chr9.24406_chr9_90082672_90088793_-_2.R 

fitVsDatCorrelation=0.938048824908242
cont.fitVsDatCorrelation=0.269484070304457

fstatistic=8169.25031157394,58,830
cont.fstatistic=1045.18638825176,58,830

residuals=-1.10620113291825,-0.0871766793523513,-1.16015309621332e-05,0.092892416079911,1.18596585536035
cont.residuals=-0.908425436830372,-0.351612239022882,-0.149308294623595,0.206773545206955,1.67893592830646

predictedValues:
Include	Exclude	Both
chr9.24406_chr9_90082672_90088793_-_2.R.tl.Lung	76.3498338842382	56.6896852998175	56.8761816585311
chr9.24406_chr9_90082672_90088793_-_2.R.tl.cerebhem	70.3536020395676	49.9393526036394	56.9570822694994
chr9.24406_chr9_90082672_90088793_-_2.R.tl.cortex	73.7262153348698	57.2925262068423	57.7828891325841
chr9.24406_chr9_90082672_90088793_-_2.R.tl.heart	81.3466332897084	58.2319448523325	61.4189591401941
chr9.24406_chr9_90082672_90088793_-_2.R.tl.kidney	79.811916936825	60.2189657763443	61.2946858773755
chr9.24406_chr9_90082672_90088793_-_2.R.tl.liver	79.6530544457985	56.81581709899	62.3745821368488
chr9.24406_chr9_90082672_90088793_-_2.R.tl.stomach	83.5039136844274	61.2462301611309	63.2911142119452
chr9.24406_chr9_90082672_90088793_-_2.R.tl.testicle	77.918118934854	63.1831075852798	64.2470179005191


diffExp=19.6601485844207,20.4142494359282,16.4336891280275,23.1146884373759,19.5929511604807,22.8372373468084,22.2576835232966,14.7350113495741
diffExpScore=0.993751783044531
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,1,0,0,0,1,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,1,0,1,1,1,1,0
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	82.1047469653392	60.2777647994339	79.9885923250763
cerebhem	82.7860622155062	68.1986944677564	72.5341763551044
cortex	70.4988198600492	66.8874102927368	78.1886043431146
heart	83.5557786132924	71.8026251382355	92.9400867941852
kidney	73.2906099109423	75.672936545201	82.7198016069702
liver	79.6268496749849	83.9185632934075	59.8593882988355
stomach	73.4308783478722	64.1812306778895	82.9989526257084
testicle	87.7198016061135	60.7767357158092	77.4354624236402
cont.diffExp=21.8269821659053,14.5873677477498,3.61140956731236,11.7531534750569,-2.38232663425870,-4.29171361842268,9.24964766998274,26.9430658903043
cont.diffExpScore=1.15004183070215

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,0,0,1
cont.diffExp1.4Score=0.5
cont.diffExp1.3=1,0,0,0,0,0,0,1
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=1,1,0,0,0,0,0,1
cont.diffExp1.2Score=0.75

tran.correlation=0.733951987272524
cont.tran.correlation=-0.215325372889446

tran.covariance=0.00296520988754581
cont.tran.covariance=-0.00191494882370616

tran.mean=67.8925573834166
cont.tran.mean=74.0455942577856

weightedLogRatios:
wLogRatio
Lung	1.24644944068322
cerebhem	1.39906090258385
cortex	1.05270043976684
heart	1.41455417330485
kidney	1.19401688618114
liver	1.42199113659125
stomach	1.32363078287867
testicle	0.89106924163705

cont.weightedLogRatios:
wLogRatio
Lung	1.31446359897158
cerebhem	0.837236719725595
cortex	0.222398795664047
heart	0.659388407817385
kidney	-0.137882318282853
liver	-0.231169138589988
stomach	0.569369736710172
testicle	1.57442365320325

varWeightedLogRatios=0.0364007686415823
cont.varWeightedLogRatios=0.414084686282722

