chr13.6726_chr13_30394697_30400497_-_2.R 

fitVsDatCorrelation=0.887396050331145
cont.fitVsDatCorrelation=0.208540806316384

fstatistic=11592.2282674036,62,922
cont.fstatistic=2564.12412721124,62,922

residuals=-0.665859778479325,-0.0858885403029285,-0.00354513867218822,0.0834085811415729,0.531005466446889
cont.residuals=-0.542318913188944,-0.194537837787842,-0.076568809723802,0.126302483444357,1.54828055779199

predictedValues:
Include	Exclude	Both
chr13.6726_chr13_30394697_30400497_-_2.R.tl.Lung	60.8915264113662	42.7460313916302	70.0511860121416
chr13.6726_chr13_30394697_30400497_-_2.R.tl.cerebhem	60.0156809968818	47.4958173223039	61.8462270513964
chr13.6726_chr13_30394697_30400497_-_2.R.tl.cortex	68.473602760051	43.4553012313833	73.6372291206725
chr13.6726_chr13_30394697_30400497_-_2.R.tl.heart	62.1624603453386	45.7977805917727	67.2376041240734
chr13.6726_chr13_30394697_30400497_-_2.R.tl.kidney	56.4997307627352	43.4904719460546	69.378315615776
chr13.6726_chr13_30394697_30400497_-_2.R.tl.liver	56.7765170248026	49.0647636994191	63.1762422786478
chr13.6726_chr13_30394697_30400497_-_2.R.tl.stomach	58.1926727526042	44.7408598714576	62.8671096852425
chr13.6726_chr13_30394697_30400497_-_2.R.tl.testicle	59.0447311881849	44.9434913607598	64.8694812666402


diffExp=18.145495019736,12.5198636745779,25.0183015286677,16.3646797535659,13.0092588166806,7.71175332538345,13.4518128811466,14.1012398274251
diffExpScore=0.991757499355338
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=1,0,1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,0,1,1,0,0,1,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	59.0841151312209	58.6913781766784	52.7537317117316
cerebhem	55.9812512752704	60.1716845988781	56.6018442147551
cortex	60.9442121471286	50.0907367671649	53.0007304148406
heart	56.7070011002873	51.8755633040117	54.0678509077753
kidney	56.8414120479142	58.9019711260453	52.1084811800472
liver	56.1471356545938	56.8419191541875	51.3492914672753
stomach	58.027462327134	60.702593400755	54.1046005277569
testicle	59.164079473332	48.8918934403734	55.6715146407237
cont.diffExp=0.392736954542421,-4.19043332360766,10.8534753799637,4.83143779627552,-2.06055907813106,-0.694783499593633,-2.67513107362106,10.2721860329586
cont.diffExpScore=2.02892925769281

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=0,0,1,0,0,0,0,1
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.36147705734045
cont.tran.correlation=-0.529240341765929

tran.covariance=-0.00104888937818446
cont.tran.covariance=-0.00139998236996075

tran.mean=52.7369649785466
cont.tran.mean=56.816525570311

weightedLogRatios:
wLogRatio
Lung	1.39127659284859
cerebhem	0.93062167026452
cortex	1.81844830479870
heart	1.21503376094520
kidney	1.02149342694961
liver	0.578982584384118
stomach	1.03369877082271
testicle	1.07568909348107

cont.weightedLogRatios:
wLogRatio
Lung	0.0271814689028972
cerebhem	-0.293151382858043
cortex	0.78682454813794
heart	0.355609212946349
kidney	-0.144505474379666
liver	-0.0496132784773526
stomach	-0.184041298359554
testicle	0.759944368541008

varWeightedLogRatios=0.130963828071701
cont.varWeightedLogRatios=0.181294106327896

