chr15.8669_chr15_81747701_81748212_+_0.R 

fitVsDatCorrelation=0.804718991268884
cont.fitVsDatCorrelation=0.227572226790458

fstatistic=7923.45909969437,55,761
cont.fstatistic=2936.26684826162,55,761

residuals=-0.563611238759556,-0.090684714654339,-0.00566355052060467,0.078449474580101,1.29235335127029
cont.residuals=-0.699791783947989,-0.191579985182176,-0.0157480884214160,0.144235620045563,1.8082015657511

predictedValues:
Include	Exclude	Both
chr15.8669_chr15_81747701_81748212_+_0.R.tl.Lung	43.3195963554652	52.3860396953489	67.1249617448382
chr15.8669_chr15_81747701_81748212_+_0.R.tl.cerebhem	46.8170702116566	46.2721190772275	89.1135472745088
chr15.8669_chr15_81747701_81748212_+_0.R.tl.cortex	44.1395892547323	61.792228040043	73.8667488686584
chr15.8669_chr15_81747701_81748212_+_0.R.tl.heart	46.123384803311	51.8649970312516	62.8064021183174
chr15.8669_chr15_81747701_81748212_+_0.R.tl.kidney	43.5097551321568	48.5732475874708	58.492985320344
chr15.8669_chr15_81747701_81748212_+_0.R.tl.liver	48.1845418420661	52.8211277895509	58.9722437539081
chr15.8669_chr15_81747701_81748212_+_0.R.tl.stomach	45.9612557528261	56.3996347830209	71.7379992607569
chr15.8669_chr15_81747701_81748212_+_0.R.tl.testicle	43.4233804920468	47.6767982907598	62.5161594744616


diffExp=-9.06644333988364,0.544951134429169,-17.6526387853107,-5.74161222794061,-5.06349245531397,-4.63658594748479,-10.4383790301948,-4.253417798713
diffExpScore=1.00156876644483
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=-1,0,-1,0,0,0,-1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	54.8914181757973	54.3515570940194	60.6978979838956
cerebhem	57.1156198954459	64.969062602947	57.8609183850319
cortex	63.1422250376846	60.6681968406549	57.9952353537722
heart	63.30397266383	52.5948846448781	56.8212789602849
kidney	57.6439609702285	59.2302994757223	62.0424137137201
liver	63.5130917572361	55.0086283964511	56.7269077839809
stomach	58.6645836740455	60.6803301274235	61.4902447470022
testicle	61.5589353622598	56.6627908511183	59.6750204973204
cont.diffExp=0.539861081777907,-7.85344270750115,2.47402819702967,10.7090880189519,-1.58633850549384,8.50446336078508,-2.01574645337806,4.89614451114144
cont.diffExpScore=2.31455362020385

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

tran.correlation=-0.018409389912626
cont.tran.correlation=-0.314627852933523

tran.covariance=-1.79155582563856e-05
cont.tran.covariance=-0.00117861974895387

tran.mean=48.7040478836834
cont.tran.mean=58.9999723481089

weightedLogRatios:
wLogRatio
Lung	-0.734223654054516
cerebhem	0.0449644228329129
cortex	-1.33073394990080
heart	-0.456388446408
kidney	-0.421419101730264
liver	-0.360231703328191
stomach	-0.804355912662926
testicle	-0.356754056452715

cont.weightedLogRatios:
wLogRatio
Lung	0.0395391286501974
cerebhem	-0.529440811868633
cortex	0.164892843523081
heart	0.751562529780431
kidney	-0.110433182215048
liver	0.58643363874899
stomach	-0.138131108576209
testicle	0.338019598948626

varWeightedLogRatios=0.165824259646639
cont.varWeightedLogRatios=0.173060240545707

