chr17.10015_chr17_23011169_23012644_+_2.R 

fitVsDatCorrelation=0.9250865181419
cont.fitVsDatCorrelation=0.209484132087315

fstatistic=8375.7697178819,64,968
cont.fstatistic=1250.50785553803,64,968

residuals=-0.935944237899158,-0.116457382002744,4.8775884311265e-05,0.116832159797124,0.773249163080751
cont.residuals=-1.01822939199188,-0.383654517825622,-0.103779672374754,0.304105664532626,1.78412252377303

predictedValues:
Include	Exclude	Both
chr17.10015_chr17_23011169_23012644_+_2.R.tl.Lung	91.505921318181	307.37222242711	142.059546925365
chr17.10015_chr17_23011169_23012644_+_2.R.tl.cerebhem	81.7248240576423	428.209173431639	119.207881200545
chr17.10015_chr17_23011169_23012644_+_2.R.tl.cortex	85.3053119815717	303.85463831111	139.7414609158
chr17.10015_chr17_23011169_23012644_+_2.R.tl.heart	79.2190861579547	188.652723954288	100.340658662340
chr17.10015_chr17_23011169_23012644_+_2.R.tl.kidney	84.3474069053048	139.082626736635	104.014005573494
chr17.10015_chr17_23011169_23012644_+_2.R.tl.liver	84.4101568498788	132.146464400777	88.2068555549028
chr17.10015_chr17_23011169_23012644_+_2.R.tl.stomach	78.3660708561945	185.553642368967	91.8420932194924
chr17.10015_chr17_23011169_23012644_+_2.R.tl.testicle	84.0774492067173	200.919530905435	103.792408262555


diffExp=-215.866301108929,-346.484349373997,-218.549326329538,-109.433637796333,-54.7352198313303,-47.7363075508983,-107.187571512772,-116.842081698718
diffExpScore=0.99917887056279
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
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	84.487762417773	106.772997578804	92.748972741611
cerebhem	93.6074961636384	114.365293985034	92.1153645883312
cortex	98.7836343359216	101.729103983796	103.796462159233
heart	96.5969722632456	82.6202198200789	91.5179720031944
kidney	91.3273616308128	84.6680740273255	103.734225217912
liver	95.8987945871237	110.541095973291	95.9626154095424
stomach	102.463057842208	95.58847368278	92.1947634546373
testicle	88.3104361084274	109.849073919221	96.5564793334767
cont.diffExp=-22.2852351610308,-20.7577978213957,-2.94546964787440,13.9767524431668,6.6592876034873,-14.6423013861671,6.87458415942834,-21.5386378107935
cont.diffExpScore=1.97057844059570

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,-1,0,0,0,0,0,-1
cont.diffExp1.2Score=0.75

tran.correlation=0.211646112362070
cont.tran.correlation=-0.284400632315762

tran.covariance=0.00447911446836389
cont.tran.covariance=-0.00211925358549789

tran.mean=159.671703116838
cont.tran.mean=97.3506155199675

weightedLogRatios:
wLogRatio
Lung	-6.20638149329222
cerebhem	-8.66466762199304
cortex	-6.45495814143878
heart	-4.17017545468327
kidney	-2.34308460092520
liver	-2.08862992595883
stomach	-4.13079535569752
testicle	-4.24024541231334

cont.weightedLogRatios:
wLogRatio
Lung	-1.06600338520615
cerebhem	-0.929183165402344
cortex	-0.135378821515769
heart	0.70213061537338
kidney	0.338931169244224
liver	-0.658511725371608
stomach	0.319107639362538
testicle	-1.00175952293856

varWeightedLogRatios=4.88528206587743
cont.varWeightedLogRatios=0.489486482606085

