chr4.16587_chr4_105914101_105918579_+_2.R 

fitVsDatCorrelation=0.906794256749398
cont.fitVsDatCorrelation=0.245745352407778

fstatistic=9145.85741376938,56,784
cont.fstatistic=1718.55867731699,56,784

residuals=-0.596232015570795,-0.103754575883673,0.0070681799093564,0.100531358108795,0.898989212308945
cont.residuals=-0.804335054213887,-0.310786878849439,-0.083502264274984,0.278560260982949,1.18938504914889

predictedValues:
Include	Exclude	Both
chr4.16587_chr4_105914101_105918579_+_2.R.tl.Lung	66.0003059326938	62.257437723808	98.507587288824
chr4.16587_chr4_105914101_105918579_+_2.R.tl.cerebhem	64.187059513932	61.1350573596128	93.0348812908222
chr4.16587_chr4_105914101_105918579_+_2.R.tl.cortex	64.0131021683848	60.9005863208229	89.139504281461
chr4.16587_chr4_105914101_105918579_+_2.R.tl.heart	80.2005041020994	58.0907994156545	105.104323714452
chr4.16587_chr4_105914101_105918579_+_2.R.tl.kidney	116.179943339295	64.4341926374394	138.207717128113
chr4.16587_chr4_105914101_105918579_+_2.R.tl.liver	104.244558451136	59.3959519015348	138.737350460591
chr4.16587_chr4_105914101_105918579_+_2.R.tl.stomach	74.5798112242872	60.8049997668824	110.989782955755
chr4.16587_chr4_105914101_105918579_+_2.R.tl.testicle	95.5252484954047	65.5728928409891	130.236115809488


diffExp=3.74286820888575,3.05200215431914,3.11251584756196,22.1097046864449,51.7457507018553,44.848606549601,13.7748114574048,29.9523556544156
diffExpScore=0.9942309450292
diffExp1.5=0,0,0,0,1,1,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,0,0,1,1,0,1
diffExp1.4Score=0.75
diffExp1.3=0,0,0,1,1,1,0,1
diffExp1.3Score=0.8
diffExp1.2=0,0,0,1,1,1,1,1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	81.4947444252295	68.8869044499101	87.0581296724948
cerebhem	84.137412865623	82.9056957473799	72.1770453839301
cortex	80.6771601013679	67.6845429714829	74.1103999658293
heart	83.2589173460367	85.6732369082334	77.7648451725005
kidney	80.140280558319	80.9504835342418	80.7068876783912
liver	78.9687114213692	90.2967821698413	86.678325087216
stomach	78.6341537269617	67.9557209060688	70.371304069336
testicle	81.5842019092042	86.215281175422	81.2398848717372
cont.diffExp=12.6078399753194,1.23171711824322,12.9926171298850,-2.41431956219672,-0.81020297592282,-11.3280707484721,10.6784328208929,-4.63107926621787
cont.diffExpScore=2.9334336297354

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=0.360025826957910
cont.tran.correlation=0.256082242406989

tran.covariance=0.00304088083094042
cont.tran.covariance=0.000765919820729466

tran.mean=72.3451531996235
cont.tran.mean=79.9665143885432

weightedLogRatios:
wLogRatio
Lung	0.242893852373315
cerebhem	0.201560334322275
cortex	0.206067392627082
heart	1.36209915879920
kidney	2.62938250470709
liver	2.45564343140247
stomach	0.859626067724423
testicle	1.64459749997831

cont.weightedLogRatios:
wLogRatio
Lung	0.725485070320636
cerebhem	0.0652591129942871
cortex	0.755536511062993
heart	-0.126811255666468
kidney	-0.0441472285548769
liver	-0.594656573925536
stomach	0.626391913628281
testicle	-0.244546391784490

varWeightedLogRatios=0.980058256318135
cont.varWeightedLogRatios=0.250901430716353

