chr11.4050_chr11_70554247_70564006_-_2.R 

fitVsDatCorrelation=0.913992863716181
cont.fitVsDatCorrelation=0.247701128526186

fstatistic=10309.1445011498,59,853
cont.fstatistic=1796.06969062650,59,853

residuals=-0.649622439389813,-0.097610301487092,0.00805425369318212,0.0930553880662399,0.683403331244985
cont.residuals=-0.843632594163549,-0.271322758805200,-0.0942082568571255,0.179266737449144,1.34051543554164

predictedValues:
Include	Exclude	Both
chr11.4050_chr11_70554247_70564006_-_2.R.tl.Lung	64.9493406150555	57.2621693503252	72.5914667433057
chr11.4050_chr11_70554247_70564006_-_2.R.tl.cerebhem	62.2783957199455	60.9693674724917	69.3861084848994
chr11.4050_chr11_70554247_70564006_-_2.R.tl.cortex	83.0035795323428	59.2871903626185	90.876084087853
chr11.4050_chr11_70554247_70564006_-_2.R.tl.heart	72.2368069340975	60.2482504278577	77.8430235862473
chr11.4050_chr11_70554247_70564006_-_2.R.tl.kidney	69.5877143871605	62.6636094575742	72.1320350231182
chr11.4050_chr11_70554247_70564006_-_2.R.tl.liver	67.0529768597313	66.3440893123497	66.0897244843104
chr11.4050_chr11_70554247_70564006_-_2.R.tl.stomach	64.3969900072397	57.4016653988846	70.8877998875541
chr11.4050_chr11_70554247_70564006_-_2.R.tl.testicle	77.4726109929332	61.5416873973707	79.3285099603699


diffExp=7.68717126473024,1.30902824745384,23.7163891697242,11.9885565062398,6.92410492958636,0.708887547381678,6.99532460835507,15.9309235955625
diffExpScore=0.98688703199434
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
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=0,0,1,0,0,0,0,1
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	70.2812976075478	73.2113847629827	84.3952937039011
cerebhem	67.344086294848	63.5668755279485	80.1590947324945
cortex	72.6144269079816	85.0095338540312	68.8433664124156
heart	69.3872802216792	77.8547434584831	75.626554422522
kidney	68.5840786699115	77.9393371331443	71.8622424794471
liver	77.6908784048397	73.6936865674624	72.19488714142
stomach	77.9547713553254	80.5153982123134	77.2601017640567
testicle	75.7574743521867	78.7044046460627	77.1103276372203
cont.diffExp=-2.93008715543499,3.77721076689955,-12.3951069460497,-8.46746323680397,-9.35525846323286,3.99719183737729,-2.56062685698802,-2.94693029387594
cont.diffExpScore=1.45634619696565

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=0.0217670681830576
cont.tran.correlation=0.424919525389827

tran.covariance=0.000232906787316514
cont.tran.covariance=0.00223825878110325

tran.mean=65.4185277642486
cont.tran.mean=74.3818536235468

weightedLogRatios:
wLogRatio
Lung	0.517804628015346
cerebhem	0.0875423466702296
cortex	1.43029932671109
heart	0.760242096618135
kidney	0.439161277143035
liver	0.0446407930987687
stomach	0.472344175763585
testicle	0.974896464324653

cont.weightedLogRatios:
wLogRatio
Lung	-0.174528663076407
cerebhem	0.241335456146653
cortex	-0.687759748429559
heart	-0.494793559532651
kidney	-0.54881909461683
liver	0.228519749825362
stomach	-0.141310812662973
testicle	-0.165875686482052

varWeightedLogRatios=0.210701765159468
cont.varWeightedLogRatios=0.11822671092185

