chr9.24757_chr9_105824059_105829033_+_2.R 

fitVsDatCorrelation=0.90652846889864
cont.fitVsDatCorrelation=0.254427669625437

fstatistic=10384.0452894417,52,692
cont.fstatistic=1967.80888697113,52,692

residuals=-0.687184771198875,-0.0852891475367113,-0.00530128680495596,0.0795759129495786,0.748728826464484
cont.residuals=-0.684162100935016,-0.211283340204516,-0.0792337246817914,0.128553614928257,1.59510783203664

predictedValues:
Include	Exclude	Both
chr9.24757_chr9_105824059_105829033_+_2.R.tl.Lung	61.650245833515	46.9122031120003	77.8054006114945
chr9.24757_chr9_105824059_105829033_+_2.R.tl.cerebhem	61.0218221163237	47.2921589516490	97.8139627039382
chr9.24757_chr9_105824059_105829033_+_2.R.tl.cortex	59.2208877299265	53.6902934367789	121.940815810864
chr9.24757_chr9_105824059_105829033_+_2.R.tl.heart	59.8529472917417	56.0250758796967	143.867123549033
chr9.24757_chr9_105824059_105829033_+_2.R.tl.kidney	62.7771530971969	48.3888503658258	89.8724261623058
chr9.24757_chr9_105824059_105829033_+_2.R.tl.liver	60.8605170264336	48.4190332723411	76.2097723921553
chr9.24757_chr9_105824059_105829033_+_2.R.tl.stomach	61.529970845963	45.7193990577112	78.6217489480523
chr9.24757_chr9_105824059_105829033_+_2.R.tl.testicle	60.1698784984205	48.5643544355628	90.3930557525835


diffExp=14.7380427215146,13.7296631646747,5.53059429314756,3.82787141204509,14.3883027313711,12.4414837540925,15.8105717882519,11.6055240628576
diffExpScore=0.989255636275373
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=1,0,0,0,0,0,1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,0,0,1,1,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	57.8541296712808	61.7638342601481	53.0588920448144
cerebhem	60.1713741252333	59.0326953366906	61.2220647862193
cortex	61.813938496082	68.3758339038061	72.4693176649016
heart	59.8075948546899	57.6612491065074	69.7646150453114
kidney	63.9684400515093	59.4987369500464	64.1761494119068
liver	55.2988267600346	57.5256146710092	50.4838496899349
stomach	61.2934296351112	58.5495579981791	54.019731866099
testicle	55.3632398673527	63.1453010848631	73.8822192686589
cont.diffExp=-3.9097045888673,1.13867878854275,-6.56189540772414,2.14634574818253,4.46970310146281,-2.22678791097463,2.74387163693207,-7.78206121751033
cont.diffExpScore=2.82093170307915

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=-0.702280606592563
cont.tran.correlation=0.100427644871103

tran.covariance=-0.000920719291408508
cont.tran.covariance=0.000288522599724207

tran.mean=55.1309244344429
cont.tran.mean=60.070237298284

weightedLogRatios:
wLogRatio
Lung	1.08866607471007
cerebhem	1.01541590067816
cortex	0.395330510414399
heart	0.268254349688798
kidney	1.04374203687811
liver	0.913449080271552
stomach	1.17940071087151
testicle	0.854992012121617

cont.weightedLogRatios:
wLogRatio
Lung	-0.267498619905106
cerebhem	0.0780955753777654
cortex	-0.421175191036245
heart	0.148852007552770
kidney	0.298588543805117
liver	-0.159197215428292
stomach	0.18744537571922
testicle	-0.536569760405258

varWeightedLogRatios=0.111337172792835
cont.varWeightedLogRatios=0.09405492064012

