chr13.6481_chr13_64407844_64419107_-_2.R 

fitVsDatCorrelation=0.827667388416553
cont.fitVsDatCorrelation=0.283039559346524

fstatistic=9766.22895075404,65,991
cont.fstatistic=3333.89726563912,65,991

residuals=-0.811058623333825,-0.0839783021248182,-0.00710909589230803,0.0699107163311364,1.45236868326743
cont.residuals=-0.527939478571552,-0.194758130381443,-0.055679401697986,0.159080139096289,1.78213550145561

predictedValues:
Include	Exclude	Both
chr13.6481_chr13_64407844_64419107_-_2.R.tl.Lung	57.8223668630316	42.8680610410629	56.3030712161765
chr13.6481_chr13_64407844_64419107_-_2.R.tl.cerebhem	70.0314946750364	54.8159632898753	61.8844439566237
chr13.6481_chr13_64407844_64419107_-_2.R.tl.cortex	60.9119148663902	43.8021021650511	59.6424748712383
chr13.6481_chr13_64407844_64419107_-_2.R.tl.heart	64.1302953829705	44.6164289566874	61.5787708014579
chr13.6481_chr13_64407844_64419107_-_2.R.tl.kidney	59.326479752933	43.7068181613371	59.7103765686133
chr13.6481_chr13_64407844_64419107_-_2.R.tl.liver	61.5004487706463	47.8441136715626	59.5643186837471
chr13.6481_chr13_64407844_64419107_-_2.R.tl.stomach	61.2062604287175	47.5929441495735	56.2338513361721
chr13.6481_chr13_64407844_64419107_-_2.R.tl.testicle	61.8665212698632	47.2679271211892	63.7900398703644


diffExp=14.9543058219687,15.2155313851611,17.1098127013391,19.5138664262831,15.6196615915959,13.6563350990838,13.6133162791440,14.598594148674
diffExpScore=0.992017970642127
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,1,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,1,1,1,0,0,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	58.0495444758275	56.9520425366764	57.1009033705525
cerebhem	55.5449986423498	61.2734729900897	56.2921106227345
cortex	56.194487174082	53.3724528330503	56.9886892438963
heart	61.1669160097849	55.6488646309202	54.9877226083397
kidney	55.4686257353803	68.8772470257206	61.1406877986112
liver	58.0943060575903	60.8630454283374	58.5803696621072
stomach	59.081301801973	56.8690124242108	61.597143553755
testicle	55.4751593723382	57.1286073355327	62.4318517502799
cont.diffExp=1.09750193915117,-5.72847434773987,2.82203434103172,5.51805137886467,-13.4086212903403,-2.76873937074708,2.21228937776215,-1.65344796319452
cont.diffExpScore=2.72740358352153

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

tran.correlation=0.866689105922063
cont.tran.correlation=-0.417680714282514

tran.covariance=0.00393466889219509
cont.tran.covariance=-0.00116406788042527

tran.mean=54.3318837853705
cont.tran.mean=58.1287552796165

weightedLogRatios:
wLogRatio
Lung	1.16938920198803
cerebhem	1.01083334605581
cortex	1.30070524361445
heart	1.44382465357150
kidney	1.20090345125981
liver	1.00275245143451
stomach	1.00335854402784
testicle	1.07400644888697

cont.weightedLogRatios:
wLogRatio
Lung	0.077337054631866
cerebhem	-0.399118777971835
cortex	0.206253136479936
heart	0.38445065847885
kidney	-0.892895893538021
liver	-0.190207732883373
stomach	0.154939028246297
testicle	-0.118377918319248

varWeightedLogRatios=0.0257622668741583
cont.varWeightedLogRatios=0.164499183127235

