chr5.19093_chr5_88682250_88721511_+_0.R 

fitVsDatCorrelation=0.899257460813895
cont.fitVsDatCorrelation=0.268673956401455

fstatistic=4423.27944385577,61,899
cont.fstatistic=900.480490729706,61,899

residuals=-1.07839494009604,-0.134903489679766,-0.000160481968944373,0.143329649330215,1.17143516575800
cont.residuals=-1.20201773171768,-0.438860581763115,-0.130322791914873,0.403646522209148,2.17666869843679

predictedValues:
Include	Exclude	Both
chr5.19093_chr5_88682250_88721511_+_0.R.tl.Lung	59.8242398922069	228.614191412021	59.4093087298684
chr5.19093_chr5_88682250_88721511_+_0.R.tl.cerebhem	121.804270735226	100.508925718210	141.923848369674
chr5.19093_chr5_88682250_88721511_+_0.R.tl.cortex	94.6779728109477	150.247155001675	99.6208814718216
chr5.19093_chr5_88682250_88721511_+_0.R.tl.heart	71.4936676270027	188.100485491424	75.9988479809725
chr5.19093_chr5_88682250_88721511_+_0.R.tl.kidney	131.177423334715	272.074501762397	151.962759226764
chr5.19093_chr5_88682250_88721511_+_0.R.tl.liver	63.7500717743594	241.118812778157	66.0633630454332
chr5.19093_chr5_88682250_88721511_+_0.R.tl.stomach	63.2427956046649	204.543418339184	65.6464378338862
chr5.19093_chr5_88682250_88721511_+_0.R.tl.testicle	55.0421600870176	205.000702401709	54.9878001121907


diffExp=-168.789951519814,21.295345017016,-55.5691821907278,-116.606817864421,-140.897078427682,-177.368741003797,-141.300622734519,-149.958542314691
diffExpScore=1.04471176861588
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=1.14285714285714

cont.predictedValues:
Include	Exclude	Both
Lung	120.476127267415	89.7743271940106	108.010123380260
cerebhem	87.7681827971981	79.9129380287933	85.538348986615
cortex	103.592969945951	94.238894502349	104.788467851222
heart	101.365508160701	90.211649646077	93.2053095555875
kidney	100.813587367364	116.439982337827	85.5798787651182
liver	92.003610704099	108.276712309068	85.2830723922191
stomach	90.8509662078143	95.7573259653323	77.2507903625927
testicle	91.2956398386723	85.8786215299945	92.1799768288673
cont.diffExp=30.7018000734048,7.85524476840475,9.35407544360258,11.1538585146243,-15.6263949704627,-16.2731016049687,-4.90635975751805,5.4170183086778
cont.diffExpScore=3.53212987178755

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

tran.correlation=-0.225382355498827
cont.tran.correlation=0.0593812608892993

tran.covariance=-0.0401689835052716
cont.tran.covariance=0.00132445528430380

tran.mean=140.701299673182
cont.tran.mean=96.7910652376667

weightedLogRatios:
wLogRatio
Lung	-6.38368493265364
cerebhem	0.904410359884102
cortex	-2.20804324489455
heart	-4.59818047527605
kidney	-3.82366990768337
liver	-6.41231301768442
stomach	-5.55662471813572
testicle	-6.13480530253123

cont.weightedLogRatios:
wLogRatio
Lung	1.36615495689458
cerebhem	0.415157943019721
cortex	0.434679667622619
heart	0.531630790165451
kidney	-0.675168505887637
liver	-0.749697242464326
stomach	-0.238553350434567
testicle	0.274248374169604

varWeightedLogRatios=6.50326032858472
cont.varWeightedLogRatios=0.489951048327676

