chr13.6581_chr13_98198076_98198935_-_1.R 

fitVsDatCorrelation=0.928903639501486
cont.fitVsDatCorrelation=0.322822077333128

fstatistic=8672.52906717666,46,554
cont.fstatistic=1317.49871361454,46,554

residuals=-0.871678839881277,-0.0873914543290099,-0.00567215508663506,0.0755218676321388,1.03012473917335
cont.residuals=-0.73632673195152,-0.285702910835784,-0.106033864778777,0.20679556855378,1.46028299232105

predictedValues:
Include	Exclude	Both
chr13.6581_chr13_98198076_98198935_-_1.R.tl.Lung	50.9494316302095	45.9305366460173	95.3982607385528
chr13.6581_chr13_98198076_98198935_-_1.R.tl.cerebhem	61.0660497455474	52.2893219324473	93.2078742314472
chr13.6581_chr13_98198076_98198935_-_1.R.tl.cortex	54.7635201794015	49.6106028817546	94.2395520737118
chr13.6581_chr13_98198076_98198935_-_1.R.tl.heart	54.2103758542242	50.0883242560136	89.0843795901991
chr13.6581_chr13_98198076_98198935_-_1.R.tl.kidney	57.5780823017625	48.8223373340828	104.524330411705
chr13.6581_chr13_98198076_98198935_-_1.R.tl.liver	60.3719206684113	52.6339285568751	88.8458340400659
chr13.6581_chr13_98198076_98198935_-_1.R.tl.stomach	54.1508330208413	50.6581572702392	88.9795211290138
chr13.6581_chr13_98198076_98198935_-_1.R.tl.testicle	53.4996863442712	48.0347059658072	94.9576046545389


diffExp=5.01889498419215,8.77672781310003,5.1529172976469,4.12205159821051,8.75574496767964,7.73799211153616,3.49267575060206,5.46498037846396
diffExpScore=0.979806948328295
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	63.789888073988	86.8643065851174	65.3378298611227
cerebhem	60.6436428909146	72.1289853288662	74.4517358337438
cortex	73.4523530635198	68.5357762159249	80.8023095814073
heart	58.4864780374445	76.6546270400156	67.7518410808401
kidney	56.8461760139901	69.4824824622202	65.5046234366024
liver	72.6981397822901	72.9290914036238	72.0513314532985
stomach	66.5609609188855	100.429203955535	71.0983879335863
testicle	70.9111825437207	86.2159572696952	71.0040950060055
cont.diffExp=-23.0744185111294,-11.4853424379516,4.91657684759495,-18.1681490025711,-12.6363064482302,-0.230951621333702,-33.8682430366495,-15.3047747259744
cont.diffExpScore=1.07968448793802

cont.diffExp1.5=0,0,0,0,0,0,-1,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,0,0,0,-1,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=-1,0,0,-1,0,0,-1,0
cont.diffExp1.3Score=0.75
cont.diffExp1.2=-1,0,0,-1,-1,0,-1,-1
cont.diffExp1.2Score=0.833333333333333

tran.correlation=0.835286176718968
cont.tran.correlation=0.106262627429252

tran.covariance=0.00234068364800221
cont.tran.covariance=0.00171525757433507

tran.mean=52.7911134117441
cont.tran.mean=72.2893282241095

weightedLogRatios:
wLogRatio
Lung	0.402263695802569
cerebhem	0.625989143691356
cortex	0.390695005622127
heart	0.312646719143673
kidney	0.654977223887725
liver	0.553034001163219
stomach	0.263921203806216
testicle	0.423012781291943

cont.weightedLogRatios:
wLogRatio
Lung	-1.33071440332739
cerebhem	-0.727019472920224
cortex	0.295275582041095
heart	-1.13725675734378
kidney	-0.831147345005835
liver	-0.0136004602112838
stomach	-1.81143031830454
testicle	-0.851894579336222

varWeightedLogRatios=0.020504455811077
cont.varWeightedLogRatios=0.46363225778751