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.26315356259507	0.068513809736077	62.2232740963788	0	***
df.mm.trans1	0.138288872180741	0.05919483734302	2.33616440872013	0.0196961583402036	*  
df.mm.trans2	-0.529401091110841	0.0519405712900017	-10.1924387422506	3.44316554266854e-23	***
df.mm.exp2	0.215452253230375	0.0664187215815696	3.24384824188125	0.00122166261142346	** 
df.mm.exp3	0.083886253235457	0.0664187215815695	1.26299108501261	0.206911687331794	   
df.mm.exp4	0.130610060971923	0.0664187215815696	1.96646454285512	0.0495445984837807	*  
df.mm.exp5	-0.0479407925258332	0.0664187215815695	-0.721796375844973	0.470602684779596	   
df.mm.exp6	0.171191443784792	0.0664187215815696	2.57745767621483	0.0101073249088632	*  
df.mm.exp7	0.108479315604208	0.0664187215815696	1.63326413127334	0.102754870801424	   
df.mm.exp8	0.096179814614984	0.0664187215815696	1.44808289477334	0.147933826571497	   
df.mm.trans1:exp2	-0.229940400854827	0.0616213803932545	-3.73150356884893	0.000201983517957116	***
df.mm.trans2:exp2	-0.110086960568719	0.0442090302618202	-2.49014646819323	0.0129441850424420	*  
df.mm.trans1:exp3	0.0334680309462529	0.0616213803932545	0.543123680979995	0.587175923664425	   
df.mm.trans2:exp3	-0.0674297594688098	0.0442090302618201	-1.52524855373368	0.127540045711260	   
df.mm.trans1:exp4	-0.109952800499251	0.0616213803932545	-1.78432874754761	0.0746990151288628	.  
df.mm.trans2:exp4	-0.0616507878500639	0.0442090302618202	-1.39452929604988	0.163493734203157	   
df.mm.trans1:exp5	-0.0269173602430854	0.0616213803932545	-0.436818520963089	0.662345245833867	   
df.mm.trans2:exp5	0.0652063127765595	0.0442090302618202	1.47495460521044	0.140566127429705	   
df.mm.trans1:exp6	-0.241162661855093	0.0616213803932545	-3.91361992081392	9.76105655377819e-05	***
df.mm.trans2:exp6	-0.0333266683393012	0.0442090302618201	-0.75384300768259	0.451135963905697	   
df.mm.trans1:exp7	-0.153813892162812	0.0616213803932545	-2.49611240743399	0.0127299680961687	*  
df.mm.trans2:exp7	-0.0628684985566596	0.0442090302618202	-1.42207368459186	0.155343052576517	   
df.mm.trans1:exp8	-0.126978527880828	0.0616213803932545	-2.06062452789077	0.0396189284242795	*  
df.mm.trans2:exp8	-0.0460502196241632	0.0442090302618202	-1.04164735918067	0.297848258007972	   
df.mm.trans1:probe2	-0.279708671878613	0.0429273358698119	-6.51586375466908	1.18745574047454e-10	***
df.mm.trans1:probe3	-0.0918163444801672	0.0429273358698119	-2.13887823736893	0.032707688416617	*  
df.mm.trans1:probe4	-0.349150388761549	0.0429273358698119	-8.13352102307108	1.33978559554938e-15	***
df.mm.trans1:probe5	-0.433131749403994	0.0429273358698119	-10.0898819045649	8.85495021922766e-23	***
df.mm.trans1:probe6	-0.302513513778075	0.0429273358698119	-7.04710664308458	3.57581370957231e-12	***
df.mm.trans1:probe7	-0.215322284360077	0.0429273358698119	-5.01597129188489	6.32890007517453e-07	***
df.mm.trans1:probe8	-0.48257902873221	0.0429273358698119	-11.2417651585869	1.43832534999226e-27	***
df.mm.trans1:probe9	-0.613285776458907	0.0429273358698119	-14.286602325354	5.6357403847405e-42	***
df.mm.trans1:probe10	-0.510299721537577	0.0429273358698119	-11.8875236768755	2.0175161286238e-30	***
df.mm.trans1:probe11	-0.580324578879384	0.0429273358698119	-13.5187653070148	4.00671039916375e-38	***
df.mm.trans1:probe12	-0.548926575335245	0.0429273358698119	-12.7873431745218	1.38200544633857e-34	***
df.mm.trans1:probe13	-0.553927234356878	0.0429273358698119	-12.9038344246846	3.85470558444653e-35	***
df.mm.trans1:probe14	-0.515051000290633	0.0429273358698119	-11.9982055688864	6.37120639982459e-31	***
df.mm.trans1:probe15	-0.543598221299492	0.0429273358698119	-12.6632182101422	5.34021596363766e-34	***
df.mm.trans1:probe16	-0.552576622653556	0.0429273358698119	-12.8723716824493	5.44626092043864e-35	***
df.mm.trans1:probe17	-0.585283109472352	0.0429273358698119	-13.6342751678644	1.07683787431450e-38	***
df.mm.trans1:probe18	-0.519678873739225	0.0429273358698119	-12.1060127121628	2.05795844840663e-31	***
