chr2.14301_chr2_13083772_13086171_+_0.R 

fitVsDatCorrelation=0.860732530525086
cont.fitVsDatCorrelation=0.246622980303047

fstatistic=7889.07999536872,58,830
cont.fstatistic=2166.40796165077,58,830

residuals=-0.619590258357392,-0.108890810527943,-0.0041241612887558,0.0961651992968438,0.734760935499084
cont.residuals=-0.639044819613172,-0.224571415276120,-0.0669349667519464,0.141948979070541,1.58490847627699

predictedValues:
Include	Exclude	Both
chr2.14301_chr2_13083772_13086171_+_0.R.tl.Lung	58.5368182898837	48.2826044980259	55.3960421452537
chr2.14301_chr2_13083772_13086171_+_0.R.tl.cerebhem	62.5912646786358	53.9503527290914	52.5953322244181
chr2.14301_chr2_13083772_13086171_+_0.R.tl.cortex	62.1855204854202	48.0817747085861	57.8596636968051
chr2.14301_chr2_13083772_13086171_+_0.R.tl.heart	72.4910364586371	49.1229304774398	68.7707997535523
chr2.14301_chr2_13083772_13086171_+_0.R.tl.kidney	127.862142650971	51.6960961620372	120.594039946442
chr2.14301_chr2_13083772_13086171_+_0.R.tl.liver	77.9920492373836	53.0005895284369	68.845493431011
chr2.14301_chr2_13083772_13086171_+_0.R.tl.stomach	64.2603270841209	48.4940209362045	55.3954644055199
chr2.14301_chr2_13083772_13086171_+_0.R.tl.testicle	64.1740156467556	50.5994770418116	57.6015075220009


diffExp=10.2542137918577,8.64091194954444,14.1037457768341,23.3681059811973,76.1660464889334,24.9914597089467,15.7663061479164,13.574538604944
diffExpScore=0.994677038023729
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,1,1,1,0,0
diffExp1.4Score=0.75
diffExp1.3=0,0,0,1,1,1,1,0
diffExp1.3Score=0.8
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	56.0180980699413	73.8807176656625	62.268477472946
cerebhem	67.3393742159223	57.1538621543267	57.4860590953085
cortex	59.1062347957738	53.8286252101711	64.5989516713984
heart	63.532705527365	58.0521627153381	69.0351947153918
kidney	64.3240788672999	65.325125657994	61.0081680204556
liver	60.1649890935022	56.4596152371081	60.4178328429324
stomach	61.0557379377654	61.1055299303462	64.1718431516999
testicle	64.3118573066546	67.2914113485257	67.227911047284
cont.diffExp=-17.8626195957213,10.1855120615956,5.27760958560273,5.48054281202687,-1.00104679069410,3.70537385639408,-0.0497919925807793,-2.97955404187118
cont.diffExpScore=12.3913018814691

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=-1,0,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=-1,0,0,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.326945158847046
cont.tran.correlation=-0.286930228168452

tran.covariance=0.00408019630426142
cont.tran.covariance=-0.00165160746065939

tran.mean=62.0825637883401
cont.tran.mean=61.809382858356

weightedLogRatios:
wLogRatio
Lung	0.765208599641205
cerebhem	0.603508050214801
cortex	1.02926502071455
heart	1.59114002015314
kidney	3.9828502926256
liver	1.60835868958406
stomach	1.13225467575498
testicle	0.96079313996358

cont.weightedLogRatios:
wLogRatio
Lung	-1.15251717195564
cerebhem	0.676942729647056
cortex	0.377170476988835
heart	0.370454443453008
kidney	-0.0644216097111183
liver	0.258411304145323
stomach	-0.00335219806880407
testicle	-0.189595627542738

varWeightedLogRatios=1.16606382617453
cont.varWeightedLogRatios=0.309086172948289