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81901071616061	0.0752152229895954	50.7744385294038	5.48666535522527e-278	***
df.mm.trans1	0.286810967919170	0.0643608841866079	4.45629315917398	9.28917079972763e-06	***
df.mm.trans2	-0.0834378935585348	0.0562774102834419	-1.48261785924936	0.138493891473476	   
df.mm.exp2	0.342904041406021	0.0710635010886417	4.82531871006885	1.61765829854513e-06	***
df.mm.exp3	0.0159889899988558	0.0710635010886417	0.224995810140451	0.822028918799779	   
df.mm.exp4	0.053948214326401	0.0710635010886416	0.759155030359512	0.447940351963547	   
df.mm.exp5	-0.0136995970515732	0.0710635010886416	-0.192779652588252	0.847171038684093	   
df.mm.exp6	0.115182213957897	0.0710635010886417	1.62083505869242	0.105371133711836	   
df.mm.exp7	0.162661447474296	0.0710635010886417	2.28895909971279	0.0222913759043434	*  
df.mm.exp8	0.0404604732055515	0.0710635010886417	0.569356597771377	0.569243188337189	   
df.mm.trans1:exp2	-0.151334647386407	0.0649165280279232	-2.33121905905553	0.0199415664250255	*  
df.mm.trans2:exp2	-0.0970496397422665	0.0445553671566758	-2.17818067576457	0.0296277315266096	*  
df.mm.trans1:exp3	0.0360641412176667	0.0649165280279232	0.555546365667525	0.578646392893111	   
df.mm.trans2:exp3	0.00556577008723308	0.0445553671566758	0.124918061333923	0.900613753997129	   
df.mm.trans1:exp4	0.0495929940533184	0.0649165280279232	0.763950192037172	0.445078770297108	   
df.mm.trans2:exp4	-0.0139731116520144	0.0445553671566758	-0.313612310788034	0.753881505428929	   
df.mm.trans1:exp5	0.0393796714483370	0.0649165280279232	0.60662011115872	0.544241989032343	   
df.mm.trans2:exp5	0.0330766582010361	0.0445553671566758	0.742372026353735	0.458037882106337	   
df.mm.trans1:exp6	-0.0535134128617834	0.0649165280279232	-0.824341881604715	0.409943685559264	   
df.mm.trans2:exp6	-0.00536117067481667	0.0445553671566757	-0.120326035154519	0.904249270738918	   
df.mm.trans1:exp7	-0.105787639374278	0.0649165280279232	-1.62959484414777	0.103504946926572	   
df.mm.trans2:exp7	-0.0581039801566864	0.0445553671566758	-1.30408486933500	0.192507460463644	   
df.mm.trans1:exp8	0.0271430375384126	0.0649165280279232	0.418122138736953	0.675948404368386	   
df.mm.trans2:exp8	0.057244467968219	0.0445553671566758	1.28479399051798	0.199164503029629	   
df.mm.trans1:probe2	-0.293767800213182	0.0479440333449523	-6.12730677245183	1.28782364302302e-09	***
df.mm.trans1:probe3	-0.0272338315654350	0.0479440333449523	-0.568033802443996	0.570140689928088	   
df.mm.trans1:probe4	-0.212520304118374	0.0479440333449523	-4.43267471030884	1.03454152452119e-05	***
df.mm.trans1:probe5	0.207783266143615	0.0479440333449523	4.33387121706337	1.61449515399695e-05	***
df.mm.trans1:probe6	-0.270372273310775	0.0479440333449523	-5.63933099590256	2.22600638483935e-08	***
df.mm.trans1:probe7	-0.245559454920102	0.0479440333449523	-5.12179384561426	3.6358801073191e-07	***
df.mm.trans1:probe8	-0.267805156020145	0.0479440333449523	-5.58578695482951	3.00473577808645e-08	***
df.mm.trans1:probe9	-0.347880289695874	0.0479440333449523	-7.25596628871233	8.03990847641991e-13	***
df.mm.trans1:probe10	-0.329042678360317	0.0479440333449523	-6.8630579324207	1.18655689789609e-11	***