df.mm.trans1:probe19	-0.597699702670426	0.0429273358698119	-13.9235219367701	3.88493460438407e-40	***
df.mm.trans1:probe20	-0.614659258577061	0.0429273358698119	-14.318597838011	3.86822463779441e-42	***
df.mm.trans1:probe21	-0.413544899608668	0.0429273358698119	-9.63360272025379	5.40292945500104e-21	***
df.mm.trans1:probe22	-0.342763965324238	0.0429273358698119	-7.98474814192423	4.17577161869644e-15	***
df.mm.trans1:probe23	-0.362161412965213	0.0429273358698119	-8.43661516902794	1.25223508984957e-16	***
df.mm.trans1:probe24	-0.224191384887548	0.0429273358698119	-5.22257858180311	2.18159306870528e-07	***
df.mm.trans2:probe2	0.083257891876697	0.0429273358698119	1.93950754664109	0.0527443001957931	.  
df.mm.trans2:probe3	0.0361360422840396	0.0429273358698119	0.84179559601908	0.400120662063408	   
df.mm.trans2:probe4	0.052937472540942	0.0429273358698119	1.23318793184577	0.217819964450633	   
df.mm.trans2:probe5	0.116129734159141	0.0429273358698119	2.70526301728424	0.00695092807395746	** 
df.mm.trans2:probe6	0.0774449321037641	0.0429273358698119	1.80409360456553	0.0715428969726665	.  
df.mm.trans3:probe2	0.568823950288431	0.0429273358698119	13.250856098164	8.20210284657249e-37	***
df.mm.trans3:probe3	0.0931092414226199	0.0429273358698119	2.16899650388269	0.030337924987652	*  
df.mm.trans3:probe4	0.126477288927112	0.0429273358698119	2.94631116430535	0.00329682277616487	** 
df.mm.trans3:probe5	0.0852706095601293	0.0429273358698119	1.98639416661528	0.0472852690483126	*  
df.mm.trans3:probe6	0.173523154480069	0.0429273358698119	4.04225305307373	5.73580721781601e-05	***
df.mm.trans3:probe7	0.512159221247741	0.0429273358698119	11.9308410566403	1.28616599201897e-30	***
df.mm.trans3:probe8	1.46349520252950	0.0429273358698119	34.0923836263197	1.76643108209967e-165	***
df.mm.trans3:probe9	0.285385495548275	0.0429273358698119	6.6481063817652	5.07684323066864e-11	***
df.mm.trans3:probe10	0.210910986932247	0.0429273358698119	4.91320932591504	1.05990264326974e-06	***
df.mm.trans3:probe11	0.629878308093566	0.0429273358698119	14.6731283302517	5.77192862955509e-44	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.23645136409726	0.145349890175550	29.1465742353200	6.78640610887286e-133	***
df.mm.trans1	-0.078959835654195	0.125579983654550	-0.62876131495128	0.529661064427266	   
df.mm.trans2	-0.158762178979569	0.110190286625993	-1.44080012713316	0.149980659339972	   
df.mm.exp2	-0.0994432779709893	0.140905226620293	-0.705745843189807	0.480524479526698	   
df.mm.exp3	-0.132131140170263	0.140905226620293	-0.937730582033882	0.348628452473227	   
df.mm.exp4	-0.189114671629307	0.140905226620293	-1.34214092809295	0.179880702848443	   
df.mm.exp5	-0.022808453094475	0.140905226620293	-0.16187088046023	0.87144299947668	   
df.mm.exp6	-0.0560218360454405	0.140905226620293	-0.3975852236937	0.691027983019297	   
df.mm.exp7	-0.00963684296928947	0.140905226620293	-0.068392374083174	0.945488121107984	   
df.mm.exp8	-0.23516284498565	0.140905226620293	-1.66894337865380	0.095468094020102	.  
df.mm.trans1:exp2	0.0454980052138735	0.130727818335127	0.348036139463731	0.727892486807113	   
df.mm.trans2:exp2	0.124352327530643	0.0937880657647843	1.32588647091318	0.185205690829995	   
df.mm.trans1:exp3	0.163127922139123	0.130727818335127	1.24784398773440	0.212405014652315	   
df.mm.trans2:exp3	-0.0263256003039602	0.0937880657647843	-0.280692432339776	0.779009285234732	   
df.mm.trans1:exp4	0.148050242157200	0.130727818335127	1.13250755686648	0.257715539227082	   
df.mm.trans2:exp4	0.0656696723908169	0.0937880657647843	0.700192203083845	0.483983916980197	   
df.mm.trans1:exp5	-0.0158885102602242	0.130727818335127	-0.121538861908437	0.903290718381831	   
df.mm.trans2:exp5	0.0263901722265846	0.0937880657647843	0.281380919964485	0.778481387342942	   
df.mm.trans1:exp6	0.00503539476512472	0.130727818335127	0.0385181580267503	0.969282892510991	   
df.mm.trans2:exp6	0.0240030662496994	0.0937880657647843	0.255928790661894	0.798062909965623	   
df.mm.trans1:exp7	-0.00840887892448827	0.130727818335127	-0.0643235619746343	0.94872653551447	   
