207	0.781363779734319	   
df.mm.trans2	0.0583720690021624	0.118894940918973	0.490955027615035	0.623543278928297	   
df.mm.exp2	-0.331822368835355	0.147945972576964	-2.24286178971676	0.0250780452094703	*  
df.mm.exp3	0.0643548981334012	0.147945972576964	0.434989185663184	0.663644357736521	   
df.mm.exp4	-0.220662599599161	0.147945972576964	-1.49150798602760	0.136077101774400	   
df.mm.exp5	-0.0831087124744438	0.147945972576964	-0.561750421635908	0.574385427905174	   
df.mm.exp6	0.0343864068220891	0.147945972576964	0.232425433576441	0.816245133757259	   
df.mm.exp7	-0.0960468873616266	0.147945972576964	-0.649202446600306	0.516325193036575	   
df.mm.exp8	0.0257824845657029	0.147945972576964	0.174269593937682	0.861681434765739	   
df.mm.trans1:exp2	0.327998617106002	0.137730204971572	2.38145740924224	0.0173912668153644	*  
df.mm.trans2:exp2	0.0768553412601226	0.085416647092831	0.899770055087693	0.36841360941447	   
df.mm.trans1:exp3	-0.0831289293031339	0.137730204971572	-0.60356353437717	0.546241899294319	   
df.mm.trans2:exp3	0.118357567036408	0.085416647092831	1.38564988283580	0.166097693305370	   
df.mm.trans1:exp4	0.212157683693258	0.137730204971572	1.54038603033407	0.123715983890503	   
df.mm.trans2:exp4	0.152128636775337	0.085416647092831	1.78101859477115	0.075148934273593	.  
df.mm.trans1:exp5	-0.000133391664666129	0.137730204971572	-0.000968499717935233	0.999227401626241	   
df.mm.trans2:exp5	0.0704773030156129	0.085416647092831	0.82510032194331	0.409469953626206	   
df.mm.trans1:exp6	-0.0147033816133378	0.137730204971572	-0.106754953398731	0.915000285475262	   
df.mm.trans2:exp6	0.009029514839901	0.085416647092831	0.105711417472144	0.915828054589378	   
df.mm.trans1:exp7	0.0652187901395801	0.137730204971572	0.473525688523019	0.635919755016847	   
df.mm.trans2:exp7	0.0550218042429362	0.085416647092831	0.644157855823332	0.51958966863143	   
df.mm.trans1:exp8	-0.00459535870034333	0.137730204971572	-0.0333649303817695	0.973388830820513	   
df.mm.trans2:exp8	0.0363033114122699	0.085416647092831	0.425014474904586	0.67089829151897	   
df.mm.trans1:probe2	0.212374558044760	0.10461360045841	2.03008554446218	0.0425564203480781	*  
df.mm.trans1:probe3	0.158400111707432	0.10461360045841	1.51414453774015	0.130238592305383	   
df.mm.trans1:probe4	0.239410321827522	0.10461360045841	2.28852004690061	0.0222712620708652	*  
df.mm.trans1:probe5	0.167989330180174	0.10461360045841	1.60580774817094	0.108565346507440	   
df.mm.trans1:probe6	0.0388164363160348	0.10461360045841	0.371045792764456	0.710665455138301	   
df.mm.trans1:probe7	0.302064151800232	0.10461360045841	2.88742716507802	0.00395002305604921	** 
df.mm.trans1:probe8	0.104058135049111	0.10461360045841	0.994690313622084	0.3200768321699	   
df.mm.trans1:probe9	0.0351260396494322	0.10461360045841	0.335769340654677	0.737100403595708	   
df.mm.trans1:probe10	0.156023480247329	0.10461360045841	1.49142634957257	0.136098517987266	   
df.mm.trans1:probe11	0.269784104782917	0.10461360045841	2.57886262972253	0.0100245835011342	*  
df.mm.trans1:probe12	0.184394488168688	0.10461360045841	1.76262443277627	0.0782049228094809	.  
df.mm.trans1:probe13	0.0674949824277462	0.10461360045841	0.645183629394147	0.518925003386163	   
df.mm.trans1:probe14	0.181883405400313	0.10461360045841	1.73862102636093	0.0823443561169483	.  
df.mm.trans1:probe15	0.103246041077398	0.10461360045841	0.986927518267039	0.323866602551016	   
df.mm.trans1:probe16	0.176133584438614	0.10461360045841	1.68365856510825	0.0924940059699265	.  