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.1285022164225	0.088217414664259	46.7991748810017	5.20790255107079e-235	***
df.mm.trans1	-0.0788067376016293	0.0763740785386974	-1.03185189411744	0.302442074838101	   
df.mm.trans2	-0.132000031860995	0.0676629150013927	-1.95084754859111	0.0514113549539895	.  
df.mm.exp2	-0.209996215910314	0.087452140703774	-2.40127016011686	0.0165572901708069	*  
df.mm.exp3	-0.04020555384228	0.087452140703774	-0.459743506776672	0.64582073456481	   
df.mm.exp4	0.0133934842637707	0.0874521407037739	0.153152160209987	0.878315508078156	   
df.mm.exp5	0.0299255488515936	0.087452140703774	0.342193439871989	0.73229195118697	   
df.mm.exp6	-0.047704193412231	0.087452140703774	-0.545489144443235	0.585563704949402	   
df.mm.exp7	0.0600093773204325	0.0874521407037739	0.686196779604308	0.49278066499576	   
df.mm.exp8	0.00691871019411557	0.087452140703774	0.0791142462429967	0.936960821767132	   
df.mm.trans1:exp2	0.128204343911260	0.0810703041975757	1.58139709947078	0.114168239275698	   
df.mm.trans2:exp2	0.0832132601205424	0.0608641140579587	1.36719742673493	0.171933574826745	   
df.mm.trans1:exp3	0.00523813716436076	0.0810703041975757	0.0646122796282464	0.948498265424107	   
df.mm.trans2:exp3	0.0507834592915487	0.0608641140579586	0.834374410563004	0.404310107765022	   
df.mm.trans1:exp4	0.05000010709821	0.0810703041975757	0.616749962802103	0.537568790568669	   
df.mm.trans2:exp4	0.013448323444039	0.0608641140579587	0.220956530004407	0.825180623125384	   
df.mm.trans1:exp5	0.0144214235544304	0.0810703041975757	0.177887867785522	0.85885444399087	   
df.mm.trans2:exp5	0.0304695228912698	0.0608641140579587	0.500615565721614	0.616774423122546	   
df.mm.trans1:exp6	0.0900587214312037	0.0810703041975757	1.11087188240619	0.266945199172656	   
df.mm.trans2:exp6	0.0499266733524098	0.0608641140579587	0.820297380897822	0.41228211053428	   
df.mm.trans1:exp7	0.0295582679618726	0.0810703041975756	0.364600432358517	0.715502564003197	   
df.mm.trans2:exp7	0.0173006447465438	0.0608641140579587	0.28425033394997	0.776289442438743	   
df.mm.trans1:exp8	0.0134139519206631	0.0810703041975757	0.165460732550998	0.868621646331424	   
df.mm.trans2:exp8	0.101525993114662	0.0608641140579587	1.66807641392730	0.095677739078782	.  
df.mm.trans1:probe2	0.140571111646153	0.0543836133427097	2.58480639674151	0.00991295664978716	** 
df.mm.trans1:probe3	-0.245865067303506	0.0543836133427097	-4.52094026474733	7.05167690297741e-06	***
df.mm.trans1:probe4	-0.196060863141262	0.0543836133427097	-3.60514594544764	0.000330587979979485	***
df.mm.trans1:probe5	0.0624702824011277	0.0543836133427097	1.14869679598996	0.251011957239539	   
df.mm.trans1:probe6	-0.190775146311765	0.0543836133427097	-3.50795275609135	0.000475836596554864	***
df.mm.trans1:probe7	-0.240964095433734	0.0543836133427097	-4.43082172409635	1.06455747658268e-05	***
df.mm.trans1:probe8	0.025685816923697	0.0543836133427097	0.472308023408309	0.636831098390938	   
df.mm.trans1:probe9	-0.0751749346771952	0.0543836133427097	-1.38230856790380	0.167248825846985	   
df.mm.trans1:probe10	-0.095859690347786	0.0543836133427097	-1.76265761790608	0.0783262484399716	.  
df.mm.trans1:probe11	0.757264514399413	0.0543836133427097	13.9244979848498	9.19199883776154e-40	***
df.mm.trans1:probe12	1.24011410315723	0.0543836133427097	22.8030839977916	9.55191056480931e-90	***
df.mm.trans1:probe13	1.57943701936274	0.0543836133427097	29.0425170797237	1.60966859848077e-128	***
df.mm.trans1:probe14	1.33821384823737	0.0543836133427097	24.6069314998313	7.76154572654815e-101	***
df.mm.trans1:probe15	0.991453098334303	0.0543836133427097	18.2307323363465	1.03261912126515e-62	***
df.mm.trans1:probe16	1.27482148652344	0.0543836133427097	23.4412796091699	1.18878183559496e-93	***
