chr1.1321_chr1_83776921_83778065_+_1.R 

fitVsDatCorrelation=0.790669467954906
cont.fitVsDatCorrelation=0.288679461349835

fstatistic=7167.30505434236,48,600
cont.fstatistic=2923.46181477586,48,600

residuals=-0.584641701509956,-0.0987322203731747,-0.00662052401020555,0.093573705182444,0.821485505913836
cont.residuals=-0.66713611135087,-0.196946801217563,-0.0272752381983474,0.161293589416991,1.18507449635443

predictedValues:
Include	Exclude	Both
chr1.1321_chr1_83776921_83778065_+_1.R.tl.Lung	109.001792390119	92.6766060144962	86.7469049651051
chr1.1321_chr1_83776921_83778065_+_1.R.tl.cerebhem	146.577559740167	89.6296945483572	85.061214557911
chr1.1321_chr1_83776921_83778065_+_1.R.tl.cortex	102.562534324019	89.476678919723	97.594690240701
chr1.1321_chr1_83776921_83778065_+_1.R.tl.heart	99.0195871431558	84.7799085957974	89.2475628475732
chr1.1321_chr1_83776921_83778065_+_1.R.tl.kidney	103.144675408287	93.4520083477225	81.2312344232795
chr1.1321_chr1_83776921_83778065_+_1.R.tl.liver	97.2314030067075	84.1025767591132	70.6060944878838
chr1.1321_chr1_83776921_83778065_+_1.R.tl.stomach	95.0416500739312	77.8209541764305	73.089514747196
chr1.1321_chr1_83776921_83778065_+_1.R.tl.testicle	105.069212452221	87.397021553037	84.6949567759022


diffExp=16.3251863756224,56.9478651918101,13.0858554042960,14.2396785473585,9.69266706056487,13.1288262475944,17.2206958975007,17.6721908991836
diffExpScore=0.993723046984383
diffExp1.5=0,1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,1,0,0,0,0,1,1
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	77.0715449975373	88.5371048055256	84.5817883365452
cerebhem	80.9649436502943	91.4292743543508	90.7689775165873
cortex	70.4907974962634	90.3944999093464	78.4106208065229
heart	76.2279571644212	96.9722881424752	85.0453241350852
kidney	76.0360648818162	86.7147040423715	81.9096617796994
liver	72.545642787671	84.2235923114397	82.8511115601318
stomach	76.3435154223689	102.688609912936	86.0254765987699
testicle	63.4841332394166	88.4207156252845	78.0136347174656
cont.diffExp=-11.4655598079883,-10.4643307040565,-19.9037024130830,-20.744330978054,-10.6786391605553,-11.6779495237687,-26.3450944905675,-24.936582385868
cont.diffExpScore=0.992712230211999

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,-1,-1
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=0,0,-1,-1,0,0,-1,-1
cont.diffExp1.2Score=0.8

tran.correlation=0.395330651442597
cont.tran.correlation=0.298695467707967

tran.covariance=0.00361250758794033
cont.tran.covariance=0.00141701824933207

tran.mean=97.3114914658303
cont.tran.mean=82.65908679647

weightedLogRatios:
wLogRatio
Lung	0.748003393919407
cerebhem	2.33225162371618
cortex	0.622719504192723
heart	0.701412160835022
kidney	0.452645538243874
liver	0.653416635204396
stomach	0.89044715192675
testicle	0.840228723236687

cont.weightedLogRatios:
wLogRatio
Lung	-0.612177792377858
cerebhem	-0.541477390238874
cortex	-1.08926991103741
heart	-1.07208274256969
kidney	-0.57782374595673
liver	-0.650597601231373
stomach	-1.32916167350097
testicle	-1.43010953042781

varWeightedLogRatios=0.350735873193709
cont.varWeightedLogRatios=0.129527454951898

