chr1.955_chr1_91735088_91738256_+_1.R 

fitVsDatCorrelation=0.915983983338584
cont.fitVsDatCorrelation=0.374002021092123

fstatistic=7734.30243063652,43,485
cont.fstatistic=1438.31928279348,43,485

residuals=-0.756449708878669,-0.0846170187044848,-0.0108203254782759,0.0725163918765731,0.9248321690169
cont.residuals=-1.08766984384462,-0.260251318560521,-0.0588098663579368,0.18020475788966,1.65400378513609

predictedValues:
Include	Exclude	Both
chr1.955_chr1_91735088_91738256_+_1.R.tl.Lung	48.8232510878647	59.0709301086618	70.0946193806653
chr1.955_chr1_91735088_91738256_+_1.R.tl.cerebhem	64.7692459858013	67.6317319509918	60.3330215567772
chr1.955_chr1_91735088_91738256_+_1.R.tl.cortex	48.0385875329213	64.5139048388518	69.5605828934011
chr1.955_chr1_91735088_91738256_+_1.R.tl.heart	49.4107670176858	69.720808754955	74.8117779727107
chr1.955_chr1_91735088_91738256_+_1.R.tl.kidney	46.1295429277026	74.5625002013205	90.331799405222
chr1.955_chr1_91735088_91738256_+_1.R.tl.liver	50.9914069749013	79.7951388152834	84.4690113370266
chr1.955_chr1_91735088_91738256_+_1.R.tl.stomach	49.6753416113197	79.3490463077353	76.616813914671
chr1.955_chr1_91735088_91738256_+_1.R.tl.testicle	54.6178663042934	64.1962083471629	68.1977841408444


diffExp=-10.2476790207971,-2.86248596519057,-16.4753173059305,-20.3100417372692,-28.4329572736179,-28.8037318403822,-29.6737046964157,-9.57834204286942
diffExpScore=0.993215014949375
diffExp1.5=0,0,0,0,-1,-1,-1,0
diffExp1.5Score=0.75
diffExp1.4=0,0,0,-1,-1,-1,-1,0
diffExp1.4Score=0.8
diffExp1.3=0,0,-1,-1,-1,-1,-1,0
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,0,-1,-1,-1,-1,-1,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	55.1723732317847	55.4218778209966	66.6220293642834
cerebhem	58.6515397251303	70.0956529935685	58.9836144977378
cortex	85.3268541221493	62.5929657154193	60.7904561479252
heart	69.5920160816052	61.8489539648868	55.3166707457795
kidney	99.3817594483717	63.0460768536217	58.3323612478826
liver	66.3204833784646	66.7440601656688	68.825768653476
stomach	65.9618957937015	75.6571657929256	63.4109805474344
testicle	61.6702160021024	80.0960001251357	74.080035710521
cont.diffExp=-0.249504589211895,-11.4441132684382,22.7338884067299,7.74306211671833,36.3356825947501,-0.423576787204183,-9.6952699992241,-18.4257841230333
cont.diffExpScore=3.88225827718586

cont.diffExp1.5=0,0,0,0,1,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,0,1,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,1,0,1,0,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=0,0,1,0,1,0,0,-1
cont.diffExp1.2Score=1.5

tran.correlation=-0.153065284701232
cont.tran.correlation=-0.225046850256009

tran.covariance=-0.00158277369803295
cont.tran.covariance=-0.00394123654275725

tran.mean=60.7060174229658
cont.tran.mean=68.5987432009708

weightedLogRatios:
wLogRatio
Lung	-0.758980117284777
cerebhem	-0.181308499243714
cortex	-1.18523792373682
heart	-1.40222851553239
kidney	-1.95509153608550
liver	-1.86088238344333
stomach	-1.93881105926452
testicle	-0.65944528060043

cont.weightedLogRatios:
wLogRatio
Lung	-0.0181057033391581
cerebhem	-0.741638747041682
cortex	1.32968459249630
heart	0.493483682124406
kidney	1.98944400673227
liver	-0.0267245749488453
stomach	-0.583871764633104
testicle	-1.11171259977513

varWeightedLogRatios=0.443385128021122
cont.varWeightedLogRatios=1.12515107685840