df.mm.trans1:probe17	0.399975812387986	0.0543836133427097	7.35471197670251	4.59502229176606e-13	***
df.mm.trans1:probe18	0.479892355160955	0.0543836133427097	8.82420872141785	6.43367720368407e-18	***
df.mm.trans1:probe19	0.391902223533892	0.0543836133427097	7.2062556980949	1.29278520768590e-12	***
df.mm.trans1:probe20	0.356491651608049	0.0543836133427097	6.55512993153917	9.75840546521536e-11	***
df.mm.trans1:probe21	0.404742401846264	0.0543836133427097	7.44235950810579	2.47419438465533e-13	***
df.mm.trans1:probe22	0.456205762642988	0.0543836133427097	8.38866222014546	2.10179766917618e-16	***
df.mm.trans2:probe2	0.142290610374763	0.0543836133427097	2.61642435338176	0.00904714259654518	** 
df.mm.trans2:probe3	0.277207660171119	0.0543836133427097	5.09726447237401	4.26983449792947e-07	***
df.mm.trans2:probe4	0.0871539370423847	0.0543836133427097	1.6025771677429	0.109408506593503	   
df.mm.trans2:probe5	0.083761575439813	0.0543836133427097	1.54019878951352	0.123893042944973	   
df.mm.trans2:probe6	0.0259376046590643	0.0543836133427097	0.476937869052816	0.633531910294155	   
df.mm.trans3:probe2	0.000879853930366426	0.0543836133427097	0.0161786589063482	0.987095749047658	   
df.mm.trans3:probe3	-0.00228029013095085	0.0543836133427097	-0.0419297282911513	0.966564801637	   
df.mm.trans3:probe4	-0.173242477327575	0.0543836133427097	-3.1855639351481	0.00149869806275563	** 
df.mm.trans3:probe5	0.280342465608242	0.0543836133427097	5.15490693569044	3.17398709379079e-07	***
df.mm.trans3:probe6	-0.0860957642195946	0.0543836133427097	-1.58311960032968	0.113775145743429	   
df.mm.trans3:probe7	0.963819480320017	0.0543836133427097	17.7226083571591	7.54919082089654e-60	***
df.mm.trans3:probe8	-0.0871287112095902	0.0543836133427097	-1.60211331785791	0.109511036084927	   
df.mm.trans3:probe9	0.212336301611420	0.0543836133427097	3.90441694768124	0.000102120981695858	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20245796256969	0.245174708676105	17.1406666913652	1.29775669660229e-56	***
df.mm.trans1	0.339481771825328	0.212259592138303	1.59937069701016	0.110118821606026	   
df.mm.trans2	-0.161316905502482	0.188049440541626	-0.85784304934836	0.391226774011197	   
df.mm.exp2	0.229552191370503	0.243047851739372	0.944473237379849	0.345202719341931	   
df.mm.exp3	-0.0255922781419223	0.243047851739372	-0.105297281826484	0.916165360779718	   
df.mm.exp4	0.042405297557368	0.243047851739372	0.174473039995599	0.861536264481314	   
df.mm.exp5	0.0803189723137607	0.243047851739372	0.330465674717789	0.741131522726869	   
df.mm.exp6	0.590124805630953	0.243047851739372	2.42801901521747	0.0153931542824972	*  
df.mm.exp7	-0.0858477430419414	0.243047851739372	-0.353213338145439	0.724018197330805	   
df.mm.exp8	0.106834796557799	0.243047851739372	0.439562809517695	0.660368203383106	   
df.mm.trans1:exp2	-0.221288308975865	0.225311388795173	-0.98214435656883	0.326314930740062	   
df.mm.trans2:exp2	-0.106090062175269	0.169154145922109	-0.627179792708838	0.530713871196801	   
df.mm.trans1:exp3	-0.126807585850501	0.225311388795173	-0.562810368923602	0.573716003848186	   
df.mm.trans2:exp3	0.129639747842670	0.169154145922109	0.766400061529477	0.443656209355795	   
df.mm.trans1:exp4	-0.0248867154700994	0.225311388795173	-0.110454760423688	0.91207542783684	   
df.mm.trans2:exp4	0.132552446872867	0.169154145922109	0.783619261297347	0.433487101537473	   
df.mm.trans1:exp5	-0.193882310622523	0.225311388795173	-0.860508257746254	0.389757427489134	   
df.mm.trans2:exp5	0.147138322128089	0.169154145922109	0.869847566111932	0.384635195554611	   
df.mm.trans1:exp6	-0.620769296278075	0.225311388795173	-2.75516164361494	0.00599479450028973	** 
df.mm.trans2:exp6	-0.259241254413529	0.169154145922109	-1.53257404954711	0.125761891478707	   
