chr5.17812_chr5_29714487_29715175_+_2.R 

fitVsDatCorrelation=0.902189463366605
cont.fitVsDatCorrelation=0.273609405250095

fstatistic=10006.8012080804,54,738
cont.fstatistic=2001.54627486235,54,738

residuals=-0.533249721315255,-0.0924415977756034,-0.00032323639696102,0.084907164657281,1.25451640545145
cont.residuals=-0.663343956321484,-0.261729257913830,-0.0604274333480744,0.240347278610617,1.04078915932363

predictedValues:
Include	Exclude	Both
chr5.17812_chr5_29714487_29715175_+_2.R.tl.Lung	66.200978962034	41.4640218613676	55.7229419883884
chr5.17812_chr5_29714487_29715175_+_2.R.tl.cerebhem	63.3157426959684	44.5844252724863	55.0973895703976
chr5.17812_chr5_29714487_29715175_+_2.R.tl.cortex	72.7148018937216	41.8896363921191	59.3693293418615
chr5.17812_chr5_29714487_29715175_+_2.R.tl.heart	67.7598438783345	42.6473593306299	56.9002087950885
chr5.17812_chr5_29714487_29715175_+_2.R.tl.kidney	85.8875092268738	40.4109361710839	73.887968130331
chr5.17812_chr5_29714487_29715175_+_2.R.tl.liver	76.6603748701783	45.5593662438512	61.4530142040684
chr5.17812_chr5_29714487_29715175_+_2.R.tl.stomach	59.1075642955031	42.9723843303592	55.4685605180453
chr5.17812_chr5_29714487_29715175_+_2.R.tl.testicle	69.565451689165	43.1406045323807	57.0363526879652


diffExp=24.7369571006665,18.7313174234821,30.8251655016024,25.1124845477047,45.4765730557899,31.1010086263271,16.1351799651438,26.4248471567843
diffExpScore=0.995445094717135
diffExp1.5=1,0,1,1,1,1,0,1
diffExp1.5Score=0.857142857142857
diffExp1.4=1,1,1,1,1,1,0,1
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	57.6704332181154	70.2990791353679	55.5234167013443
cerebhem	64.3505036199576	58.198752520159	67.1751843521913
cortex	62.9875267357081	63.7716505639147	60.8598436982
heart	62.2495931429253	73.4961416980443	65.6066833232499
kidney	58.4255425709785	64.158995398792	67.398905353245
liver	54.3453934712767	56.8149837079379	64.7457812740834
stomach	63.4312287612425	76.1371125869176	72.4236750212683
testicle	64.0308481280919	64.3874999194756	56.9398900958736
cont.diffExp=-12.6286459172525,6.15175109979852,-0.78412382820656,-11.2465485551189,-5.73345282781352,-2.46959023666120,-12.7058838256750,-0.356651791383726
cont.diffExpScore=1.27722909172187

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

tran.correlation=-0.31609012462998
cont.tran.correlation=0.283015818787611

tran.covariance=-0.00140382821537753
cont.tran.covariance=0.00194212725405235

tran.mean=56.4925626028785
cont.tran.mean=63.4222053236816

weightedLogRatios:
wLogRatio
Lung	1.85218196507234
cerebhem	1.39344297470180
cortex	2.21197774401693
heart	1.84482596489571
kidney	3.07310347714860
liver	2.12268839672398
stomach	1.24968749961757
testicle	1.91282208172587

cont.weightedLogRatios:
wLogRatio
Lung	-0.822501313610186
cerebhem	0.413388660475789
cortex	-0.0513330485081606
heart	-0.699896924069666
kidney	-0.385168528478623
liver	-0.178542199552458
stomach	-0.774364673052873
testicle	-0.0231188039025987

varWeightedLogRatios=0.311651355248812
cont.varWeightedLogRatios=0.189361160205436