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.55762215755395	0.0843712252023308	42.1662972064517	1.49695311803846e-208	***
df.mm.trans1	0.537225497185101	0.0714179383588713	7.5222767490931	1.39945024303927e-13	***
df.mm.trans2	0.336078503643388	0.0638506695510269	5.26350790064634	1.80091740708407e-07	***
df.mm.exp2	0.229843461996256	0.0815151312885173	2.81964168324456	0.00492225879657091	** 
df.mm.exp3	0.0127857775440849	0.0815151312885173	0.156851584999974	0.875399961984382	   
df.mm.exp4	0.0147905086907184	0.0815151312885173	0.181444947176351	0.856062641046781	   
df.mm.exp5	0.071685959724264	0.0815151312885173	0.87941905497933	0.379428636722842	   
df.mm.exp6	0.162826037733678	0.0815151312885173	1.99749463884645	0.0460974757496179	*  
df.mm.exp7	0.097666114469174	0.0815151312885173	1.19813478706783	0.231206558193052	   
df.mm.exp8	0.0997717701136354	0.0815151312885173	1.22396625677385	0.221312279239258	   
df.mm.trans1:exp2	-0.162873664244563	0.0723940830224653	-2.24982011574095	0.0247216195125087	*  
df.mm.trans2:exp2	-0.118850572706152	0.053862921409778	-2.20653781108457	0.0276195030191778	*  
df.mm.trans1:exp3	0.0476804767352099	0.0723940830224653	0.658623947490483	0.510319936822468	   
df.mm.trans2:exp3	-0.0169539168887466	0.053862921409778	-0.314760440856238	0.753022658484466	   
df.mm.trans1:exp4	0.199016481730528	0.0723940830224653	2.74907110390181	0.00610628842889629	** 
df.mm.trans2:exp4	0.00246409245895754	0.053862921409778	0.0457474714416478	0.963522530815335	   
df.mm.trans1:exp5	0.709610784520789	0.0723940830224653	9.80205501464232	1.54206625738541e-21	***
df.mm.trans2:exp5	-0.00337503096586259	0.053862921409778	-0.0626596344484557	0.950052627215903	   
df.mm.trans1:exp6	0.124124922163693	0.0723940830224653	1.71457275210150	0.0867968859345081	.  
df.mm.trans2:exp6	-0.0695943415320125	0.053862921409778	-1.2920639970965	0.196694611470647	   
df.mm.trans1:exp7	-0.0043795995258556	0.0723940830224653	-0.060496650320117	0.951774646911672	   
df.mm.trans2:exp7	-0.0932969442578343	0.053862921409778	-1.73211815876176	0.0836240380309471	.  
df.mm.trans1:exp8	-0.00782931079474664	0.0723940830224653	-0.108148490427278	0.913904041550194	   
df.mm.trans2:exp8	-0.0529018694823357	0.053862921409778	-0.982157448903842	0.326308485618586	   
df.mm.trans1:probe2	-0.140997345561803	0.0529870431769996	-2.66097779962567	0.00794190936540974	** 
df.mm.trans1:probe3	-0.177433935420974	0.0529870431769996	-3.34862873605285	0.000848719373523976	***
df.mm.trans1:probe4	-0.0575026840431169	0.0529870431769996	-1.08522160504471	0.278138616361386	   
df.mm.trans1:probe5	-0.0970032782942962	0.0529870431769996	-1.83069808160956	0.06750401813489	.  
df.mm.trans1:probe6	-0.158850885970131	0.0529870431769996	-2.99791942417886	0.00279936612702582	** 
df.mm.trans1:probe7	0.178059259432282	0.0529870431769996	3.36043018738538	0.000813746401388344	***
df.mm.trans1:probe8	0.155708995457464	0.0529870431769996	2.93862397524861	0.00338807926328836	** 
df.mm.trans1:probe9	0.20618162405014	0.0529870431769996	3.89117058978748	0.00010775433921239	***
df.mm.trans1:probe10	0.182022643345422	0.0529870431769996	3.43522930195194	0.000621434818326422	***
df.mm.trans1:probe11	-0.185670834153499	0.0529870431769996	-3.50407992258181	0.000482707508494047	***
df.mm.trans1:probe12	-0.176002577669590	0.0529870431769996	-3.32161538211644	0.000934094536199118	***
df.mm.trans1:probe13	-0.00499353043272154	0.0529870431769996	-0.0942405941777313	0.924940783740012	   
df.mm.trans1:probe14	-0.139918134230859	0.0529870431769996	-2.64061034248451	0.0084311833733793	** 
df.mm.trans1:probe15	-0.0916869182255238	0.0529870431769996	-1.73036487277182	0.0839368033454678	.  
df.mm.trans1:probe16	-0.247664178174630	0.0529870431769996	-4.67405167990456	3.44501706420196e-06	***