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.48901773852322	0.0911701714643735	60.20628951727	0	***
df.mm.trans1	-0.703761777364122	0.0780973570832109	-9.01133922642581	1.06356573470780e-18	***
df.mm.trans2	0.268176261841255	0.0683729787986353	3.92225505679749	9.39155228413066e-05	***
df.mm.exp2	0.393883895149811	0.0865327788362703	4.55184613792518	5.99401898195957e-06	***
df.mm.exp3	-0.0652246911967974	0.0865327788362703	-0.753757039516897	0.451178424160386	   
df.mm.exp4	-0.284662675073483	0.0865327788362703	-3.28965137722084	0.00103930016023822	** 
df.mm.exp5	-0.562730133003096	0.0865327788362703	-6.50308635144891	1.25905179220871e-10	***
df.mm.exp6	-0.448302898801397	0.0865327788362703	-5.18072925462889	2.689190580661e-07	***
df.mm.exp7	-0.223552476774062	0.0865327788362703	-2.58344271131115	0.00992760961959127	** 
df.mm.exp8	-0.195966791237095	0.0865327788362703	-2.26465385571271	0.0237543647564403	*  
df.mm.trans1:exp2	-0.506929779447841	0.0791647964424164	-6.40347480482157	2.36505069636486e-10	***
df.mm.trans2:exp2	-0.0623315600478858	0.0548766509195746	-1.13584847113278	0.256301123277560	   
df.mm.trans1:exp3	-0.00494226618218017	0.0791647964424164	-0.0624301003006447	0.950233213129328	   
df.mm.trans2:exp3	0.0537146507313086	0.0548766509195746	0.97882523498074	0.327910898746982	   
df.mm.trans1:exp4	0.140476247656534	0.0791647964424164	1.77447873258558	0.0762982445702618	.  
df.mm.trans2:exp4	-0.203488903489456	0.0548766509195746	-3.70811447272325	0.000220672468957969	***
df.mm.trans1:exp5	0.481270515392069	0.0791647964424164	6.07935012808553	1.73404812363919e-09	***
df.mm.trans2:exp5	-0.230261137584481	0.0548766509195746	-4.19597649867416	2.96672249547109e-05	***
df.mm.trans1:exp6	0.367586950938494	0.0791647964424164	4.64331328390231	3.90124857131052e-06	***
df.mm.trans2:exp6	-0.395845679010853	0.0548766509195746	-7.21337166859894	1.10037239415950e-12	***
df.mm.trans1:exp7	0.0685398567196721	0.0791647964424164	0.865787064450134	0.386821470191903	   
df.mm.trans2:exp7	-0.281162969855734	0.0548766509195746	-5.12354462497715	3.61861782087623e-07	***
df.mm.trans1:exp8	0.111301495604409	0.0791647964424164	1.40594684261418	0.160060808504863	   
df.mm.trans2:exp8	-0.229188188681657	0.0548766509195746	-4.17642448730239	3.22849328403432e-05	***
df.mm.trans1:probe2	-0.640901682959421	0.0579426979673324	-11.0609568667437	7.29758260446174e-27	***
df.mm.trans1:probe3	-0.747223039387513	0.0579426979673324	-12.8958965598873	3.14644682896162e-35	***
df.mm.trans1:probe4	-0.520572752737521	0.0579426979673324	-8.98426844105559	1.33524051944635e-18	***
df.mm.trans1:probe5	-0.62750454952216	0.0579426979673324	-10.8297433763947	7.06672668187085e-26	***
df.mm.trans1:probe6	-0.363923076132953	0.0579426979673325	-6.28074095441895	5.08276922685693e-10	***
df.mm.trans1:probe7	-0.599059495022576	0.0579426979673324	-10.338826392936	7.75108907230199e-24	***
df.mm.trans1:probe8	-0.627319034096484	0.0579426979673324	-10.8265416713968	7.29055208052421e-26	***
df.mm.trans1:probe9	-0.636359216176077	0.0579426979673324	-10.9825610214915	1.58230108752340e-26	***