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.34499491793826	0.124461426201388	42.9449917220912	5.29698103229356e-220	***
df.mm.trans1	-1.34252296864522	0.105028051524037	-12.7825180907785	1.69308600816098e-34	***
df.mm.trans2	0.093506617328816	0.0936413212161549	0.99856149095731	0.318275856004118	   
df.mm.exp2	-0.981624510404416	0.118883682107287	-8.25701637940982	5.30293763690142e-16	***
df.mm.exp3	-0.47760461492524	0.118883682107287	-4.01741102276952	6.3760562559134e-05	***
df.mm.exp4	-0.263128814153522	0.118883682107287	-2.21332994982491	0.0271253438382151	*  
df.mm.exp5	0.0199953994705681	0.118883682107287	0.168192969094978	0.86646930221823	   
df.mm.exp6	0.0106499627967259	0.118883682107287	0.0895830496494451	0.928638507934618	   
df.mm.exp7	-0.155517684699441	0.118883682107287	-1.30814996593977	0.191156818918757	   
df.mm.exp8	-0.114994330960987	0.118883682107287	-0.967284398688207	0.33366202207623	   
df.mm.trans1:exp2	1.69262900038037	0.105028051524037	16.1159706937246	1.59920652743967e-51	***
df.mm.trans2:exp2	0.159835217974932	0.076551689505749	2.08793847669330	0.0370843484237026	*  
df.mm.trans1:exp3	0.936675060195837	0.105028051524037	8.91833226080053	2.60155789582982e-18	***
df.mm.trans2:exp3	0.057850423962803	0.076551689505749	0.755704078333352	0.450024592805842	   
df.mm.trans1:exp4	0.441326767081364	0.105028051524037	4.20198947497719	2.90979952406927e-05	***
df.mm.trans2:exp4	0.0680693023839983	0.076551689505749	0.889193991974355	0.374136695775929	   
df.mm.trans1:exp5	0.765144455798028	0.105028051524037	7.28514377535527	7.0252549904562e-13	***
df.mm.trans2:exp5	0.154044703757447	0.076551689505749	2.01229659008216	0.0444865244747489	*  
df.mm.trans1:exp6	0.0529094189207463	0.105028051524037	0.503764643378512	0.614550196456881	   
df.mm.trans2:exp6	0.0426040191878177	0.076551689505749	0.556539241170088	0.577980780429365	   
df.mm.trans1:exp7	0.211087974143285	0.105028051524037	2.00982471901781	0.0447481123385133	*  
df.mm.trans2:exp7	0.0442621231622942	0.076551689505749	0.578199167752792	0.563274464386994	   
df.mm.trans1:exp8	0.0316828413902813	0.105028051524037	0.301660755679451	0.76298045291588	   
df.mm.trans2:exp8	0.00597190734184367	0.076551689505749	0.078011437505833	0.937836314176263	   
df.mm.trans1:probe2	-0.0655473680623055	0.0787710386430275	-0.832125222562461	0.405559247659432	   
df.mm.trans1:probe3	-0.184796472454014	0.0787710386430275	-2.34599512254078	0.0191927721180993	*  
df.mm.trans1:probe4	0.152641006510484	0.0787710386430275	1.93778080294483	0.0529628307671652	.  
df.mm.trans1:probe5	-0.193507120158191	0.0787710386430275	-2.45657697920072	0.0142148471435689	*  
df.mm.trans1:probe6	-0.129425178694232	0.0787710386430275	-1.64305537826862	0.100721153048049	   
df.mm.trans1:probe7	-0.312104157625288	0.0787710386430275	-3.96216887579296	8.01544135347383e-05	***
df.mm.trans1:probe8	-0.171750174221487	0.0787710386430275	-2.18037209081144	0.0294885804545719	*  
df.mm.trans1:probe9	0.00287944625926607	0.0787710386430275	0.0365546311038892	0.970848233531216	   
df.mm.trans1:probe10	-0.103761843735969	0.0787710386430275	-1.31725879870892	0.18808752216558	   
df.mm.trans1:probe11	0.608905546570379	0.0787710386430275	7.73006877984433	2.87240050811257e-14	***
df.mm.trans1:probe12	0.285906594028947	0.0787710386430275	3.62959025238465	0.00029989177735723	***
df.mm.trans1:probe13	0.668062839215574	0.0787710386430275	8.48107185996471	9.08971228193215e-17	***
df.mm.trans1:probe14	0.62710463773855	0.0787710386430275	7.96110662676985	5.12743120965051e-15	***
df.mm.trans1:probe15	0.555117603680255	0.0787710386430275	7.04722970831858	3.63256486643777e-12	***
df.mm.trans1:probe16	0.572688092339902	0.0787710386430275	7.27028743311605	7.79492913132249e-13	***
df.mm.trans2:probe2	-0.0846445618021847	0.0787710386430275	-1.07456450061265	0.282858071940261	   
df.mm.trans2:probe3	0.243980752708280	0.0787710386430275	3.09734081092857	0.00201327574999684	** 
