chr1.401_chr1_172548606_172549628_+_2.R 

fitVsDatCorrelation=0.883944426455187
cont.fitVsDatCorrelation=0.290797547690163

fstatistic=9107.45460747456,58,830
cont.fstatistic=2164.32530937185,58,830

residuals=-0.66573106584877,-0.097042260826082,-0.0114057542685329,0.0886099030157079,1.44610917635824
cont.residuals=-0.80350881348671,-0.258636881939661,-0.0457189186007226,0.189233676504217,1.83954938820518

predictedValues:
Include	Exclude	Both
chr1.401_chr1_172548606_172549628_+_2.R.tl.Lung	78.1512053313884	115.857631769604	72.6749841924685
chr1.401_chr1_172548606_172549628_+_2.R.tl.cerebhem	79.179947129488	164.784198376046	69.9304712693782
chr1.401_chr1_172548606_172549628_+_2.R.tl.cortex	69.1499553986855	117.593850828567	77.4223495394716
chr1.401_chr1_172548606_172549628_+_2.R.tl.heart	69.6882663704929	111.199091289415	65.4897890105304
chr1.401_chr1_172548606_172549628_+_2.R.tl.kidney	81.9640443138303	113.315920670261	68.2623382203918
chr1.401_chr1_172548606_172549628_+_2.R.tl.liver	79.01456436597	119.732814464496	66.8081246692961
chr1.401_chr1_172548606_172549628_+_2.R.tl.stomach	73.1021660578525	136.462005839617	69.5281620309527
chr1.401_chr1_172548606_172549628_+_2.R.tl.testicle	73.5022294354267	125.262281643077	71.3007177498458


diffExp=-37.7064264382160,-85.6042512465581,-48.443895429881,-41.5108249189222,-31.3518763564306,-40.7182500985256,-63.3598397817645,-51.7600522076501
diffExpScore=0.997509063375522
diffExp1.5=0,-1,-1,-1,0,-1,-1,-1
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,-1,-1,-1,0,-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	74.6361804953143	75.0279763565045	75.0102081964117
cerebhem	84.3551822274803	79.3592337024948	74.0882219617451
cortex	71.6546324685664	74.0013319670365	74.4172968860854
heart	75.9219139607638	96.0522305009303	80.9920290065789
kidney	73.1073465858098	74.748866986484	79.1767842188057
liver	72.4788932134467	81.0515121478881	75.69293760674
stomach	74.224832293938	91.540860681108	81.2009045606177
testicle	81.3897058684698	90.5881610818197	76.8721330381753
cont.diffExp=-0.391795861190133,4.99594852498551,-2.34669949847007,-20.1303165401665,-1.64152040067421,-8.5726189344414,-17.3160283871700,-9.19845521334987
cont.diffExpScore=1.16172044394210

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,-1,0,0,-1,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.215611844181773
cont.tran.correlation=0.271718290606298

tran.covariance=0.00175645380522579
cont.tran.covariance=0.00178811561396639

tran.mean=100.497510830264
cont.tran.mean=79.3836787836285

weightedLogRatios:
wLogRatio
Lung	-1.7935776732172
cerebhem	-3.47267675107858
cortex	-2.39024993206819
heart	-2.09237484904560
kidney	-1.47964522485322
liver	-1.90252686424988
stomach	-2.87373273319848
testicle	-2.43296810328511

cont.weightedLogRatios:
wLogRatio
Lung	-0.0225932291647126
cerebhem	0.26890169835716
cortex	-0.138181075295692
heart	-1.04594572314917
kidney	-0.0955496422510047
liver	-0.485075956079956
stomach	-0.92512547440622
testicle	-0.476780879432131

varWeightedLogRatios=0.40864657894156
cont.varWeightedLogRatios=0.206780698520138