df.mm.trans2:probe2	-0.0863132040265997	0.0529870431769996	-1.62894924591803	0.103703221881865	   
df.mm.trans2:probe3	-0.111284350776831	0.0529870431769996	-2.10021816852647	0.0360108884953086	*  
df.mm.trans2:probe4	-0.0702310840742774	0.0529870431769996	-1.32543882170732	0.185390358228168	   
df.mm.trans2:probe5	0.0230823028645862	0.0529870431769996	0.435621644096677	0.663224527758568	   
df.mm.trans2:probe6	-0.10446939933136	0.0529870431769996	-1.97160273658575	0.0489867143951481	*  
df.mm.trans3:probe2	-0.685468412803194	0.0529870431769996	-12.9365288512785	5.27594692077638e-35	***
df.mm.trans3:probe3	-0.435757551639984	0.0529870431769996	-8.22385106835205	7.57209212254338e-16	***
df.mm.trans3:probe4	-0.156404724365439	0.0529870431769996	-2.95175414568764	0.00324874605996015	** 
df.mm.trans3:probe5	-0.484208704038948	0.0529870431769996	-9.13824729606976	4.77076962073114e-19	***
df.mm.trans3:probe6	-0.661466362467261	0.0529870431769996	-12.4835492378331	6.73004836560612e-33	***
df.mm.trans3:probe7	-0.439700857851033	0.0529870431769996	-8.2982712657176	4.25592240907081e-16	***
df.mm.trans3:probe8	-0.471395612976521	0.0529870431769996	-8.89643174467873	3.55968624951154e-18	***
df.mm.trans3:probe9	-0.585463986825956	0.0529870431769996	-11.0491914951784	1.41857107045750e-26	***
df.mm.trans3:probe10	-0.511631778019614	0.0529870431769996	-9.65579030916187	5.60491366392836e-21	***
df.mm.trans3:probe11	-0.292014930721640	0.0529870431769996	-5.51106295450729	4.7618168131421e-08	***
df.mm.trans3:probe12	-0.490261002192929	0.0529870431769996	-9.25246952458256	1.81903525632669e-19	***
df.mm.trans3:probe13	-0.161133109237757	0.0529870431769996	-3.04099077013041	0.00243214111039471	** 
df.mm.trans3:probe14	-0.383283570143119	0.0529870431769996	-7.23353384454359	1.07043931416231e-12	***
df.mm.trans3:probe15	-0.488079525756754	0.0529870431769996	-9.21129952706283	2.57779621768598e-19	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.13310452698578	0.160620615951826	25.7320923749004	8.02442528882677e-108	***
df.mm.trans1	-0.158510765000832	0.135960965621897	-1.16585495164579	0.244008001753574	   
df.mm.trans2	0.158958977397270	0.121554876649333	1.30771369918660	0.191332526843963	   
df.mm.exp2	0.00727825071667764	0.155183364536396	0.0469009725264119	0.96260343521187	   
df.mm.exp3	-0.299727744729958	0.155183364536396	-1.93144249466032	0.053768523438617	.  
df.mm.exp4	-0.218391027775318	0.155183364536395	-1.40730952977951	0.159709988958235	   
df.mm.exp5	0.0356316464984575	0.155183364536396	0.229609962413856	0.818451435256904	   
df.mm.exp6	-0.167339461869702	0.155183364536396	-1.07833376579779	0.281198115941347	   
df.mm.exp7	-0.133846351727995	0.155183364536396	-0.862504509602919	0.388659086289073	   
df.mm.exp8	-0.0319832422972498	0.155183364536395	-0.206099683382936	0.836763588327213	   
df.mm.trans1:exp2	0.176792051726046	0.137819288252019	1.28278163360386	0.199926692288079	   
df.mm.trans2:exp2	-0.263983152726741	0.102540831818623	-2.57441984860902	0.0102131034075444	*  
df.mm.trans1:exp3	0.353389340931924	0.137819288252019	2.56415009403988	0.0105178171549546	*  
df.mm.trans2:exp3	-0.0169187317170849	0.102540831818623	-0.164995069934787	0.868988037839342	   
df.mm.trans1:exp4	0.344271030413295	0.137819288252019	2.49798874148700	0.0126825053172642	*  
df.mm.trans2:exp4	-0.0227188779174144	0.102540831818623	-0.221559329239695	0.824711443673392	   
df.mm.trans1:exp5	0.102627573242848	0.137819288252019	0.744653194371326	0.45669218416447	   
df.mm.trans2:exp5	-0.158706780732452	0.102540831818623	-1.54774227902869	0.122065545543073	   
df.mm.trans1:exp6	0.238755250139179	0.137819288252019	1.73237906803427	0.0835775758470647	.  