df.mm.trans1:exp7	-0.0258035579595624	0.225311388795173	-0.114523984329172	0.908850132409735	   
df.mm.trans2:exp7	0.148595261181545	0.169154145922109	0.878460651209631	0.379948010310638	   
df.mm.trans1:exp8	-0.0406829687796490	0.225311388795173	-0.180563303955457	0.856754439191442	   
df.mm.trans2:exp8	-0.0985910098321868	0.169154145922109	-0.58284713800385	0.560154520706852	   
df.mm.trans1:probe2	-0.206678096821129	0.151143474435267	-1.36742983839270	0.171860785409743	   
df.mm.trans1:probe3	-0.251392327037221	0.151143474435267	-1.66326947277429	0.0966360162301964	.  
df.mm.trans1:probe4	-0.215957208869563	0.151143474435267	-1.42882257852326	0.153431451458620	   
df.mm.trans1:probe5	-0.129590941161362	0.151143474435267	-0.857403481331667	0.391469433950626	   
df.mm.trans1:probe6	-0.206709418731255	0.151143474435267	-1.36763707135624	0.171795901238509	   
df.mm.trans1:probe7	0.0753325791561217	0.151143474435267	0.498417675242642	0.61832172911871	   
df.mm.trans1:probe8	-0.261972483782951	0.151143474435267	-1.7332702239497	0.0834190385525293	.  
df.mm.trans1:probe9	-0.309808484239376	0.151143474435267	-2.04976420845786	0.0407012065962471	*  
df.mm.trans1:probe10	-0.0422995069725057	0.151143474435267	-0.279863269853717	0.7796521379907	   
df.mm.trans1:probe11	-0.136859102783062	0.151143474435267	-0.905491310785478	0.36546792970375	   
df.mm.trans1:probe12	-0.315463705787627	0.151143474435267	-2.08718045530133	0.0371763566381098	*  
df.mm.trans1:probe13	-0.243551481469363	0.151143474435267	-1.61139263457697	0.107474349993447	   
df.mm.trans1:probe14	-0.300428492928181	0.151143474435267	-1.98770402791587	0.0471726300299063	*  
df.mm.trans1:probe15	-0.176967653800274	0.151143474435267	-1.17085871197216	0.241991613990229	   
df.mm.trans1:probe16	-0.188987888230575	0.151143474435267	-1.25038734842314	0.21151048043072	   
df.mm.trans1:probe17	-0.242726501752330	0.151143474435267	-1.60593437896842	0.108668692621004	   
df.mm.trans1:probe18	-0.171049880455102	0.151143474435267	-1.13170536203573	0.258085186707633	   
df.mm.trans1:probe19	-0.337048890435319	0.151143474435267	-2.22999300297064	0.0260146266323241	*  
df.mm.trans1:probe20	-0.189101148364464	0.151143474435267	-1.25113670352638	0.211237141558805	   
df.mm.trans1:probe21	-0.0393886385409756	0.151143474435267	-0.260604294615744	0.794462317822555	   
df.mm.trans1:probe22	-0.261611636239227	0.151143474435267	-1.73088277358128	0.0838443174430771	.  
df.mm.trans2:probe2	0.270418806312342	0.151143474435267	1.78915303702482	0.0739547127103342	.  
df.mm.trans2:probe3	0.0950671414731164	0.151143474435267	0.628986079804804	0.529531235066997	   
df.mm.trans2:probe4	0.110110439152642	0.151143474435267	0.728515998219962	0.466503269264593	   
df.mm.trans2:probe5	0.124299830432404	0.151143474435267	0.822396275438542	0.411087591465883	   
df.mm.trans2:probe6	0.267437318162989	0.151143474435267	1.76942682548646	0.077189820135245	.  
df.mm.trans3:probe2	-0.152817373108336	0.151143474435267	-1.01107489872999	0.312275235334310	   
df.mm.trans3:probe3	-0.15850604318132	0.151143474435267	-1.0487124487085	0.294615752262607	   
df.mm.trans3:probe4	-0.00676643671314241	0.151143474435267	-0.0447683020284172	0.964302759222747	   
df.mm.trans3:probe5	0.202021293616084	0.151143474435267	1.33661935701105	0.181713184182426	   
df.mm.trans3:probe6	0.164319175157088	0.151143474435267	1.08717346727042	0.277275764730157	   
df.mm.trans3:probe7	-0.060431110006888	0.151143474435267	-0.39982612701397	0.689387456241366	   
df.mm.trans3:probe8	0.0481695796327651	0.151143474435267	0.318701021084410	0.750033421210468	   
df.mm.trans3:probe9	-0.0246382883029629	0.151143474435267	-0.163012583871195	0.87054820709902