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.99257005533410	0.0778307281196725	38.4497245192507	1.98446527228572e-188	***
df.mm.trans1	0.917853468757452	0.0672127130004649	13.6559503073638	1.52729915975502e-38	***
df.mm.trans2	1.05968197534961	0.0593821281398666	17.8451330146618	9.0594884236856e-61	***
df.mm.exp2	0.0658990725509286	0.0763844036383353	0.862729423966538	0.388528738734992	   
df.mm.exp3	0.0553797382509848	0.0763844036383353	0.72501368883099	0.468642465948342	   
df.mm.exp4	0.0873285611717936	0.0763844036383353	1.14327738402301	0.253244201958308	   
df.mm.exp5	0.165470263829778	0.0763844036383353	2.16628337655481	0.0305658681223654	*  
df.mm.exp6	0.272924000185376	0.0763844036383353	3.57303306938961	0.000372653211207097	***
df.mm.exp7	0.0176414671826011	0.0763844036383353	0.230956403955576	0.817404054572976	   
df.mm.exp8	0.159641273220207	0.0763844036383353	2.08997210970025	0.0369159346233595	*  
df.mm.trans1:exp2	-0.107892075941929	0.0706036926953228	-1.52813644475392	0.126849378868699	   
df.mm.trans2:exp2	-0.00316769200737917	0.0521442828150834	-0.0607485967083409	0.951573658441602	   
df.mm.trans1:exp3	0.189896404639438	0.0706036926953228	2.68961009530905	0.00729327549753264	** 
df.mm.trans2:exp3	-0.0206266546195989	0.0521442828150834	-0.395568862127152	0.69252202140385	   
df.mm.trans1:exp4	0.0190135552865891	0.0706036926953227	0.269299728679046	0.787764167487458	   
df.mm.trans2:exp4	-0.0364952126812142	0.0521442828150834	-0.69988905227895	0.484187420294267	   
df.mm.trans1:exp5	-0.0964898203682269	0.0706036926953228	-1.36663985529214	0.172098320122839	   
df.mm.trans2:exp5	-0.0753295610262327	0.0521442828150834	-1.44463701405905	0.148927091218442	   
df.mm.trans1:exp6	-0.241048584705454	0.0706036926953227	-3.4141073292817	0.000670088918465522	***
df.mm.trans2:exp6	-0.125709511753301	0.0521442828150834	-2.41080143338242	0.0161280385147754	*  
df.mm.trans1:exp7	-0.0261821650545455	0.0706036926953227	-0.370832800028318	0.710854143017542	   
df.mm.trans2:exp7	-0.0152083352769036	0.0521442828150834	-0.291658729507052	0.77061847931871	   
df.mm.trans1:exp8	0.016675603261555	0.0706036926953227	0.236185992898637	0.813345047797302	   
df.mm.trans2:exp8	-0.0875666690972117	0.0521442828150834	-1.67931486195227	0.093456906177785	.  
df.mm.trans1:probe2	-0.0349849131712125	0.0483390439154887	-0.723740279852798	0.469423626362912	   
df.mm.trans1:probe3	0.0962532041530555	0.0483390439154887	1.99121034171332	0.0467763480195642	*  
df.mm.trans1:probe4	-0.171838295017503	0.0483390439154887	-3.55485506328856	0.00039898089468679	***
df.mm.trans1:probe5	0.221111765233441	0.0483390439154887	4.57418573731023	5.48902046878606e-06	***
df.mm.trans1:probe6	0.352835455546381	0.0483390439154887	7.29918150973867	6.63222818338704e-13	***
df.mm.trans1:probe7	0.0855454763339164	0.0483390439154887	1.76969731721372	0.0771347913177007	.  
df.mm.trans1:probe8	0.194464499487058	0.0483390439154887	4.02292812880290	6.25790822079966e-05	***
df.mm.trans1:probe9	0.303418310717786	0.0483390439154887	6.27687860869266	5.49251650867221e-10	***
df.mm.trans1:probe10	0.00994740089269804	0.0483390439154887	0.205783980959348	0.837008756122337	   
df.mm.trans1:probe11	0.260544528735842	0.0483390439154887	5.38993963536707	9.12630516552835e-08	***
df.mm.trans1:probe12	0.285058221526076	0.0483390439154887	5.89705956999159	5.32949685426364e-09	***
df.mm.trans1:probe13	0.314039284299443	0.0483390439154887	6.49659692997814	1.39491794388021e-10	***
df.mm.trans1:probe14	0.219194689159894	0.0483390439154887	4.53452678011407	6.5988642266078e-06	***
df.mm.trans1:probe15	0.167071935524859	0.0483390439154887	3.45625237886275	0.000574800519825488	***
df.mm.trans1:probe16	0.383157918265733	0.0483390439154887	7.92646869341497	7.03827434993093e-15	***