df.mm.trans1:probe11	-0.360687711077464	0.0479440333449523	-7.52309903679471	1.19781497146332e-13	***
df.mm.trans1:probe12	-0.293268412981012	0.0479440333449523	-6.11689072696442	1.37157102164853e-09	***
df.mm.trans1:probe13	-0.373307658778483	0.0479440333449523	-7.78632152394385	1.73206961702996e-14	***
df.mm.trans1:probe14	-0.210026739911504	0.0479440333449523	-4.38066481391718	1.30909589225096e-05	***
df.mm.trans1:probe15	-0.280636014212875	0.0479440333449523	-5.85340853978071	6.54186282843098e-09	***
df.mm.trans1:probe16	-0.231954851813764	0.0479440333449523	-4.8380337579209	1.51979499491476e-06	***
df.mm.trans1:probe17	0.382032189608162	0.0479440333449523	7.96829475858822	4.40116583493716e-15	***
df.mm.trans1:probe18	0.199988841338669	0.0479440333449523	4.17129781092404	3.29417795545229e-05	***
df.mm.trans1:probe19	0.145230874771331	0.0479440333449523	3.02917515775967	0.00251578470537259	** 
df.mm.trans1:probe20	0.458557377033085	0.0479440333449523	9.56443054621235	8.61995712169567e-21	***
df.mm.trans1:probe21	0.174801609550465	0.0479440333449523	3.64595127599687	0.000280256544503670	***
df.mm.trans1:probe22	0.334720512892092	0.0479440333449523	6.98148423358155	5.34415658666347e-12	***
df.mm.trans2:probe2	0.0730290124456222	0.0479440333449523	1.52321378387559	0.128024255086054	   
df.mm.trans2:probe3	0.0618193527748326	0.0479440333449523	1.28940659476955	0.197557626235424	   
df.mm.trans2:probe4	-0.0142176753438185	0.0479440333449523	-0.296547335546925	0.766874260188285	   
df.mm.trans2:probe5	0.118580686046373	0.0479440333449523	2.47331477502524	0.0135533611199877	*  
df.mm.trans2:probe6	0.256981644001772	0.0479440333449523	5.36003389937631	1.03513680394955e-07	***
df.mm.trans3:probe2	-0.100225315761072	0.0479440333449523	-2.09046483511225	0.0368303927728281	*  
df.mm.trans3:probe3	0.0210992274580572	0.0479440333449523	0.440080360078396	0.659974868889797	   
df.mm.trans3:probe4	0.370269565510578	0.0479440333449523	7.72295402947281	2.77333188897819e-14	***
df.mm.trans3:probe5	-0.0672612974277202	0.0479440333449523	-1.40291278674412	0.160956059421728	   
df.mm.trans3:probe6	0.419086098643925	0.0479440333449523	8.74115232710283	9.72074041135527e-18	***
df.mm.trans3:probe7	0.0904747433147433	0.0479440333449523	1.88709078069813	0.0594401868915876	.  
df.mm.trans3:probe8	0.0597973350554659	0.0479440333449523	1.24723205128009	0.212607093595551	   
df.mm.trans3:probe9	-0.211748072755935	0.0479440333449523	-4.41656777669143	1.11306050344506e-05	***
df.mm.trans3:probe10	0.285478220260411	0.0479440333449523	5.95440559217089	3.62025118804977e-09	***
df.mm.trans3:probe11	-0.117470850607813	0.0479440333449523	-2.45016621281365	0.0144510756146011	*  
df.mm.trans3:probe12	-0.153439606524164	0.0479440333449523	-3.20039003435907	0.00141603693361321	** 
df.mm.trans3:probe13	-0.075296723444495	0.0479440333449523	-1.57051291247741	0.116615042683751	   
df.mm.trans3:probe14	-0.0724718379513454	0.0479440333449523	-1.51159243174053	0.130956378024540	   
df.mm.trans3:probe15	-0.160118599097150	0.0479440333449523	-3.33969814231343	0.000869962017688273	***