df.mm.trans1:probe10	-0.603516989561031	0.0579426979673324	-10.4157557506433	3.75428032173243e-24	***
df.mm.trans1:probe11	-0.634329496057339	0.0579426979673324	-10.9475312387933	2.23297947159633e-26	***
df.mm.trans1:probe12	-0.536707017030816	0.0579426979673324	-9.2627205128316	1.25234887642182e-19	***
df.mm.trans1:probe13	-0.388814045044821	0.0579426979673324	-6.71032000035675	3.30083679999895e-11	***
df.mm.trans1:probe14	-0.661015799693446	0.0579426979673324	-11.4080949434926	2.25523918383089e-28	***
df.mm.trans1:probe15	-0.470328960868171	0.0579426979673324	-8.1171394734387	1.44174646618203e-15	***
df.mm.trans1:probe16	-0.485840635559962	0.0579426979673324	-8.38484662612492	1.77772573317523e-16	***
df.mm.trans1:probe17	-0.148565392898711	0.0579426979673324	-2.5640054417637	0.0104971494271512	*  
df.mm.trans1:probe18	0.120729127800323	0.0579426979673324	2.08359520760302	0.0374588602589497	*  
df.mm.trans1:probe19	-0.254145069742986	0.0579426979673324	-4.38614490968769	1.28022310416465e-05	***
df.mm.trans1:probe20	-0.15953925483463	0.0579426979673324	-2.75339707040526	0.0060084546930068	** 
df.mm.trans1:probe21	-0.327138999528880	0.0579426979673324	-5.64590554125247	2.15847487106939e-08	***
df.mm.trans1:probe22	-0.635458874073053	0.0579426979673325	-10.9670225302819	1.84370597472491e-26	***
df.mm.trans2:probe2	-0.119246610624043	0.0579426979673324	-2.05800928861257	0.0398565265957575	*  
df.mm.trans2:probe3	-0.00348583712356788	0.0579426979673324	-0.0601600761761761	0.952040560351684	   
df.mm.trans2:probe4	-0.0899511416805057	0.0579426979673324	-1.55241548695608	0.120889704449657	   
df.mm.trans2:probe5	-0.143468576052572	0.0579426979673324	-2.47604238472738	0.0134548584416254	*  
df.mm.trans2:probe6	-0.255673096812163	0.0579426979673325	-4.41251625798145	1.13645749271682e-05	***
df.mm.trans3:probe2	-0.0104542097317305	0.0579426979673325	-0.180423247423247	0.856858069441236	   
df.mm.trans3:probe3	-0.3862504574496	0.0579426979673325	-6.66607650315773	4.40645782298694e-11	***
df.mm.trans3:probe4	-0.583180434795623	0.0579426979673325	-10.0647787426884	9.9079835777413e-23	***
df.mm.trans3:probe5	0.194974175142978	0.0579426979673325	3.36494816401028	0.000795647975221601	***
df.mm.trans3:probe6	0.288101157746169	0.0579426979673325	4.97217367939267	7.82889311856923e-07	***
df.mm.trans3:probe7	-0.450177630384941	0.0579426979673325	-7.76935914580206	2.00668629641784e-14	***
df.mm.trans3:probe8	-0.334018173816448	0.0579426979673325	-5.76462928952263	1.09973731384182e-08	***
df.mm.trans3:probe9	0.107843637843337	0.0579426979673324	1.86121188047091	0.0630172304419156	.  
df.mm.trans3:probe10	0.255444185547598	0.0579426979673324	4.40856560893342	1.15696568288444e-05	***
df.mm.trans3:probe11	0.162838163759587	0.0579426979673324	2.81033105934062	0.00504885465666343	** 
df.mm.trans3:probe12	-0.447151928248895	0.0579426979673324	-7.71714027712335	2.95466906532852e-14	***
df.mm.trans3:probe13	0.0478009433698988	0.0579426979673324	0.824969237656978	0.409592300093074	   
df.mm.trans3:probe14	-0.279973568283603	0.0579426979673325	-4.83190424514664	1.57162745785986e-06	***
