chr9.25014_chr9_61969208_61970612_+_0.R 

fitVsDatCorrelation=0.870469602714641
cont.fitVsDatCorrelation=0.242456436048598

fstatistic=7778.01336913476,55,761
cont.fstatistic=1991.90133795968,55,761

residuals=-0.749537901793619,-0.0922199019283005,-0.00134853213238261,0.0889370436832467,0.978878172444726
cont.residuals=-0.817218318377403,-0.270186949886651,-0.0795118752300414,0.204943297065419,1.34908296757953

predictedValues:
Include	Exclude	Both
chr9.25014_chr9_61969208_61970612_+_0.R.tl.Lung	69.6929732517891	63.7611529119671	101.128630449028
chr9.25014_chr9_61969208_61970612_+_0.R.tl.cerebhem	95.286405053922	95.459539075931	97.4058569944858
chr9.25014_chr9_61969208_61970612_+_0.R.tl.cortex	66.395335672628	57.8463473317131	84.0203722090527
chr9.25014_chr9_61969208_61970612_+_0.R.tl.heart	67.0044704943783	64.7211597462894	85.8968890977929
chr9.25014_chr9_61969208_61970612_+_0.R.tl.kidney	67.2472644897223	63.3650954604313	93.481622969941
chr9.25014_chr9_61969208_61970612_+_0.R.tl.liver	62.4216949012683	69.4032774355824	76.8126597996712
chr9.25014_chr9_61969208_61970612_+_0.R.tl.stomach	74.5548502696111	66.1358734437765	100.166207638205
chr9.25014_chr9_61969208_61970612_+_0.R.tl.testicle	91.2131030407097	71.7283151398357	157.396759337146


diffExp=5.93182033982205,-0.173134022008995,8.548988340915,2.28331074808888,3.88216902929098,-6.98158253431419,8.41897682583463,19.4847879008741
diffExpScore=1.31393625268913
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,1
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	88.0712599345894	87.631048431042	87.7849442276391
cerebhem	84.0148893988875	104.655243774101	79.3638297612699
cortex	78.426025717215	76.9211251946186	86.4273971897273
heart	84.8458931115841	75.5996686406233	87.4059472920331
kidney	85.676564602421	82.6519393270774	76.1790163438984
liver	92.6298241324	82.1254416472927	90.76077461547
stomach	88.1827450561391	88.0105619266896	85.519188981426
testicle	74.495931277924	77.6726312775195	80.0887769431028
cont.diffExp=0.440211503547317,-20.6403543752132,1.50490052259634,9.2462244709608,3.02462527534361,10.5043824851073,0.172183129449508,-3.17669999959548
cont.diffExpScore=23.4691472621326

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

tran.correlation=0.792136236690908
cont.tran.correlation=0.274982274642917

tran.covariance=0.0180868516967470
cont.tran.covariance=0.00231156179358325

tran.mean=71.6398036074722
cont.tran.mean=84.4756745906328

weightedLogRatios:
wLogRatio
Lung	0.373578962685104
cerebhem	-0.00827393282793275
cortex	0.568811051219038
heart	0.145182793041186
kidney	0.248475487854535
liver	-0.443902661346945
stomach	0.509445586535319
testicle	1.05570502911685

cont.weightedLogRatios:
wLogRatio
Lung	0.0224269414892632
cerebhem	-0.997518876072512
cortex	0.084330308254323
heart	0.505747733927624
kidney	0.159312576424712
liver	0.537834966839759
stomach	0.00875301169804563
testicle	-0.180881890284711

varWeightedLogRatios=0.195339645923558
cont.varWeightedLogRatios=0.229061557437937