df.mm.trans1:probe17	1.21498048792173	0.0483390439154887	25.1345576889332	9.14797621843808e-105	***
df.mm.trans1:probe18	0.87425767061556	0.0483390439154887	18.0859528819814	3.88835190289647e-62	***
df.mm.trans1:probe19	0.757707820346683	0.0483390439154887	15.6748615399052	7.48905645378472e-49	***
df.mm.trans1:probe20	0.91688802885228	0.0483390439154887	18.9678560969323	3.27950930624222e-67	***
df.mm.trans1:probe21	0.884545504897958	0.0483390439154887	18.2987794802978	2.36961759184522e-63	***
df.mm.trans1:probe22	1.08769114265041	0.0483390439154887	22.5012961479343	2.18565890488637e-88	***
df.mm.trans2:probe2	0.0602725664179537	0.0483390439154887	1.24687129773043	0.212786953895687	   
df.mm.trans2:probe3	-0.0256007881018668	0.0483390439154887	-0.529608904690476	0.596520926768097	   
df.mm.trans2:probe4	-0.0814068814321515	0.0483390439154887	-1.68408133132454	0.0925318003377635	.  
df.mm.trans2:probe5	-0.00628340628826636	0.0483390439154887	-0.129986151551770	0.896608029015913	   
df.mm.trans2:probe6	-0.0207710228666740	0.0483390439154887	-0.429694532291288	0.667526380987165	   
df.mm.trans3:probe2	-0.59730338785042	0.0483390439154887	-12.3565412028977	2.20177791344381e-32	***
df.mm.trans3:probe3	-0.112345720998997	0.0483390439154887	-2.32411963288747	0.0203531462776582	*  
df.mm.trans3:probe4	-0.783783319893038	0.0483390439154887	-16.2142909004019	9.67532772503889e-52	***
df.mm.trans3:probe5	-0.63199144307449	0.0483390439154887	-13.0741403197672	9.8058615387812e-36	***
df.mm.trans3:probe6	-0.949843528818672	0.0483390439154887	-19.6496134776535	3.36691005272312e-71	***
df.mm.trans3:probe7	-1.04755422507597	0.0483390439154887	-21.6709752660273	2.60852529252506e-83	***
df.mm.trans3:probe8	-1.05142648876677	0.0483390439154887	-21.7510816019652	8.48950766379113e-84	***
df.mm.trans3:probe9	-0.801081480585804	0.0483390439154887	-16.5721416002008	1.09954440081763e-53	***
df.mm.trans3:probe10	-0.87725162983126	0.0483390439154887	-18.1478895479390	1.72500652363868e-62	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14578826089592	0.185850691628435	22.3070908403422	3.40043076145237e-87	***
df.mm.trans1	0.0683812823810667	0.160496111229397	0.426061926717524	0.670170209136725	   
df.mm.trans2	0.157800506417395	0.141797588841688	1.11285747315191	0.266083161111329	   
df.mm.exp2	-0.132450767670320	0.182397037632518	-0.726167318227906	0.467935405832919	   
df.mm.exp3	0.385748134166358	0.182397037632517	2.11488157468621	0.0347294953941123	*  
df.mm.exp4	0.158395898564374	0.182397037632517	0.868412670624073	0.385412718191851	   
df.mm.exp5	0.198895199325878	0.182397037632517	1.09045191691435	0.275822058590181	   
df.mm.exp6	0.262940732120198	0.182397037632517	1.44158444420547	0.149786651197455	   
df.mm.exp7	0.287054551238986	0.182397037632517	1.57378954705024	0.115906918449224	   
df.mm.exp8	0.237653982725481	0.182397037632517	1.30294869812684	0.192943892920578	   
df.mm.trans1:exp2	0.0897601345558288	0.168593374827115	0.532406060723762	0.594583439135796	   
df.mm.trans2:exp2	-0.00880766111507331	0.124514459260242	-0.0707360507960349	0.943624408471982	   
df.mm.trans1:exp3	-0.353090240882113	0.168593374827115	-2.09433046372191	0.0365251110037362	*  
df.mm.trans2:exp3	-0.236335659399980	0.124514459260242	-1.89805795089248	0.0580257951198436	.  
df.mm.trans1:exp4	-0.171198056503746	0.168593374827115	-1.01544949010780	0.310179545131971	   
df.mm.trans2:exp4	-0.0969020098185082	0.124514459260242	-0.778239012514825	0.436643933584968	   
df.mm.trans1:exp5	-0.223340507436377	0.168593374827115	-1.32472884931216	0.18561588164547	   
df.mm.trans2:exp5	-0.136315342709428	0.124514459260242	-1.09477520538013	0.273924158846761	   
df.mm.trans1:exp6	-0.162708603230586	0.168593374827115	-0.965094882271836	0.334770815769303	   
df.mm.trans2:exp6	-0.256374538959182	0.124514459260242	-2.05899411588292	0.0397979139929613	*  