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.00106142834189	0.0831026754603056	48.1460001880808	2.31198355076134e-236	***
df.mm.trans1	0.452782979260224	0.0717294878544283	6.31236877334362	4.59578528257042e-10	***
df.mm.trans2	0.164803743488998	0.063873799846881	2.58014622402405	0.0100566950235672	*  
df.mm.exp2	0.0111087929904344	0.0827093395266064	0.134311228381419	0.893190945698008	   
df.mm.exp3	0.0473241661282879	0.0827093395266064	0.572174392869676	0.567367857476726	   
df.mm.exp4	0.0607798643599609	0.0827093395266065	0.734860956547825	0.462643925592909	   
df.mm.exp5	0.261223095038216	0.0827093395266064	3.15832645422328	0.00164759678651537	** 
df.mm.exp6	0.0675793422772578	0.0827093395266065	0.817070268775613	0.414136488161225	   
df.mm.exp7	-0.0207001030193016	0.0827093395266064	-0.250275278919893	0.802440040947935	   
df.mm.exp8	0.142400094330037	0.0827093395266065	1.72169304150022	0.0855195932763726	.  
df.mm.trans1:exp2	-0.0389665451745936	0.0761401186313284	-0.511774158945959	0.608953193375444	   
df.mm.trans2:exp2	-0.0293013308640698	0.0579865867749758	-0.505312219492713	0.613481582643877	   
df.mm.trans1:exp3	-0.0778957596996926	0.0761401186313284	-1.02305802906435	0.306595975685703	   
df.mm.trans2:exp3	-0.0693593735734887	0.0579865867749758	-1.19612788803463	0.232008274537137	   
df.mm.trans1:exp4	0.134090558695563	0.0761401186313284	1.76110257123227	0.0786108344457483	.  
df.mm.trans2:exp4	-0.130050580490433	0.0579865867749759	-2.24277005637719	0.0251903134945591	*  
df.mm.trans1:exp5	0.30425775246894	0.0761401186313284	3.99602414519684	7.04879423006551e-05	***
df.mm.trans2:exp5	-0.226856670966797	0.0579865867749759	-3.91222666454128	9.93832530212556e-05	***
df.mm.trans1:exp6	0.389500942568402	0.0761401186313284	5.11558097846381	3.9358523051692e-07	***
df.mm.trans2:exp6	-0.114631277751200	0.0579865867749759	-1.9768585137808	0.0484076259150175	*  
df.mm.trans1:exp7	0.14291056922311	0.0761401186313284	1.87694177251135	0.0608972359684962	.  
df.mm.trans2:exp7	-0.00290588809601818	0.0579865867749759	-0.0501131081795665	0.960045018135805	   
df.mm.trans1:exp8	0.227331123005153	0.0761401186313284	2.98569436312403	0.00291739043262458	** 
df.mm.trans2:exp8	-0.0905157123802319	0.0579865867749759	-1.56097672607442	0.118932789486256	   
df.mm.trans1:probe2	-0.694882062920847	0.0510763443223646	-13.6047728579624	5.35816902508058e-38	***
df.mm.trans1:probe3	0.175000477706316	0.0510763443223646	3.42625299496403	0.00064383325484226	***
df.mm.trans1:probe4	0.00948082632024073	0.0510763443223646	0.185620690870184	0.852790238505435	   
df.mm.trans1:probe5	-0.095062309734216	0.0510763443223646	-1.86118076764142	0.0630927502367091	.  
df.mm.trans1:probe6	-0.724818223562353	0.0510763443223646	-14.1908790297856	7.52245153665083e-41	***
df.mm.trans1:probe7	-0.790729863824459	0.0510763443223646	-15.4813323920331	2.27529477415922e-47	***
df.mm.trans1:probe8	-0.0144628233787843	0.0510763443223646	-0.283160895139701	0.777128252834871	   
df.mm.trans1:probe9	-0.321621507013342	0.0510763443223646	-6.29687796337678	5.05470292427984e-10	***
df.mm.trans1:probe10	-0.27507931123468	0.0510763443223646	-5.38564994977983	9.54839542444874e-08	***
df.mm.trans1:probe11	-0.654088601546567	0.0510763443223646	-12.8060966426715	3.13873154362852e-34	***
df.mm.trans1:probe12	-0.314033566165445	0.0510763443223646	-6.14831719716363	1.24646289323507e-09	***
df.mm.trans1:probe13	-0.481738512309031	0.0510763443223646	-9.43173437136717	4.45027813424541e-20	***
df.mm.trans1:probe14	-0.571155256917567	0.0510763443223646	-11.1823832440467	4.87866057537938e-27	***
