chr1.928_chr1_55696805_55700318_+_2.R 

fitVsDatCorrelation=0.807338199939163
cont.fitVsDatCorrelation=0.260844905900148

fstatistic=9056.841839265,51,669
cont.fstatistic=3375.66056597232,51,669

residuals=-0.479767631043,-0.0877267936615104,-0.00184847569095759,0.0815855890799643,1.53780233175519
cont.residuals=-0.527527728893189,-0.182780132381052,-0.0378083117728544,0.141566359764544,1.69825296369777

predictedValues:
Include	Exclude	Both
chr1.928_chr1_55696805_55700318_+_2.R.tl.Lung	51.7483783281431	48.1503864441858	62.6205149315007
chr1.928_chr1_55696805_55700318_+_2.R.tl.cerebhem	60.5482853580129	54.0630310804731	58.9140591477663
chr1.928_chr1_55696805_55700318_+_2.R.tl.cortex	63.0914630238996	44.8517090973783	77.9308564820004
chr1.928_chr1_55696805_55700318_+_2.R.tl.heart	54.8521479371054	47.4943218786588	64.2248135880925
chr1.928_chr1_55696805_55700318_+_2.R.tl.kidney	55.1418285485863	44.8385698385452	68.1689831465027
chr1.928_chr1_55696805_55700318_+_2.R.tl.liver	56.433298776533	53.5365368208669	59.2515256190529
chr1.928_chr1_55696805_55700318_+_2.R.tl.stomach	55.482164764361	47.720877140624	64.6440316082347
chr1.928_chr1_55696805_55700318_+_2.R.tl.testicle	70.4389686872171	49.5713869455213	73.6513447762224


diffExp=3.59799188395731,6.48525427753984,18.2397539265213,7.3578260584466,10.3032587100411,2.89676195566604,7.761287623737,20.8675817416958
diffExpScore=0.987262723027328
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,1,0,0,0,0,1
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,1,0,0,0,0,1
diffExp1.3Score=0.666666666666667
diffExp1.2=0,0,1,0,1,0,0,1
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	56.1371708816941	53.9238313516698	54.634550830176
cerebhem	55.0383071748038	53.0811806173184	57.4491376869265
cortex	55.1839888165418	55.783860328033	57.1756965767299
heart	59.1930104988294	50.1108484819802	55.9194237798466
kidney	56.6613690784996	52.4668482304549	56.3262630049819
liver	58.5542639240492	59.102954007997	56.8519867469296
stomach	56.98391136768	57.3057264734662	56.0399601573685
testicle	56.0375469502526	62.7179304331147	65.7982933407057
cont.diffExp=2.2133395300243,1.95712655748539,-0.599871511491138,9.08216201684922,4.19452084804472,-0.548690083947783,-0.321815105786179,-6.68038348286204
cont.diffExpScore=2.48610553782306

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=0.121395090372142
cont.tran.correlation=-0.152369071113310

tran.covariance=0.000886520036802957
cont.tran.covariance=-0.000304878759708644

tran.mean=53.622709666882
cont.tran.mean=56.142671788524

weightedLogRatios:
wLogRatio
Lung	0.281795994421937
cerebhem	0.458463705538493
cortex	1.35601425854732
heart	0.566420734560237
kidney	0.808017578148254
liver	0.211133826563261
stomach	0.593836773531354
testicle	1.43311488340676

cont.weightedLogRatios:
wLogRatio
Lung	0.161211769656251
cerebhem	0.144463190560651
cortex	-0.0434207691347737
heart	0.665850790138819
kidney	0.307539773640578
liver	-0.0380039306602441
stomach	-0.0227830581026736
testicle	-0.459774701958861

varWeightedLogRatios=0.211334125159148
cont.varWeightedLogRatios=0.105160052551481

