chr9.24538_chr9_107023792_107025113_-_0.R 

fitVsDatCorrelation=0.907959574331476
cont.fitVsDatCorrelation=0.264331976422931

fstatistic=7103.56892073661,45,531
cont.fstatistic=1331.59030058585,45,531

residuals=-0.74326495937609,-0.119474721517710,-0.00556890788725907,0.100317919805788,0.961894333175162
cont.residuals=-0.907449304980706,-0.359724596004444,-0.0460320149269265,0.344617274701302,1.28122852341671

predictedValues:
Include	Exclude	Both
chr9.24538_chr9_107023792_107025113_-_0.R.tl.Lung	252.447485705379	86.4382243659293	159.210897155437
chr9.24538_chr9_107023792_107025113_-_0.R.tl.cerebhem	147.485328528931	72.0155818189811	89.8764063225062
chr9.24538_chr9_107023792_107025113_-_0.R.tl.cortex	110.427845473519	79.8136499197182	92.7824726502906
chr9.24538_chr9_107023792_107025113_-_0.R.tl.heart	212.734014321411	84.2685565536491	178.805283121477
chr9.24538_chr9_107023792_107025113_-_0.R.tl.kidney	153.657902106383	95.0841741112959	112.670491404658
chr9.24538_chr9_107023792_107025113_-_0.R.tl.liver	179.850767040093	87.9909961642466	165.314583095946
chr9.24538_chr9_107023792_107025113_-_0.R.tl.stomach	127.920607859871	78.0447822356275	113.733743582556
chr9.24538_chr9_107023792_107025113_-_0.R.tl.testicle	139.709397962025	83.3796578226837	120.555550540725


diffExp=166.009261339450,75.4697467099502,30.6141955538008,128.465457767762,58.5737279950873,91.8597708758468,49.8758256242434,56.3297401393417
diffExpScore=0.998480699703919
diffExp1.5=1,1,0,1,1,1,1,1
diffExp1.5Score=0.875
diffExp1.4=1,1,0,1,1,1,1,1
diffExp1.4Score=0.875
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	132.64963971822	137.013288319909	121.948870689436
cerebhem	122.398439051605	148.934647204284	127.840145940947
cortex	134.115999451659	121.389599329116	125.973105323807
heart	146.864896978741	111.755489546496	127.579726578726
kidney	146.581092674681	153.250128132146	120.465558624471
liver	108.698539347667	135.558857315163	118.957787754496
stomach	135.674149822318	131.586158672403	124.433361620524
testicle	138.540859091304	120.387242352831	106.736766568464
cont.diffExp=-4.36364860168854,-26.5362081526791,12.7264001225427,35.1094074322446,-6.6690354574649,-26.8603179674962,4.08799114991473,18.1536167384729
cont.diffExpScore=20.2320205656057

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=0,0,0,1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,-1,0,1,0,-1,0,0
cont.diffExp1.2Score=1.5

tran.correlation=0.364479074327378
cont.tran.correlation=-0.267006047516075

tran.covariance=0.00905534132545587
cont.tran.covariance=-0.0031489525220323

tran.mean=124.454310749359
cont.tran.mean=132.837439188034

weightedLogRatios:
wLogRatio
Lung	5.35384677848891
cerebhem	3.32280117962089
cortex	1.47465030672309
heart	4.53481145507963
kidney	2.30131571485306
liver	3.45627929841327
stomach	2.27513195287895
testicle	2.41639580076598

cont.weightedLogRatios:
wLogRatio
Lung	-0.158722158982692
cerebhem	-0.962565880138448
cortex	0.483430328787266
heart	1.32581463280888
kidney	-0.222900154631887
liver	-1.05974972773203
stomach	0.149757438860001
testicle	0.682727337196567

varWeightedLogRatios=1.67548816046726
cont.varWeightedLogRatios=0.656777839150467