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.52645844015582	0.0811824830375168	43.4386619897563	7.39099436071595e-200	***
df.mm.trans1	0.823214494258806	0.0729137418144912	11.2902516558984	2.99757232389165e-27	***
df.mm.trans2	0.295816837322380	0.0670540881926419	4.41161523921582	1.18962133366226e-05	***
df.mm.exp2	-0.231035493596585	0.091847494831024	-2.51542509702232	0.0121142886921927	*  
df.mm.exp3	-0.354573475665340	0.091847494831024	-3.86045886518370	0.000123773948020666	***
df.mm.exp4	-0.466744278624915	0.091847494831024	-5.08173118367143	4.81748110417757e-07	***
df.mm.exp5	-0.0950747594083138	0.091847494831024	-1.03513720851317	0.300966433493342	   
df.mm.exp6	0.0394437168487908	0.091847494831024	0.42944793346141	0.66773095298059	   
df.mm.exp7	-0.0381454912161611	0.091847494831024	-0.415313354886152	0.678041265546377	   
df.mm.exp8	-0.139649971339565	0.091847494831024	-1.52045487573162	0.128853458015907	   
df.mm.trans1:exp2	0.220789816238646	0.0879372813307325	2.51076463699457	0.0122738900396634	*  
df.mm.trans2:exp2	0.239102166766957	0.0765395790258534	3.12390229747924	0.00185919568731484	** 
df.mm.trans1:exp3	0.314370571430507	0.0879372813307326	3.5749407608834	0.000374665520182651	***
df.mm.trans2:exp3	0.4895278695814	0.0765395790258534	6.3957481320357	2.9406369981345e-10	***
df.mm.trans1:exp4	0.437157736887495	0.0879372813307325	4.97124462198624	8.3962584453982e-07	***
df.mm.trans2:exp4	0.644265816807641	0.0765395790258534	8.41742043799355	2.20820130123296e-16	***
df.mm.trans1:exp5	0.113188745335351	0.0879372813307326	1.28715311211007	0.198471283926893	   
df.mm.trans2:exp5	0.126066346357224	0.0765395790258534	1.64707394477101	0.0999968399688227	.  
df.mm.trans1:exp6	-0.0523362942449302	0.0879372813307326	-0.595154790470417	0.551934573354656	   
df.mm.trans2:exp6	-0.007828566893254	0.0765395790258534	-0.102281290188566	0.918563036190095	   
df.mm.trans1:exp7	0.0361926609806417	0.0879372813307325	0.411573571902012	0.680779446430696	   
df.mm.trans2:exp7	0.0123903501935239	0.0765395790258534	0.161881608851529	0.87144633944306	   
df.mm.trans1:exp8	0.115344624095269	0.0879372813307326	1.31166920730079	0.19006676416771	   
df.mm.trans2:exp8	0.174261949420346	0.0765395790258534	2.2767560475017	0.0231052854336841	*  
df.mm.trans1:probe2	-0.548034057878752	0.0439686406653663	-12.4642028860909	2.58938088320151e-32	***
df.mm.trans1:probe3	-0.391707062395378	0.0439686406653663	-8.9087826338903	4.51591588400708e-18	***
df.mm.trans1:probe4	-0.0305735923177458	0.0439686406653663	-0.695349955219979	0.487069444922402	   
df.mm.trans1:probe5	-0.102462695843851	0.0439686406653663	-2.33035850763882	0.0200737603955377	*  
df.mm.trans1:probe6	-0.232426715011171	0.0439686406653663	-5.28619287505633	1.67613840869559e-07	***
df.mm.trans1:probe7	0.479425715573078	0.0439686406653663	10.9038102683651	1.17589856126156e-25	***
df.mm.trans1:probe8	-0.421720286012863	0.0439686406653663	-9.5913878535037	1.53738137134768e-20	***
df.mm.trans1:probe9	-0.521528549794073	0.0439686406653663	-11.8613753325533	1.13208837062274e-29	***
df.mm.trans1:probe10	-0.437215765978385	0.0439686406653663	-9.94380902757306	7.23647623857536e-22	***
df.mm.trans1:probe11	-0.452051307381441	0.0439686406653663	-10.2812209006388	3.59874793798874e-23	***
df.mm.trans1:probe12	-0.133550946546875	0.0439686406653663	-3.03741358672641	0.00247591185394878	** 
