chr4.16754_chr4_134891806_134898403_+_1.R 

fitVsDatCorrelation=0.900514892826171
cont.fitVsDatCorrelation=0.310509738696197

fstatistic=7557.77852812688,37,347
cont.fstatistic=1574.03277432193,37,347

residuals=-0.519015348762384,-0.0830339876743597,-0.00864932441666153,0.0849568617252362,0.855158777630224
cont.residuals=-0.689002358232663,-0.237974058186593,-0.0548800034841147,0.171760575855906,1.17025761206750

predictedValues:
Include	Exclude	Both
chr4.16754_chr4_134891806_134898403_+_1.R.tl.Lung	105.029926835169	66.6126746636805	59.1548908697893
chr4.16754_chr4_134891806_134898403_+_1.R.tl.cerebhem	71.83443710901	84.2093404339445	52.8384226620589
chr4.16754_chr4_134891806_134898403_+_1.R.tl.cortex	83.0860588894266	61.2264087877091	61.436844703818
chr4.16754_chr4_134891806_134898403_+_1.R.tl.heart	92.2481212030916	61.863942044963	53.9477804619257
chr4.16754_chr4_134891806_134898403_+_1.R.tl.kidney	112.791932866284	60.8596115183563	57.8379751259292
chr4.16754_chr4_134891806_134898403_+_1.R.tl.liver	100.279150457163	69.3786722426978	52.8985720299678
chr4.16754_chr4_134891806_134898403_+_1.R.tl.stomach	93.3806791840773	85.3776777354881	62.5111065033103
chr4.16754_chr4_134891806_134898403_+_1.R.tl.testicle	90.3968499766706	65.9701575766488	59.7441193067795


diffExp=38.417252171488,-12.3749033249345,21.8596501017175,30.3841791581286,51.9323213479278,30.9004782144650,8.00300144858917,24.4266924000218
diffExpScore=1.12207642676061
diffExp1.5=1,0,0,0,1,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,0,0,1,1,1,0,0
diffExp1.4Score=0.8
diffExp1.3=1,0,1,1,1,1,0,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,0,1,1,1,1,0,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	82.9715178858473	68.6561999384618	85.0479569034847
cerebhem	73.1970023073744	74.7042308504898	68.1616454428206
cortex	79.8732114590552	88.050926012722	80.1230643338886
heart	84.3742068386803	86.076850603687	86.2028411670216
kidney	75.6186643186526	73.9357183338373	96.0964172986592
liver	82.6039708363278	87.3505976727795	64.472007067529
stomach	68.0013401252484	78.6000502263705	68.552535246155
testicle	73.1575092152961	70.8379863490032	73.6778826352795
cont.diffExp=14.3153179473855,-1.50722854311542,-8.17771455366689,-1.70264376500671,1.68294598481532,-4.74662683645172,-10.5987101011221,2.31952286629294
cont.diffExpScore=4.78492353257293

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

tran.correlation=-0.467294804708822
cont.tran.correlation=0.389403380159933

tran.covariance=-0.00900807181738617
cont.tran.covariance=0.00270521745841457

tran.mean=81.5341025952737
cont.tran.mean=78.0006239358646

weightedLogRatios:
wLogRatio
Lung	2.01564071865163
cerebhem	-0.692006620083456
cortex	1.30277743529998
heart	1.72791365545795
kidney	2.72521417839076
liver	1.62962036549913
stomach	0.402471874203373
testicle	1.36924253897691

cont.weightedLogRatios:
wLogRatio
Lung	0.818867808950304
cerebhem	-0.0877119104998838
cortex	-0.431733371181191
heart	-0.0888106062112633
kidney	0.0971057082853394
liver	-0.248183639854852
stomach	-0.621667161012655
testicle	0.137786385309388

varWeightedLogRatios=1.08753836433088
cont.varWeightedLogRatios=0.189498557957958

