chr1.397_chr1_172566830_172567852_-_2.R 

fitVsDatCorrelation=0.901062278875184
cont.fitVsDatCorrelation=0.262779441747901

fstatistic=9320.64024223891,65,991
cont.fstatistic=1870.95873161978,65,991

residuals=-0.804716421641997,-0.106077455988801,-0.00158445792554234,0.107650145183209,1.20852750609416
cont.residuals=-0.88876454434579,-0.324381910368017,-0.0336440934076495,0.283738022324068,1.92404682332966

predictedValues:
Include	Exclude	Both
chr1.397_chr1_172566830_172567852_-_2.R.tl.Lung	92.2424789963395	64.8190905858645	91.0024708763459
chr1.397_chr1_172566830_172567852_-_2.R.tl.cerebhem	99.7750366027026	105.834477028986	88.2643327083242
chr1.397_chr1_172566830_172567852_-_2.R.tl.cortex	93.8049581612247	56.0447234564584	120.158843877694
chr1.397_chr1_172566830_172567852_-_2.R.tl.heart	91.0646626961824	55.0840893588492	111.622061195436
chr1.397_chr1_172566830_172567852_-_2.R.tl.kidney	107.565918087989	63.6599884889407	115.170357064080
chr1.397_chr1_172566830_172567852_-_2.R.tl.liver	107.955729621902	59.04650276369	111.265128475195
chr1.397_chr1_172566830_172567852_-_2.R.tl.stomach	99.215879151531	61.6469036773799	112.304359977242
chr1.397_chr1_172566830_172567852_-_2.R.tl.testicle	92.744951531808	68.0979223782512	110.189535400484


diffExp=27.4233884104751,-6.05944042628344,37.7602347047663,35.9805733373332,43.9059295990482,48.9092268582118,37.5689754741512,24.6470291535568
diffExpScore=1.04427435541863
diffExp1.5=0,0,1,1,1,1,1,0
diffExp1.5Score=0.833333333333333
diffExp1.4=1,0,1,1,1,1,1,0
diffExp1.4Score=0.857142857142857
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	97.7858360017366	79.7758022157682	91.4916566136937
cerebhem	87.0750115654954	102.329634079875	104.005031152861
cortex	94.163715498993	97.3687060142208	97.4288148463452
heart	90.7554053328919	85.9516597157096	104.333087428446
kidney	101.612103675726	82.6575018758548	95.1373217371558
liver	95.283986105997	121.267047071458	101.852948447323
stomach	94.9619612924744	107.129635777584	101.220005207698
testicle	90.3828446653668	86.0309708563793	112.696045000244
cont.diffExp=18.0100337859684,-15.2546225143799,-3.20499051522772,4.80374561718226,18.954601799871,-25.9830609654607,-12.1676744851091,4.35187380898751
cont.diffExpScore=8.94079789488105

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=1,0,0,0,1,-1,0,0
cont.diffExp1.2Score=1.5

tran.correlation=0.111596340461793
cont.tran.correlation=-0.206225286282716

tran.covariance=0.00188223399568104
cont.tran.covariance=-0.00164076699406859

tran.mean=82.4127070367562
cont.tran.mean=94.6582388590956

weightedLogRatios:
wLogRatio
Lung	1.53406758660526
cerebhem	-0.27311834767956
cortex	2.20638722861969
heart	2.1416482618863
kidney	2.31631309740121
liver	2.64288815681153
stomach	2.07451099911919
testicle	1.35158977946078

cont.weightedLogRatios:
wLogRatio
Lung	0.912150171413671
cerebhem	-0.734097533905268
cortex	-0.152682182607519
heart	0.243688912920165
kidney	0.932759449447144
liver	-1.12788400929435
stomach	-0.55624964624768
testicle	0.221044549146221

varWeightedLogRatios=0.840472302352002
cont.varWeightedLogRatios=0.563645339968136