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.39812963518091	0.102222954376214	43.0248730534077	3.32288607101805e-175	***
df.mm.trans1	1.04369774184466	0.0813814663870766	12.8247595942852	5.45290994844006e-33	***
df.mm.trans2	0.0669691455301102	0.0813814663870765	0.822904139028207	0.410931903041693	   
df.mm.exp2	-0.148227769232176	0.108508621849435	-1.36604600358720	0.172502780587111	   
df.mm.exp3	-0.366604401426627	0.108508621849435	-3.37857393429363	0.000782119795197996	***
df.mm.exp4	-0.312649501608716	0.108508621849435	-2.88133326439757	0.00412011216782808	** 
df.mm.exp5	-0.0553798132884246	0.108508621849435	-0.510372469436287	0.610002629624359	   
df.mm.exp6	-0.358891813744730	0.108508621849435	-3.30749582501123	0.00100507589557166	** 
df.mm.exp7	-0.445571039264771	0.108508621849435	-4.10631921842153	4.65398678028296e-05	***
df.mm.exp8	-0.349545260732077	0.108508621849435	-3.22135932402772	0.00135401311794462	** 
df.mm.trans1:exp2	-0.38924677721632	0.0840504170685563	-4.63111059756941	4.5792980272389e-06	***
df.mm.trans2:exp2	-0.0343197106309511	0.0840504170685563	-0.408322906987576	0.683201226616897	   
df.mm.trans1:exp3	-0.46023652272203	0.0840504170685563	-5.4757196784239	6.73244112626518e-08	***
df.mm.trans2:exp3	0.286868953262737	0.0840504170685563	3.41305805810280	0.000691408246744863	***
df.mm.trans1:exp4	0.141488878233759	0.0840504170685563	1.68338103686449	0.0928891149298663	.  
df.mm.trans2:exp4	0.287228312800562	0.0840504170685563	3.41733358165591	0.000680871296613208	***
df.mm.trans1:exp5	-0.441094719874394	0.0840504170685563	-5.24797776451974	2.22663250830918e-07	***
df.mm.trans2:exp5	0.150712366243601	0.0840504170685563	1.79311860071642	0.0735229434676915	.  
df.mm.trans1:exp6	0.0198159990845188	0.0840504170685563	0.235763245152677	0.813707293266716	   
df.mm.trans2:exp6	0.376696317055352	0.0840504170685563	4.48178998026978	9.07271377052839e-06	***
df.mm.trans1:exp7	-0.234222390026332	0.0840504170685563	-2.78668920625684	0.0055155004541867	** 
df.mm.trans2:exp7	0.343423842812546	0.0840504170685563	4.08592669483638	5.06855348976276e-05	***
df.mm.trans1:exp8	-0.242093452492929	0.0840504170685563	-2.88033612367995	0.00413296244572542	** 
df.mm.trans2:exp8	0.313519639729122	0.0840504170685563	3.73013782279507	0.000211973150034838	***
df.mm.trans1:probe2	0.440934377838835	0.0594326198707337	7.41906345030506	4.70635805831871e-13	***
df.mm.trans1:probe3	-0.108410565636585	0.0594326198707337	-1.82409198639364	0.0687000394133189	.  
df.mm.trans1:probe4	0.336841591375989	0.0594326198707337	5.6676214528086	2.37779920786674e-08	***
df.mm.trans1:probe5	0.317448736033347	0.0594326198707337	5.34132159618405	1.37078236761014e-07	***
df.mm.trans1:probe6	0.621951568180409	0.0594326198707337	10.4648183023592	1.98785311639692e-23	***
df.mm.trans2:probe2	-0.114753165889123	0.0594326198707337	-1.93081116293900	0.0540384378891292	.  
df.mm.trans2:probe3	-0.0766224087428883	0.0594326198707337	-1.28923155178995	0.197878909731866	   
df.mm.trans2:probe4	0.0597618167501345	0.0594326198707337	1.00553899323497	0.315095372360191	   
df.mm.trans2:probe5	-0.0659527950912402	0.0594326198707337	-1.10970701333187	0.267627594431612	   
df.mm.trans2:probe6	0.0955283132904367	0.0594326198707337	1.60733808299569	0.108574643395544	   
df.mm.trans3:probe2	-0.319012310723358	0.0594326198707337	-5.36762995501817	1.19406411380773e-07	***
df.mm.trans3:probe3	-0.984574841746917	0.0594326198707337	-16.5662365867157	5.3972569151826e-50	***
df.mm.trans3:probe4	-0.333730355923886	0.0594326198707337	-5.61527249934045	3.16783170372846e-08	***
df.mm.trans3:probe5	-0.385306432833358	0.0594326198707337	-6.48308006060311	2.05678496724224e-10	***
df.mm.trans3:probe6	-0.535055889618757	0.0594326198707337	-9.00273100500208	3.92512204744099e-18	***
df.mm.trans3:probe7	-0.623387371429359	0.0594326198707337	-10.4889768074372	1.60871587087757e-23	***
df.mm.trans3:probe8	-0.394130476526338	0.0594326198707337	-6.63155145076178	8.19146137444026e-11	***
df.mm.trans3:probe9	-0.73407472163074	0.0594326198707337	-12.3513774628707	5.52143564162342e-31	***
df.mm.trans3:probe10	-0.348136446680657	0.0594326198707337	-5.8576661678024	8.23809344908948e-09	***
df.mm.trans3:probe11	-0.319598576440651	0.0594326198707337	-5.37749433115653	1.13367642951932e-07	***