df.mm.trans1:probe15	-0.504742395382774	0.0510763443223646	-9.88211670351992	8.76381948272141e-22	***
df.mm.trans1:probe16	-0.626542694016439	0.0510763443223646	-12.2667881252828	9.07595588457595e-32	***
df.mm.trans1:probe17	-0.298717734114627	0.0510763443223646	-5.8484556417995	7.28180281453395e-09	***
df.mm.trans1:probe18	-0.555696974470893	0.0510763443223646	-10.8797327186075	8.98489550122467e-26	***
df.mm.trans1:probe19	-0.499372394107677	0.0510763443223646	-9.77697994507839	2.21979642282320e-21	***
df.mm.trans1:probe20	-0.42310295036189	0.0510763443223646	-8.28373596378603	5.14486950207209e-16	***
df.mm.trans2:probe2	-0.153789164346539	0.0510763443223646	-3.01096655187203	0.00268776693317735	** 
df.mm.trans2:probe3	-0.162120092852580	0.0510763443223646	-3.17407392802763	0.00156184754058315	** 
df.mm.trans2:probe4	-0.0758837888402054	0.0510763443223646	-1.48569342318766	0.137762216657827	   
df.mm.trans2:probe5	0.0281071609821202	0.0510763443223646	0.550297037797457	0.58227240288584	   
df.mm.trans2:probe6	-0.155121546689747	0.0510763443223646	-3.03705264634267	0.00246820836041966	** 
df.mm.trans3:probe2	-0.0321726764844013	0.0510763443223646	-0.629893875750893	0.528947547420323	   
df.mm.trans3:probe3	0.273146866741316	0.0510763443223646	5.34781551744129	1.16884940392440e-07	***
df.mm.trans3:probe4	-0.00977839578706762	0.0510763443223646	-0.191446665120589	0.848225232161612	   
df.mm.trans3:probe5	0.148082522748332	0.0510763443223646	2.89923886904904	0.00384517428786065	** 
df.mm.trans3:probe6	0.0182116810279389	0.0510763443223646	0.356558036201597	0.721518634395536	   
df.mm.trans3:probe7	-0.289793171688683	0.0510763443223646	-5.67372578310763	1.96607037996421e-08	***
df.mm.trans3:probe8	-0.206263051777381	0.0510763443223646	-4.03832839867252	5.91233567377752e-05	***
df.mm.trans3:probe9	-0.158064972880035	0.0510763443223646	-3.09468061931801	0.00204025496605743	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.23755871174467	0.1910800677645	22.1768746542801	5.80819637853395e-85	***
df.mm.trans1	0.269202708337313	0.164929412007726	1.63222984342358	0.103032630776885	   
df.mm.trans2	-0.00244085831087694	0.146866631375159	-0.0166195567231468	0.986744351084237	   
df.mm.exp2	0.404604457550894	0.190175660578460	2.12753018088752	0.0336875454759109	*  
df.mm.exp3	0.133328903576958	0.190175660578460	0.701082899732853	0.483459310896093	   
df.mm.exp4	0.352377685765493	0.190175660578460	1.85290633245948	0.0642713755502157	.  
df.mm.exp5	0.22036376478624	0.190175660578460	1.15873800104575	0.246915817169456	   
df.mm.exp6	0.243521113611858	0.190175660578460	1.28050620605779	0.200745865207713	   
df.mm.exp7	0.163448319548668	0.190175660578460	0.859459717671046	0.390349648897305	   
df.mm.exp8	0.294648183562909	0.190175660578460	1.54934749623939	0.121701761751176	   
df.mm.trans1:exp2	-0.372691660420928	0.175070886070577	-2.12880433055375	0.0335815907174037	*  
df.mm.trans2:exp2	-0.219366785444534	0.133330014575607	-1.64529184327163	0.100310472155621	   
df.mm.trans1:exp3	-0.14341192323483	0.175070886070577	-0.819164890597603	0.412941279671846	   
df.mm.trans2:exp3	-0.150937160164319	0.133330014575607	-1.13205687890125	0.257956644434308	   
df.mm.trans1:exp4	-0.330960979961190	0.175070886070577	-1.89043985204809	0.0590677120249591	.  
df.mm.trans2:exp4	-0.134303290968212	0.133330014575607	-1.00729975464042	0.314101528826153	   
df.mm.trans1:exp5	-0.237123691319402	0.175070886070577	-1.35444388636846	0.175985018852785	   
df.mm.trans2:exp5	-0.0589922053504415	0.133330014575607	-0.442452553074530	0.658283792303848	   
df.mm.trans1:exp6	-0.275007930141384	0.175070886070577	-1.57083759792316	0.116623847183157	   