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.70953725574312	0.0823514911968038	32.9021031236443	2.05704689667230e-132	***
df.mm.trans1	1.21452721107846	0.0654165520895337	18.5660535794698	3.45122229771199e-60	***
df.mm.trans2	1.10143447020552	0.0654165520895337	16.8372443215598	1.12238291895732e-51	***
df.mm.exp2	0.334012596794679	0.0870710354528985	3.83609308258847	0.000139381333228952	***
df.mm.exp3	0.161485454540653	0.0870710354528985	1.85464033706144	0.0641787355661988	.  
df.mm.exp4	0.217172813435731	0.0870710354528985	2.49420271972316	0.0129143744781999	*  
df.mm.exp5	0.0920066094739735	0.0870710354528985	1.0566844530492	0.291116181695736	   
df.mm.exp6	0.37707894331596	0.0870710354528985	4.3307047097188	1.76452235490290e-05	***
df.mm.exp7	0.228563943391423	0.0870710354528985	2.62502842882885	0.00890341011124756	** 
df.mm.exp8	0.0982656171600127	0.0870710354528985	1.12856837694517	0.259568432827987	   
df.mm.trans1:exp2	-0.152890139857972	0.067092738774857	-2.27878817663154	0.0230593324719974	*  
df.mm.trans2:exp2	-0.204350598388129	0.067092738774857	-3.04579306374521	0.00243108063813197	** 
df.mm.trans1:exp3	-0.089294776577771	0.067092738774857	-1.33091565806275	0.183764242950127	   
df.mm.trans2:exp3	-0.0844110581447465	0.067092738774857	-1.25812509201636	0.208876514033713	   
df.mm.trans1:exp4	-0.155134091051052	0.067092738774857	-2.31223369151221	0.0211305208900471	*  
df.mm.trans2:exp4	-0.130515063554371	0.067092738774857	-1.94529342426072	0.0522451649477093	.  
df.mm.trans1:exp5	0.0303017660549337	0.067092738774857	0.451640022575578	0.651705087623706	   
df.mm.trans2:exp5	-0.0309488513537382	0.067092738774857	-0.461284662377447	0.64477543799658	   
df.mm.trans1:exp6	-0.207388440229809	0.067092738774857	-3.0910713143749	0.00209460356980027	** 
df.mm.trans2:exp6	-0.240848184183297	0.067092738774857	-3.58978018458168	0.000360325529278128	***
df.mm.trans1:exp7	-0.167624190647146	0.067092738774857	-2.49839541071118	0.0127643340158712	*  
df.mm.trans2:exp7	-0.130593856199114	0.067092738774857	-1.9464678083473	0.052103698050597	.  
df.mm.trans1:exp8	-0.0494234301632481	0.067092738774857	-0.736643503689696	0.461650992747456	   
df.mm.trans2:exp8	-0.0534720088331947	0.067092738774857	-0.796986526554396	0.425800281170272	   
df.mm.trans1:probe2	-0.0823683666932655	0.0480619102189721	-1.71379719029043	0.0871253571589248	.  
df.mm.trans1:probe3	0.0945071556317873	0.0480619102189721	1.96636286825073	0.04975553610383	*  
df.mm.trans1:probe4	-0.0441005484640391	0.0480619102189721	-0.917577937770579	0.359239154605711	   
df.mm.trans1:probe5	0.05681688703023	0.0480619102189721	1.18216040043706	0.237649205695002	   
df.mm.trans1:probe6	0.103758482003207	0.0480619102189721	2.15885056441741	0.0312904417772071	*  
df.mm.trans2:probe2	-0.00853496632149223	0.0480619102189721	-0.177582752799599	0.8591155798273	   
df.mm.trans2:probe3	0.219914005692206	0.0480619102189721	4.57564014185597	5.86400809494005e-06	***
df.mm.trans2:probe4	0.0203939412130405	0.0480619102189721	0.424326480577339	0.671492454862483	   
df.mm.trans2:probe5	-0.00129106910989756	0.0480619102189721	-0.026862625809407	0.978578976389968	   
df.mm.trans2:probe6	0.07652875857884	0.0480619102189721	1.59229540045686	0.111888730378637	   
df.mm.trans3:probe2	-0.896814705663733	0.0480619102189721	-18.6595726548905	1.17906057049835e-60	***
df.mm.trans3:probe3	-0.826461124935459	0.0480619102189721	-17.1957610750398	2.01464085789173e-53	***
df.mm.trans3:probe4	-0.473438410570647	0.0480619102189721	-9.8505949599931	3.33368905069994e-21	***
df.mm.trans3:probe5	-0.117347134301053	0.0480619102189721	-2.44158282029189	0.0149348503089853	*  
df.mm.trans3:probe6	-1.08322610333964	0.0480619102189721	-22.5381408771398	2.56236455384312e-80	***
df.mm.trans3:probe7	-0.580858582154893	0.0480619102189721	-12.0856324583954	5.20810132071271e-30	***
df.mm.trans3:probe8	-0.421215397936954	0.0480619102189721	-8.76401699428671	2.30385766651272e-17	***
df.mm.trans3:probe9	-0.660292208499277	0.0480619102189721	-13.7383679818583	3.59063521920575e-37	***
df.mm.trans3:probe10	0.359616211824002	0.0480619102189721	7.48235370141501	2.88319719422144e-13	***
df.mm.trans3:probe11	-0.746266592410557	0.0480619102189721	-15.5271937592687	2.10694880146083e-45	***
df.mm.trans3:probe12	-0.108846852546432	0.0480619102189721	-2.26472173183548	0.0239152827152876	*  