df.mm.trans1:probe13	-0.205927208356482	0.0439686406653663	-4.68350181493533	3.39521885709272e-06	***
df.mm.trans1:probe14	-0.148555364488054	0.0439686406653663	-3.37866630034505	0.000769212858692706	***
df.mm.trans1:probe15	-0.123272198983755	0.0439686406653663	-2.80363907362857	0.00519451323405601	** 
df.mm.trans1:probe16	-0.0137460852418831	0.0439686406653663	-0.312633846165518	0.754653049789265	   
df.mm.trans1:probe17	-0.424087332909122	0.0439686406653663	-9.64522274265286	9.69008881326291e-21	***
df.mm.trans1:probe18	-0.292349267255772	0.0439686406653663	-6.64904038041033	5.98885965679114e-11	***
df.mm.trans1:probe19	-0.326276444808664	0.0439686406653663	-7.42066254201189	3.42678983246682e-13	***
df.mm.trans1:probe20	-0.463237311771686	0.0439686406653663	-10.5356295933109	3.57137064643519e-24	***
df.mm.trans1:probe21	-0.478549585290202	0.0439686406653663	-10.8838840147986	1.41745113814443e-25	***
df.mm.trans1:probe22	-0.437046868433632	0.0439686406653663	-9.93996770925625	7.4848570992188e-22	***
df.mm.trans2:probe2	-0.00965580694215439	0.0439686406653663	-0.219606674121272	0.826242255229136	   
df.mm.trans2:probe3	0.0264107360590287	0.0439686406653663	0.60067210765131	0.548255076738015	   
df.mm.trans2:probe4	0.0805705264317207	0.0439686406653663	1.83245434046783	0.067313310016329	.  
df.mm.trans2:probe5	0.02978731203536	0.0439686406653663	0.677467203547714	0.498335998528738	   
df.mm.trans2:probe6	0.106910258151873	0.0439686406653663	2.43151156219586	0.0152887419555273	*  
df.mm.trans3:probe2	-1.03813050913352	0.0439686406653663	-23.6107028423839	7.35100894300194e-91	***
df.mm.trans3:probe3	0.164293730048434	0.0439686406653663	3.73661153863797	0.000201901013364226	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.28213856499096	0.185981055578194	23.0245954442955	1.57081599781608e-87	***
df.mm.trans1	-0.267869591132478	0.167038185596617	-1.60364284475263	0.109249090060600	   
df.mm.trans2	-0.109534253910626	0.153614297522013	-0.713047259776911	0.476056890417177	   
df.mm.exp2	-0.149059841885688	0.210413544914517	-0.708413719022913	0.478926932307528	   
df.mm.exp3	-0.143855286560849	0.210413544914517	-0.683678831699223	0.494406850334042	   
df.mm.exp4	-0.309249277654956	0.210413544914517	-1.46972134222914	0.142091722520959	   
df.mm.exp5	-0.127127071689640	0.210413544914517	-0.604177225098731	0.545923840382817	   
df.mm.exp6	-0.066511820112664	0.210413544914517	-0.316100468435552	0.752021569838942	   
df.mm.exp7	-0.0136435470768601	0.210413544914517	-0.0648415817641537	0.948318860188606	   
df.mm.exp8	-0.352958279899585	0.210413544914517	-1.67745037536903	0.0939060200927512	.  
df.mm.trans1:exp2	0.188331731299101	0.201455631740265	0.934854636091364	0.350189318118114	   
df.mm.trans2:exp2	0.103833303534195	0.175344620762098	0.592167031317564	0.553932169300405	   
df.mm.trans1:exp3	0.210059330662131	0.201455631740265	1.04270766147138	0.297447838588600	   
df.mm.trans2:exp3	0.245556756245127	0.175344620762098	1.40042366385617	0.161834698152688	   
df.mm.trans1:exp4	0.342457097866825	0.201455631740265	1.69991325091548	0.0895966117452227	.  
df.mm.trans2:exp4	0.240516645924257	0.175344620762098	1.37167963795469	0.170607745618161	   
df.mm.trans1:exp5	0.227592072289034	0.201455631740265	1.12973794935883	0.258978104631652	   