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.943904236293	0.0877083316176898	44.9661299394454	1.59950068262027e-241	***
df.mm.trans1	0.449802589895609	0.0756143640869386	5.94863945927579	3.74553197816344e-09	***
df.mm.trans2	0.227837627668585	0.0659490798446492	3.45475066832295	0.00057413457653763	***
df.mm.exp2	0.59932400080469	0.0836773533573992	7.1623202307186	1.54524765146035e-12	***
df.mm.exp3	-0.406581108063309	0.0836773533573992	-4.85891453003702	1.37132903470412e-06	***
df.mm.exp4	-0.379822223270191	0.0836773533573993	-4.5391280678765	6.34184356245057e-06	***
df.mm.exp5	-0.0998865660490742	0.0836773533573992	-1.19371086729336	0.232876759341186	   
df.mm.exp6	-0.137003610269910	0.0836773533573992	-1.63728422055542	0.101888571717169	   
df.mm.exp7	-0.187625867897461	0.0836773533573992	-2.24225385207968	0.0251654778956080	*  
df.mm.exp8	-0.136536219007961	0.0836773533573992	-1.63169858426083	0.103060706888081	   
df.mm.trans1:exp2	-0.52082673411956	0.0775518521729545	-6.71585164668953	3.14541470252600e-11	***
df.mm.trans2:exp2	-0.109047832401768	0.0537294992738512	-2.02957097824359	0.0426669666059897	*  
df.mm.trans1:exp3	0.423378070438134	0.0775518521729545	5.45929025001138	6.04260502127712e-08	***
df.mm.trans2:exp3	0.261130945435317	0.0537294992738512	4.86010383428984	1.36330747791782e-06	***
df.mm.trans1:exp4	0.366971305422143	0.0775518521729545	4.73194766004726	2.5466856160429e-06	***
df.mm.trans2:exp4	0.217082970586096	0.0537294992738512	4.04029394503859	5.75130076453821e-05	***
df.mm.trans1:exp5	0.253569666105824	0.0775518521729545	3.26967904699837	0.00111376796506766	** 
df.mm.trans2:exp5	0.081842639305671	0.0537294992738512	1.52323472974374	0.128019016883628	   
df.mm.trans1:exp6	0.294304091347326	0.0775518521729545	3.7949331073483	0.000156639081577845	***
df.mm.trans2:exp6	0.0437287583819735	0.0537294992738512	0.813868712215137	0.41591567395482	   
df.mm.trans1:exp7	0.260503190368121	0.0775518521729545	3.35908405884559	0.0008118083984609	***
df.mm.trans2:exp7	0.137448704258922	0.0537294992738512	2.55816090074404	0.0106704843482580	*  
df.mm.trans1:exp8	0.141968736988143	0.0775518521729545	1.83062986905235	0.067455980516154	.  
df.mm.trans2:exp8	0.185882755525893	0.0537294992738512	3.4596033471013	0.000564009931223728	***
df.mm.trans1:probe2	0.106970046618496	0.0555543002791882	1.92550434585474	0.0544521802868463	.  
df.mm.trans1:probe3	-0.0262863552817970	0.0555543002791882	-0.473165086225458	0.636199606875162	   
df.mm.trans1:probe4	0.083099483636496	0.0555543002791882	1.49582450357361	0.135017771674324	   
df.mm.trans1:probe5	-0.268515260177135	0.0555543002791882	-4.8333838933748	1.55490324734690e-06	***
df.mm.trans1:probe6	-0.16038998652471	0.0555543002791882	-2.88708499105686	0.00397277393226207	** 
df.mm.trans1:probe7	-0.317353942898393	0.0555543002791882	-5.71250004596459	1.47137293701847e-08	***
df.mm.trans1:probe8	-0.362183471368044	0.0555543002791882	-6.51944979142733	1.12217339823905e-10	***
df.mm.trans1:probe9	-0.20537412587788	0.0555543002791882	-3.69681779530608	0.000230289433146461	***
df.mm.trans1:probe10	-0.267568264325505	0.0555543002791882	-4.81633758288451	1.69040934165548e-06	***