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09882691733466	0.0836259497180003	49.0138160601648	5.24450540330435e-190	***
df.mm.trans1	-0.185377394095928	0.066946946013781	-2.76901942708138	0.00583792087727642	** 
df.mm.trans2	-0.0110465112250610	0.066946946013781	-0.165003960341776	0.869009614824002	   
df.mm.exp2	0.567929048750144	0.0896465215952594	6.3352045193037	5.41675614344623e-10	***
df.mm.exp3	0.0795877567570535	0.0896465215952594	0.887795257872696	0.375090948477607	   
df.mm.exp4	0.112592296362920	0.0896465215952593	1.25595833903359	0.209735756271921	   
df.mm.exp5	-0.0774978080064037	0.0896465215952594	-0.864482041548636	0.387750495461891	   
df.mm.exp6	0.157635427557131	0.0896465215952594	1.75841097626555	0.0793082778460304	.  
df.mm.exp7	0.223448989553551	0.0896465215952594	2.49255615920481	0.0130153270230937	*  
df.mm.exp8	0.222793577717145	0.0896465215952594	2.4852450909699	0.0132822082068548	*  
df.mm.trans1:exp2	-0.285304813142388	0.070324517375376	-4.05697506062497	5.79253786384943e-05	***
df.mm.trans2:exp2	-0.432590695346664	0.0703245173753761	-6.15134965004586	1.60846744301765e-09	***
df.mm.trans1:exp3	-0.095789817981588	0.0703245173753761	-1.36211127436942	0.173795038794004	   
df.mm.trans2:exp3	0.0085540956405837	0.0703245173753761	0.121637459592135	0.903236525274503	   
df.mm.trans1:exp4	-0.100630596202543	0.0703245173753761	-1.43094613313022	0.153089648260991	   
df.mm.trans2:exp4	0.0531675971967789	0.0703245173753761	0.756032166036526	0.449996954438236	   
df.mm.trans1:exp5	0.0207447410664408	0.0703245173753761	0.294985900233202	0.76813088007063	   
df.mm.trans2:exp5	0.310396583497336	0.0703245173753761	4.41377481256658	1.25305664453269e-05	***
df.mm.trans1:exp6	-0.114184955825345	0.0703245173753761	-1.62368630581354	0.105092496903686	   
df.mm.trans2:exp6	0.143088230874174	0.070324517375376	2.03468486119004	0.0424259867548145	*  
df.mm.trans1:exp7	-0.206146980321080	0.0703245173753761	-2.93136715351653	0.00353410688013351	** 
df.mm.trans2:exp7	0.0716685114733084	0.0703245173753761	1.01911131633877	0.308658207079665	   
df.mm.trans1:exp8	-0.110639182909451	0.0703245173753761	-1.57326615295324	0.116309286286605	   
df.mm.trans2:exp8	-0.139588355845606	0.0703245173753761	-1.9849173667345	0.0477165918863643	*  
df.mm.trans1:probe2	-0.0841272346665595	0.0481479056401969	-1.74726675123175	0.0812239934806737	.  
df.mm.trans1:probe3	-0.0896220818964102	0.0481479056401969	-1.86139107620058	0.0632937223725682	.  
df.mm.trans1:probe4	-0.0493746430199659	0.0481479056401969	-1.02547852006142	0.305648640274208	   
df.mm.trans1:probe5	-0.0765395927555726	0.0481479056401969	-1.58967647165265	0.112559360262078	   
df.mm.trans1:probe6	-0.104222321750344	0.0481479056401969	-2.16462835432935	0.0309033137770137	*  
df.mm.trans2:probe2	-0.174421792542196	0.0481479056401969	-3.62262470657868	0.000322377129481022	***
df.mm.trans2:probe3	-0.0068364152860491	0.0481479056401969	-0.141987801860724	0.8871486019642	   
df.mm.trans2:probe4	0.00116111183315563	0.0481479056401969	0.0241155210744257	0.980770381463058	   
df.mm.trans2:probe5	0.0967845482712986	0.0481479056401969	2.01015074247584	0.0449684264373112	*  
df.mm.trans2:probe6	-0.0613511169541983	0.0481479056401969	-1.27422192384997	0.203194989245038	   
df.mm.trans3:probe2	0.0672092480522044	0.0481479056401969	1.39589141331402	0.163385845262941	   
df.mm.trans3:probe3	0.125863042715734	0.0481479056401969	2.61409174588595	0.00922469576493499	** 
df.mm.trans3:probe4	0.775590798954506	0.0481479056401969	16.1085054197455	4.50317481532373e-47	***
df.mm.trans3:probe5	0.611157038104597	0.0481479056401969	12.6933254931522	4.45782782744233e-32	***
df.mm.trans3:probe6	1.41559859351933	0.0481479056401969	29.4010419497355	7.70278977351835e-110	***
df.mm.trans3:probe7	0.282299337928256	0.0481479056401969	5.86316962648059	8.40493922245003e-09	***
df.mm.trans3:probe8	0.115312404372419	0.0481479056401969	2.39496199967935	0.0170005127567704	*  
df.mm.trans3:probe9	-0.171353378067772	0.0481479056401969	-3.55889577727999	0.000409064966335526	***