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9248447052056	0.0899858971386645	43.6162202079019	3.34065100071326e-209	***
df.mm.trans1	0.305477915605559	0.0750063148962919	4.07269595937264	5.13416962516139e-05	***
df.mm.trans2	0.231965995028903	0.0686686925313789	3.37804589657657	0.000767157762522006	***
df.mm.exp2	0.753852947595672	0.0883446792165765	8.53308828874238	7.67172541779181e-17	***
df.mm.exp3	0.0395074841575937	0.0883446792165765	0.447197097866429	0.654860007157819	   
df.mm.exp4	0.138849580512115	0.0883446792165765	1.57168017070645	0.116440395949124	   
df.mm.exp5	0.0366742952311937	0.0883446792165765	0.415127380125370	0.67816566685371	   
df.mm.exp6	0.249627174094912	0.0883446792165765	2.82560507671269	0.00484276372391064	** 
df.mm.exp7	0.113565191903655	0.0883446792165765	1.28547856996855	0.199016149710169	   
df.mm.exp8	-0.0555359452056975	0.0883446792165765	-0.628628070169924	0.529781206951711	   
df.mm.trans1:exp2	-0.441065299826505	0.0756538634416326	-5.8300433019761	8.18581441851462e-09	***
df.mm.trans2:exp2	-0.350294580857691	0.0603509052244503	-5.80429704500562	9.48588660780952e-09	***
df.mm.trans1:exp3	-0.0879801745430457	0.0756538634416326	-1.16293035861841	0.245222292055679	   
df.mm.trans2:exp3	-0.13686128922042	0.0603509052244503	-2.26775868085857	0.023623477503902	*  
df.mm.trans1:exp4	-0.178189738056719	0.0756538634416326	-2.35532899379545	0.0187592128144557	*  
df.mm.trans2:exp4	-0.123905504856063	0.0603509052244502	-2.05308444662508	0.0404057429171859	*  
df.mm.trans1:exp5	-0.0723974528156464	0.0756538634416326	-0.956956453010512	0.338893138706195	   
df.mm.trans2:exp5	-0.0429052465572736	0.0603509052244503	-0.710929627280739	0.477345676479238	   
df.mm.trans1:exp6	-0.359813782950888	0.0756538634416326	-4.75605298371162	2.36167685219023e-06	***
df.mm.trans2:exp6	-0.164837198776337	0.0603509052244503	-2.7313127808654	0.00645433660550697	** 
df.mm.trans1:exp7	-0.0461295906903515	0.0756538634416326	-0.609745340050488	0.542212526836018	   
df.mm.trans2:exp7	-0.0769979939062056	0.0603509052244503	-1.27583825992076	0.202401979033259	   
df.mm.trans1:exp8	0.324635007151573	0.0756538634416326	4.2910565618639	2.00766663095573e-05	***
df.mm.trans2:exp8	0.173277409773428	0.0603509052244503	2.87116504928956	0.00420319713967127	** 
df.mm.trans1:probe2	0.0899164758703102	0.0558740810962643	1.60926988160029	0.107972066356639	   
df.mm.trans1:probe3	-0.119381730420140	0.0558740810962644	-2.13662091756747	0.0329471641585197	*  
df.mm.trans1:probe4	0.0381196513797876	0.0558740810962643	0.682242117129622	0.495293467281163	   
df.mm.trans1:probe5	0.51250113035937	0.0558740810962643	9.1724305850577	4.25462163606834e-19	***
df.mm.trans1:probe6	-0.0687582018858839	0.0558740810962644	-1.23059208378607	0.218855696014464	   
df.mm.trans1:probe7	0.063384872321606	0.0558740810962643	1.13442353015885	0.256974139245726	   
df.mm.trans1:probe8	-0.0529769051392598	0.0558740810962643	-0.948148123420355	0.343355036424623	   
df.mm.trans1:probe9	-0.0206633965217376	0.0558740810962644	-0.369820784813214	0.711618919061016	   
df.mm.trans1:probe10	-0.0211234090915608	0.0558740810962644	-0.378053807366742	0.705496049203135	   
df.mm.trans1:probe11	-0.0250736436132646	0.0558740810962643	-0.448752679620192	0.653737830451811	   
df.mm.trans1:probe12	-0.0928535339656249	0.0558740810962644	-1.66183554420608	0.0969575925190204	.  
df.mm.trans2:probe2	-0.157783790248436	0.0558740810962643	-2.82391740772602	0.00486806619967076	** 
df.mm.trans2:probe3	-0.198215550490005	0.0558740810962644	-3.54754022976241	0.000412672047310266	***