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.65940911185973	0.0877533478266647	53.0966536007636	1.14545050932510e-168	***
df.mm.trans1	-0.135793877202347	0.0731105812919893	-1.85737652201140	0.0641046305513916	.  
df.mm.trans2	-0.524243908071777	0.0731105812919893	-7.17056134430187	4.53528055708669e-12	***
df.mm.exp2	-0.0325497186835664	0.100731022509057	-0.323134997270972	0.746787642867223	   
df.mm.exp3	-0.356535118328161	0.100731022509057	-3.53947681108969	0.000455543620422472	***
df.mm.exp4	-0.111578067208715	0.100731022509057	-1.10768325814108	0.268765846056508	   
df.mm.exp5	0.00348808772432096	0.100731022509057	0.0346277406645737	0.972396491201701	   
df.mm.exp6	0.106180036858110	0.100731022509057	1.05409469906417	0.292572966671039	   
df.mm.exp7	0.075443975796532	0.100731022509057	0.748964657732415	0.454385897345331	   
df.mm.exp8	-0.169639784925964	0.100731022509057	-1.68408679571094	0.0930641413069732	.  
df.mm.trans1:exp2	-0.347331621797664	0.0851332522566835	-4.07985848761459	5.59501308716276e-05	***
df.mm.trans2:exp2	0.266960695593817	0.0851332522566835	3.13579815779749	0.0018602606773379	** 
df.mm.trans1:exp3	0.122166716026754	0.0851332522566835	1.43500586185069	0.152186111929142	   
df.mm.trans2:exp3	0.27221886120405	0.0851332522566835	3.19756210397424	0.00151343002041352	** 
df.mm.trans1:exp4	-0.0181853434657497	0.0851332522566835	-0.213610345942376	0.830976383439519	   
df.mm.trans2:exp4	0.0376206879962891	0.0851332522566835	0.441903568805991	0.658834257724451	   
df.mm.trans1:exp5	0.0678114046137393	0.0851332522566834	0.796532527728209	0.42626719829913	   
df.mm.trans2:exp5	-0.0938131961824904	0.0851332522566835	-1.10195715182637	0.271244029010691	   
df.mm.trans1:exp6	-0.152467562337046	0.0851332522566835	-1.79092843625126	0.0741762594389916	.  
df.mm.trans2:exp6	-0.0654954026694639	0.0851332522566835	-0.769328093704093	0.442221912119779	   
df.mm.trans1:exp7	-0.193004839970157	0.0851332522566835	-2.26709111720803	0.0239994467103707	*  
df.mm.trans2:exp7	0.172745836295558	0.0851332522566835	2.02912295391600	0.0432087449763746	*  
df.mm.trans1:exp8	0.019603879300615	0.0851332522566835	0.230272881406055	0.81801542407331	   
df.mm.trans2:exp8	0.159947397063490	0.0851332522566835	1.87878875555271	0.0611114288819635	.  
df.mm.trans1:probe2	0.250791443261924	0.0466294026547631	5.37839708389029	1.38422494811972e-07	***
df.mm.trans1:probe3	-0.00629079910211245	0.0466294026547631	-0.134910565951028	0.892760805675704	   
df.mm.trans1:probe4	0.614226258616388	0.0466294026547631	13.1725096965969	2.05849506530201e-32	***
df.mm.trans1:probe5	-0.101865706272525	0.0466294026547631	-2.18458098266286	0.0295882557288706	*  
df.mm.trans1:probe6	0.54943972717797	0.0466294026547631	11.7831174301317	3.47285816030745e-27	***
df.mm.trans2:probe2	0.07962274713158	0.0466294026547631	1.70756523992157	0.0886114893941325	.  
df.mm.trans2:probe3	0.0105898431185674	0.0466294026547631	0.227106557572116	0.82047462422381	   
df.mm.trans2:probe4	0.155640470716187	0.0466294026547631	3.33781824031769	0.00093595029291038	***
df.mm.trans2:probe5	0.316607015126966	0.0466294026547631	6.78985783864906	4.87993111422474e-11	***
df.mm.trans2:probe6	0.074836583078275	0.0466294026547631	1.60492262001196	0.109420517382898	   
df.mm.trans3:probe2	0.0539504982015578	0.0466294026547631	1.15700599042623	0.248065762993509	   
df.mm.trans3:probe3	0.251581833456792	0.0466294026547631	5.39534755183258	1.26891120811971e-07	***