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.71696808425677	0.0784099158529845	47.4043116080617	3.78893109878624e-226	***
df.mm.trans1	0.528370580658363	0.0690721542482994	7.64954541245356	6.30568593334823e-14	***
df.mm.trans2	0.00256862385113676	0.0623259140399463	0.0412127746652935	0.967137416409738	   
df.mm.exp2	0.0392868362081708	0.082999565814943	0.473337852101121	0.636112136814786	   
df.mm.exp3	0.0406762572075011	0.082999565814943	0.490077951711139	0.624224395323462	   
df.mm.exp4	0.0305066809322972	0.082999565814943	0.367552295397728	0.713312470953502	   
df.mm.exp5	-0.0475405603038742	0.082999565814943	-0.57278083128617	0.566967559900346	   
df.mm.exp6	0.142998880077126	0.082999565814943	1.72288708589100	0.0853278511580253	.  
df.mm.exp7	-0.073029202784379	0.082999565814943	-0.879874515816214	0.379213707597394	   
df.mm.exp8	0.065914471570015	0.082999565814943	0.794154414216802	0.427360827196639	   
df.mm.trans1:exp2	-0.0838480889801283	0.078288694575879	-1.07101145873445	0.284514406100945	   
df.mm.trans2:exp2	0.0332716445684890	0.0640543864711737	0.519428042347453	0.603618022367505	   
df.mm.trans1:exp3	0.0531734582176313	0.078288694575879	0.67919714980168	0.497225904370153	   
df.mm.trans2:exp3	-0.0304639104747983	0.0640543864711737	-0.475594446424178	0.634504071040415	   
df.mm.trans1:exp4	-0.00723218540965447	0.078288694575879	-0.0923784136245226	0.926422459694214	   
df.mm.trans2:exp4	-0.00236743190984541	0.0640543864711737	-0.0369597156458726	0.97052712085144	   
df.mm.trans1:exp5	0.307883717311071	0.078288694575879	3.93267149208451	9.1924070834119e-05	***
df.mm.trans2:exp5	0.0218148978185592	0.0640543864711737	0.340568367294823	0.733525467986953	   
df.mm.trans1:exp6	0.0036908191198123	0.078288694575879	0.0471437049730735	0.96241144876674	   
df.mm.trans2:exp6	-0.0488087600826959	0.0640543864711737	-0.761989346423	0.446309807890412	   
df.mm.trans1:exp7	-0.0403071403098597	0.078288694575879	-0.514852630104762	0.606810164034566	   
df.mm.trans2:exp7	0.10876077916327	0.0640543864711737	1.69794428071238	0.0899398331114446	.  
df.mm.trans1:exp8	-0.0163416620249736	0.078288694575879	-0.208735911532347	0.83471200199063	   
df.mm.trans2:exp8	-0.0262759255637788	0.0640543864711737	-0.410212742816664	0.681768950440968	   
df.mm.trans1:probe2	-0.425991614728599	0.0457107495503293	-9.31928745249661	1.32531502199627e-19	***
df.mm.trans1:probe3	-0.363175821359803	0.0457107495503293	-7.94508567311793	7.25139020154595e-15	***
df.mm.trans1:probe4	0.200864087999450	0.0457107495503293	4.39424183535408	1.27458497622164e-05	***
df.mm.trans1:probe5	0.344285302055734	0.0457107495503293	7.53182359603757	1.46466898479885e-13	***
df.mm.trans1:probe6	-0.0494401727576283	0.0457107495503293	-1.08158744374106	0.279789176534639	   
df.mm.trans1:probe7	-0.0916334334646327	0.0457107495503293	-2.00463642285587	0.0453667702027169	*  
df.mm.trans1:probe8	0.0898921541465694	0.0457107495503293	1.96654299110967	0.0496102695756587	*  
df.mm.trans1:probe9	0.0581762693344163	0.0457107495503293	1.27270433993566	0.203523778194218	   
df.mm.trans1:probe10	-0.0474351991527699	0.0457107495503293	-1.03772525323703	0.299737864369705	   
df.mm.trans1:probe11	-0.510561606057171	0.0457107495503293	-11.1693991255825	7.1532050111971e-27	***
df.mm.trans1:probe12	-0.371531985174263	0.0457107495503293	-8.12789089719897	1.84052662723865e-15	***
df.mm.trans1:probe13	-0.429599207434019	0.0457107495503293	-9.39820964784255	6.79798451461567e-20	***