df.mm.trans1:probe11	0.101880106321127	0.0555543002791882	1.83388335032802	0.0669710517561351	.  
df.mm.trans1:probe12	-0.136864585163484	0.0555543002791882	-2.46361819833336	0.0139232136133432	*  
df.mm.trans1:probe13	0.0157450368178973	0.0555543002791882	0.283417066523575	0.776916342465974	   
df.mm.trans1:probe14	1.18858755339448	0.0555543002791882	21.3950593819242	8.61965277416258e-84	***
df.mm.trans1:probe15	0.526561880800972	0.0555543002791882	9.47832801699841	1.84170905461955e-20	***
df.mm.trans1:probe16	0.552806852284785	0.0555543002791882	9.95074817802858	2.67064030893046e-22	***
df.mm.trans1:probe17	0.536046539691986	0.0555543002791882	9.64905573462511	4.06531567338941e-21	***
df.mm.trans1:probe18	0.94899338159846	0.0555543002791882	17.0822668421579	1.53089939249992e-57	***
df.mm.trans1:probe19	0.763924162445246	0.0555543002791882	13.7509456262818	1.65949884211164e-39	***
df.mm.trans1:probe20	0.0953986599121982	0.0555543002791882	1.71721467884164	0.0862523907915455	.  
df.mm.trans1:probe21	0.198879639396287	0.0555543002791882	3.57991439720809	0.000360373649498694	***
df.mm.trans1:probe22	0.447398338959194	0.0555543002791882	8.05335206655099	2.29852226271246e-15	***
df.mm.trans1:probe23	0.573627121857503	0.0555543002791882	10.3255214983312	8.26729479411593e-24	***
df.mm.trans1:probe24	0.281087950476061	0.0555543002791882	5.05969743230413	5.0028210033888e-07	***
df.mm.trans1:probe25	0.290658384116133	0.0555543002791882	5.23196913030007	2.04650953056462e-07	***
df.mm.trans2:probe2	0.0107719281724919	0.0555543002791882	0.19389908824983	0.846294634197176	   
df.mm.trans2:probe3	0.145370037826565	0.0555543002791882	2.61671980559575	0.00901306651082083	** 
df.mm.trans2:probe4	-0.222211962879466	0.0555543002791882	-3.99990570959835	6.80800783302159e-05	***
df.mm.trans2:probe5	0.0945376100707091	0.0555543002791882	1.70171543148974	0.0891223835429249	.  
df.mm.trans2:probe6	-0.0311598407084766	0.0555543002791882	-0.560889806043506	0.574999451471612	   
df.mm.trans3:probe2	0.0336781142041159	0.0555543002791882	0.606219753194019	0.54450766873661	   
df.mm.trans3:probe3	-0.0307051322208245	0.0555543002791882	-0.55270486832731	0.580590161310188	   
df.mm.trans3:probe4	-0.305506491276341	0.0555543002791882	-5.49924109818715	4.85356362889313e-08	***
df.mm.trans3:probe5	-0.54760044676183	0.0555543002791882	-9.85703076107274	6.26711901555069e-22	***
df.mm.trans3:probe6	-0.531187104562141	0.0555543002791882	-9.5615839258646	8.83984052275845e-21	***
df.mm.trans3:probe7	-0.211223432529701	0.0555543002791882	-3.80210769406144	0.000152234283088700	***
df.mm.trans3:probe8	-0.338559134365402	0.0555543002791882	-6.09420211691936	1.57281335580364e-09	***
df.mm.trans3:probe9	0.223116783937548	0.0555543002791882	4.01619285665151	6.3614550251906e-05	***
df.mm.trans3:probe10	0.325815222846727	0.0555543002791882	5.86480652639566	6.12203857021305e-09	***
df.mm.trans3:probe11	-0.0927206271010568	0.0555543002791882	-1.66900899903498	0.0954314488716013	.  
df.mm.trans3:probe12	-0.0220147202433723	0.0555543002791882	-0.396273918179823	0.691988273233362	   