df.mm.trans3:probe16	-0.154328181944749	0.0479440333449523	-3.21892363194339	0.00132851430463667	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04708596991382	0.128540815697848	31.484831864044	2.29874041193814e-151	***
df.mm.trans1	-0.0087800526792831	0.109991039360818	-0.0798251633069918	0.936392427082725	   
df.mm.trans2	0.00433718885890592	0.0961765974448653	0.0450960937913433	0.964039800089006	   
df.mm.exp2	0.0432996144877362	0.121445633386509	0.356534963673269	0.721515867978243	   
df.mm.exp3	-0.0954258847266833	0.121445633386509	-0.78574982126351	0.432201892891633	   
df.mm.exp4	0.0668717175012813	0.121445633386509	0.550630892495389	0.582010825949237	   
df.mm.exp5	0.0762794061962592	0.121445633386509	0.62809509135248	0.530086361999046	   
df.mm.exp6	0.0416077096475574	0.121445633386509	0.342603587196404	0.731969355416137	   
df.mm.exp7	-0.0596368892424931	0.121445633386509	-0.491058324449547	0.623493921664803	   
df.mm.exp8	-0.131521731163744	0.121445633386509	-1.08296797090405	0.279086064505289	   
df.mm.trans1:exp2	-0.0874029969092096	0.110940619907964	-0.787835843911082	0.430981127003048	   
df.mm.trans2:exp2	0.0298378393258145	0.0761439374955848	0.391860997831176	0.695245172282169	   
df.mm.trans1:exp3	0.0629476828142208	0.110940619907964	0.567399775361286	0.570571109495721	   
df.mm.trans2:exp3	0.0305110783515874	0.0761439374955848	0.400702660712241	0.688725422826792	   
df.mm.trans1:exp4	-0.0145621232416367	0.110940619907964	-0.131260518047559	0.89559586527767	   
df.mm.trans2:exp4	-0.0900195966356846	0.0761439374955848	-1.18222933560409	0.237398298242664	   
df.mm.trans1:exp5	-0.121758708365356	0.110940619907964	-1.09751242120664	0.272684051366895	   
df.mm.trans2:exp5	0.113836930749774	0.0761439374955848	1.49502290653639	0.135226816581575	   
df.mm.trans1:exp6	-0.0408369140561706	0.110940619907964	-0.368097042274044	0.712879484912197	   
df.mm.trans2:exp6	0.0248089187819673	0.0761439374955848	0.325816074108406	0.744632299902725	   
df.mm.trans1:exp7	0.0772545202129503	0.110940619907964	0.696359189961622	0.486367204146126	   
df.mm.trans2:exp7	0.0581779302261323	0.0761439374955848	0.764052032763682	0.44501810880389	   
df.mm.trans1:exp8	0.0861602118332092	0.110940619907964	0.776633589254208	0.437560303798295	   
df.mm.trans2:exp8	0.13461717180223	0.0761439374955848	1.76793026772533	0.0773802182664338	.  
df.mm.trans1:probe2	0.0433775217328966	0.0819350778878566	0.529413321511292	0.596637328857768	   
df.mm.trans1:probe3	0.00458648203410592	0.0819350778878566	0.055977026596391	0.95537139196883	   
df.mm.trans1:probe4	0.0530715828810367	0.0819350778878567	0.647727252467802	0.517311400543765	   
df.mm.trans1:probe5	0.0582180229500018	0.0819350778878566	0.710538446423203	0.477537475661902	   
df.mm.trans1:probe6	0.0548582580880323	0.0819350778878566	0.669533239025122	0.503311342743535	   
df.mm.trans1:probe7	-0.0360948482182145	0.0819350778878566	-0.440529857890865	0.659649465784307	   
df.mm.trans1:probe8	0.0867480614712752	0.0819350778878566	1.05874142928144	0.289975544176425	   
df.mm.trans1:probe9	0.0286815507491833	0.0819350778878567	0.350052157007031	0.726373937672431	   
df.mm.trans1:probe10	0.072093326608505	0.0819350778878566	0.879883542762699	0.379135714771350	   
