chr15.8563_chr15_79322286_79327910_+_2.R 

fitVsDatCorrelation=0.890930603717331
cont.fitVsDatCorrelation=0.218296926840295

fstatistic=10342.6055215055,49,623
cont.fstatistic=2229.86108344746,49,623

residuals=-0.45736325656305,-0.0914395852790342,-0.0101526902250044,0.0794299415765147,1.28522786856424
cont.residuals=-0.466484654881268,-0.210254411466007,-0.0878377722719128,0.131481470806209,1.15912150731113

predictedValues:
Include	Exclude	Both
chr15.8563_chr15_79322286_79327910_+_2.R.tl.Lung	46.2605936784973	53.2381850175255	88.0961781238001
chr15.8563_chr15_79322286_79327910_+_2.R.tl.cerebhem	50.7234479727408	47.968103425954	61.6093206568792
chr15.8563_chr15_79322286_79327910_+_2.R.tl.cortex	46.5325423822661	50.5021196471359	70.8812288576262
chr15.8563_chr15_79322286_79327910_+_2.R.tl.heart	47.5845212385813	53.127441792296	81.5661711419838
chr15.8563_chr15_79322286_79327910_+_2.R.tl.kidney	45.4248470935898	49.3066939224977	62.8836441926639
chr15.8563_chr15_79322286_79327910_+_2.R.tl.liver	46.6925195498121	53.8183428871918	68.4125616927548
chr15.8563_chr15_79322286_79327910_+_2.R.tl.stomach	50.3476716626257	52.223414686995	74.5056927196417
chr15.8563_chr15_79322286_79327910_+_2.R.tl.testicle	47.1844018096181	52.252700241703	74.7070785034775


diffExp=-6.97759133902817,2.75534454678682,-3.96957726486981,-5.54292055371469,-3.88184682890789,-7.12582333737971,-1.87574302436931,-5.06829843208485
diffExpScore=1.13799871914354
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	54.2912764538928	55.0658097052833	65.9179046037122
cerebhem	58.0519443726291	54.0018844367118	57.297912946883
cortex	57.7745222590142	52.1255845115514	59.0642176898934
heart	56.8420526228912	54.4831417573882	55.3966414972488
kidney	55.7549970288088	54.2418419631658	56.6281684923339
liver	59.3534663650902	52.0790382197774	55.166860334771
stomach	56.7979927897718	58.7458593955528	55.9230273019617
testicle	53.6461455507798	53.4293628513724	59.303374637588
cont.diffExp=-0.77453325139048,4.0500599359173,5.64893774746275,2.35891086550296,1.51315506564304,7.27442814531278,-1.94786660578104,0.216782699407382
cont.diffExpScore=1.22982567394032

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=-0.283900665272838
cont.tran.correlation=-0.259115495444573

tran.covariance=-0.000454743641798489
cont.tran.covariance=-0.000340685696931425

tran.mean=49.5742216880644
cont.tran.mean=55.4178075177301

weightedLogRatios:
wLogRatio
Lung	-0.548529955249693
cerebhem	0.217736955560197
cortex	-0.317718717592665
heart	-0.431664989782245
kidney	-0.316281209053063
liver	-0.555992022388989
stomach	-0.144018695339774
testicle	-0.398428028613497

cont.weightedLogRatios:
wLogRatio
Lung	-0.056682289818261
cerebhem	0.291097933498589
cortex	0.412092917386704
heart	0.170349217977086
kidney	0.110256120453301
liver	0.525363292450052
stomach	-0.13677921052359
testicle	0.0161172205637947

varWeightedLogRatios=0.0637368402490961
cont.varWeightedLogRatios=0.0531721670489608