df.mm.trans1:exp7	-0.183431473694434	0.168593374827115	-1.08801116225673	0.276897495943164	   
df.mm.trans2:exp7	-0.191957041388769	0.124514459260242	-1.54164458111301	0.123530860962749	   
df.mm.trans1:exp8	-0.162622598433244	0.168593374827115	-0.96458475073535	0.335026216719322	   
df.mm.trans2:exp8	-0.165305799742547	0.124514459260242	-1.32760324162071	0.184664391201705	   
df.mm.trans1:probe2	-0.0209110330830953	0.115427993048418	-0.18116084782245	0.856284360574311	   
df.mm.trans1:probe3	-0.103995226288392	0.115427993048418	-0.900953257021197	0.367867471452915	   
df.mm.trans1:probe4	0.0599321217844304	0.115427993048418	0.519216527998464	0.603744440257965	   
df.mm.trans1:probe5	-0.0469722168317181	0.115427993048418	-0.406939561116816	0.684154467602416	   
df.mm.trans1:probe6	0.0736199062074346	0.115427993048418	0.637799412977351	0.523775428942226	   
df.mm.trans1:probe7	0.0597631671531182	0.115427993048418	0.51775280479883	0.604764997990486	   
df.mm.trans1:probe8	0.107040558731614	0.115427993048418	0.927336219791281	0.354014303391949	   
df.mm.trans1:probe9	-0.0289574989345892	0.115427993048418	-0.250870678505539	0.801974511168847	   
df.mm.trans1:probe10	0.205442421814580	0.115427993048418	1.77983187950261	0.0754594124478164	.  
df.mm.trans1:probe11	-0.0964175655208883	0.115427993048418	-0.835304876871979	0.403779968723726	   
df.mm.trans1:probe12	0.114965196502577	0.115427993048418	0.995990603894097	0.319537158593823	   
df.mm.trans1:probe13	0.141201530958368	0.115427993048418	1.22328671953205	0.221559249788898	   
df.mm.trans1:probe14	0.107225876368035	0.115427993048418	0.928941702408857	0.35318209163617	   
df.mm.trans1:probe15	0.128612552800105	0.115427993048418	1.11422324345669	0.265497273911950	   
df.mm.trans1:probe16	0.0167178961857940	0.115427993048418	0.144833984757765	0.88487616267727	   
df.mm.trans1:probe17	0.0463834708791902	0.115427993048418	0.401839013693446	0.68790316781851	   
df.mm.trans1:probe18	0.0906188265255641	0.115427993048418	0.785068024942206	0.432631671810686	   
df.mm.trans1:probe19	0.0982718651949377	0.115427993048418	0.851369434741157	0.394803104552776	   
df.mm.trans1:probe20	-0.0102501388048372	0.115427993048418	-0.088801152425284	0.929260785971837	   
df.mm.trans1:probe21	0.106717004109471	0.115427993048418	0.92453313352427	0.355470272950977	   
df.mm.trans1:probe22	0.177749148842230	0.115427993048418	1.53991370852018	0.123952249624436	   
df.mm.trans2:probe2	0.0609252405040845	0.115427993048418	0.527820322393794	0.597761319758874	   
df.mm.trans2:probe3	-0.0713588187442648	0.115427993048418	-0.61821068581113	0.53660149392707	   
df.mm.trans2:probe4	-0.0150305940997145	0.115427993048418	-0.130216195419855	0.896426082533994	   
df.mm.trans2:probe5	-0.108103372466958	0.115427993048418	-0.936543810664822	0.349258339707618	   
df.mm.trans2:probe6	-0.0302377174422529	0.115427993048418	-0.261961736002542	0.793414187364331	   
df.mm.trans3:probe2	-0.0725507093814842	0.115427993048418	-0.628536522774435	0.529820787848232	   
df.mm.trans3:probe3	0.125173854745312	0.115427993048418	1.08443239321337	0.278479531249647	   
df.mm.trans3:probe4	0.0790464420406988	0.115427993048418	0.684811716405237	0.493648771582397	   
df.mm.trans3:probe5	0.0887270418931445	0.115427993048418	0.768678719519334	0.442296826325843	   
df.mm.trans3:probe6	0.0525082225465975	0.115427993048418	0.454900246984041	0.64929675490113	   
df.mm.trans3:probe7	0.0131525754440346	0.115427993048418	0.113946150294041	0.909307294388663	   
df.mm.trans3:probe8	0.225254217296323	0.115427993048418	1.95146958157573	0.0513281873186932	.  
df.mm.trans3:probe9	0.113300815007921	0.115427993048418	0.981571385031319	0.326589365473031	   
df.mm.trans3:probe10	0.0108034259141207	0.115427993048418	0.0935945053604894	0.925453253287674