df.mm.trans3:probe4	0.0176194535427774	0.0466294026547631	0.377861446633343	0.70576455225095	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27670863739282	0.191837370879746	22.2934072635602	5.92512416719629e-69	***
df.mm.trans1	0.139566568815085	0.159826856136005	0.873236026718315	0.383138327613149	   
df.mm.trans2	-0.0292916729730685	0.159826856136005	-0.183271282944731	0.854692203695953	   
df.mm.exp2	0.180415638283313	0.220207832552847	0.8192970985263	0.413179274683875	   
df.mm.exp3	0.270398580541145	0.220207832552847	1.22792444485939	0.220307601900339	   
df.mm.exp4	0.229405585237844	0.220207832552847	1.04176850831493	0.298244287278397	   
df.mm.exp5	-0.140846389617313	0.220207832552847	-0.639606629721091	0.522850405006217	   
df.mm.exp6	0.513362965568375	0.220207832552847	2.33126569394471	0.0203114772088633	*  
df.mm.exp7	0.151905826861240	0.220207832552847	0.689829353934469	0.490762581114951	   
df.mm.exp8	0.0489139750988324	0.220207832552847	0.222126409091709	0.824346154186733	   
df.mm.trans1:exp2	-0.305758561142235	0.186109586606584	-1.64289527862193	0.101310402681120	   
df.mm.trans2:exp2	-0.0959903502794552	0.186109586606584	-0.515773271166137	0.606341236878912	   
df.mm.trans1:exp3	-0.308455450745641	0.186109586606584	-1.65738614742981	0.0983451994603297	.  
df.mm.trans2:exp3	-0.0215946691803770	0.186109586606584	-0.116032008743461	0.907694294200624	   
df.mm.trans1:exp4	-0.212641227422368	0.186109586606584	-1.14255923781008	0.254009364933665	   
df.mm.trans2:exp4	-0.00327651676924086	0.186109586606584	-0.0176053089418069	0.985963839638971	   
df.mm.trans1:exp5	0.0480521339516072	0.186109586606584	0.258192685437448	0.796411269627082	   
df.mm.trans2:exp5	0.214930993721209	0.186109586606584	1.15486256049534	0.248941372475423	   
df.mm.trans1:exp6	-0.517802604020465	0.186109586606584	-2.78224573737325	0.0056935922696371	** 
df.mm.trans2:exp6	-0.272544527158529	0.186109586606584	-1.46443035056899	0.143981763675427	   
df.mm.trans1:exp7	-0.350875805057665	0.186109586606584	-1.88531827648073	0.0602221427240491	.  
df.mm.trans2:exp7	-0.0166449288364138	0.186109586606584	-0.0894361711285696	0.928786875777154	   
df.mm.trans1:exp8	-0.174796588775972	0.186109586606584	-0.939213245073044	0.348274570105918	   
df.mm.trans2:exp8	-0.0176300283494321	0.186109586606584	-0.0947292864966714	0.924584532742615	   
df.mm.trans1:probe2	0.0949736561475217	0.101936418752387	0.931695043929503	0.352141918184624	   
df.mm.trans1:probe3	-0.0127923600131604	0.101936418752387	-0.125493520075825	0.900205528811151	   
df.mm.trans1:probe4	-0.0244366242121258	0.101936418752387	-0.239724178180956	0.810685632396733	   
df.mm.trans1:probe5	-0.0283888503599454	0.101936418752387	-0.278495661387757	0.780797778132463	   
df.mm.trans1:probe6	-0.0071339746155097	0.101936418752387	-0.0699845521632339	0.944246268861738	   
df.mm.trans2:probe2	0.0419212718118063	0.101936418752387	0.411249211271948	0.681143676845396	   
df.mm.trans2:probe3	0.00389112066958926	0.101936418752387	0.0381720362282017	0.969572467516224	   
df.mm.trans2:probe4	-0.100847446817923	0.101936418752387	-0.989317145454076	0.323197337671306	   
df.mm.trans2:probe5	-0.0974023780024603	0.101936418752387	-0.955520894245455	0.339979536462865	   
df.mm.trans2:probe6	-0.0306178076337044	0.101936418752387	-0.300361813848668	0.764081054067834	   
df.mm.trans3:probe2	0.0478383924215361	0.101936418752387	0.469296381087704	0.639152535144559	   
df.mm.trans3:probe3	0.0154461592957330	0.101936418752387	0.151527388197275	0.87964776285984	   
df.mm.trans3:probe4	0.161642500361418	0.101936418752387	1.58571884651022	0.113713941785886