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.46723539049152	0.0702512008789745	49.3548202323931	1.92006178749742e-217	***
df.mm.trans1	0.308250016068257	0.0591730382282931	5.20929844567065	2.57982307514644e-07	***
df.mm.trans2	0.538681966602515	0.0545271739149037	9.87914700004799	1.76216547590453e-21	***
df.mm.exp2	0.345474156240529	0.0712113985047375	4.8513884503693	1.55091421165348e-06	***
df.mm.exp3	0.170524327590986	0.0712113985047375	2.39462124282872	0.0169320185020895	*  
df.mm.exp4	0.103149265236204	0.0712113985047376	1.44849374400844	0.147982108245358	   
df.mm.exp5	0.242195756463091	0.0712113985047375	3.40108130929318	0.000714133328592655	***
df.mm.exp6	0.273004625852747	0.0712113985047376	3.8337208871777	0.000139040742107188	***
df.mm.exp7	0.232970550060387	0.0712113985047375	3.27153454295506	0.00112871351365759	** 
df.mm.exp8	0.165942848166178	0.0712113985047376	2.33028492138289	0.0201094904558816	*  
df.mm.trans1:exp2	-0.253376358124983	0.0621584045698333	-4.07630086194252	5.16824309448392e-05	***
df.mm.trans2:exp2	-0.449713780543763	0.0515390263813124	-8.72569414130077	2.41114438637499e-17	***
df.mm.trans1:exp3	-0.164662913829016	0.0621584045698333	-2.64908526801104	0.00827565393962088	** 
df.mm.trans2:exp3	-0.223284921345811	0.0515390263813124	-4.33234651531508	1.71944628797662e-05	***
df.mm.trans1:exp4	-0.0749322311840123	0.0621584045698333	-1.20550441573557	0.228466297738532	   
df.mm.trans2:exp4	-0.105231578264696	0.0515390263813124	-2.04178436523302	0.0415935928985579	*  
df.mm.trans1:exp5	-0.260426998553494	0.0621584045698333	-4.18973106462073	3.19651001036572e-05	***
df.mm.trans2:exp5	-0.318911807636524	0.0515390263813124	-6.18777322794283	1.10601678050026e-09	***
df.mm.trans1:exp6	-0.263711145192404	0.0621584045698333	-4.24256618260096	2.54572590818997e-05	***
df.mm.trans2:exp6	-0.262166173301158	0.0515390263813124	-5.08675059869228	4.82670614321395e-07	***
df.mm.trans1:exp7	-0.148308665249184	0.0621584045698333	-2.38597927787164	0.0173313911776884	*  
df.mm.trans2:exp7	-0.252215500887286	0.0515390263813124	-4.89367996634754	1.2618666562294e-06	***
df.mm.trans1:exp8	-0.146169970565129	0.0621584045698333	-2.35157210962375	0.0190047342695694	*  
df.mm.trans2:exp8	-0.184627181755443	0.0515390263813124	-3.58227919149027	0.000367189412756723	***
df.mm.trans1:probe2	0.051778431809522	0.0425569504017874	1.21668567227381	0.224184558756967	   
df.mm.trans1:probe3	0.0163345761982871	0.0425569504017874	0.383828635371417	0.701236418982128	   
df.mm.trans1:probe4	0.00157407612622204	0.0425569504017874	0.036987521694128	0.970506803492354	   
df.mm.trans1:probe5	0.0759578139382734	0.0425569504017874	1.78485096373548	0.074771951636572	.  
df.mm.trans1:probe6	0.0402786106314912	0.0425569504017874	0.946463744493297	0.344279050292895	   
df.mm.trans1:probe7	0.725483791462072	0.0425569504017874	17.0473632300401	9.06583818528047e-54	***
df.mm.trans1:probe8	0.143632097122250	0.0425569504017874	3.37505614867125	0.000783855258178516	***
df.mm.trans1:probe9	0.0562160880620212	0.0425569504017874	1.32096138307082	0.186999222168183	   
df.mm.trans1:probe10	0.064109635857846	0.0425569504017874	1.50644337182472	0.132460250126056	   