df.mm.trans2:probe4	0.100672041572035	0.0558740810962643	1.80176639323320	0.0719778304589209	.  
df.mm.trans2:probe5	0.308179274794863	0.0558740810962643	5.51560345599074	4.76524639189343e-08	***
df.mm.trans2:probe6	-0.0895168205225884	0.0558740810962643	-1.60211709555208	0.109544693185100	   
df.mm.trans3:probe2	0.423716653642096	0.0558740810962643	7.58342052931632	9.81982872340647e-14	***
df.mm.trans3:probe3	0.647430859265688	0.0558740810962643	11.5873200339571	1.04477663168358e-28	***
df.mm.trans3:probe4	0.0718399798550247	0.0558740810962644	1.28574785384395	0.198922172063487	   
df.mm.trans3:probe5	0.232478096542455	0.0558740810962643	4.16075024378339	3.533844969112e-05	***
df.mm.trans3:probe6	0.0351441055240863	0.0558740810962643	0.628987624217697	0.52954591276191	   
df.mm.trans3:probe7	0.377226453622790	0.0558740810962643	6.75136747167034	2.90612598822855e-11	***
df.mm.trans3:probe8	0.568759996668486	0.0558740810962643	10.1793172345614	6.65035677242506e-23	***
df.mm.trans3:probe9	-0.141650444478832	0.0558740810962643	-2.53517269008480	0.0114383495923891	*  
df.mm.trans3:probe10	-0.0517339686085223	0.0558740810962643	-0.925902808484508	0.35479001824806	   
df.mm.trans3:probe11	-0.261032174898972	0.0558740810962643	-4.67179360765227	3.53012005783444e-06	***
df.mm.trans3:probe12	-0.103530793099045	0.0558740810962643	-1.85293057295517	0.0642792503403746	.  
df.mm.trans3:probe13	0.370850685880537	0.0558740810962643	6.63725789497291	6.07166854583335e-11	***
df.mm.trans3:probe14	-0.210408646364715	0.0558740810962643	-3.76576477387085	0.000178843089138362	***
df.mm.trans3:probe15	-0.106987548809610	0.0558740810962643	-1.91479746441438	0.0558929741522747	.  
df.mm.trans3:probe16	0.613571297300408	0.0558740810962643	10.9813223817193	3.82443358425459e-26	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.5192932303137	0.177360811056311	25.4807880241303	4.85470142787197e-104	***
df.mm.trans1	-0.0187106385104687	0.147836286211069	-0.126563234169419	0.899319529104299	   
df.mm.trans2	-0.0319714000909409	0.135344930581447	-0.23622162982825	0.813324259397422	   
df.mm.exp2	0.231231253188074	0.174125995923745	1.32795365770276	0.184591573065089	   
df.mm.exp3	-0.230759961143082	0.174125995923745	-1.32524704263078	0.185486964644246	   
df.mm.exp4	-0.180666458462526	0.174125995923745	-1.03756166621809	0.299803733453371	   
df.mm.exp5	0.0557399491062947	0.174125995923745	0.320112736817912	0.748970739983898	   
df.mm.exp6	-0.0477597956177245	0.174125995923745	-0.274282971731802	0.783941605704194	   
df.mm.exp7	0.0317357352957484	0.174125995923745	0.182257308148557	0.855429339096136	   
df.mm.exp8	-0.196279881670789	0.174125995923745	-1.12722905405087	0.260000943407649	   
df.mm.trans1:exp2	-0.2783834740776	0.149112594375480	-1.86693468277135	0.0622950260462789	.  
df.mm.trans2:exp2	-0.0536950671096409	0.118950700486953	-0.451406060576585	0.651825525608978	   
df.mm.trans1:exp3	0.114769535317718	0.149112594375480	0.769683713159178	0.441726373036352	   
df.mm.trans2:exp3	0.100405140582121	0.118950700486953	0.84409036828777	0.398884169438654	   
df.mm.trans1:exp4	0.143356788448468	0.149112594375480	0.961399599067278	0.336656654124216	   
df.mm.trans2:exp4	0.0329829892881502	0.118950700486953	0.277282850400430	0.781638230668642	   
df.mm.trans1:exp5	-0.0833068782210804	0.149112594375480	-0.558684385916493	0.576541486342427	   
df.mm.trans2:exp5	-0.114237030071808	0.118950700486953	-0.960372907466294	0.337172598708880	   
df.mm.trans1:exp6	0.0982247015050808	0.149112594375480	0.658728405313244	0.510269439296433	   