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.92509134486678	0.085190193273626	46.0744505210784	1.00513150675500e-209	***
df.mm.trans1	-0.024374517935959	0.0764283783953613	-0.318919731750299	0.749886928327632	   
df.mm.trans2	0.0189200169755726	0.0704006535515564	0.268747746236715	0.788206661148513	   
df.mm.exp2	0.333882682454589	0.0964656210183919	3.46115723850399	0.000572062243702113	***
df.mm.exp3	-0.0915041359372781	0.0964656210183919	-0.94856732348027	0.343183235558875	   
df.mm.exp4	0.0192325500739125	0.0964656210183918	0.199372065103335	0.842032290985776	   
df.mm.exp5	-0.0926417190009169	0.0964656210183919	-0.96035995023817	0.337221044898676	   
df.mm.exp6	0.248002891564783	0.0964656210183918	2.57089405475863	0.0103586730503533	*  
df.mm.exp7	0.0289054890390467	0.0964656210183919	0.299645497887124	0.764540647625124	   
df.mm.exp8	0.175188711860101	0.0964656210183918	1.81607405841196	0.069806480922693	.  
df.mm.trans1:exp2	-0.176834625643419	0.092176092535534	-1.91844350068592	0.0554800899330934	.  
df.mm.trans2:exp2	-0.218061238408620	0.0804578897732073	-2.71025301587310	0.00689551709950636	** 
df.mm.trans1:exp3	0.289696508524651	0.092176092535534	3.14285950462668	0.00174698009426753	** 
df.mm.trans2:exp3	0.0205366666292721	0.0804578897732073	0.255247393228437	0.798610478651621	   
df.mm.trans1:exp4	0.0390157010828193	0.0921760925355339	0.423273540997398	0.672231731141645	   
df.mm.trans2:exp4	-0.0329515496270916	0.0804578897732073	-0.409550259396246	0.682266986855336	   
df.mm.trans1:exp5	0.156157190972245	0.092176092535534	1.6941181457876	0.0907082742573755	.  
df.mm.trans2:exp5	0.0213812578752262	0.0804578897732073	0.265744701178408	0.790517705812229	   
df.mm.trans1:exp6	-0.161336598441545	0.092176092535534	-1.75030850195076	0.0805234937119182	.  
df.mm.trans2:exp6	-0.141967703574796	0.0804578897732073	-1.76449698065622	0.078104622214496	.  
df.mm.trans1:exp7	0.0407630294702329	0.092176092535534	0.442229957345163	0.658465736829979	   
df.mm.trans2:exp7	-0.0378656750445589	0.0804578897732073	-0.470627245523016	0.63806034825793	   
df.mm.trans1:exp8	0.133164835649943	0.092176092535534	1.44467867954597	0.149016351219475	   
df.mm.trans2:exp8	-0.146104084736331	0.0804578897732072	-1.81590749083981	0.0698320636375591	.  
df.mm.trans1:probe2	0.042175792145685	0.0460880462677669	0.915113474341001	0.360461779428195	   
df.mm.trans1:probe3	0.151487055633528	0.0460880462677669	3.28690556231010	0.00106590034689781	** 
df.mm.trans1:probe4	-0.142783422083727	0.0460880462677669	-3.09805760162124	0.00202951142656733	** 
df.mm.trans1:probe5	0.171868753991193	0.0460880462677669	3.7291395038239	0.000208415286297181	***
df.mm.trans1:probe6	0.000972512404518012	0.046088046267767	0.0211011853023192	0.983171231189214	   
df.mm.trans1:probe7	0.166250932008726	0.0460880462677669	3.60724624868723	0.000332523395066318	***
df.mm.trans1:probe8	0.016457169508731	0.0460880462677669	0.357081083739512	0.721143711408368	   
df.mm.trans1:probe9	0.093216473007986	0.0460880462677669	2.02257375950388	0.0435144681390782	*  
df.mm.trans1:probe10	-0.163237458177042	0.046088046267767	-3.54186109839955	0.000424909941137151	***
df.mm.trans1:probe11	-0.212397906406254	0.0460880462677669	-4.60852484768487	4.85725704886053e-06	***