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.57715711623738	0.0945760894425183	48.3965571342352	2.53837641983695e-209	***
df.mm.trans1	0.224223627766032	0.0748548557102526	2.99544532734062	0.00285349757374434	** 
df.mm.trans2	-0.0511196171571868	0.0748548557102526	-0.682916514528598	0.494923191318726	   
df.mm.exp2	0.282384551366419	0.0993475915081532	2.84238950416069	0.00462996088958480	** 
df.mm.exp3	-0.213858028461025	0.0993475915081532	-2.15262418760776	0.0317462711925528	*  
df.mm.exp4	-0.213523508876358	0.0993475915081532	-2.14925702410042	0.0320133060976599	*  
df.mm.exp5	0.0187951456172747	0.0993475915081532	0.18918571987457	0.850011236534078	   
df.mm.exp6	-0.0054711876423405	0.0993475915081532	-0.0550711653829222	0.956100100340511	   
df.mm.exp7	-0.140444637101463	0.0993475915081533	-1.41366926937466	0.157977645533850	   
df.mm.exp8	-0.0714612186384375	0.0993475915081532	-0.719304993242567	0.472233079192978	   
df.mm.trans1:exp2	0.0138058289800582	0.0758779763662774	0.181947775114809	0.855685163436933	   
df.mm.trans2:exp2	-0.315813952340341	0.0758779763662774	-4.16212934851936	3.61455280750242e-05	***
df.mm.trans1:exp3	0.152966405935707	0.0758779763662773	2.01595262895928	0.0442501523429744	*  
df.mm.trans2:exp3	0.178719970638116	0.0758779763662773	2.35536026653375	0.0188256943063321	*  
df.mm.trans1:exp4	0.117476863332112	0.0758779763662774	1.54823400620266	0.122093236524632	   
df.mm.trans2:exp4	0.124466018246327	0.0758779763662773	1.64034446102655	0.101457673194641	   
df.mm.trans1:exp5	-0.0740268533629255	0.0758779763662774	-0.975603948708171	0.329653935704043	   
df.mm.trans2:exp5	-0.0104631992191714	0.0758779763662773	-0.137895074700775	0.890369606408532	   
df.mm.trans1:exp6	-0.108799402908435	0.0758779763662774	-1.43387328074274	0.152129350145039	   
df.mm.trans2:exp6	-0.0916076850395882	0.0758779763662774	-1.20730269080162	0.227791311453422	   
df.mm.trans1:exp7	0.0033955284141021	0.0758779763662774	0.0447498546575786	0.964321584137527	   
df.mm.trans2:exp7	-0.0342607125106859	0.0758779763662774	-0.45152380376228	0.651775232408091	   
df.mm.trans1:exp8	0.0347161918129738	0.0758779763662774	0.457526590395508	0.647458219698456	   
df.mm.trans2:exp8	0.0128063439587936	0.0758779763662774	0.168775507361648	0.866030104053505	   
df.mm.trans1:probe2	-0.326891306078536	0.0555369920032779	-5.88601028408672	6.58439258646366e-09	***
df.mm.trans1:probe3	-0.543676626769741	0.0555369920032779	-9.7894503673813	4.29119061026715e-21	***
df.mm.trans1:probe4	-0.569963318631246	0.0555369920032779	-10.2627689774341	7.08425202953413e-23	***
df.mm.trans1:probe5	-0.382997919186240	0.0555369920032779	-6.89626689114951	1.35741600926734e-11	***
df.mm.trans1:probe6	-0.486815606566123	0.0555369920032779	-8.76560989362533	1.90562311025775e-17	***
df.mm.trans2:probe2	-0.0119617385399054	0.0555369920032779	-0.215383262730532	0.829541710789357	   
df.mm.trans2:probe3	-0.0966320455919369	0.0555369920032779	-1.73995821715072	0.0823790996926972	.  
df.mm.trans2:probe4	0.118458897466086	0.0555369920032779	2.13297287435182	0.0333321342181498	*  