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.71690546689731	0.0824409027459025	57.2155969887381	2.78005983009358e-290	***
df.mm.trans1	-0.379532125180224	0.069783973107385	-5.43867177920915	7.0649566032978e-08	***
df.mm.trans2	0.0867591622429106	0.0623898352322558	1.39059771387336	0.164720176048390	   
df.mm.exp2	0.403847946207377	0.0796501531743844	5.07027205990678	4.90108154246251e-07	***
df.mm.exp3	-0.170771710443376	0.0796501531743844	-2.14402237330908	0.0323209886909093	*  
df.mm.exp4	-0.0515504448862396	0.0796501531743844	-0.64721086943017	0.517674431425652	   
df.mm.exp5	0.0880917694747304	0.0796501531743844	1.10598367942701	0.269054045393786	   
df.mm.exp6	0.128059832688212	0.0796501531743844	1.60777886274544	0.108263922556330	   
df.mm.exp7	0.141161972428702	0.0796501531743845	1.77227496499153	0.0767157088753318	.  
df.mm.exp8	0.0358087602838432	0.0796501531743844	0.449575535723632	0.65313390892741	   
df.mm.trans1:exp2	-0.390770352310062	0.0707377846359517	-5.52420964723649	4.43041919509635e-08	***
df.mm.trans2:exp2	-0.0515733419804208	0.052630595975167	-0.9799117989231	0.327415190817902	   
df.mm.trans1:exp3	0.0484036435053723	0.0707377846359517	0.6842685808508	0.493996590271316	   
df.mm.trans2:exp3	0.185646330657268	0.052630595975167	3.52734616086929	0.000442783793408024	***
df.mm.trans1:exp4	-0.0630630763299123	0.0707377846359517	-0.89150482524245	0.372916768351518	   
df.mm.trans2:exp4	0.0105105298007634	0.052630595975167	0.199703795976823	0.841761160105582	   
df.mm.trans1:exp5	-0.0404565824052286	0.0707377846359517	-0.571923231882879	0.56752883996299	   
df.mm.trans2:exp5	-0.11027421849219	0.052630595975167	-2.09524928321582	0.0364513339120411	*  
df.mm.trans1:exp6	-0.117073118205183	0.0707377846359517	-1.65502946986104	0.0982965622586505	.  
df.mm.trans2:exp6	-0.0951592434855197	0.052630595975167	-1.80805939439522	0.0709591093052026	.  
df.mm.trans1:exp7	-0.207949454732463	0.0707377846359517	-2.93972246660909	0.00337621681782990	** 
df.mm.trans2:exp7	0.0225221329429131	0.052630595975167	0.427928518110260	0.668814173711473	   
df.mm.trans1:exp8	-0.0971385021251474	0.0707377846359517	-1.37321945584055	0.170054972914832	   
df.mm.trans2:exp8	0.0422389068936182	0.052630595975167	0.802554219860023	0.422462174008647	   
df.mm.trans1:probe2	-0.337812141657220	0.0517747568898322	-6.5246495000648	1.18453526891998e-10	***
df.mm.trans1:probe3	-0.348422249068431	0.0517747568898322	-6.7295776938135	3.17089841020801e-11	***
df.mm.trans1:probe4	0.201145537273467	0.0517747568898322	3.8850117191563	0.000110472448896095	***
df.mm.trans1:probe5	0.32645000258006	0.0517747568898322	6.30519624215116	4.67260926686239e-10	***
df.mm.trans1:probe6	0.388299548377714	0.0517747568898322	7.49978506328766	1.64373262236098e-13	***
df.mm.trans1:probe7	0.213230075224124	0.0517747568898322	4.11841770069227	4.19669676972970e-05	***
df.mm.trans1:probe8	0.291824501811739	0.0517747568898322	5.63642437631705	2.37901002486855e-08	***
df.mm.trans1:probe9	0.498831882498536	0.0517747568898322	9.63465426906716	6.74555960701141e-21	***
df.mm.trans1:probe10	0.433946916468908	0.0517747568898322	8.3814380315155	2.22422604066226e-16	***
df.mm.trans1:probe11	-0.136468453990253	0.0517747568898322	-2.63581061868884	0.00855034544584482	** 
df.mm.trans1:probe12	-0.220988693160795	0.0517747568898322	-4.26827099605741	2.19719598310564e-05	***
df.mm.trans1:probe13	-0.111687426555425	0.0517747568898322	-2.15717915958690	0.0312781253220594	*  
df.mm.trans1:probe14	-0.216134327485011	0.0517747568898322	-4.17451168230317	3.3016317891835e-05	***
df.mm.trans1:probe15	-0.270499800285047	0.0517747568898322	-5.22454988751804	2.20951043469852e-07	***
df.mm.trans1:probe16	-0.0522790835613053	0.0517747568898322	-1.00974078299481	0.312913765213564	   