df.mm.trans3:probe15	-0.0235844920180345	0.0579426979673325	-0.407031305848603	0.684074980277753	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.53355248895333	0.234748614283807	19.3123716737784	1.50944367119365e-70	***
df.mm.trans1	-0.0923919237487486	0.201088207470088	-0.459459681455921	0.646007262266397	   
df.mm.trans2	0.155705040056032	0.176049488222228	0.884439038297486	0.376678878680698	   
df.mm.exp2	0.178051234293532	0.222808069741093	0.799123813156457	0.424414654973943	   
df.mm.exp3	-0.00460164071945977	0.222808069741093	-0.0206529356176684	0.983526769147713	   
df.mm.exp4	-0.109148665504850	0.222808069741093	-0.489877523878209	0.624331569616435	   
df.mm.exp5	-0.266058206350294	0.222808069741093	-1.19411386966126	0.232725959631754	   
df.mm.exp6	0.127306943152035	0.222808069741093	0.571374920576118	0.567878160304542	   
df.mm.exp7	0.0882360960366067	0.222808069741093	0.396018403369045	0.692178723002902	   
df.mm.exp8	0.0324224055763111	0.222808069741093	0.145517196096113	0.884332834261529	   
df.mm.trans1:exp2	-0.0755474672153274	0.203836693146715	-0.370627417709091	0.710996144803862	   
df.mm.trans2:exp2	-0.109358638531449	0.141298601867172	-0.773954144530404	0.43914688436245	   
df.mm.trans1:exp3	0.160926886962584	0.203836693146715	0.789489293994551	0.430019418595923	   
df.mm.trans2:exp3	-0.0437899852006321	0.141298601867172	-0.309910958933599	0.756695373075916	   
df.mm.trans1:exp4	0.243089362784645	0.203836693146715	1.19256920347348	0.233330379877370	   
df.mm.trans2:exp4	-0.147301954813894	0.141298601867172	-1.04248699468637	0.297446234871271	   
df.mm.trans1:exp5	0.343901937911524	0.203836693146715	1.68714441253222	0.091897763764347	.  
df.mm.trans2:exp5	0.034091744032916	0.141298601867172	0.241274461193636	0.809393499632228	   
df.mm.trans1:exp6	-0.000620231235863315	0.203836693146715	-0.0030427850172044	0.997572839497658	   
df.mm.trans2:exp6	-0.0926246449744247	0.141298601867172	-0.655524143554489	0.51228610488996	   
df.mm.trans1:exp7	0.104659525865677	0.203836693146715	0.513447918772633	0.607755199899199	   
df.mm.trans2:exp7	-0.198888914319576	0.141298601867172	-1.40757878486683	0.159576861813100	   
df.mm.trans1:exp8	0.0118291838563309	0.203836693146715	0.0580326518926433	0.953734596940972	   
df.mm.trans2:exp8	-0.00402010009305906	0.141298601867172	-0.0284510960472076	0.977308236317223	   
df.mm.trans1:probe2	-0.0808811124441541	0.1491931827836	-0.542123379467476	0.587858284183537	   
df.mm.trans1:probe3	0.0487974092222994	0.1491931827836	0.327075328187605	0.743681624916233	   
df.mm.trans1:probe4	-0.0636116077373489	0.1491931827836	-0.426370739939341	0.66993251542084	   
df.mm.trans1:probe5	-0.0535349803307111	0.1491931827836	-0.358829936675873	0.719800563748259	   
df.mm.trans1:probe6	-0.0403231878239903	0.1491931827836	-0.270275002326868	0.787006306596508	   
df.mm.trans1:probe7	-0.095822435508731	0.1491931827836	-0.642270871368957	0.520849399268225	   
df.mm.trans1:probe8	0.158387426747935	0.1491931827836	1.06162643488658	0.288670098282648	   
df.mm.trans1:probe9	0.0094784752478463	0.1491931827836	0.0635315573473255	0.94935634714852	   