df.mm.trans2:probe4	-0.269628243129143	0.0787710386430275	-3.42293624375117	0.000647455004781077	***
df.mm.trans2:probe5	0.134204138429971	0.0787710386430275	1.70372437309293	0.088778179687316	.  
df.mm.trans2:probe6	-0.179089034181337	0.0787710386430275	-2.27353907307136	0.0232288137071557	*  
df.mm.trans3:probe2	0.815592877941857	0.0787710386430275	10.3539688188947	8.21675045696525e-24	***
df.mm.trans3:probe3	0.115930791881331	0.0787710386430275	1.47174385254335	0.141440019835615	   
df.mm.trans3:probe4	0.43051451610843	0.0787710386430275	5.46539087873939	5.98367840419641e-08	***
df.mm.trans3:probe5	-0.453437201420597	0.0787710386430275	-5.75639485313215	1.17852804802985e-08	***
df.mm.trans3:probe6	0.0860870064348251	0.0787710386430275	1.09287636570278	0.274740851979896	   
df.mm.trans3:probe7	0.442493234373793	0.0787710386430275	5.61746096022768	2.58363930005976e-08	***
df.mm.trans3:probe8	-0.0786568844868793	0.0787710386430275	-0.998550810575629	0.318281029268812	   
df.mm.trans3:probe9	0.169435634787160	0.0787710386430275	2.15098896378661	0.0317429971784557	*  
df.mm.trans3:probe10	0.109075668311519	0.0787710386430275	1.38471791397630	0.166482295606816	   
df.mm.trans3:probe11	-0.160086329550208	0.0787710386430275	-2.0322993362533	0.0424168847715751	*  
df.mm.trans3:probe12	-0.225633697612514	0.0787710386430275	-2.86442455881576	0.00427494849108007	** 
df.mm.trans3:probe13	-0.344882802004222	0.0787710386430275	-4.37829445879408	1.33668569983858e-05	***
df.mm.trans3:probe14	-0.00744532303972434	0.0787710386430275	-0.0945185333084772	0.924718322880467	   
df.mm.trans3:probe15	-0.353593449708399	0.0787710386430275	-4.48887631545402	8.09012006522747e-06	***
df.mm.trans3:probe16	-0.331836503771695	0.0787710386430275	-4.21267142706474	2.77804577903818e-05	***
df.mm.trans3:probe17	-0.157206883290942	0.0787710386430275	-1.99574470514941	0.0462630486088497	*  
df.mm.trans3:probe18	-0.263848173286176	0.0787710386430275	-3.34955813496222	0.000843078394002556	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.41289629677244	0.274073432128939	16.1011458224684	1.92616772402538e-51	***
df.mm.trans1	0.387986588303793	0.231279677805000	1.67756454862808	0.0937797345592468	.  
df.mm.trans2	0.0875420505794011	0.206205240274788	0.424538437833797	0.67127475662188	   
df.mm.exp2	-0.199853689198924	0.261790819643576	-0.76340984558214	0.445419296916920	   
df.mm.exp3	-0.072166974546867	0.261790819643576	-0.275666559450485	0.782867532633016	   
df.mm.exp4	-0.0204389475639779	0.261790819643576	-0.0780735840615237	0.937786893086267	   
df.mm.exp5	0.314673207363197	0.261790819643576	1.20200245291878	0.229679100036685	   
df.mm.exp6	0.154016230269243	0.261790819643576	0.588317919165134	0.556466686487177	   
df.mm.exp7	0.117454712216687	0.261790819643576	0.448658636603834	0.653786047038507	   
df.mm.exp8	-0.163230691454062	0.261790819643576	-0.623515720208595	0.53310383395537	   
df.mm.trans1:exp2	-0.116898877896518	0.231279677805000	-0.505443794309847	0.613371059872938	   
df.mm.trans2:exp2	0.0834924109058315	0.168572584442034	0.495290566862854	0.620516064890768	   
df.mm.trans1:exp3	-0.078815174934935	0.231279677805000	-0.340778643774257	0.733349849292871	   
df.mm.trans2:exp3	0.120700917992594	0.168572584442034	0.716017485240005	0.474166491487922	   
df.mm.trans1:exp4	-0.152279794691473	0.231279677805000	-0.658422720650215	0.510435118720476	   
df.mm.trans2:exp4	0.0252984740710773	0.168572584442034	0.150074664601091	0.880739370969937	   
df.mm.trans1:exp5	-0.492851684839035	0.231279677805000	-2.13097704699578	0.0333618116487850	*  
df.mm.trans2:exp5	-0.0545962858966049	0.168572584442034	-0.32387405150912	0.746108720998741	   
df.mm.trans1:exp6	-0.423640026546842	0.231279677805000	-1.83172179487395	0.0673236166878928	.  
df.mm.trans2:exp6	0.0333748254256626	0.168572584442034	0.197984894970504	0.8431016405063	   
df.mm.trans1:exp7	-0.399685901572412	0.231279677805000	-1.72814968165686	0.0843047766491595	.  