df.mm.trans2:probe5	0.0408040606495116	0.0555369920032779	0.734718593457551	0.462797943322988	   
df.mm.trans2:probe6	0.0139809921504982	0.0555369920032779	0.251741976765199	0.801326710187602	   
df.mm.trans3:probe2	-0.0289073670294546	0.0555369920032779	-0.520506530633644	0.602902590180096	   
df.mm.trans3:probe3	0.0268517703676429	0.0555369920032779	0.483493423015385	0.628921821031332	   
df.mm.trans3:probe4	-0.42076421213087	0.0555369920032779	-7.57628738888192	1.35313635772214e-13	***
df.mm.trans3:probe5	-0.586436407077542	0.0555369920032779	-10.5593836814736	5.05976360099154e-24	***
df.mm.trans3:probe6	-0.406972974832348	0.0555369920032779	-7.3279621411316	7.58043468862527e-13	***
df.mm.trans3:probe7	-0.00124120234118382	0.0555369920032779	-0.022349109960989	0.98217690487342	   
df.mm.trans3:probe8	0.192785574350657	0.0555369920032779	3.4713002522585	0.000555166145326884	***
df.mm.trans3:probe9	-0.647027982522394	0.0555369920032779	-11.6503965948355	2.02347159485953e-28	***
df.mm.trans3:probe10	-0.542587720830642	0.0555369920032779	-9.76984350896458	5.07151191952302e-21	***
df.mm.trans3:probe11	-0.510451518603184	0.0555369920032779	-9.19119851815266	6.30248668384621e-19	***
df.mm.trans3:probe12	-0.219436120086547	0.0555369920032779	-3.95117042121394	8.70211055740939e-05	***
df.mm.trans3:probe13	-0.286932744453144	0.0555369920032779	-5.16651576009391	3.25109799272996e-07	***
df.mm.trans3:probe14	-0.307439866255895	0.0555369920032779	-5.53576733572011	4.63674835148494e-08	***
df.mm.trans3:probe15	-0.570435022925965	0.0555369920032779	-10.2712624927957	6.57328395379096e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.41794292680001	0.147898060071752	29.8715407399979	8.15875392910935e-121	***
df.mm.trans1	-0.114645820484414	0.117058000724655	-0.97939329028936	0.327780419763538	   
df.mm.trans2	0.0883974669726233	0.117058000724655	0.755159548475059	0.450449578561809	   
df.mm.exp2	0.0108275139404037	0.155359733559158	0.0696931804165379	0.944461090654272	   
df.mm.exp3	0.0072693073091055	0.155359733559158	0.0467901633362256	0.962696038191779	   
df.mm.exp4	0.0745322980589335	0.155359733559158	0.479740125394544	0.631587018480251	   
df.mm.exp5	-0.00222262070274821	0.155359733559158	-0.0143062854951529	0.98859038069129	   
df.mm.exp6	-0.0897910633360195	0.155359733559158	-0.57795582728538	0.563510780148798	   
df.mm.exp7	0.121863910079963	0.155359733559158	0.784398294771532	0.433116007303578	   
df.mm.exp8	-0.114424227415311	0.155359733559158	-0.736511480767574	0.461707345815084	   
df.mm.trans1:exp2	0.0384546063408815	0.118657956497169	0.324079458942974	0.745990818012475	   
df.mm.trans2:exp2	0.0213164740923170	0.118657956497169	0.179646394743243	0.857490830856391	   
df.mm.trans1:exp3	-0.096521284246614	0.118657956497169	-0.81344131566025	0.41628775522575	   
df.mm.trans2:exp3	0.0134923890404831	0.118657956497169	0.113708253865008	0.909507105861268	   
df.mm.trans1:exp4	-0.0855381570009753	0.118657956497169	-0.720880078556014	0.47126407391253	   
df.mm.trans2:exp4	0.0164712228878272	0.118657956497169	0.138812628955228	0.88964479103523	   