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.36935914143456	0.0857557998465899	39.2901605193126	2.74620859189883e-185	***
df.mm.trans1	0.403373075275442	0.0673399832566168	5.9900976473113	3.23315250466697e-09	***
df.mm.trans2	0.612182067651288	0.0673399832566167	9.0909150558949	8.38723287968775e-19	***
df.mm.exp2	-0.329813143398119	0.0888053400764246	-3.71388863681267	0.000219013556442213	***
df.mm.exp3	0.088182653679474	0.0888053400764247	0.992988187462434	0.321031334755883	   
df.mm.exp4	0.119217943507023	0.0888053400764246	1.34246367847278	0.179846020175941	   
df.mm.exp5	0.0664619898217721	0.0888053400764246	0.748400825497384	0.454449654801454	   
df.mm.exp6	0.244193371556160	0.0888053400764246	2.74975999580668	0.00610540730004489	** 
df.mm.exp7	0.066551616151912	0.0888053400764247	0.749410070325035	0.453841674194045	   
df.mm.exp8	-0.0206714465241835	0.0888053400764246	-0.23277256194722	0.816000609862893	   
df.mm.trans1:exp2	0.407455923581117	0.0664558313363852	6.13122904924117	1.39990315403923e-09	***
df.mm.trans2:exp2	0.205712605546035	0.0664558313363852	3.09547862707129	0.00203697300952491	** 
df.mm.trans1:exp3	-0.0694306627539574	0.0664558313363852	-1.04476403887740	0.296463787152132	   
df.mm.trans2:exp3	0.0769548052021658	0.0664558313363852	1.15798423787127	0.247233759532713	   
df.mm.trans1:exp4	-0.0565029639223688	0.0664558313363852	-0.850233347264333	0.395462783653962	   
df.mm.trans2:exp4	-0.129213949666868	0.0664558313363852	-1.94435833648389	0.052220395869923	.  
df.mm.trans1:exp5	-0.0620819253055831	0.0664558313363852	-0.934183262132975	0.350505684327412	   
df.mm.trans2:exp5	-0.142029209441559	0.0664558313363852	-2.13719709144315	0.0329001273152811	*  
df.mm.trans1:exp6	-0.137760214956960	0.0664558313363852	-2.07295901934695	0.0385121325101979	*  
df.mm.trans2:exp6	-0.235922251155959	0.0664558313363852	-3.55006094140590	0.000408804898851163	***
df.mm.trans1:exp7	-0.00735794515974102	0.0664558313363852	-0.110719330595635	0.91186810947191	   
df.mm.trans2:exp7	0.0072709290490658	0.0664558313363852	0.109409947973744	0.91290618732348	   
df.mm.trans1:exp8	0.0230643592388709	0.0664558313363852	0.347062985671251	0.728639954219443	   
df.mm.trans2:exp8	-0.0735238207192154	0.0664558313363852	-1.10635619539621	0.268921988926312	   
df.mm.trans1:probe2	-0.0302076650863273	0.0510147839416136	-0.59213550959855	0.55393573050103	   
df.mm.trans1:probe3	0.0300032243446920	0.0510147839416136	0.588128029298933	0.556620851259972	   
df.mm.trans1:probe4	-0.0457837373829997	0.0510147839416136	-0.897460184000763	0.369757243218072	   
df.mm.trans1:probe5	-0.0462790481378863	0.0510147839416136	-0.907169345083431	0.36460441215021	   
df.mm.trans1:probe6	-0.0232919198595715	0.0510147839416136	-0.456571959340827	0.64810901994196	   
df.mm.trans2:probe2	-0.151935487944257	0.0510147839416136	-2.97826387186404	0.00299096217347495	** 
df.mm.trans2:probe3	-0.182948993935233	0.0510147839416136	-3.5861956044863	0.000356967828163954	***
df.mm.trans2:probe4	-0.0619735564441836	0.0510147839416136	-1.21481562119546	0.224813214551962	   
df.mm.trans2:probe5	-0.161991097597004	0.0510147839416136	-3.17537554961327	0.00155673603023577	** 
df.mm.trans2:probe6	-0.0823808599791337	0.0510147839416136	-1.61484286738171	0.106759232933680	   
df.mm.trans3:probe2	-0.123974722313750	0.0510147839416136	-2.43017244678796	0.0153218364351380	*  
df.mm.trans3:probe3	0.0317161431580297	0.0510147839416136	0.621704939382452	0.53432210078297	   
df.mm.trans3:probe4	-0.191348154645615	0.0510147839416136	-3.75083730364541	0.000189627376430273	***
