chr18.11444_chr18_74170877_74178218_-_2.R 

fitVsDatCorrelation=0.871713696091189
cont.fitVsDatCorrelation=0.224509979346986

fstatistic=9401.35936802687,58,830
cont.fstatistic=2366.54157321937,58,830

residuals=-0.714745708180666,-0.0877820659348609,0.000913648424474089,0.07752851130307,1.18530232284713
cont.residuals=-0.51937515052699,-0.199102695010455,-0.0738866446941205,0.114932799400524,1.88639625292119

predictedValues:
Include	Exclude	Both
chr18.11444_chr18_74170877_74178218_-_2.R.tl.Lung	52.4936252976572	42.1671530088151	63.4518577671356
chr18.11444_chr18_74170877_74178218_-_2.R.tl.cerebhem	55.6745345056733	41.0326060664314	71.617764219138
chr18.11444_chr18_74170877_74178218_-_2.R.tl.cortex	58.1118758656051	44.7003322177731	65.2966331510443
chr18.11444_chr18_74170877_74178218_-_2.R.tl.heart	54.6887385224741	46.2760749600156	63.7478034627396
chr18.11444_chr18_74170877_74178218_-_2.R.tl.kidney	50.2036530404522	41.6348357315776	57.9817002970365
chr18.11444_chr18_74170877_74178218_-_2.R.tl.liver	54.7432666307656	50.3812523317832	61.0948916214834
chr18.11444_chr18_74170877_74178218_-_2.R.tl.stomach	72.6548442631095	46.365046566374	90.5756288203849
chr18.11444_chr18_74170877_74178218_-_2.R.tl.testicle	52.0480426944357	43.2718931101539	61.6979576018852


diffExp=10.3264722888421,14.6419284392419,13.4115436478321,8.41266356245849,8.56881730887463,4.36201429898235,26.2897976967355,8.77614958428184
diffExpScore=0.989560430094375
diffExp1.5=0,0,0,0,0,0,1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,1,0
diffExp1.4Score=0.5
diffExp1.3=0,1,1,0,0,0,1,0
diffExp1.3Score=0.75
diffExp1.2=1,1,1,0,1,0,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	55.3965000853929	50.5923220495427	58.6878970020967
cerebhem	54.9238729781415	52.6521917118459	61.6139478266869
cortex	56.8166565199385	53.0691449652321	57.5162647703052
heart	54.0107085644638	50.4573642025751	54.1921683236845
kidney	55.7152760599543	49.7534532133469	53.2167003363326
liver	57.3066356272353	53.2244015571216	59.1072062154129
stomach	57.7817054995697	51.3243955973754	64.283252039707
testicle	56.2868641460481	48.5567720204489	52.0427562688698
cont.diffExp=4.80417803585021,2.27168126629563,3.74751155470634,3.55334436188869,5.96182284660733,4.08223407011371,6.4573099021943,7.73009212559922
cont.diffExpScore=0.97475268625415

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=0.341799243353592
cont.tran.correlation=0.291404374446217

tran.covariance=0.00291155897378436
cont.tran.covariance=0.000211557157999686

tran.mean=50.4029859258185
cont.tran.mean=53.6167665498895

weightedLogRatios:
wLogRatio
Lung	0.84359877051073
cerebhem	1.18002079792169
cortex	1.03149765032760
heart	0.654457835882028
kidney	0.715385430427737
liver	0.328913976146122
stomach	1.8241561465326
testicle	0.712772174675425

cont.weightedLogRatios:
wLogRatio
Lung	0.360068489061618
cerebhem	0.168320062848084
cortex	0.273325177518093
heart	0.269162679231890
kidney	0.448586015916941
liver	0.296445202297186
stomach	0.473718259095022
testicle	0.584498320106524

varWeightedLogRatios=0.201069330983623
cont.varWeightedLogRatios=0.0182455519691383