df.mm.trans2:exp6	0.0271146173749306	0.133330014575607	0.203364692198056	0.838902764710559	   
df.mm.trans1:exp7	-0.199180721291708	0.175070886070577	-1.13771470381095	0.255587202479912	   
df.mm.trans2:exp7	-0.177058083533143	0.133330014575607	-1.32796868054597	0.18457493541011	   
df.mm.trans1:exp8	-0.293551076977929	0.175070886070577	-1.67675553352479	0.0939889394152229	.  
df.mm.trans2:exp8	-0.0702668397035352	0.133330014575607	-0.527014415525241	0.598332661870058	   
df.mm.trans1:probe2	-0.0833615034303364	0.117441120640480	-0.709815292767254	0.47802974019542	   
df.mm.trans1:probe3	-0.0412106222213302	0.117441120640480	-0.350904538347242	0.725754235832636	   
df.mm.trans1:probe4	-0.134371395567507	0.117441120640480	-1.14415968473986	0.252906621969544	   
df.mm.trans1:probe5	-0.22908211141113	0.117441120640480	-1.95061244444708	0.05145912257556	.  
df.mm.trans1:probe6	-0.149724773983795	0.117441120640480	-1.27489224529920	0.202725075727987	   
df.mm.trans1:probe7	-0.234286124612879	0.117441120640480	-1.99492412312801	0.0463969082872822	*  
df.mm.trans1:probe8	-0.0994236595881558	0.117441120640480	-0.846583028550278	0.397485907894852	   
df.mm.trans1:probe9	-0.237551729020719	0.117441120640480	-2.02273043483579	0.0434399400558054	*  
df.mm.trans1:probe10	-0.196018837251169	0.117441120640480	-1.66908180186085	0.0955004034782933	.  
df.mm.trans1:probe11	-0.194862786837690	0.117441120640480	-1.65923814227063	0.097467752776515	.  
df.mm.trans1:probe12	0.107083706751090	0.117441120640480	0.911807603393907	0.362150282241431	   
df.mm.trans1:probe13	-0.157391621055291	0.117441120640480	-1.34017472071908	0.180576805780778	   
df.mm.trans1:probe14	-0.285437886288599	0.117441120640480	-2.43047652076145	0.0153023193847733	*  
df.mm.trans1:probe15	-0.284311971910537	0.117441120640480	-2.42088946665364	0.0157087261591968	*  
df.mm.trans1:probe16	-0.132276309224164	0.117441120640480	-1.12632022329810	0.260374638989001	   
df.mm.trans1:probe17	-0.222743162283580	0.117441120640480	-1.89663689403526	0.0582432072301739	.  
df.mm.trans1:probe18	-0.208365014144729	0.117441120640480	-1.77420832676311	0.0764167392341323	.  
df.mm.trans1:probe19	-0.143076402494160	0.117441120640480	-1.21828199283075	0.223483317929558	   
df.mm.trans1:probe20	-0.154051532764505	0.117441120640480	-1.31173418581470	0.189993813562970	   
df.mm.trans2:probe2	0.0203560673981889	0.117441120640479	0.173329982608942	0.862436797903361	   
df.mm.trans2:probe3	0.00801422757448296	0.117441120640480	0.068240387445014	0.945611677079774	   
df.mm.trans2:probe4	0.0219494023042193	0.117441120640480	0.18689707816577	0.851789682911848	   
df.mm.trans2:probe5	-0.0601028485691556	0.117441120640480	-0.511770053294599	0.608956065792016	   
df.mm.trans2:probe6	-0.0299932414786061	0.117441120640480	-0.255389605574558	0.798489182385606	   
df.mm.trans3:probe2	-0.104211683435017	0.117441120640480	-0.887352597341426	0.375161224071546	   
df.mm.trans3:probe3	0.0426304923158529	0.117441120640480	0.362994597491597	0.716706763336921	   
df.mm.trans3:probe4	-0.0265621702004790	0.117441120640480	-0.226174359164993	0.821124763715989	   
df.mm.trans3:probe5	-0.125719769278216	0.117441120640480	-1.07049190771161	0.284727394467262	   
df.mm.trans3:probe6	-0.055465030571087	0.117441120640480	-0.472279473055108	0.636858748255344	   
df.mm.trans3:probe7	-0.0880681013366591	0.117441120640480	-0.74989152740002	0.453545003534647	   
df.mm.trans3:probe8	0.00121654054516204	0.117441120640480	0.0103587273224871	0.991737714475075	   
df.mm.trans3:probe9	0.0164791402492353	0.117441120640480	0.140318315759968	0.88844453490466