df.mm.trans1:probe17	0.215670748124238	0.10461360045841	2.06159378110669	0.0394500292336061	*  
df.mm.trans1:probe18	0.233092715916316	0.10461360045841	2.22813013695082	0.0260468145740603	*  
df.mm.trans1:probe19	0.213475251786019	0.10461360045841	2.04060706113339	0.041496835085933	*  
df.mm.trans1:probe20	0.0996435664695895	0.10461360045841	0.952491511934948	0.34102943832058	   
df.mm.trans1:probe21	-0.0368611504307002	0.10461360045841	-0.352355241280073	0.724630465229665	   
df.mm.trans1:probe22	0.265085320947561	0.10461360045841	2.53394701822683	0.0113982473666452	*  
df.mm.trans1:probe23	0.259365322562629	0.10461360045841	2.47926963058443	0.0132944675774032	*  
df.mm.trans1:probe24	0.101378763478253	0.10461360045841	0.969078236806854	0.332691105879193	   
df.mm.trans1:probe25	0.171937467633508	0.10461360045841	1.64354794099514	0.100517718164054	   
df.mm.trans1:probe26	0.185633773842168	0.10461360045841	1.77447074786388	0.0762254022501451	.  
df.mm.trans1:probe27	0.155924111169317	0.10461360045841	1.4904764818921	0.136347894391934	   
df.mm.trans1:probe28	0.0435894694617172	0.10461360045841	0.416671152419102	0.67698953609119	   
df.mm.trans1:probe29	0.112253405696997	0.10461360045841	1.07302879554006	0.283462522395266	   
df.mm.trans1:probe30	0.177908037710054	0.10461360045841	1.70062054006814	0.089259661760386	.  
df.mm.trans2:probe2	0.179749303057718	0.10461360045841	1.71822117076621	0.0860005932201976	.  
df.mm.trans2:probe3	0.0856610544795673	0.10461360045841	0.818832867850893	0.413035759108684	   
df.mm.trans2:probe4	0.165995899759573	0.10461360045841	1.58675257358689	0.112818212612299	   
df.mm.trans2:probe5	0.0566691888041445	0.10461360045841	0.54170001372502	0.588120423407637	   
df.mm.trans2:probe6	0.0756653178990014	0.10461360045841	0.723283756294027	0.469639072107676	   
df.mm.trans3:probe2	0.050496433162022	0.10461360045841	0.482694725549545	0.629395894402944	   
df.mm.trans3:probe3	-0.0238808231692080	0.10461360045841	-0.228276467539247	0.81946815322391	   
df.mm.trans3:probe4	0.146684973945685	0.10461360045841	1.40215969341387	0.161112513266857	   
df.mm.trans3:probe5	0.0354345216837564	0.10461360045841	0.338718116272498	0.734878216133478	   
df.mm.trans3:probe6	0.0525811340151024	0.10461360045841	0.502622352970315	0.615317159241502	   
df.mm.trans3:probe7	0.0279378411072719	0.10461360045841	0.267057447452818	0.78946839057442	   
df.mm.trans3:probe8	-0.00123891126909966	0.10461360045841	-0.0118427361611763	0.990552948944015	   
df.mm.trans3:probe9	0.00826611040024695	0.10461360045841	0.0790156381581877	0.937032661806676	   
df.mm.trans3:probe10	0.151376759185444	0.10461360045841	1.44700840542836	0.148141926614383	   
df.mm.trans3:probe11	-0.0148579006475573	0.10461360045841	-0.142026472489724	0.887081675530137	   
df.mm.trans3:probe12	0.0854779147497155	0.10461360045841	0.817082237635994	0.414035043553611	   
df.mm.trans3:probe13	0.00714445613719693	0.10461360045841	0.068293760141037	0.9455625670197	   
df.mm.trans3:probe14	-0.0747214835347574	0.10461360045841	-0.714261656298347	0.475196962038417	   
df.mm.trans3:probe15	0.196432676160958	0.10461360045841	1.87769730991193	0.060652140585582	.  
df.mm.trans3:probe16	-0.0308511521755398	0.10461360045841	-0.294905748777904	0.768114194264023	   
df.mm.trans3:probe17	0.0267087433405358	0.10461360045841	0.255308518428768	0.798526258656486	   
df.mm.trans3:probe18	0.0557483178139716	0.10461360045841	0.532897420313287	0.594198042313809	   
df.mm.trans3:probe19	0.0138216497053221	0.10461360045841	0.132120963667788	0.894909592309293	   
df.mm.trans3:probe20	-0.00877755451184909	0.10461360045841	-0.0839045255433941	0.933145595899867