df.mm.trans3:probe13	0.058355630190809	0.0555543002791882	1.05042507776253	0.293778803477227	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.16316897214518	0.195130068247318	21.3353534365015	2.06826523352749e-83	***
df.mm.trans1	0.337323274856921	0.168223881957709	2.00520443905654	0.0452127391650899	*  
df.mm.trans2	0.199671057627081	0.146720935327183	1.36089002691961	0.173857838776238	   
df.mm.exp2	0.00477779892878942	0.186162105357971	0.0256647233313256	0.979529928198857	   
df.mm.exp3	0.0986655950063281	0.186162105357971	0.529998276591276	0.59623184720882	   
df.mm.exp4	-0.131387653709703	0.186162105357971	-0.705770132203102	0.480496968611094	   
df.mm.exp5	0.0347947208951901	0.186162105357971	0.186905497379734	0.851772984823557	   
df.mm.exp6	0.285574598130559	0.186162105357971	1.53401035931253	0.125346276894416	   
df.mm.exp7	0.164467452271181	0.186162105357971	0.883463645594933	0.377200082691615	   
df.mm.exp8	-0.211684676528891	0.186162105357971	-1.13709863842506	0.255771918717629	   
df.mm.trans1:exp2	-0.120787590248844	0.172534329727946	-0.700078589804729	0.484042535237248	   
df.mm.trans2:exp2	0.244201282187022	0.119535290055457	2.04292206990694	0.0413242899222287	*  
df.mm.trans1:exp3	-0.136410413807085	0.172534329727946	-0.790627662461022	0.429350459027046	   
df.mm.trans2:exp3	0.100619042434566	0.119535290055457	0.841751773789017	0.4001299891212	   
df.mm.trans1:exp4	0.0567759475834065	0.172534329727946	0.329070438752285	0.742171977722892	   
df.mm.trans2:exp4	0.205952467505597	0.119535290055457	1.7229428013271	0.0852108253613224	.  
df.mm.trans1:exp5	0.00358819742595081	0.172534329727946	0.0207970056255397	0.983411772840966	   
df.mm.trans2:exp5	0.000690637957496204	0.119535290055457	0.00577769089927994	0.995391258102958	   
df.mm.trans1:exp6	-0.311492578647682	0.172534329727946	-1.80539478223753	0.0713163126677337	.  
df.mm.trans2:exp6	0.133200288115052	0.119535290055457	1.11431768855251	0.265413096018581	   
df.mm.trans1:exp7	-0.193770788697084	0.172534329727946	-1.12308541148086	0.261673223626295	   
df.mm.trans2:exp7	0.130351970087016	0.119535290055457	1.09048942807217	0.275762681855106	   
df.mm.trans1:exp8	0.132959414123004	0.172534329727946	0.770625847810436	0.441112413229305	   
df.mm.trans2:exp8	0.287171806096187	0.119535290055457	2.40240188452261	0.0164709099257097	*  
df.mm.trans1:probe2	0.0935472159492626	0.123595036012789	0.756884895762182	0.449298730467846	   
df.mm.trans1:probe3	0.226827727444806	0.123595036012788	1.83524949514425	0.0667682875931627	.  
df.mm.trans1:probe4	0.0594069407649992	0.123595036012788	0.480657983374448	0.630865688648405	   
df.mm.trans1:probe5	0.302670376922858	0.123595036012788	2.44888780882381	0.0145021488559069	*  
df.mm.trans1:probe6	-0.0353219331634141	0.123595036012788	-0.285787635999874	0.775100502932412	   
df.mm.trans1:probe7	0.191783397791292	0.123595036012788	1.55170793244033	0.121051402418755	   
df.mm.trans1:probe8	0.197830613608854	0.123595036012788	1.60063559177558	0.109776367644635	   
df.mm.trans1:probe9	0.169792485452877	0.123595036012788	1.37378078384401	0.169820409505245	   
df.mm.trans1:probe10	0.0850923848309203	0.123595036012788	0.688477365888026	0.491313351613597	   