df.mm.trans1:probe11	0.0591658929137665	0.0425569504017874	1.39027567424760	0.164941658118306	   
df.mm.trans1:probe12	0.0591808267983443	0.0425569504017874	1.39062658953727	0.164835213732601	   
df.mm.trans2:probe2	-0.118944489060665	0.0425569504017874	-2.79494860270037	0.00535063451182849	** 
df.mm.trans2:probe3	-0.111292593755299	0.0425569504017874	-2.61514494587997	0.00913487026790136	** 
df.mm.trans2:probe4	-0.0754695398994596	0.0425569504017874	-1.77337753732208	0.0766546423256394	.  
df.mm.trans2:probe5	-0.116339951804706	0.0425569504017874	-2.73374738336089	0.00643970909778664	** 
df.mm.trans2:probe6	-0.076216701129998	0.0425569504017874	-1.79093427537507	0.0737891938565023	.  
df.mm.trans3:probe2	-0.0121871661143372	0.0425569504017874	-0.286373107078306	0.774687573802568	   
df.mm.trans3:probe3	0.281653621660982	0.0425569504017874	6.61827548736088	7.84294678846446e-11	***
df.mm.trans3:probe4	0.0848779044191916	0.0425569504017874	1.99445457481903	0.0465377097264547	*  
df.mm.trans3:probe5	0.285845972522401	0.0425569504017874	6.71678703064202	4.19269512448443e-11	***
df.mm.trans3:probe6	0.632153768279833	0.0425569504017874	14.8543014081498	5.98733782060568e-43	***
df.mm.trans3:probe7	-0.0151405918656330	0.0425569504017874	-0.355772481878708	0.722131390900733	   
df.mm.trans3:probe8	-0.184868095472406	0.0425569504017874	-4.34401651732642	1.63309788932039e-05	***
df.mm.trans3:probe9	0.00256822689343014	0.0425569504017874	0.0603480011885972	0.951897825476144	   
df.mm.trans3:probe10	0.567714136918671	0.0425569504017874	13.3401038269609	6.87538257701087e-36	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.75584816862573	0.150960034540891	24.8797516511457	2.08433386656657e-95	***
df.mm.trans1	0.238169170401551	0.127154607794129	1.87306755557896	0.0615270900552847	.  
df.mm.trans2	0.242465821252713	0.117171293225175	2.06932785820458	0.0389270796365613	*  
df.mm.exp2	0.187610474197601	0.153023365344317	1.22602501765309	0.220652479824591	   
df.mm.exp3	0.117096022114096	0.153023365344317	0.765216618067565	0.444432343099765	   
df.mm.exp4	0.209166302699732	0.153023365344317	1.36689127329731	0.172152418413912	   
df.mm.exp5	0.163430632412066	0.153023365344317	1.06801096711167	0.285929224341856	   
df.mm.exp6	0.211428167136025	0.153023365344317	1.38167244368395	0.167567559770845	   
df.mm.exp7	0.274262954983713	0.153023365344317	1.79229462354717	0.0735708844819783	.  
df.mm.exp8	0.0636213995385175	0.153023365344317	0.415762647719604	0.677726862050157	   
df.mm.trans1:exp2	-0.120635831232551	0.133569743768993	-0.903167347847793	0.36678615721779	   
df.mm.trans2:exp2	-0.207120541311017	0.110750180856415	-1.87015984722899	0.0619303421825095	.  
df.mm.trans1:exp3	-0.0549116943884845	0.133569743768993	-0.411108779870488	0.681134145386496	   
df.mm.trans2:exp3	-0.171969138452085	0.110750180856415	-1.55276620879778	0.120986822506844	   
df.mm.trans1:exp4	-0.163253447236214	0.133569743768993	-1.22223373819267	0.222081463224486	   
df.mm.trans2:exp4	-0.219803984479009	0.110750180856415	-1.98468284908698	0.0476179836279193	*  
df.mm.trans1:exp5	-0.136827152693983	0.133569743768993	-1.02438732629916	0.306049952299242	   