df.mm.trans2:exp6	-0.101586799336310	0.102540831818623	-0.990696072331454	0.322122729214544	   
df.mm.trans1:exp7	0.219958717038564	0.137819288252019	1.59599370906882	0.110870851910822	   
df.mm.trans2:exp7	-0.0560031491857361	0.102540831818623	-0.546154621456515	0.58510642396314	   
df.mm.trans1:exp8	0.170052444209236	0.137819288252019	1.23387986083831	0.21759694452619	   
df.mm.trans2:exp8	-0.0614360158234305	0.102540831818623	-0.599137092354589	0.549244961371889	   
df.mm.trans1:probe2	0.105837206193627	0.100873390094145	1.04920837987946	0.294387628668707	   
df.mm.trans1:probe3	0.113010866058029	0.100873390094145	1.12032386293904	0.262899840322544	   
df.mm.trans1:probe4	0.0754410502867682	0.100873390094145	0.747878605213517	0.454745224148505	   
df.mm.trans1:probe5	0.184006326352743	0.100873390094145	1.824131479878	0.068491623630186	.  
df.mm.trans1:probe6	0.0640942969493623	0.100873390094145	0.635393505557245	0.525346935030714	   
df.mm.trans1:probe7	0.0694574570768184	0.100873390094145	0.688560749390834	0.49129213727886	   
df.mm.trans1:probe8	0.0789550347997698	0.100873390094145	0.782714199711947	0.434018216905106	   
df.mm.trans1:probe9	0.159105487271350	0.100873390094145	1.57727907352828	0.115112359174651	   
df.mm.trans1:probe10	0.0921364536911006	0.100873390094145	0.913387104419805	0.361304224957122	   
df.mm.trans1:probe11	0.0385308756183928	0.100873390094145	0.381972644940672	0.702579429728322	   
df.mm.trans1:probe12	0.109252989659376	0.100873390094145	1.08307046642738	0.279091680212230	   
df.mm.trans1:probe13	0.133892065461641	0.100873390094145	1.32732790418444	0.184765215539729	   
df.mm.trans1:probe14	0.0356057961501207	0.100873390094145	0.352975111839603	0.724196720848225	   
df.mm.trans1:probe15	0.117056268263557	0.100873390094145	1.16042762272894	0.24620840915126	   
df.mm.trans1:probe16	0.15604951694418	0.100873390094145	1.54698396473578	0.122248296117892	   
df.mm.trans2:probe2	0.090315849018542	0.100873390094145	0.89533869075135	0.370865638402469	   
df.mm.trans2:probe3	0.0852842957407675	0.100873390094145	0.845458803963778	0.398098379684052	   
df.mm.trans2:probe4	-0.0476834957689361	0.100873390094145	-0.47270638693151	0.636546942953112	   
df.mm.trans2:probe5	0.00699031882506126	0.100873390094145	0.0692979468474015	0.944769151453018	   
df.mm.trans2:probe6	0.0832486937776721	0.100873390094145	0.825279032458175	0.409450321489621	   
df.mm.trans3:probe2	0.0224757665627561	0.100873390094145	0.222811650741385	0.82373691933893	   
df.mm.trans3:probe3	0.0236363896262774	0.100873390094145	0.234317391377622	0.814796372201091	   
df.mm.trans3:probe4	-0.000997267195985607	0.100873390094145	-0.0098863257699068	0.992114357507682	   
df.mm.trans3:probe5	-0.0330369516854889	0.100873390094145	-0.327509084949515	0.743365459951652	   
df.mm.trans3:probe6	0.0516096852553444	0.100873390094145	0.511628341301677	0.609047231751891	   
df.mm.trans3:probe7	-0.0975500955835383	0.100873390094145	-0.967054795050458	0.333798334299692	   
df.mm.trans3:probe8	-0.0488161750931559	0.100873390094145	-0.48393510962203	0.628559577294339	   
df.mm.trans3:probe9	-0.0797044514248915	0.100873390094145	-0.79014347937056	0.429669664391479	   
df.mm.trans3:probe10	0.153476174922258	0.100873390094145	1.52147335168392	0.128521954502497	   
df.mm.trans3:probe11	0.082771572639786	0.100873390094145	0.820549131565177	0.412138725933016	   
df.mm.trans3:probe12	-0.0804461289940717	0.100873390094145	-0.797496038538919	0.425391088462320	   
df.mm.trans3:probe13	0.0149855195848576	0.100873390094145	0.148557707546774	0.881938731423739	   
df.mm.trans3:probe14	-0.0108049593752766	0.100873390094145	-0.107114070075293	0.914724369924505	   
df.mm.trans3:probe15	-0.0290603973461396	0.100873390094145	-0.288087842780118	0.77335142031338