df.mm.trans2:exp5	0.0897641692683778	0.175344620762098	0.511929986093882	0.608863339541628	   
df.mm.trans1:exp6	0.0213386755639297	0.201455631740265	0.105922457364913	0.915674572620902	   
df.mm.trans2:exp6	-0.00457584575539823	0.175344620762098	-0.0260962995928264	0.97918805209271	   
df.mm.trans1:exp7	0.07139136356851	0.201455631740265	0.354377601419227	0.723163877834856	   
df.mm.trans2:exp7	-0.0398008990595626	0.175344620762098	-0.226986712717941	0.820501112619877	   
df.mm.trans1:exp8	0.308949276172648	0.201455631740265	1.53358470797666	0.125588891253907	   
df.mm.trans2:exp8	0.375078730119623	0.175344620762098	2.13909459263377	0.0327773381116481	*  
df.mm.trans1:probe2	0.0412256482872901	0.100727815870132	0.409277694856821	0.68246252747808	   
df.mm.trans1:probe3	0.0859559725323058	0.100727815870132	0.853348916481305	0.39376111742568	   
df.mm.trans1:probe4	0.0290679159877520	0.100727815870132	0.288578837301795	0.772990111541251	   
df.mm.trans1:probe5	0.0874194893100719	0.100727815870132	0.867878336831817	0.385761802022423	   
df.mm.trans1:probe6	0.0903279548860248	0.100727815870132	0.896752839379383	0.370162675586501	   
df.mm.trans1:probe7	-0.0721635112335104	0.100727815870132	-0.716420887419522	0.473973204034518	   
df.mm.trans1:probe8	0.123165022217217	0.100727815870132	1.22275084745224	0.221840083890858	   
df.mm.trans1:probe9	0.0235637135797676	0.100727815870132	0.233934523212021	0.815105000919098	   
df.mm.trans1:probe10	0.029978271499753	0.100727815870132	0.297616614048336	0.766085117448825	   
df.mm.trans1:probe11	0.0200285441885054	0.100727815870132	0.198838265433334	0.842447666783343	   
df.mm.trans1:probe12	0.144764726305126	0.100727815870132	1.43718718662351	0.151116801369595	   
df.mm.trans1:probe13	0.0315597536101094	0.100727815870132	0.313317164057237	0.754134122691904	   
df.mm.trans1:probe14	0.10399157950857	0.100727815870132	1.03240181086271	0.302244598065430	   
df.mm.trans1:probe15	0.0162533351275784	0.100727815870132	0.161358955192017	0.871857789319461	   
df.mm.trans1:probe16	-0.0375505377877677	0.100727815870132	-0.37279213753807	0.709417308455076	   
df.mm.trans1:probe17	0.0480254970890658	0.100727815870132	0.476784855049222	0.633665852503054	   
df.mm.trans1:probe18	0.00316586208294638	0.100727815870132	0.0314298692530781	0.974935783280848	   
df.mm.trans1:probe19	-0.008583496895096	0.100727815870132	-0.0852147623866147	0.932115302218688	   
df.mm.trans1:probe20	0.216653285788782	0.100727815870132	2.15087842337524	0.0318314080140505	*  
df.mm.trans1:probe21	0.0834618524771148	0.100727815870132	0.828587930316305	0.407623309191787	   
df.mm.trans1:probe22	0.0310856984609871	0.100727815870132	0.308610865751975	0.757710435838671	   
df.mm.trans2:probe2	-0.0438243141797367	0.100727815870132	-0.435076585361873	0.663642492219235	   
df.mm.trans2:probe3	-0.109075688290190	0.100727815870132	-1.08287554284728	0.279240842006805	   
df.mm.trans2:probe4	-0.0537088522150534	0.100727815870132	-0.533207751514238	0.594060975956322	   
df.mm.trans2:probe5	-0.154836215707916	0.100727815870132	-1.53717435814895	0.124707707919938	   
df.mm.trans2:probe6	-0.0821318461750968	0.100727815870132	-0.815383967830582	0.415133106089623	   
df.mm.trans3:probe2	0.141148990711086	0.100727815870132	1.40129108818430	0.161575356136511	   
df.mm.trans3:probe3	0.0588542513928146	0.100727815870132	0.58428995887983	0.559215670132867