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.77338166074212	0.0763329073445491	36.3327135991784	9.94899094747318e-174	***
df.mm.trans1	1.20436238290502	0.0660850862928502	18.2244202204411	1.12132184745326e-62	***
df.mm.trans2	0.947067989922286	0.0585474766078806	16.1760684626126	2.2965878251434e-51	***
df.mm.exp2	-0.0895050936695928	0.0756707298533902	-1.18282318464492	0.237217933603807	   
df.mm.exp3	0.131358685764704	0.0756707298533902	1.73592465698703	0.0829482583913296	.  
df.mm.exp4	0.129296351582149	0.0756707298533902	1.70867060265782	0.0878858423666651	.  
df.mm.exp5	0.0328457397769548	0.0756707298533902	0.434061358210664	0.664356690497932	   
df.mm.exp6	0.25779343011979	0.0756707298533902	3.40677869262338	0.00068895460882564	***
df.mm.exp7	0.0640289369571671	0.0756707298533902	0.846151967626337	0.397711852001339	   
df.mm.exp8	0.0453678465136739	0.0756707298533902	0.599542869502815	0.548974553933033	   
df.mm.trans1:exp2	0.148336206532721	0.0701486440320171	2.11459834441016	0.0347617497768937	*  
df.mm.trans2:exp2	0.06223056098846	0.0526645991233805	1.18163931795377	0.237687288783263	   
df.mm.trans1:exp3	-0.0296803782348709	0.0701486440320171	-0.423106941615639	0.672326841273284	   
df.mm.trans2:exp3	-0.0730193052466964	0.0526645991233805	-1.38649693460364	0.165967513348020	   
df.mm.trans1:exp4	-0.0883302797761162	0.0701486440320171	-1.25918727289726	0.208316700950486	   
df.mm.trans2:exp4	-0.0363128168091833	0.0526645991233805	-0.68951093170027	0.490694515520792	   
df.mm.trans1:exp5	-0.077449685320189	0.0701486440320171	-1.10407957828578	0.269878596380689	   
df.mm.trans2:exp5	-0.0455500787764716	0.0526645991233805	-0.864908867335319	0.387338713776064	   
df.mm.trans1:exp6	-0.215830792222731	0.0701486440320172	-3.07676356686555	0.00216131107229942	** 
df.mm.trans2:exp6	-0.079815855026101	0.0526645991233805	-1.51555041440858	0.130013800205448	   
df.mm.trans1:exp7	0.260999390977382	0.0701486440320171	3.72066195403944	0.000212082110092909	***
df.mm.trans2:exp7	0.0308753783989087	0.0526645991233805	0.586264377073774	0.557857261652399	   
df.mm.trans1:exp8	-0.0538923958168168	0.0701486440320171	-0.768259979369227	0.442551268488175	   
df.mm.trans2:exp8	-0.0195060953714605	0.0526645991233805	-0.37038343965673	0.711191308937133	   
df.mm.trans1:probe2	-0.209230452581426	0.0470571409755076	-4.44630609178587	9.9233323203354e-06	***
df.mm.trans1:probe3	-0.0363829267299041	0.0470571409755076	-0.773164836955156	0.439644962631421	   
df.mm.trans1:probe4	0.0521815272771349	0.0470571409755076	1.10889710244603	0.267795774545551	   
df.mm.trans1:probe5	-0.215292708618275	0.0470571409755076	-4.57513363870387	5.48545910344518e-06	***
df.mm.trans1:probe6	-0.0617658762426847	0.0470571409755076	-1.31257180020420	0.189690104778364	   
df.mm.trans1:probe7	-0.190158186827957	0.0470571409755076	-4.04100595331389	5.81660735529356e-05	***
df.mm.trans1:probe8	-0.113378399695054	0.0470571409755076	-2.40937713904178	0.0161965447645212	*  
df.mm.trans1:probe9	-0.0250878131558883	0.0470571409755076	-0.53313509141888	0.594082787344613	   
df.mm.trans1:probe10	-0.09440812788359	0.0470571409755076	-2.00624444933295	0.0451541827519509	*  
df.mm.trans1:probe11	-0.225714438586604	0.0470571409755076	-4.79660331901771	1.91283420789583e-06	***
df.mm.trans1:probe12	-0.187661147088993	0.0470571409755076	-3.98794196159658	7.25266966030317e-05	***
df.mm.trans1:probe13	-0.151277535950901	0.0470571409755076	-3.21476258044742	0.00135599226383495	** 
df.mm.trans1:probe14	-0.250288099236719	0.0470571409755076	-5.31881227903305	1.34413145692338e-07	***
df.mm.trans1:probe15	-0.157294461029036	0.0470571409755076	-3.34262681005005	0.000867036732745664	***
df.mm.trans1:probe16	-0.159711636263722	0.0470571409755076	-3.39399362036994	0.000721468912292631	***