df.mm.trans1:probe12	-0.169614181831310	0.0460880462677669	-3.68022069857049	0.000251792635123798	***
df.mm.trans1:probe13	-0.211671877852002	0.0460880462677669	-4.59277177041113	5.22669601891166e-06	***
df.mm.trans1:probe14	-0.0704202937575912	0.0460880462677669	-1.52795137698952	0.126997253600393	   
df.mm.trans1:probe15	-0.118234607514557	0.046088046267767	-2.56540723873661	0.0105222838657793	*  
df.mm.trans1:probe16	0.38399115390898	0.046088046267767	8.33168652188097	4.51415048213213e-16	***
df.mm.trans1:probe17	0.254351255704674	0.0460880462677669	5.51881184606782	4.88433985239447e-08	***
df.mm.trans1:probe18	0.336422877328875	0.0460880462677669	7.2995690764215	8.21511990933126e-13	***
df.mm.trans1:probe19	0.099762704143982	0.0460880462677669	2.16461126523721	0.0307704568027072	*  
df.mm.trans1:probe20	0.281275535288732	0.0460880462677669	6.10300409903577	1.76271308051310e-09	***
df.mm.trans1:probe21	0.186357966212773	0.0460880462677669	4.04352063721799	5.8784326707363e-05	***
df.mm.trans2:probe2	-0.0785426218044313	0.0460880462677669	-1.70418640330524	0.088810609319126	.  
df.mm.trans2:probe3	-0.137478732861895	0.0460880462677669	-2.98295857592134	0.00295865636725084	** 
df.mm.trans2:probe4	-0.144857733078951	0.0460880462677669	-3.14306517219979	0.00174577112198217	** 
df.mm.trans2:probe5	-0.17447812316855	0.0460880462677669	-3.78575655289984	0.00016701059239598	***
df.mm.trans2:probe6	-0.091782569201922	0.046088046267767	-1.99146148805429	0.0468363664801325	*  
df.mm.trans3:probe2	0.184068728969641	0.0460880462677669	3.99384968284879	7.22155806107816e-05	***
df.mm.trans3:probe3	0.468299556251408	0.0460880462677669	10.1609765259008	1.18244156968759e-22	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94545965766199	0.139370450539252	28.3091547913939	1.64955648682258e-116	***
df.mm.trans1	0.044580021970411	0.125036193975201	0.356536939850016	0.721550910908426	   
df.mm.trans2	0.0434427252199064	0.115174886060222	0.377189218118186	0.706152610551835	   
df.mm.exp2	-0.0857523203374212	0.157816956931876	-0.54336569405807	0.587059042379482	   
df.mm.exp3	-0.0286756977004175	0.157816956931876	-0.181702259743836	0.855871431245483	   
df.mm.exp4	-0.0435750175469584	0.157816956931876	-0.276111125154747	0.78254798779608	   
df.mm.exp5	-0.048590958843267	0.157816956931876	-0.307894410004636	0.758258512977277	   
df.mm.exp6	0.0940794380763864	0.157816956931876	0.596130098472228	0.551289957617225	   
df.mm.exp7	0.0504003036877943	0.157816956931876	0.319359241665967	0.749553809928015	   
df.mm.exp8	-0.0366287893080204	0.157816956931876	-0.232096664516424	0.81653391407418	   
df.mm.trans1:exp2	0.0659835815386633	0.150799323865395	0.437558868616419	0.661847314066767	   
df.mm.trans2:exp2	0.0700022514743565	0.131628441211683	0.531817066509054	0.595029250583583	   
df.mm.trans1:exp3	0.0115503755581313	0.150799323865395	0.0765943457972081	0.938969157143352	   
df.mm.trans2:exp3	0.0625877635112034	0.131628441211683	0.475488146293177	0.634594291213445	   
df.mm.trans1:exp4	0.096580310889308	0.150799323865395	0.640455861562858	0.522095572981741	   
df.mm.trans2:exp4	-0.0297599815414179	0.131628441211683	-0.226090814929263	0.821199844625829	   
df.mm.trans1:exp5	0.0578854403261132	0.150799323865395	0.383857426163146	0.701206086285618	   