df.mm.trans3:probe10	0.252753141844426	0.0481479056401969	5.24951477086497	2.28520444998808e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.80400694946865	0.193305510099423	19.6787300450573	8.33109760082009e-64	***
df.mm.trans1	0.210598418894345	0.154751169851371	1.36088418004601	0.174182307526576	   
df.mm.trans2	0.235620550674596	0.154751169851371	1.52257686259105	0.128516318507609	   
df.mm.exp2	0.417813451892063	0.207222359136694	2.01625661261994	0.0443239303246445	*  
df.mm.exp3	0.649307851009512	0.207222359136694	3.13338702307311	0.00183262039453928	** 
df.mm.exp4	0.527869241001998	0.207222359136694	2.54735658449767	0.0111618563678006	*  
df.mm.exp5	0.850275935873911	0.207222359136694	4.10320555859046	4.77941938761111e-05	***
df.mm.exp6	0.337384415927293	0.207222359136694	1.62812747298537	0.104147244711369	   
df.mm.exp7	0.539250885072466	0.207222359136694	2.60228137214069	0.00954389316297538	** 
df.mm.exp8	0.373479464672639	0.207222359136694	1.80231257972637	0.0721169730210953	.  
df.mm.trans1:exp2	-0.356661967431507	0.162558592752421	-2.19405176553606	0.0287053403693685	*  
df.mm.trans2:exp2	-0.182927093592693	0.162558592752421	-1.12529944123774	0.261018726532353	   
df.mm.trans1:exp3	-0.213280969404044	0.162558592752421	-1.31202519530219	0.190132275631624	   
df.mm.trans2:exp3	-0.527629370294937	0.162558592752421	-3.24577963773668	0.00125227868297709	** 
df.mm.trans1:exp4	-0.295681734779905	0.162558592752421	-1.81892405546493	0.0695395491314743	.  
df.mm.trans2:exp4	-0.418148477072189	0.162558592752421	-2.57229390333758	0.0103991846011868	*  
df.mm.trans1:exp5	-0.261769688673613	0.162558592752421	-1.61030976118434	0.107980859392638	   
df.mm.trans2:exp5	-0.721384520494842	0.162558592752421	-4.43768925579666	1.12649150616457e-05	***
df.mm.trans1:exp6	-0.153347959616468	0.162558592752421	-0.9433396107828	0.345976889109098	   
df.mm.trans2:exp6	-0.151493531202930	0.162558592752421	-0.931931856925316	0.351835414043644	   
df.mm.trans1:exp7	-0.360635989295200	0.162558592752421	-2.21849846992988	0.026983148432705	*  
df.mm.trans2:exp7	-0.228013148665164	0.162558592752421	-1.40265208257820	0.161360393998508	   
df.mm.trans1:exp8	-0.262140716195128	0.162558592752421	-1.61259218449542	0.107483609343689	   
df.mm.trans2:exp8	-0.00522797017531951	0.162558592752421	-0.0321605280090102	0.97435726438751	   
df.mm.trans1:probe2	0.0746888945562958	0.111296260208496	0.671081799301953	0.502487890616636	   
df.mm.trans1:probe3	0.132615687875002	0.111296260208496	1.1915556517943	0.234018276993004	   
df.mm.trans1:probe4	-0.123568235907657	0.111296260208496	-1.11026404369897	0.267435412011103	   
df.mm.trans1:probe5	-0.0408781604608368	0.111296260208496	-0.36729141108837	0.713561869812504	   
df.mm.trans1:probe6	-0.109146590795162	0.111296260208496	-0.980685160406045	0.327236956817883	   
df.mm.trans2:probe2	-0.142005374434905	0.111296260208496	-1.27592224724246	0.202593716429698	   
df.mm.trans2:probe3	-0.159744540180708	0.111296260208496	-1.43530914589091	0.151843590481546	   
df.mm.trans2:probe4	-0.046572848722888	0.111296260208496	-0.418458343844089	0.675797268150246	   
df.mm.trans2:probe5	-0.0559983943276674	0.111296260208496	-0.503147133809917	0.615089246726997	   
df.mm.trans2:probe6	0.00987191240405267	0.111296260208496	0.0886994080983423	0.92935741645442	   
df.mm.trans3:probe2	-0.0887402103246827	0.111296260208496	-0.797333263116317	0.425647678144793	   
df.mm.trans3:probe3	-0.0442442019968678	0.111296260208496	-0.397535388107231	0.69114759652949	   
df.mm.trans3:probe4	-0.0564703452316454	0.111296260208496	-0.507387625836278	0.61211350052349	   
df.mm.trans3:probe5	0.0306792827868142	0.111296260208496	0.275654210926238	0.782931073843535	   
df.mm.trans3:probe6	-0.0523352147942238	0.111296260208496	-0.470233363602533	0.638399677679455	   
df.mm.trans3:probe7	-0.111588230798742	0.111296260208496	-1.00262336389111	0.316542576016610	   
df.mm.trans3:probe8	0.0158840042015060	0.111296260208496	0.142718220466257	0.886571996596027	   
df.mm.trans3:probe9	-0.115826372306219	0.111296260208496	-1.04070318346040	0.298531831327338	   
df.mm.trans3:probe10	-0.0892649610210152	0.111296260208496	-0.802048162748609	0.422917901921322