df.mm.trans1:exp5	-0.0113037599175951	0.118657956497169	-0.095263396162269	0.924137422565233	   
df.mm.trans2:exp5	-0.0185756405796813	0.118657956497169	-0.156547787675110	0.875653913937064	   
df.mm.trans1:exp6	0.0292728365338141	0.118657956497169	0.246699314550495	0.805225318051853	   
df.mm.trans2:exp6	0.0398444115730925	0.118657956497169	0.335792160503313	0.737144988114507	   
df.mm.trans1:exp7	-0.131354960544568	0.118657956497169	-1.10700507932397	0.268735342512341	   
df.mm.trans2:exp7	0.0264155668192463	0.118657956497169	0.222619431507541	0.823907472351051	   
df.mm.trans1:exp8	-0.0795199144220203	0.118657956497169	-0.67016082839685	0.503013113727777	   
df.mm.trans2:exp8	0.113108781771361	0.118657956497169	0.95323385898745	0.340855450985192	   
df.mm.trans1:probe2	0.252120212910607	0.0868487313011333	2.90298095474098	0.00383211652715849	** 
df.mm.trans1:probe3	0.193923657038382	0.0868487313011333	2.23288992404488	0.0259242629930754	*  
df.mm.trans1:probe4	0.101794137792851	0.0868487313011334	1.17208549011380	0.241627915208203	   
df.mm.trans1:probe5	0.109348043320574	0.0868487313011334	1.25906322041053	0.208497111255542	   
df.mm.trans1:probe6	0.212991787933777	0.0868487313011333	2.45244558835596	0.0144725848812051	*  
df.mm.trans2:probe2	-0.0955572506691683	0.0868487313011334	-1.10027226923833	0.271654745596637	   
df.mm.trans2:probe3	-0.104752420866143	0.0868487313011334	-1.20614796896609	0.228235826471881	   
df.mm.trans2:probe4	-0.111549483766992	0.0868487313011333	-1.28441120665554	0.199493803103750	   
df.mm.trans2:probe5	-0.0825481989883093	0.0868487313011334	-0.950482496999148	0.342249842670321	   
df.mm.trans2:probe6	-0.0868846377110463	0.0868487313011334	-1.0004134362054	0.317513753347734	   
df.mm.trans3:probe2	0.033717945746213	0.0868487313011334	0.388237631581533	0.697977897349552	   
df.mm.trans3:probe3	-0.0845437008750489	0.0868487313011334	-0.973459250451314	0.330717386979034	   
df.mm.trans3:probe4	0.0402009081045461	0.0868487313011334	0.46288422988191	0.643615185396356	   
df.mm.trans3:probe5	0.0353011077750184	0.0868487313011334	0.406466591349708	0.684544736781644	   
df.mm.trans3:probe6	0.178648481092539	0.0868487313011334	2.05700737841645	0.0401172416259833	*  
df.mm.trans3:probe7	0.0154226623004169	0.0868487313011334	0.177580743775535	0.859112181705645	   
df.mm.trans3:probe8	0.124841272904590	0.0868487313011334	1.43745649515275	0.151109620647665	   
df.mm.trans3:probe9	0.0599304032194891	0.0868487313011333	0.690055022354795	0.490426404072139	   
df.mm.trans3:probe10	0.00108124225044916	0.0868487313011333	0.0124497184270906	0.990070956870074	   
df.mm.trans3:probe11	-0.0286609414093987	0.0868487313011334	-0.330009903196187	0.741507666203068	   
df.mm.trans3:probe12	0.00212827164676876	0.0868487313011334	0.0245055007123747	0.98045754374267	   
df.mm.trans3:probe13	0.128243762546487	0.0868487313011334	1.47663368969459	0.140298492938061	   
df.mm.trans3:probe14	0.100393041747006	0.0868487313011334	1.15595288777345	0.248160506515197	   
df.mm.trans3:probe15	0.0836615768653357	0.0868487313011334	0.96330223380297	0.335783941649436