df.mm.trans3:probe5	-0.266408896930958	0.0510147839416136	-5.22219004663164	2.28404731349679e-07	***
df.mm.trans3:probe6	-0.128011752549906	0.0510147839416136	-2.50930696278977	0.0123035094527534	*  
df.mm.trans3:probe7	-0.315925384478163	0.0510147839416136	-6.19282019972366	9.66436924387047e-10	***
df.mm.trans3:probe8	-0.167755479328564	0.0510147839416136	-3.28836988745380	0.00105399541012344	** 
df.mm.trans3:probe9	-0.28362147988778	0.0510147839416136	-5.55959386620916	3.74340714230725e-08	***
df.mm.trans3:probe10	-0.124851335806448	0.0510147839416136	-2.44735596546562	0.0146158973772503	*  
df.mm.trans3:probe11	-0.220895221053988	0.0510147839416136	-4.33002365170855	1.69039682074241e-05	***
df.mm.trans3:probe12	-0.142692836175956	0.0510147839416136	-2.79708792532118	0.00528673114184692	** 
df.mm.trans3:probe13	-0.138998544202370	0.0510147839416136	-2.72467181986802	0.00658428574858956	** 
df.mm.trans3:probe14	-0.250056656474963	0.0510147839416136	-4.90165079913213	1.16195773058948e-06	***
df.mm.trans3:probe15	-0.271462206448594	0.0510147839416136	-5.32124583256654	1.35687150449997e-07	***
df.mm.trans3:probe16	-0.206605994495708	0.0510147839416136	-4.04992393444552	5.64853030003762e-05	***
df.mm.trans3:probe17	-0.316532982684454	0.0510147839416136	-6.20473043748898	8.99281021248382e-10	***
df.mm.trans3:probe18	-0.218357974423653	0.0510147839416136	-4.28028813517202	2.10490428996013e-05	***
df.mm.trans3:probe19	-0.182905834208446	0.0510147839416136	-3.58534958058004	0.000358107653339206	***
df.mm.trans3:probe20	-0.0241946381724652	0.0510147839416136	-0.474267188902651	0.635445436642194	   
df.mm.trans3:probe21	-0.307187526862907	0.0510147839416136	-6.02153930935948	2.68700685772357e-09	***
df.mm.trans3:probe22	-0.07381115373134	0.0510147839416136	-1.44685810716785	0.148348437361558	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.91528446609634	0.140663419332239	27.834418391669	3.806339383371e-118	***
df.mm.trans1	0.0957952049759647	0.110456346038362	0.867267553307389	0.386068808832906	   
df.mm.trans2	0.075877909553694	0.110456346038362	0.686949299656727	0.492323954675212	   
df.mm.exp2	0.266025443683981	0.145665515480688	1.82627605995909	0.0682003469097796	.  
df.mm.exp3	0.295527271315779	0.145665515480688	2.02880736968221	0.0428256033257214	*  
df.mm.exp4	0.175734852666060	0.145665515480688	1.20642728710460	0.228027689459843	   
df.mm.exp5	0.112979257420047	0.145665515480688	0.77560743905118	0.438221762095236	   
df.mm.exp6	0.225566231081621	0.145665515480688	1.54852183330601	0.121912454110563	   
df.mm.exp7	0.163656041302860	0.145665515480688	1.12350572997874	0.261577063449955	   
df.mm.exp8	0.173278034520759	0.145665515480688	1.18956112535593	0.234589938302622	   
df.mm.trans1:exp2	-0.226304830011512	0.109006090399310	-2.07607509986382	0.0382222011563476	*  
df.mm.trans2:exp2	-0.0875875095887116	0.109006090399310	-0.803510237527662	0.421930782509272	   
df.mm.trans1:exp3	-0.155494567974814	0.109006090399310	-1.42647596483102	0.154141113225591	   
df.mm.trans2:exp3	-0.185580913117836	0.109006090399310	-1.70248205800260	0.0890734146614319	.  
df.mm.trans1:exp4	-0.0331437850989093	0.109006090399310	-0.304054433816471	0.761169539046075	   
df.mm.trans2:exp4	-0.208589250289682	0.109006090399310	-1.91355592633017	0.0560518217269778	.  
df.mm.trans1:exp5	-0.064050788443053	0.109006090399310	-0.587589080650656	0.556982445896396	   
df.mm.trans2:exp5	-0.0270192935354810	0.109006090399310	-0.247869577163112	0.804302184196163	   
df.mm.trans1:exp6	-0.0796771959435815	0.109006090399310	-0.73094260744247	0.465039194674349	   