df.mm.trans1:probe17	0.268515883308527	0.0470571409755076	5.70616654012799	1.60759480837817e-08	***
df.mm.trans1:probe18	0.166352635035603	0.0470571409755076	3.53511989013923	0.000430147761752133	***
df.mm.trans1:probe19	0.321307793881133	0.0470571409755076	6.82803475137532	1.66265706987999e-11	***
df.mm.trans1:probe20	0.181860410743707	0.0470571409755076	3.86467190682838	0.00011991670407029	***
df.mm.trans1:probe21	0.223484828196602	0.0470571409755076	4.74922240416013	2.40518797268666e-06	***
df.mm.trans1:probe22	0.335327297413814	0.0470571409755076	7.12595985353946	2.24528784126825e-12	***
df.mm.trans2:probe2	0.100466093600458	0.0470571409755076	2.13498082369153	0.0330548426342333	*  
df.mm.trans2:probe3	0.0259683188341226	0.0470571409755076	0.551846506094339	0.581202067683213	   
df.mm.trans2:probe4	0.0546319271487539	0.0470571409755076	1.16096996154503	0.245987903635728	   
df.mm.trans2:probe5	0.0862132264462141	0.0470571409755076	1.83209656725823	0.0672952117020039	.  
df.mm.trans2:probe6	0.0505989725869547	0.0470571409755076	1.07526661284608	0.282567847125666	   
df.mm.trans3:probe2	-1.00574032302633	0.0470571409755076	-21.3727460312517	4.33855292474358e-81	***
df.mm.trans3:probe3	-0.963422237871513	0.0470571409755076	-20.4734545682016	9.97545606801615e-76	***
df.mm.trans3:probe4	-0.894548465728543	0.0470571409755076	-19.0098345794986	3.61071425775343e-67	***
df.mm.trans3:probe5	-1.22455657733932	0.0470571409755076	-26.0227576931772	1.24101362741733e-109	***
df.mm.trans3:probe6	-0.844203873969458	0.0470571409755076	-17.9399737525246	4.54000814029965e-61	***
df.mm.trans3:probe7	-0.97592202958236	0.0470571409755076	-20.7390846394665	2.6462700566373e-77	***
df.mm.trans3:probe8	-0.270119145581584	0.0470571409755076	-5.74023708159779	1.32543382317950e-08	***
df.mm.trans3:probe9	-0.792262392370916	0.0470571409755076	-16.8361778031367	6.10651871614909e-55	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.89144746848712	0.151799807082355	25.6353913966170	3.20854377000571e-107	***
df.mm.trans1	0.109378766001165	0.131420427955071	0.83228131047145	0.405489575190825	   
df.mm.trans2	-0.00550036580067599	0.116430723830796	-0.0472415323009538	0.962332090798247	   
df.mm.exp2	-0.0173150276330979	0.150482964597128	-0.115063041716739	0.908422983449588	   
df.mm.exp3	0.0932747218037224	0.150482964597128	0.61983575385717	0.53553604784136	   
df.mm.exp4	0.0516919165791547	0.150482964597128	0.343506766480472	0.731304244052495	   
df.mm.exp5	0.0868792532742517	0.150482964597128	0.577336135733666	0.563868973540168	   
df.mm.exp6	0.0774978181367793	0.150482964597128	0.514993961902971	0.606694357881274	   
df.mm.exp7	-0.0345434542029201	0.150482964597128	-0.229550595945526	0.818497555526246	   
df.mm.exp8	0.095046521444684	0.150482964597128	0.631609841679703	0.527815765425853	   
df.mm.trans1:exp2	0.00874671000220835	0.139501441797361	0.0626997820919569	0.950020666597892	   
df.mm.trans2:exp2	0.0572230666732581	0.104731710937112	0.546377655451638	0.584953203873401	   
df.mm.trans1:exp3	-0.0679616068787873	0.139501441797361	-0.487174942446172	0.626262995753449	   
df.mm.trans2:exp3	-0.0454788631745941	0.104731710937112	-0.434241575618893	0.664225883255913	   
df.mm.trans1:exp4	-0.077025999329409	0.139501441797361	-0.552151994538498	0.580992863183438	   
df.mm.trans2:exp4	-0.0543630367422247	0.104731710937112	-0.519069499159311	0.603850649071844	   
df.mm.trans1:exp5	-0.0811413043324505	0.139501441797361	-0.581652083928393	0.560958985527837	   
df.mm.trans2:exp5	-0.103599207383640	0.104731710937112	-0.989186622243262	0.322860117961672	   
df.mm.trans1:exp6	-0.0435978125118093	0.139501441797361	-0.312525891848051	0.754719390939902	   