df.mm.trans3:probe13	-0.521550769349976	0.0480619102189721	-10.8516446178225	5.23252985622892e-25	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.56400305528476	0.210472146096010	21.6845940897226	5.90477063797152e-76	***
df.mm.trans1	-0.445031384041801	0.167190197874886	-2.66182700719592	0.0079974168417972	** 
df.mm.trans2	-0.0808441745225859	0.167190197874886	-0.483546138171834	0.628899066746993	   
df.mm.exp2	-0.367050979231516	0.222534254428711	-1.64941339109257	0.0996298317322947	.  
df.mm.exp3	-0.308383418918583	0.222534254428711	-1.38577954980578	0.166371807276234	   
df.mm.exp4	-0.248116779533229	0.222534254428711	-1.11495994254994	0.265351017936777	   
df.mm.exp5	-0.341067801315768	0.222534254428711	-1.53265303892812	0.125932195468462	   
df.mm.exp6	-0.141946084472204	0.222534254428711	-0.637861729811475	0.523827240406285	   
df.mm.exp7	0.103135913284920	0.222534254428711	0.463460843588734	0.643216098616045	   
df.mm.exp8	0.0151751743247149	0.222534254428711	0.0681925322628311	0.945656975977995	   
df.mm.trans1:exp2	0.316471109168774	0.171474159267576	1.84559067395652	0.0654850570855905	.  
df.mm.trans2:exp2	0.181159751751640	0.171474159267576	1.05648426868185	0.291207501348015	   
df.mm.trans1:exp3	0.44942567363725	0.171474159267576	2.62095277537385	0.00900920711983396	** 
df.mm.trans2:exp3	0.071392101729121	0.171474159267576	0.416343208994643	0.677320125982332	   
df.mm.trans1:exp4	0.161317678742825	0.171474159267576	0.940769614686358	0.347232952940328	   
df.mm.trans2:exp4	0.123079542206882	0.171474159267576	0.717773119475241	0.473199743168621	   
df.mm.trans1:exp5	0.225822071113191	0.171474159267576	1.31694520082649	0.188401470080805	   
df.mm.trans2:exp5	0.117795264571953	0.171474159267576	0.68695636167628	0.492397676577438	   
df.mm.trans1:exp6	0.272667197444351	0.171474159267576	1.59013578844186	0.112374659245218	   
df.mm.trans2:exp6	-0.0329135037225308	0.171474159267576	-0.191944394788786	0.84785609465426	   
df.mm.trans1:exp7	-0.0606123636449864	0.171474159267576	-0.353478121157627	0.723864497997149	   
df.mm.trans2:exp7	0.0419699210865289	0.171474159267576	0.244759451020472	0.806733321245228	   
df.mm.trans1:exp8	0.0906582858858557	0.171474159267576	0.528699404464719	0.597225731324067	   
df.mm.trans2:exp8	-0.0226671012905937	0.171474159267576	-0.132189604471091	0.894882318517483	   
df.mm.trans1:probe2	0.171630185571322	0.122835582480058	1.39723508535635	0.162902230139627	   
df.mm.trans1:probe3	0.135353479604791	0.122835582480058	1.10190774425452	0.270980263098805	   
df.mm.trans1:probe4	0.148589470232334	0.122835582480058	1.20966146154317	0.226924752274515	   
df.mm.trans1:probe5	0.0728687211602833	0.122835582480058	0.593221603130454	0.553274951907214	   
df.mm.trans1:probe6	0.167395379186019	0.122835582480058	1.36275968091896	0.17351193452089	   
df.mm.trans2:probe2	0.0670035998970541	0.122835582480058	0.545473864691705	0.585647198256956	   
df.mm.trans2:probe3	-0.159756049345553	0.122835582480058	-1.30056817511724	0.193947046332382	   
df.mm.trans2:probe4	-0.145969562084037	0.122835582480058	-1.18833288479529	0.235211450956877	   
df.mm.trans2:probe5	-0.0651819866268024	0.122835582480058	-0.530644177450653	0.595878018722622	   
df.mm.trans2:probe6	-0.0535178101412736	0.122835582480058	-0.435686541804464	0.663233875573615	   
df.mm.trans3:probe2	0.148889274524224	0.122835582480058	1.21210215735653	0.225989958971046	   
df.mm.trans3:probe3	0.396733375897228	0.122835582480058	3.22979195349715	0.00131222297668343	** 
df.mm.trans3:probe4	-0.0637959053291141	0.122835582480058	-0.519360140124471	0.603717044651672	   
df.mm.trans3:probe5	0.149836130898638	0.122835582480058	1.21981048059069	0.223055754159172	   
df.mm.trans3:probe6	0.340176882828998	0.122835582480058	2.76936760473476	0.00580476687289654	** 
df.mm.trans3:probe7	0.208405833736910	0.122835582480058	1.69662429671584	0.0903294396195909	.  
df.mm.trans3:probe8	0.118083762970574	0.122835582480058	0.961315610562146	0.336812881106363	   
df.mm.trans3:probe9	0.0840758904316604	0.122835582480058	0.684458759702708	0.493971749384694	   
df.mm.trans3:probe10	0.0825677541727487	0.122835582480058	0.672181077385725	0.501748655616795	   
df.mm.trans3:probe11	0.224049547282421	0.122835582480058	1.82397919852576	0.068693783650345	.  
df.mm.trans3:probe12	0.00334449892056468	0.122835582480058	0.0272274438158637	0.9782881316405	   
df.mm.trans3:probe13	0.146401000088040	0.122835582480058	1.19184520586132	0.233832248003650