df.mm.trans1:probe11	0.200282972405893	0.123595036012788	1.62047747925062	0.105447876881239	   
df.mm.trans1:probe12	0.0397249001711216	0.123595036012788	0.321411777144603	0.74796613474074	   
df.mm.trans1:probe13	-0.00663723888908574	0.123595036012788	-0.0537015005068568	0.95718381066176	   
df.mm.trans1:probe14	0.142500944623538	0.123595036012788	1.15296656905212	0.249202126788297	   
df.mm.trans1:probe15	0.160689796756323	0.123595036012789	1.30013147728438	0.19385820722843	   
df.mm.trans1:probe16	0.0608416752760328	0.123595036012788	0.492266333979121	0.622640108438616	   
df.mm.trans1:probe17	0.105253959176206	0.123595036012788	0.85160345084826	0.394639960489743	   
df.mm.trans1:probe18	0.142725892032433	0.123595036012789	1.15478660500301	0.248456199700099	   
df.mm.trans1:probe19	0.0785604794259935	0.123595036012788	0.635628112263868	0.525165590311874	   
df.mm.trans1:probe20	0.0461807372076012	0.123595036012788	0.373645566176483	0.708747946574148	   
df.mm.trans1:probe21	0.0702221193938618	0.123595036012788	0.56816294294049	0.57005303981589	   
df.mm.trans1:probe22	0.282221544556729	0.123595036012788	2.28343753650047	0.0226155445728381	*  
df.mm.trans1:probe23	0.228807100809937	0.123595036012788	1.85126448594798	0.0644288673116502	.  
df.mm.trans1:probe24	0.185404631434497	0.123595036012788	1.50009771764064	0.133907597125385	   
df.mm.trans1:probe25	0.0985160249213635	0.123595036012789	0.797087230195636	0.425591298756481	   
df.mm.trans2:probe2	0.0772611015620755	0.123595036012788	0.625114924146964	0.532039543266099	   
df.mm.trans2:probe3	0.0362890911799327	0.123595036012788	0.29361285332024	0.769115230451639	   
df.mm.trans2:probe4	0.0552080131674403	0.123595036012788	0.446684712820731	0.655200315735845	   
df.mm.trans2:probe5	0.00367569070550186	0.123595036012788	0.0297397923418343	0.976280564239986	   
df.mm.trans2:probe6	0.138789871484553	0.123595036012788	1.12294049956984	0.261734737846786	   
df.mm.trans3:probe2	-0.321847597147201	0.123595036012788	-2.60404954381744	0.00935077650294473	** 
df.mm.trans3:probe3	-0.222366897291120	0.123595036012788	-1.79915718676688	0.0722979582986202	.  
df.mm.trans3:probe4	-0.221042231374698	0.123595036012788	-1.78843939453868	0.074010539050102	.  
df.mm.trans3:probe5	-0.309776467273862	0.123595036012788	-2.50638275829953	0.0123565984732773	*  
df.mm.trans3:probe6	-0.0085721188009359	0.123595036012788	-0.0693564974571384	0.944719847437657	   
df.mm.trans3:probe7	-0.152898427777395	0.123595036012789	-1.23709197966150	0.216345997972352	   
df.mm.trans3:probe8	-0.285475057764566	0.123595036012788	-2.30976151610998	0.0211061425885609	*  
df.mm.trans3:probe9	-0.1827289810104	0.123595036012788	-1.47844919104593	0.139605346160315	   
df.mm.trans3:probe10	-0.148061879384207	0.123595036012788	-1.19795975761427	0.231219114364894	   
df.mm.trans3:probe11	-0.291662263685256	0.123595036012788	-2.35982182694682	0.0184767672664430	*  
df.mm.trans3:probe12	-0.179554366545705	0.123595036012788	-1.45276357642006	0.146605959023657	   
df.mm.trans3:probe13	-0.0669154856605386	0.123595036012789	-0.541409168355393	0.588347277967059