df.mm.trans2:exp6	-0.0267806776917581	0.104731710937112	-0.255707440011545	0.798240105458874	   
df.mm.trans1:exp7	0.0766992495282242	0.139501441797361	0.549809726265318	0.582597796234642	   
df.mm.trans2:exp7	0.0489098144210997	0.104731710937112	0.467001006509559	0.640621721928422	   
df.mm.trans1:exp8	-0.0791017488237026	0.139501441797361	-0.567031765439424	0.570845913561149	   
df.mm.trans2:exp8	-0.136112677575104	0.104731710937112	-1.29963194869255	0.194087981594390	   
df.mm.trans1:probe2	0.0611044188165034	0.0935804120454389	0.65296163460825	0.513961830246047	   
df.mm.trans1:probe3	0.0855294036157758	0.0935804120454389	0.913966948278087	0.360999633750816	   
df.mm.trans1:probe4	0.0732986293921374	0.0935804120454389	0.783268931927191	0.433692639898516	   
df.mm.trans1:probe5	0.0248119507143560	0.0935804120454389	0.265140430267697	0.790967116460831	   
df.mm.trans1:probe6	-0.0502889504702102	0.0935804120454389	-0.537387572580808	0.591143963850581	   
df.mm.trans1:probe7	-0.0735396133174758	0.0935804120454389	-0.785844085424286	0.432183114309896	   
df.mm.trans1:probe8	0.0312136242319181	0.0935804120454389	0.333548694108785	0.738804384219878	   
df.mm.trans1:probe9	0.138556800478055	0.0935804120454389	1.48061755071967	0.139087999532739	   
df.mm.trans1:probe10	-0.0345421370635686	0.0935804120454389	-0.369117172157741	0.712134531143757	   
df.mm.trans1:probe11	-0.0950214983018388	0.0935804120454389	-1.01539944337604	0.310211353359387	   
df.mm.trans1:probe12	0.0247162359483784	0.0935804120454389	0.264117622568035	0.791754849465239	   
df.mm.trans1:probe13	-0.0542659385478269	0.093580412045439	-0.579885655146267	0.56214910309536	   
df.mm.trans1:probe14	-0.0686570586880876	0.0935804120454389	-0.733669121426292	0.463357602507026	   
df.mm.trans1:probe15	0.0292912359454634	0.0935804120454389	0.313006058695711	0.754354691429441	   
df.mm.trans1:probe16	-0.0102550570521946	0.0935804120454389	-0.10958550863417	0.91276459287724	   
df.mm.trans1:probe17	0.0991684800273373	0.0935804120454389	1.05971407754846	0.289582978883003	   
df.mm.trans1:probe18	0.0630989285343419	0.0935804120454389	0.674274959418896	0.50032426857015	   
df.mm.trans1:probe19	0.0374401297130482	0.0935804120454389	0.400085112842512	0.689196773713565	   
df.mm.trans1:probe20	0.0054323879920947	0.0935804120454389	0.0580504816484132	0.95372239016483	   
df.mm.trans1:probe21	0.091399178999296	0.0935804120454389	0.976691350268005	0.329006553359729	   
df.mm.trans1:probe22	0.0459044884095991	0.0935804120454389	0.49053522426584	0.623884863196291	   
df.mm.trans2:probe2	0.108343650537401	0.0935804120454389	1.15775992186050	0.247295070230889	   
df.mm.trans2:probe3	0.152173511014820	0.0935804120454389	1.62612567831963	0.104302509190479	   
df.mm.trans2:probe4	0.111728722240762	0.0935804120454389	1.19393278784144	0.232845374832853	   
df.mm.trans2:probe5	0.134286316146515	0.0935804120454389	1.43498316807273	0.151668588446446	   
df.mm.trans2:probe6	0.0612586591942395	0.0935804120454389	0.6546098468181	0.512900335281654	   
df.mm.trans3:probe2	0.0305701754035565	0.0935804120454389	0.326672801875598	0.743997731733047	   
df.mm.trans3:probe3	0.145325541303165	0.0935804120454389	1.55294829469868	0.120816687583598	   
df.mm.trans3:probe4	0.060063227261896	0.0935804120454389	0.64183546480573	0.52115723400758	   
df.mm.trans3:probe5	0.174389697407230	0.0935804120454389	1.86352777889622	0.0627410011603239	.  
df.mm.trans3:probe6	0.0372724363490677	0.093580412045439	0.398293142062355	0.690516545748196	   
df.mm.trans3:probe7	0.0624584796146213	0.0935804120454389	0.667431124200371	0.504682309790306	   
df.mm.trans3:probe8	0.0874407212886766	0.0935804120454389	0.934391283148218	0.350373846338117	   
df.mm.trans3:probe9	0.094648597804411	0.0935804120454389	1.01141462978869	0.312112771921103