df.mm.trans2:exp7	0.0433304284080289	0.0937880657647843	0.462003646782731	0.644187697480311	   
df.mm.trans1:exp8	0.236515328286860	0.130727818335127	1.80921957773779	0.0707424849363502	.  
df.mm.trans2:exp8	0.0524816128053494	0.0937880657647843	0.559576662311926	0.57590413451852	   
df.mm.trans1:probe2	-0.0514883826939413	0.0910689914667028	-0.565377763217755	0.571954395384949	   
df.mm.trans1:probe3	-0.057040893829402	0.0910689914667028	-0.626348144530156	0.531241649075653	   
df.mm.trans1:probe4	-0.133767441546152	0.0910689914667028	-1.46885827318139	0.142212327276908	   
df.mm.trans1:probe5	-0.0665072195802387	0.0910689914667028	-0.730294895212005	0.465395463171653	   
df.mm.trans1:probe6	-0.140495388134298	0.0910689914667028	-1.54273574211774	0.123238038565410	   
df.mm.trans1:probe7	-0.101200522545872	0.0910689914667028	-1.11125116152048	0.266749949303196	   
df.mm.trans1:probe8	-0.146540796974904	0.0910689914667028	-1.60911847836246	0.107932757243125	   
df.mm.trans1:probe9	-0.146829241667264	0.0910689914667028	-1.61228579895879	0.107241981373616	   
df.mm.trans1:probe10	-0.108127455311795	0.0910689914667028	-1.18731363519414	0.235409732022689	   
df.mm.trans1:probe11	-0.123663113569470	0.0910689914667028	-1.35790582038766	0.174825782775205	   
df.mm.trans1:probe12	-0.239555736031613	0.0910689914667028	-2.63048631782862	0.00866847146218184	** 
df.mm.trans1:probe13	-0.0963435416474074	0.0910689914667028	-1.05791817934684	0.290369835949342	   
df.mm.trans1:probe14	-0.0427306634770569	0.0910689914667028	-0.469211998385645	0.639029042058252	   
df.mm.trans1:probe15	-0.208394109566004	0.0910689914667028	-2.28831028223474	0.0223449811556169	*  
df.mm.trans1:probe16	-0.124078619930595	0.0910689914667028	-1.36246836527185	0.173382833826060	   
df.mm.trans1:probe17	-0.114101028946448	0.0910689914667028	-1.25290757159825	0.210557007740879	   
df.mm.trans1:probe18	-0.136598213458171	0.0910689914667028	-1.49994209069632	0.133971734875582	   
df.mm.trans1:probe19	-0.139724229015921	0.0910689914667028	-1.53426788597969	0.125306819709393	   
df.mm.trans1:probe20	-0.144685557772245	0.0910689914667028	-1.58874667921567	0.112460428460005	   
df.mm.trans1:probe21	-0.0582413783488019	0.0910689914667028	-0.639530288090392	0.52263688002378	   
df.mm.trans1:probe22	-0.132211207311144	0.0910689914667028	-1.45176975369804	0.146905813799383	   
df.mm.trans1:probe23	-0.131575757703738	0.0910689914667028	-1.44479208108773	0.148856051553131	   
df.mm.trans1:probe24	-0.104629189186985	0.0910689914667028	-1.14890027331905	0.250895066266023	   
df.mm.trans2:probe2	0.060746113123359	0.0910689914667028	0.66703399417319	0.504917314514534	   
df.mm.trans2:probe3	0.0161345844531901	0.0910689914667028	0.177168805686064	0.859414709390985	   
df.mm.trans2:probe4	0.00815035899968506	0.0910689914667028	0.0894965329957018	0.928706745450398	   
df.mm.trans2:probe5	-0.057105461241216	0.0910689914667028	-0.62705713900538	0.530777021585784	   
df.mm.trans2:probe6	-0.119663520086294	0.0910689914667028	-1.31398754020513	0.189177221831088	   
df.mm.trans3:probe2	-0.0108046913262765	0.0910689914667028	-0.118642922824362	0.905584101145766	   
df.mm.trans3:probe3	-0.0167396065218811	0.0910689914667028	-0.183812363047871	0.854201073498684	   
df.mm.trans3:probe4	-0.0344781712186655	0.0910689914667028	-0.37859397214552	0.705076556628655	   
df.mm.trans3:probe5	0.0225615459867892	0.0910689914667028	0.247741252246527	0.804389772304342	   
df.mm.trans3:probe6	-0.0350003740183449	0.0910689914667028	-0.384328117119228	0.700823892891852	   
df.mm.trans3:probe7	-0.085025785854922	0.0910689914667028	-0.933641456719212	0.350733309371707	   
df.mm.trans3:probe8	-0.0486011179374482	0.0910689914667028	-0.533673615516188	0.593695996707613	   
df.mm.trans3:probe9	-0.0142428968205507	0.0910689914667028	-0.156396777774335	0.875754514268659	   
df.mm.trans3:probe10	-0.06413425162201	0.0910689914667028	-0.70423807916506	0.481462349361043	   
df.mm.trans3:probe11	-0.0775778495173483	0.0910689914667028	-0.851858006418275	0.394514110355984