df.mm.trans1:probe11	-0.0194619381978964	0.0819350778878567	-0.237528769113196	0.81229569969781	   
df.mm.trans1:probe12	0.188624173782252	0.0819350778878566	2.30211746476178	0.0215351194881493	*  
df.mm.trans1:probe13	0.052912339929589	0.0819350778878566	0.645783726501235	0.518568927314615	   
df.mm.trans1:probe14	-0.0391493641282901	0.0819350778878566	-0.477809567495295	0.632891122774651	   
df.mm.trans1:probe15	0.0294280370985742	0.0819350778878566	0.359162862319505	0.719549767225623	   
df.mm.trans1:probe16	-0.0858180673368639	0.0819350778878566	-1.04739105092842	0.295174639342922	   
df.mm.trans1:probe17	0.107858316805963	0.0819350778878566	1.31638755446827	0.188348370908363	   
df.mm.trans1:probe18	0.081377544171361	0.0819350778878566	0.993195420925104	0.320857169703129	   
df.mm.trans1:probe19	0.0363231470921115	0.0819350778878566	0.443316196535828	0.657633803836819	   
df.mm.trans1:probe20	0.0302031208884205	0.0819350778878566	0.368622593240945	0.712487785299973	   
df.mm.trans1:probe21	0.0378147697666528	0.0819350778878566	0.461521130405336	0.644526049012556	   
df.mm.trans1:probe22	0.0880038254055493	0.0819350778878566	1.07406775796319	0.283053763224948	   
df.mm.trans2:probe2	0.056617802607816	0.0819350778878566	0.691008101381291	0.489722275177430	   
df.mm.trans2:probe3	-0.120040041013192	0.0819350778878566	-1.46506287792255	0.143220936572091	   
df.mm.trans2:probe4	-0.0519868704784067	0.0819350778878566	-0.63448857093369	0.525908463604821	   
df.mm.trans2:probe5	-0.0418101988906623	0.0819350778878566	-0.510284483379479	0.60996580686715	   
df.mm.trans2:probe6	-0.0454799820955656	0.0819350778878566	-0.555073397962878	0.578969720919577	   
df.mm.trans3:probe2	-0.13780571215387	0.0819350778878566	-1.68188907249814	0.0929054025353133	.  
df.mm.trans3:probe3	0.0130085326046029	0.0819350778878566	0.158766342083760	0.87388531549132	   
df.mm.trans3:probe4	0.0656194443643836	0.0819350778878566	0.800871202614782	0.423398173880524	   
df.mm.trans3:probe5	-0.0948221901909796	0.0819350778878567	-1.15728443342376	0.247435033347233	   
df.mm.trans3:probe6	-0.00888293645775257	0.0819350778878566	-0.108414328596972	0.913688981801305	   
df.mm.trans3:probe7	-0.0651529384876063	0.0819350778878566	-0.79517759874202	0.426700604975273	   
df.mm.trans3:probe8	0.0407825912937552	0.0819350778878566	0.497742753715005	0.618775796005163	   
df.mm.trans3:probe9	-0.143811712004804	0.0819350778878566	-1.75519100868662	0.0795356090059919	.  
df.mm.trans3:probe10	0.0842911402816753	0.0819350778878566	1.02875523468768	0.303845727316489	   
df.mm.trans3:probe11	-0.0689013121036798	0.0819350778878566	-0.840925692387624	0.400592418376061	   
df.mm.trans3:probe12	0.108678099950825	0.0819350778878566	1.32639283140209	0.185015199771878	   
df.mm.trans3:probe13	0.144115850017422	0.0819350778878566	1.7589029477054	0.078902587515343	.  
df.mm.trans3:probe14	-0.00237251830904246	0.0819350778878566	-0.0289560755930408	0.976905452550852	   
df.mm.trans3:probe15	0.0958779198733789	0.0819350778878566	1.17016938709213	0.242214140845346	   
df.mm.trans3:probe16	0.00420504202047447	0.0819350778878566	0.0513216332842187	0.959079572375122