df.mm.trans2:exp6	-0.213549440758860	0.109006090399310	-1.95905971837526	0.0504705686251472	.  
df.mm.trans1:exp7	-0.097176860035106	0.109006090399310	-0.891481014309646	0.372952947405053	   
df.mm.trans2:exp7	-0.0535097089130209	0.109006090399310	-0.490887332230746	0.623647659480509	   
df.mm.trans1:exp8	-0.0586400389258071	0.109006090399310	-0.53795195030844	0.590767508132144	   
df.mm.trans2:exp8	-0.131633547763555	0.109006090399310	-1.20757975340054	0.227584121503409	   
df.mm.trans1:probe2	-0.0207874025666837	0.0836784679119069	-0.248419970936464	0.803876504588041	   
df.mm.trans1:probe3	-0.130972945249265	0.0836784679119069	-1.56519291661922	0.117953397577395	   
df.mm.trans1:probe4	-0.0631580982341681	0.0836784679119069	-0.754771207100234	0.450619781311984	   
df.mm.trans1:probe5	-0.0297695973267547	0.083678467911907	-0.355761739783463	0.722117597755439	   
df.mm.trans1:probe6	0.084453782997924	0.083678467911907	1.00926540728295	0.313168155913053	   
df.mm.trans2:probe2	0.0356218783759779	0.0836784679119069	0.425699457278294	0.670447221098064	   
df.mm.trans2:probe3	0.0467647492005499	0.0836784679119069	0.55886239754989	0.576420043696702	   
df.mm.trans2:probe4	-0.0536824840957731	0.0836784679119069	-0.641532827205772	0.521369731407225	   
df.mm.trans2:probe5	-0.00260842029402044	0.0836784679119069	-0.0311719413501508	0.975140589916148	   
df.mm.trans2:probe6	0.0946091097552582	0.083678467911907	1.13062669664147	0.258568445989453	   
df.mm.trans3:probe2	-0.00403954406695151	0.0836784679119069	-0.0482745940234489	0.961510068202333	   
df.mm.trans3:probe3	0.0603934474820276	0.0836784679119069	0.721732232784271	0.47068081241477	   
df.mm.trans3:probe4	-0.0578221670460031	0.083678467911907	-0.691004131515359	0.489773665529612	   
df.mm.trans3:probe5	-0.0425930654843967	0.0836784679119069	-0.509008667907697	0.610893694475422	   
df.mm.trans3:probe6	-0.0143796755764331	0.083678467911907	-0.171844393608777	0.86360554972457	   
df.mm.trans3:probe7	0.0359416805659465	0.0836784679119069	0.429521255142772	0.667665510277255	   
df.mm.trans3:probe8	0.0142311652910715	0.0836784679119069	0.170069620610805	0.865000586058	   
df.mm.trans3:probe9	-0.0160276504253007	0.0836784679119069	-0.191538526281025	0.84815479239446	   
df.mm.trans3:probe10	0.0121409383294816	0.0836784679119069	0.145090351585584	0.884677954265583	   
df.mm.trans3:probe11	0.0332812089494973	0.0836784679119069	0.397727274172065	0.690942769975049	   
df.mm.trans3:probe12	0.116683085354717	0.083678467911907	1.39442186582044	0.163597184846736	   
df.mm.trans3:probe13	-0.0148652138562503	0.0836784679119069	-0.177646821544340	0.859047624887998	   
df.mm.trans3:probe14	0.0371106740887275	0.083678467911907	0.443491318791783	0.65753645392591	   
df.mm.trans3:probe15	0.0645832861020603	0.083678467911907	0.771802922707079	0.4404707570149	   
df.mm.trans3:probe16	-0.0243106331025074	0.0836784679119069	-0.290524357210992	0.771494272109972	   
df.mm.trans3:probe17	0.0636724141091959	0.083678467911907	0.760917541848728	0.446942026562024	   
df.mm.trans3:probe18	-0.0329161235968608	0.083678467911907	-0.393364319618201	0.694160595911672	   
df.mm.trans3:probe19	0.0575831975642489	0.083678467911907	0.688148325383658	0.491569084400621	   
df.mm.trans3:probe20	0.0420728220696707	0.0836784679119069	0.5027914960628	0.615256345863043	   
df.mm.trans3:probe21	0.00715550540089791	0.083678467911907	0.0855119074172214	0.93187692131383	   
df.mm.trans3:probe22	0.0790373673467764	0.083678467911907	0.944536501671895	0.345195339563283