df.mm.trans2:exp6	-0.0171277191368659	0.118950700486953	-0.1439900653527	0.885546435692718	   
df.mm.trans1:exp7	-0.0304706844797556	0.149112594375480	-0.204346819981063	0.838137121984666	   
df.mm.trans2:exp7	-0.0274142754151103	0.118950700486953	-0.230467540778519	0.817790423600423	   
df.mm.trans1:exp8	0.0288781330720443	0.149112594375480	0.193666626169258	0.846488605591226	   
df.mm.trans2:exp8	0.0756474718945087	0.118950700486953	0.635956506223398	0.524995977215902	   
df.mm.trans1:probe2	-0.0192783404199124	0.110126949392846	-0.175055611057948	0.861082501136876	   
df.mm.trans1:probe3	-0.0889955544835307	0.110126949392846	-0.808117858291568	0.419275276328754	   
df.mm.trans1:probe4	-0.0556645740357088	0.110126949392846	-0.505458240172817	0.613383383129987	   
df.mm.trans1:probe5	-0.0452535898166	0.110126949392846	-0.410922031946702	0.681245389050084	   
df.mm.trans1:probe6	0.00013537332723264	0.110126949392846	0.00122924795410191	0.999019524424378	   
df.mm.trans1:probe7	-0.143729088521212	0.110126949392846	-1.30512185540071	0.192245834917633	   
df.mm.trans1:probe8	-0.0445135328371881	0.110126949392846	-0.404201996719249	0.686177753607258	   
df.mm.trans1:probe9	0.0359901491273279	0.110126949392846	0.326806011841329	0.743904476153312	   
df.mm.trans1:probe10	-0.0672611206389424	0.110126949392846	-0.6107598640457	0.541540934361338	   
df.mm.trans1:probe11	0.00665800666736313	0.110126949392846	0.0604575601528071	0.95180708930453	   
df.mm.trans1:probe12	-0.0716870550177681	0.110126949392846	-0.650949249143779	0.515275745414862	   
df.mm.trans2:probe2	-0.116420831464415	0.110126949392846	-1.05715115243152	0.290778072664805	   
df.mm.trans2:probe3	-0.0845668223572782	0.110126949392846	-0.767903068445223	0.442782977527344	   
df.mm.trans2:probe4	-0.0598254583570719	0.110126949392846	-0.543240856910164	0.587123060988447	   
df.mm.trans2:probe5	-0.0553530102942321	0.110126949392846	-0.502629107583615	0.615370479241693	   
df.mm.trans2:probe6	0.0040639819461176	0.110126949392846	0.0369027015505580	0.970572263506065	   
df.mm.trans3:probe2	-0.0270462786362719	0.110126949392846	-0.245591826391123	0.80606443451683	   
df.mm.trans3:probe3	-0.0400344998720414	0.110126949392846	-0.363530453651539	0.716309631726766	   
df.mm.trans3:probe4	-0.0107805248641024	0.110126949392846	-0.0978917960003235	0.922043991450953	   
df.mm.trans3:probe5	0.0613203925658216	0.110126949392846	0.556815501599694	0.577817201074354	   
df.mm.trans3:probe6	0.115947150549480	0.110126949392846	1.05284992627800	0.292743958806002	   
df.mm.trans3:probe7	0.0320572160837201	0.110126949392846	0.29109329061105	0.771059289783503	   
df.mm.trans3:probe8	0.064383357497194	0.110126949392846	0.584628538719665	0.558970792741265	   
df.mm.trans3:probe9	-0.0994710220359127	0.110126949392846	-0.903239602879389	0.366684559237818	   
df.mm.trans3:probe10	-0.0254666451757799	0.110126949392846	-0.231248076117454	0.817184242747645	   
df.mm.trans3:probe11	0.127297040048303	0.110126949392846	1.15591179770364	0.248080002969835	   
df.mm.trans3:probe12	0.0396045734333488	0.110126949392846	0.359626536934851	0.719226207167747	   
df.mm.trans3:probe13	-0.00302895572394596	0.110126949392846	-0.027504218909588	0.978064785784028	   
df.mm.trans3:probe14	-0.141365439103231	0.110126949392846	-1.28365890349828	0.199652049204476	   
df.mm.trans3:probe15	-0.0696338693658392	0.110126949392846	-0.632305441581248	0.527377229523343	   
df.mm.trans3:probe16	0.0766785634733469	0.110126949392846	0.696274289772781	0.48646967085534