df.mm.trans2:probe2	-0.55933199472957	0.0517747568898322	-10.8031795478969	1.51638080834306e-25	***
df.mm.trans2:probe3	-0.432792183803486	0.0517747568898322	-8.3591350264453	2.64837466667792e-16	***
df.mm.trans2:probe4	-0.153442556106400	0.0517747568898322	-2.96365575280052	0.00312699001898644	** 
df.mm.trans2:probe5	-0.525246953986892	0.0517747568898322	-10.1448463602548	7.0667384628914e-23	***
df.mm.trans2:probe6	0.593461101908306	0.0517747568898322	11.4623638537036	2.43260636156292e-28	***
df.mm.trans3:probe2	-0.411666607206616	0.0517747568898322	-7.95110652248106	6.03054647573294e-15	***
df.mm.trans3:probe3	-0.524303011311942	0.0517747568898322	-10.1266146440353	8.34284898744168e-23	***
df.mm.trans3:probe4	-0.0975622054690266	0.0517747568898322	-1.88435854323029	0.0598657354551111	.  
df.mm.trans3:probe5	-0.0104903272830993	0.0517747568898322	-0.202614708658525	0.839485847694242	   
df.mm.trans3:probe6	0.153092213904352	0.0517747568898322	2.95688909230623	0.00319569325824328	** 
df.mm.trans3:probe7	-0.425161366749235	0.0517747568898322	-8.21175013248069	8.3124242099678e-16	***
df.mm.trans3:probe8	-0.253626582366763	0.0517747568898322	-4.89865327434443	1.16012777023371e-06	***
df.mm.trans3:probe9	0.265236837209612	0.0517747568898322	5.12289874724067	3.74356937501137e-07	***
df.mm.trans3:probe10	-0.236261374059406	0.0517747568898322	-4.56325414645846	5.7972339402346e-06	***
df.mm.trans3:probe11	0.0996214986854363	0.0517747568898322	1.92413262118089	0.0546795993045745	.  
df.mm.trans3:probe12	-0.0364873324921645	0.0517747568898322	-0.704732087295036	0.48117467587567	   
df.mm.trans3:probe13	-0.176373023736250	0.0517747568898322	-3.40654470114735	0.000689537223464165	***
df.mm.trans3:probe14	-0.185864787218654	0.0517747568898322	-3.58987271758981	0.000350256842279159	***
df.mm.trans3:probe15	-0.232183057280850	0.0517747568898322	-4.48448377603965	8.33744868133487e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21027234311718	0.168690208105535	24.9586054247036	5.14156219142255e-103	***
df.mm.trans1	0.053154671435145	0.142791655038019	0.372253346464765	0.709799255555006	   
df.mm.trans2	0.124673219649188	0.127661803042572	0.976589838760247	0.329056796083902	   
df.mm.exp2	0.190902248097807	0.162979788758954	1.17132467498869	0.241804442314125	   
df.mm.exp3	-0.0466097323161796	0.162979788758954	-0.285984738789391	0.774961170681325	   
df.mm.exp4	0.187384501563393	0.162979788758954	1.14974073159792	0.250581848305271	   
df.mm.exp5	-0.0784824417017342	0.162979788758954	-0.481547081999286	0.630254659993416	   
df.mm.exp6	0.0388332033887172	0.162979788758954	0.238270055964739	0.811730455226312	   
df.mm.exp7	0.114095580243684	0.162979788758954	0.700059689072431	0.484086196912762	   
df.mm.exp8	0.250566670601956	0.162979788758954	1.53740946966463	0.124574179706080	   
df.mm.trans1:exp2	-0.0684913866703303	0.144743339940638	-0.473191973450524	0.636200643183063	   
df.mm.trans2:exp2	-0.134778503203443	0.107692491130691	-1.25151254083148	0.211100145476795	   
df.mm.trans1:exp3	0.00584215575920638	0.144743339940638	0.0403621732205598	0.967814093589362	   
df.mm.trans2:exp3	0.03283176284382	0.107692491130691	0.304865849968842	0.760544754682215	   
df.mm.trans1:exp4	-0.170304520221433	0.144743339940638	-1.17659659015246	0.239693885647727	   
df.mm.trans2:exp4	0.0596465476022007	0.107692491130691	0.553859855742554	0.579823937179515	   
df.mm.trans1:exp5	0.0577859210242191	0.144743339940638	0.39923025852463	0.689826249143896	   
df.mm.trans2:exp5	0.0747554344171178	0.107692491130691	0.694156422906012	0.487778354890723	   
df.mm.trans1:exp6	-0.0681631948777724	0.144743339940638	-0.470924568313317	0.637818341858888	   
df.mm.trans2:exp6	0.038390639525637	0.107692491130691	0.356483902661773	0.72156880537334	   