df.mm.trans1:probe10	0.0067661056752111	0.1491931827836	0.0453513059308153	0.963836646969565	   
df.mm.trans1:probe11	-0.0792123793467026	0.1491931827836	-0.530938330215782	0.595583306564575	   
df.mm.trans1:probe12	-0.00402927775025121	0.1491931827836	-0.0270071170483409	0.978459624055953	   
df.mm.trans1:probe13	-0.069007437422004	0.1491931827836	-0.462537470777717	0.643799887949383	   
df.mm.trans1:probe14	-0.143574671272625	0.1491931827836	-0.962340695424905	0.336118820369148	   
df.mm.trans1:probe15	0.0607270093083195	0.1491931827836	0.407036086872696	0.684071470023556	   
df.mm.trans1:probe16	-0.0171895332586014	0.1491931827836	-0.115216613372571	0.908297325845976	   
df.mm.trans1:probe17	0.0445287116073036	0.1491931827836	0.298463447032235	0.765413525362964	   
df.mm.trans1:probe18	0.0802576492004174	0.1491931827836	0.537944480458122	0.590739034271144	   
df.mm.trans1:probe19	0.0417460582690828	0.1491931827836	0.279812103275751	0.779681444271998	   
df.mm.trans1:probe20	0.068106177430625	0.1491931827836	0.456496578194266	0.648135338215951	   
df.mm.trans1:probe21	0.0916474603334602	0.1491931827836	0.614287185403048	0.539169914159676	   
df.mm.trans1:probe22	-0.131748857291897	0.1491931827836	-0.883075585852972	0.377414684604923	   
df.mm.trans2:probe2	0.0190731165224248	0.1491931827836	0.127841742944044	0.89830076789861	   
df.mm.trans2:probe3	-0.0681585071853707	0.1491931827836	-0.456847329842359	0.647883280603888	   
df.mm.trans2:probe4	-0.306254455175484	0.1491931827836	-2.05273759471769	0.0403664189399087	*  
df.mm.trans2:probe5	0.0536239886602409	0.1491931827836	0.359426534508757	0.719354418420287	   
df.mm.trans2:probe6	-0.087885931125961	0.1491931827836	-0.589074711633686	0.55594859800097	   
df.mm.trans3:probe2	-0.0032958577571178	0.1491931827836	-0.0220912088315613	0.982379751854494	   
df.mm.trans3:probe3	-0.0832666053407527	0.1491931827836	-0.558112668335042	0.576896506720587	   
df.mm.trans3:probe4	-0.125276890518772	0.1491931827836	-0.839695810367434	0.401286296185075	   
df.mm.trans3:probe5	-0.0789503005036106	0.1491931827836	-0.529181689341164	0.596800742742181	   
df.mm.trans3:probe6	0.0428770927621923	0.1491931827836	0.287393109807062	0.773872847243039	   
df.mm.trans3:probe7	0.051479070057101	0.1491931827836	0.345049747559644	0.730131930656061	   
df.mm.trans3:probe8	-0.103146571319783	0.1491931827836	-0.691362496565233	0.489503534982164	   
df.mm.trans3:probe9	-0.0470160231276672	0.1491931827836	-0.315135197536890	0.752726925018558	   
df.mm.trans3:probe10	-0.176025856458407	0.1491931827836	-1.17985187509356	0.238349002365561	   
df.mm.trans3:probe11	-0.0811918400400504	0.1491931827836	-0.544206099268065	0.586424981955433	   
df.mm.trans3:probe12	-0.153431512019465	0.1491931827836	-1.02840833043968	0.304014690299649	   
df.mm.trans3:probe13	-0.108127439566945	0.1491931827836	-0.724747857439172	0.468781871193196	   
df.mm.trans3:probe14	0.0411268166213166	0.1491931827836	0.27566150043846	0.782866902452908	   
df.mm.trans3:probe15	-0.180288773736272	0.1491931827836	-1.20842501227268	0.227178939135179