df.mm.trans1:probe14	-0.241751608671918	0.0457107495503293	-5.2887255415871	1.62459576002969e-07	***
df.mm.trans1:probe15	-0.405364789535435	0.0457107495503293	-8.86804074584497	5.54212850914021e-18	***
df.mm.trans1:probe16	-0.525373325167697	0.0457107495503293	-11.4934305461178	3.06089630838096e-28	***
df.mm.trans1:probe17	0.163285259174681	0.0457107495503293	3.57214136239217	0.000377059298124283	***
df.mm.trans1:probe18	0.414164061448202	0.0457107495503293	9.060539709422	1.14724164565099e-18	***
df.mm.trans1:probe19	0.277736420638766	0.0457107495503293	6.07595419832193	1.97394325128651e-09	***
df.mm.trans1:probe20	-0.0245979828736013	0.0457107495503293	-0.538122501065487	0.590654726122474	   
df.mm.trans1:probe21	0.330959346848565	0.0457107495503293	7.24029577515822	1.12688115627114e-12	***
df.mm.trans1:probe22	0.185721663075923	0.0457107495503293	4.0629756655256	5.36428576437311e-05	***
df.mm.trans2:probe2	-0.0328201181203089	0.0457107495503293	-0.717995623418352	0.472987206907455	   
df.mm.trans2:probe3	0.129797840609367	0.0457107495503293	2.83954741250644	0.00464203023423199	** 
df.mm.trans2:probe4	0.0218629840468717	0.0457107495503293	0.478289773454704	0.632585624528883	   
df.mm.trans2:probe5	0.00237334705610892	0.0457107495503293	0.0519209831266446	0.958605704715979	   
df.mm.trans2:probe6	-0.0630306534087371	0.0457107495503293	-1.37890220634728	0.168342678143986	   
df.mm.trans3:probe2	-0.319874329469708	0.0457107495503293	-6.99779226147919	5.8446901400725e-12	***
df.mm.trans3:probe3	-0.196537830095224	0.0457107495503293	-4.29959762262984	1.9408683259117e-05	***
df.mm.trans3:probe4	-0.319686195942556	0.0457107495503293	-6.99367652220554	6.00784101580399e-12	***
df.mm.trans3:probe5	-0.301478353212776	0.0457107495503293	-6.59534915044078	8.08781866506147e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.26463001424714	0.174845522830020	24.3908448167305	8.00033962544397e-97	***
df.mm.trans1	-0.189302856549077	0.154023337369518	-1.22905307586552	0.219443641859936	   
df.mm.trans2	-2.93722148317721e-05	0.138979960731058	-0.00021134136660616	0.999831431092572	   
df.mm.exp2	-0.269793683398890	0.185079939465667	-1.45771434860955	0.145344682533136	   
df.mm.exp3	-0.101026579672379	0.185079939465667	-0.54585375359451	0.585331424589624	   
df.mm.exp4	-0.0459911252481872	0.185079939465667	-0.248493301764445	0.803821883267994	   
df.mm.exp5	-0.272209769008688	0.185079939465667	-1.47076863000155	0.141779924092619	   
df.mm.exp6	-0.426007067501892	0.185079939465667	-2.30174630882089	0.0216271253461403	*  
df.mm.exp7	-0.0907395787588247	0.185079939465667	-0.490272360261158	0.62408690511318	   
df.mm.exp8	-0.0084101337607359	0.185079939465667	-0.0454405473927444	0.963768454724573	   
df.mm.trans1:exp2	0.379393824054614	0.174575212661427	2.17323993636150	0.0300796154346749	*  
df.mm.trans2:exp2	0.0808989038843664	0.142834264904804	0.566383030978484	0.571305619176732	   
df.mm.trans1:exp3	0.189218678521790	0.174575212661427	1.08388055576230	0.278771731340005	   
df.mm.trans2:exp3	0.00357662308241074	0.142834264904804	0.0250403716838848	0.980029530782902	   
df.mm.trans1:exp4	0.122398505097271	0.174575212661427	0.701121901736715	0.48344795026001	   
df.mm.trans2:exp4	0.0904653365242986	0.142834264904804	0.633358785334822	0.526695593589209	   
df.mm.trans1:exp5	0.285218316396245	0.174575212661427	1.63378472835889	0.102730554907651	   
df.mm.trans2:exp5	0.180815375034195	0.142834264904804	1.26591035529679	0.205944645320334	   