df.mm.trans1:exp7	-0.119622201897950	0.144743339940638	-0.826443565189322	0.408790025293768	   
df.mm.trans2:exp7	0.0848287951091636	0.107692491130691	0.787694612860419	0.431100241323411	   
df.mm.trans1:exp8	-0.163943251996246	0.144743339940638	-1.13264798272226	0.257689199379963	   
df.mm.trans2:exp8	-0.0621042005465854	0.107692491130691	-0.576680879925217	0.564311409876775	   
df.mm.trans1:probe2	0.00500429655590348	0.105941277005162	0.0472365134475361	0.962336089587987	   
df.mm.trans1:probe3	-0.0115907688215186	0.105941277005162	-0.109407486384687	0.912905741593456	   
df.mm.trans1:probe4	0.209638728380795	0.105941277005162	1.97882010022006	0.0481664273600315	*  
df.mm.trans1:probe5	0.0923033177733737	0.105941277005162	0.87126869132299	0.383859397311509	   
df.mm.trans1:probe6	0.170520951548669	0.105941277005162	1.60957991416660	0.107869838069252	   
df.mm.trans1:probe7	-0.00702057728472478	0.105941277005162	-0.0662685733378757	0.947179961597422	   
df.mm.trans1:probe8	0.0830047164746944	0.105941277005162	0.783497413106037	0.433558583511587	   
df.mm.trans1:probe9	0.154288164859443	0.105941277005162	1.45635553224382	0.145672526151445	   
df.mm.trans1:probe10	0.254470488793436	0.105941277005162	2.401995671442	0.0165247206923163	*  
df.mm.trans1:probe11	0.132708995728135	0.105941277005162	1.25266562268896	0.210680238463234	   
df.mm.trans1:probe12	0.240206776433515	0.105941277005162	2.26735775916512	0.0236247724953858	*  
df.mm.trans1:probe13	-0.0348877022641446	0.105941277005162	-0.329311702203144	0.742003181264693	   
df.mm.trans1:probe14	0.167429180483912	0.105941277005162	1.58039609505324	0.114397171038476	   
df.mm.trans1:probe15	0.061012817584281	0.105941277005162	0.575911668322709	0.564831004018262	   
df.mm.trans1:probe16	0.00806003170391893	0.105941277005162	0.0760801826423728	0.939373640406757	   
df.mm.trans2:probe2	-0.0705924107879187	0.105941277005162	-0.666335282936783	0.505381980737662	   
df.mm.trans2:probe3	-0.155943149566967	0.105941277005162	-1.47197724980575	0.141406077875553	   
df.mm.trans2:probe4	0.128070965848071	0.105941277005162	1.20888637052988	0.227050765490783	   
df.mm.trans2:probe5	-0.170352566074467	0.105941277005162	-1.60799049143203	0.108217557403612	   
df.mm.trans2:probe6	-0.089957354061694	0.105941277005162	-0.849124690627535	0.396056749234309	   
df.mm.trans3:probe2	-0.136678816365882	0.105941277005162	-1.29013751985661	0.197362230685898	   
df.mm.trans3:probe3	0.0582793970736083	0.105941277005162	0.550110388708725	0.582391665011655	   
df.mm.trans3:probe4	0.0757451837847318	0.105941277005162	0.714973293941333	0.474826596554214	   
df.mm.trans3:probe5	-0.0207837973754151	0.105941277005162	-0.196182243247855	0.844515547604171	   
df.mm.trans3:probe6	-0.0706242495517669	0.105941277005162	-0.666635815125447	0.505190046518815	   
df.mm.trans3:probe7	-0.00741997304389456	0.105941277005162	-0.0700385463876655	0.944179850081794	   
df.mm.trans3:probe8	-0.0857707355978222	0.105941277005162	-0.809606397265186	0.418398464459938	   
df.mm.trans3:probe9	-0.127040460188187	0.105941277005162	-1.19915923027808	0.230808263982924	   
df.mm.trans3:probe10	-0.0352789678299711	0.105941277005162	-0.333004932801142	0.739214655213252	   
df.mm.trans3:probe11	-0.108178832741101	0.105941277005162	-1.02112071705375	0.307494779496991	   
df.mm.trans3:probe12	-0.189303447743045	0.105941277005162	-1.78687149234402	0.0743231131238489	.  
df.mm.trans3:probe13	-0.0449285394559708	0.105941277005162	-0.424089087143831	0.671610733355769	   
df.mm.trans3:probe14	-0.179847833697801	0.105941277005162	-1.69761814074639	0.0899547153892184	.  
df.mm.trans3:probe15	-0.185309236893655	0.105941277005162	-1.74916937129827	0.08063135781765	.