df.mm.trans2:exp7	-0.0529366214215596	0.168572584442034	-0.314028651792798	0.753572132098107	   
df.mm.trans1:exp8	-0.114117897847567	0.231279677805000	-0.493419477796851	0.621836733801278	   
df.mm.trans2:exp8	0.118866567491856	0.168572584442034	0.705135819595441	0.480908365854275	   
df.mm.trans1:probe2	0.0296633976079303	0.173459758353750	0.171010255574296	0.864254182838306	   
df.mm.trans1:probe3	-0.0728327866236305	0.173459758353750	-0.419882901457161	0.674671325350492	   
df.mm.trans1:probe4	0.187722591153989	0.173459758353750	1.08222560054045	0.279442489679962	   
df.mm.trans1:probe5	-0.128463956132567	0.173459758353750	-0.74059803467834	0.45913058034704	   
df.mm.trans1:probe6	0.035294088555158	0.173459758353750	0.203471334735634	0.838812694308692	   
df.mm.trans1:probe7	-0.0864894478299242	0.173459758353750	-0.498613907056988	0.618173375846455	   
df.mm.trans1:probe8	0.0631463041543917	0.173459758353750	0.364040079115138	0.715913680750818	   
df.mm.trans1:probe9	0.064801523850321	0.173459758353750	0.373582463536968	0.708803061779174	   
df.mm.trans1:probe10	0.038420290803561	0.173459758353750	0.221493971675018	0.824758129794186	   
df.mm.trans1:probe11	0.00524024303647168	0.173459758353750	0.0302101368421421	0.975906169373261	   
df.mm.trans1:probe12	0.0765350306573013	0.173459758353750	0.4412264342097	0.659155171327148	   
df.mm.trans1:probe13	-0.34758961849078	0.173459758353750	-2.00386315413811	0.0453843513219956	*  
df.mm.trans1:probe14	0.0815265054510698	0.173459758353750	0.470002415688868	0.638467279957411	   
df.mm.trans1:probe15	0.106997460091107	0.173459758353750	0.616843128957316	0.537494380402195	   
df.mm.trans1:probe16	-0.299184535421477	0.173459758353750	-1.72480659641718	0.0849060367989047	.  
df.mm.trans2:probe2	-0.029725652725207	0.173459758353750	-0.171369157937977	0.863972068647282	   
df.mm.trans2:probe3	0.241270987100903	0.173459758353750	1.39093349022694	0.164589754408152	   
df.mm.trans2:probe4	-0.0708279097618168	0.173459758353750	-0.408324734416913	0.683132517363092	   
df.mm.trans2:probe5	-0.145965321849389	0.173459758353750	-0.841493861369914	0.400295100442724	   
df.mm.trans2:probe6	-0.0700953417831345	0.173459758353750	-0.40410146104426	0.68623422159654	   
df.mm.trans3:probe2	-0.251743055858201	0.173459758353750	-1.45130523786850	0.147043733623042	   
df.mm.trans3:probe3	-0.096947042315521	0.173459758353750	-0.558902210147263	0.576367649452549	   
df.mm.trans3:probe4	-0.297655631591359	0.173459758353750	-1.71599242623368	0.0865079483924275	.  
df.mm.trans3:probe5	-0.0967822354038263	0.173459758353750	-0.557952094032386	0.57701601063713	   
df.mm.trans3:probe6	-0.224148236495722	0.173459758353750	-1.29222038946117	0.196612890572828	   
df.mm.trans3:probe7	-0.154991279370038	0.173459758353750	-0.89352874027388	0.371813217892739	   
df.mm.trans3:probe8	-0.141370465366910	0.173459758353750	-0.81500439472885	0.415285645891648	   
df.mm.trans3:probe9	-0.233030445262877	0.173459758353750	-1.34342655307775	0.179472898414122	   
df.mm.trans3:probe10	-0.257504299997767	0.173459758353750	-1.48451895956536	0.138021825087502	   
df.mm.trans3:probe11	-0.305907344378985	0.173459758353750	-1.76356376419668	0.078145099413602	.  
df.mm.trans3:probe12	-0.251579736863446	0.173459758353750	-1.45036369963332	0.147305917504623	   
df.mm.trans3:probe13	-0.028431631447501	0.173459758353750	-0.163909091753248	0.869839551595122	   
df.mm.trans3:probe14	-0.313572941998267	0.173459758353750	-1.80775613303216	0.0709785812501747	.  
df.mm.trans3:probe15	-0.308309815951992	0.173459758353750	-1.77741407504576	0.075838151268215	.  
df.mm.trans3:probe16	-0.24949486892287	0.173459758353750	-1.43834438195202	0.150684385755481	   
df.mm.trans3:probe17	-0.127581581929003	0.173459758353750	-0.735511124538848	0.462220107342032	   
df.mm.trans3:probe18	-0.372548989439208	0.173459758353750	-2.14775457417299	0.0319999725187869	*