df.mm.trans2:exp5	-0.178507039903592	0.110750180856415	-1.61179908261298	0.107512121095234	   
df.mm.trans1:exp6	-0.122281201717482	0.133569743768993	-0.915485784931693	0.360290869709054	   
df.mm.trans2:exp6	-0.267194646813369	0.110750180856415	-2.41258880795670	0.0161275096495494	*  
df.mm.trans1:exp7	-0.229125527498371	0.133569743768993	-1.71539991792333	0.0867691502866469	.  
df.mm.trans2:exp7	-0.20957129286168	0.110750180856415	-1.89228849326561	0.0589159314390943	.  
df.mm.trans1:exp8	-0.075575336798919	0.133569743768993	-0.565811797390473	0.571725454361204	   
df.mm.trans2:exp8	-0.0937899486077345	0.110750180856415	-0.846860455508697	0.397398092489076	   
df.mm.trans1:probe2	0.0612730475671382	0.0914489520780784	0.670024600334663	0.503090374898367	   
df.mm.trans1:probe3	0.0670337954961426	0.0914489520780783	0.733018738573513	0.463822713151334	   
df.mm.trans1:probe4	-0.0174973387627163	0.0914489520780783	-0.191334491703931	0.84832587894497	   
df.mm.trans1:probe5	-0.0270561851863052	0.0914489520780784	-0.295861074090875	0.767434766691068	   
df.mm.trans1:probe6	0.0505290334899049	0.0914489520780783	0.55253813566681	0.58077789213166	   
df.mm.trans1:probe7	-0.083538715641714	0.0914489520780784	-0.913501070743702	0.361332453893966	   
df.mm.trans1:probe8	0.00860332839130739	0.0914489520780783	0.0940779330523322	0.925077497515884	   
df.mm.trans1:probe9	-0.0616873515437366	0.0914489520780783	-0.674555040183166	0.500208738136224	   
df.mm.trans1:probe10	-0.08637903876048	0.0914489520780784	-0.944560181364683	0.345249633450988	   
df.mm.trans1:probe11	-0.0256071169954881	0.0914489520780783	-0.280015422961052	0.779558619883688	   
df.mm.trans1:probe12	0.121943390346126	0.0914489520780783	1.33345858618491	0.182868694803557	   
df.mm.trans2:probe2	0.0117976554833245	0.0914489520780784	0.129008099220774	0.897392876728226	   
df.mm.trans2:probe3	-0.0111829670107358	0.0914489520780783	-0.122286442398901	0.902711603756837	   
df.mm.trans2:probe4	-0.011632315343972	0.0914489520780783	-0.127200094475007	0.898823074481782	   
df.mm.trans2:probe5	0.0373753311969149	0.0914489520780783	0.408701579926298	0.682899120787212	   
df.mm.trans2:probe6	0.137082618338505	0.0914489520780783	1.49900698940175	0.134378351021096	   
df.mm.trans3:probe2	-0.0107324749628605	0.0914489520780784	-0.117360283731816	0.9066123801766	   
df.mm.trans3:probe3	-0.038183534845727	0.0914489520780784	-0.417539337281046	0.676427812538991	   
df.mm.trans3:probe4	-0.0634507194222824	0.0914489520780783	-0.693837578019579	0.488042690456373	   
df.mm.trans3:probe5	-0.034823287030362	0.0914489520780784	-0.380794817644604	0.703485313994463	   
df.mm.trans3:probe6	-0.0691006308563851	0.0914489520780784	-0.755619712267316	0.450162820390712	   
df.mm.trans3:probe7	-0.0273189531084693	0.0914489520780784	-0.298734457723962	0.765242279563618	   
df.mm.trans3:probe8	-0.0987356576182138	0.0914489520780783	-1.0796805799799	0.280702250597850	   
df.mm.trans3:probe9	-0.0512338547046857	0.0914489520780783	-0.56024539965141	0.575513461126095	   
df.mm.trans3:probe10	-0.0871515088734824	0.0914489520780784	-0.95300719027456	0.340956026503598