df.mm.trans2:exp5	0.0211999464025140	0.131628441211683	0.161059009795768	0.872095547541319	   
df.mm.trans1:exp6	-0.0519237010167292	0.150799323865395	-0.344323168604368	0.730711466737433	   
df.mm.trans2:exp6	-0.00237105200362104	0.131628441211683	-0.0180132194972054	0.985633678232492	   
df.mm.trans1:exp7	-0.0354295080816659	0.150799323865395	-0.234944740954479	0.814323515465573	   
df.mm.trans2:exp7	0.0104277331892839	0.131628441211683	0.079220973015355	0.936880553056677	   
df.mm.trans1:exp8	0.0348525609464142	0.150799323865395	0.231118814415401	0.817293164160852	   
df.mm.trans2:exp8	0.187703647579751	0.131628441211683	1.42601132287124	0.154331518966215	   
df.mm.trans1:probe2	0.0694146906485423	0.0753996619326975	0.920623367124677	0.357578943639099	   
df.mm.trans1:probe3	0.177817673478586	0.0753996619326975	2.35833515589642	0.0186438346437048	*  
df.mm.trans1:probe4	0.041187073486685	0.0753996619326975	0.54625010816957	0.585076143584516	   
df.mm.trans1:probe5	-0.000364459095499217	0.0753996619326975	-0.00483369667923096	0.996144724045084	   
df.mm.trans1:probe6	0.0835042684391848	0.0753996619326975	1.10748863189495	0.268480751558281	   
df.mm.trans1:probe7	0.000449270647257338	0.0753996619326975	0.0059585233639159	0.995247590646041	   
df.mm.trans1:probe8	-0.0195621761119526	0.0753996619326975	-0.259446469792053	0.795370595300423	   
df.mm.trans1:probe9	-0.06947225405494	0.0753996619326975	-0.921386810950845	0.357180652039049	   
df.mm.trans1:probe10	0.0981440966210123	0.0753996619326976	1.30165167993215	0.193483519457598	   
df.mm.trans1:probe11	0.0658013924784571	0.0753996619326975	0.872701425865703	0.383138953405126	   
df.mm.trans1:probe12	-0.00933297298281984	0.0753996619326975	-0.123780037517284	0.901526617107068	   
df.mm.trans1:probe13	0.0115333698131964	0.0753996619326975	0.152963150199416	0.878473458585465	   
df.mm.trans1:probe14	0.111373668618451	0.0753996619326975	1.47711098118536	0.140116463126303	   
df.mm.trans1:probe15	0.0592690439407981	0.0753996619326975	0.786065115168583	0.432107666472215	   
df.mm.trans1:probe16	0.0547299243112409	0.0753996619326976	0.725864319658268	0.46817580174939	   
df.mm.trans1:probe17	0.0864978066143682	0.0753996619326975	1.14719090772021	0.251712826257748	   
df.mm.trans1:probe18	0.0343442866168980	0.0753996619326975	0.455496559753199	0.648899839869958	   
df.mm.trans1:probe19	0.0717070297101069	0.0753996619326975	0.951025878260744	0.341934686780883	   
df.mm.trans1:probe20	-0.070478626872274	0.0753996619326975	-0.934733990388231	0.350262666290798	   
df.mm.trans1:probe21	0.109640798814209	0.0753996619326975	1.45412851999357	0.146379647252543	   
df.mm.trans2:probe2	-0.00409180835236008	0.0753996619326975	-0.0542682586032345	0.95673763976693	   
df.mm.trans2:probe3	-0.00923468489053965	0.0753996619326975	-0.122476476072036	0.902558456934487	   
df.mm.trans2:probe4	0.00770172344272776	0.0753996619326975	0.102145331229766	0.918671927885924	   
df.mm.trans2:probe5	-0.00429285122354371	0.0753996619326975	-0.0569346216349823	0.954614275018304	   
df.mm.trans2:probe6	-0.00205114216828391	0.0753996619326976	-0.0272035990043932	0.978305457769423	   
df.mm.trans3:probe2	-0.117248785431161	0.0753996619326975	-1.55503065167346	0.120411685635228	   
df.mm.trans3:probe3	0.0188010159904781	0.0753996619326975	0.249351462706292	0.803165464434826