df.mm.trans3:probe12	-0.285794200675412	0.0594326198707337	-4.80870944772443	1.9812632478927e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.03542999926899	0.235258885645966	21.4037824137561	9.53129518822078e-74	***
df.mm.trans1	-0.151300802849414	0.187293677934564	-0.80782653487255	0.41955217943235	   
df.mm.trans2	-0.0891280447348692	0.187293677934564	-0.475873215357587	0.634360423104383	   
df.mm.exp2	-0.0441788226238512	0.249724903912752	-0.17690995944596	0.859646571540073	   
df.mm.exp3	-0.142545555644438	0.249724903912752	-0.570810333334798	0.568369694660535	   
df.mm.exp4	-0.147102440452563	0.249724903912752	-0.589057951961241	0.556072906954146	   
df.mm.exp5	0.224098911784384	0.249724903912752	0.89738311347065	0.369921202374244	   
df.mm.exp6	-0.184971848046778	0.249724903912752	-0.74070244956988	0.459201276388655	   
df.mm.exp7	-0.0380398513608291	0.249724903912752	-0.152327023716142	0.878986872228504	   
df.mm.exp8	0.0473257659655291	0.249724903912752	0.189511599460115	0.849764281916577	   
df.mm.trans1:exp2	-0.0362509247726483	0.193436078797467	-0.187405188308247	0.851414540761864	   
df.mm.trans2:exp2	0.127608506824624	0.193436078797467	0.659693411993911	0.509736610064242	   
df.mm.trans1:exp3	0.153539284123945	0.193436078797467	0.793746880511908	0.427697356753328	   
df.mm.trans2:exp3	0.0214728416974359	0.193436078797467	0.111007428556896	0.911652400732646	   
df.mm.trans1:exp4	0.248904171910319	0.193436078797467	1.28675153806715	0.198741577478290	   
df.mm.trans2:exp4	-0.0566621199619395	0.193436078797467	-0.292924258567432	0.769694478991583	   
df.mm.trans1:exp5	-0.124231467401958	0.193436078797467	-0.642235244708573	0.520997621891116	   
df.mm.trans2:exp5	-0.112105417029824	0.193436078797467	-0.579547609353692	0.562465524851008	   
df.mm.trans1:exp6	-0.0141611599273523	0.193436078797467	-0.0732084728732502	0.941667791873686	   
df.mm.trans2:exp6	0.174299849223993	0.193436078797467	0.901072076665128	0.367958495180084	   
df.mm.trans1:exp7	0.06058454046257	0.193436078797467	0.313201864094876	0.754250283350159	   
df.mm.trans2:exp7	-0.0023762287010823	0.193436078797467	-0.0122843097102391	0.990203399333208	   
df.mm.trans1:exp8	-0.00387183690295391	0.193436078797467	-0.0200161052013871	0.984038043864709	   
df.mm.trans2:exp8	-0.176690115389924	0.193436078797467	-0.913428955385948	0.361431530114859	   
df.mm.trans1:probe2	0.198371022237537	0.136779963043824	1.45029299484442	0.147567241067634	   
df.mm.trans1:probe3	-0.179319433068564	0.136779963043824	-1.31100659101005	0.190422199811292	   
df.mm.trans1:probe4	0.104707973022533	0.136779963043824	0.765521284641556	0.444301317483045	   
df.mm.trans1:probe5	-0.169567122836564	0.136779963043824	-1.23970733039484	0.215631255725042	   
df.mm.trans1:probe6	0.110286586939396	0.136779963043824	0.806306599922534	0.420427043943703	   
df.mm.trans2:probe2	-0.0851116582502962	0.136779963043824	-0.622252385190559	0.534043021744527	   
df.mm.trans2:probe3	-0.0434313713676908	0.136779963043824	-0.317527292749563	0.750968352282611	   
df.mm.trans2:probe4	-0.083400157910822	0.136779963043824	-0.609739585059696	0.542295148754038	   
df.mm.trans2:probe5	-0.105874003234062	0.136779963043824	-0.774046145926648	0.439247987582312	   
df.mm.trans2:probe6	-0.154215499967039	0.136779963043824	-1.12747142589612	0.260052418522528	   
df.mm.trans3:probe2	0.0340310839064699	0.136779963043824	0.248801674961458	0.803610395217878	   
df.mm.trans3:probe3	-0.135609002935031	0.136779963043824	-0.991439096175088	0.321922777251878	   
df.mm.trans3:probe4	0.0554563314194428	0.136779963043824	0.405441924280054	0.685316003660389	   
df.mm.trans3:probe5	-0.0177384325533968	0.136779963043824	-0.129685899591253	0.896864048396396	   
df.mm.trans3:probe6	-0.035216851419862	0.136779963043824	-0.257470835904368	0.796915070163903	   
df.mm.trans3:probe7	0.0617251314611684	0.136779963043824	0.451273198848516	0.65197689767905	   
df.mm.trans3:probe8	-0.0305048143333089	0.136779963043824	-0.223021074538053	0.823604868573036	   
df.mm.trans3:probe9	-0.0364005476951693	0.136779963043824	-0.266124854000045	0.790246354527883	   
df.mm.trans3:probe10	0.139170737219536	0.136779963043824	1.01747897954136	0.309388988739963	   
df.mm.trans3:probe11	0.0444506742463857	0.136779963043824	0.324979428691202	0.745324637832078	   
df.mm.trans3:probe12	0.0238442406251676	0.136779963043824	0.174325537853289	0.861676072451035