df.mm.trans1:exp6	0.366622301121063	0.174575212661427	2.10008222548807	0.0360604717674258	*  
df.mm.trans2:exp6	0.213048456454803	0.142834264904804	1.49157806494678	0.136237102122080	   
df.mm.trans1:exp7	0.185951266524871	0.174575212661427	1.06516419880013	0.287149992143503	   
df.mm.trans2:exp7	0.170516706913126	0.142834264904804	1.19380813159064	0.232936622514554	   
df.mm.trans1:exp8	0.113030483378440	0.174575212661427	0.647460092731793	0.517535562438913	   
df.mm.trans2:exp8	-0.0794290522588494	0.142834264904804	-0.556092421603369	0.578316231092892	   
df.mm.trans1:probe2	0.036786539311983	0.101929964050271	0.360900149968074	0.718277372349241	   
df.mm.trans1:probe3	0.0637281900491027	0.101929964050271	0.625215466746096	0.53202292133497	   
df.mm.trans1:probe4	-0.105388783840296	0.101929964050271	-1.03393329745824	0.301506032255153	   
df.mm.trans1:probe5	0.0631825208903147	0.101929964050271	0.619862093340422	0.535539917149108	   
df.mm.trans1:probe6	-0.0247366054016098	0.101929964050271	-0.242682371489997	0.808318894129996	   
df.mm.trans1:probe7	-0.0806137911780407	0.101929964050271	-0.790874321688988	0.429271413305185	   
df.mm.trans1:probe8	-0.0265634663323823	0.101929964050271	-0.260605078986210	0.794469769818099	   
df.mm.trans1:probe9	0.0174345014479178	0.101929964050271	0.171043928155604	0.864236109063057	   
df.mm.trans1:probe10	-0.142691976313885	0.101929964050271	-1.39990215481202	0.161962845823099	   
df.mm.trans1:probe11	0.0470673365275196	0.101929964050271	0.461761533677248	0.644388418298971	   
df.mm.trans1:probe12	-0.0479540496021621	0.101929964050271	-0.470460772246636	0.638164859253393	   
df.mm.trans1:probe13	-0.0384833757124247	0.101929964050271	-0.377547231287605	0.705875521848253	   
df.mm.trans1:probe14	-0.080493788253373	0.101929964050271	-0.789697014056382	0.429958381983729	   
df.mm.trans1:probe15	-0.0485687834918139	0.101929964050271	-0.476491716094989	0.633865149478237	   
df.mm.trans1:probe16	-0.0542982772876878	0.101929964050271	-0.532701819269833	0.594400361192707	   
df.mm.trans1:probe17	0.0380685741013476	0.101929964050271	0.373477754613675	0.70890015588221	   
df.mm.trans1:probe18	-0.134113077749859	0.101929964050271	-1.31573751643546	0.188670730783286	   
df.mm.trans1:probe19	-0.0747698439713391	0.101929964050271	-0.733541355262949	0.463461267741648	   
df.mm.trans1:probe20	0.00519747760705618	0.101929964050271	0.050990674385923	0.959346749251869	   
df.mm.trans1:probe21	0.095675020626421	0.101929964050271	0.93863489031778	0.348225320415598	   
df.mm.trans1:probe22	-0.0641928695950571	0.101929964050271	-0.629774278772413	0.529037197120051	   
df.mm.trans2:probe2	-0.0332159817502016	0.101929964050271	-0.325870631464364	0.744614612752632	   
df.mm.trans2:probe3	0.0499735139795735	0.101929964050271	0.49027304625485	0.624086419985816	   
df.mm.trans2:probe4	-0.0143611544049066	0.101929964050271	-0.140892371921409	0.88799341536826	   
df.mm.trans2:probe5	-0.0851902900015272	0.101929964050271	-0.835772785709141	0.403553242387274	   
df.mm.trans2:probe6	-0.0474674539109643	0.101929964050271	-0.465686948418367	0.641576993760336	   
df.mm.trans3:probe2	-0.107565813096378	0.101929964050271	-1.05529138657724	0.291637422660506	   
df.mm.trans3:probe3	-0.0659558319854685	0.101929964050271	-0.647070099553258	0.517787781691605	   
df.mm.trans3:probe4	-0.125632648627700	0.101929964050271	-1.23253892805985	0.218140335569265	   
df.mm.trans3:probe5	0.00668963786762378	0.101929964050271	0.0656297481310259	0.947690378443376