chr9.24782_chr9_37408585_37411647_-_2.R 

fitVsDatCorrelation=0.883638196236807
cont.fitVsDatCorrelation=0.281142325521151

fstatistic=7993.63416355656,52,692
cont.fstatistic=1892.30358838982,52,692

residuals=-0.589449799886081,-0.101474982581216,-0.00881913114359157,0.092711958571815,1.26224284325667
cont.residuals=-0.66245560991774,-0.219402977051782,-0.0692100815565846,0.132303529060697,1.48884978677083

predictedValues:
Include	Exclude	Both
chr9.24782_chr9_37408585_37411647_-_2.R.tl.Lung	57.1596096409956	64.6736449191052	59.8656865397892
chr9.24782_chr9_37408585_37411647_-_2.R.tl.cerebhem	54.7452046547168	58.2240650465047	56.5224025259765
chr9.24782_chr9_37408585_37411647_-_2.R.tl.cortex	52.4791223931373	67.6286760057385	54.0342481519769
chr9.24782_chr9_37408585_37411647_-_2.R.tl.heart	56.7496278158366	73.029244620344	59.8536400498714
chr9.24782_chr9_37408585_37411647_-_2.R.tl.kidney	58.8242555133588	64.9557337345913	59.7100283915944
chr9.24782_chr9_37408585_37411647_-_2.R.tl.liver	59.7158038370719	68.6028493286701	57.8213684963583
chr9.24782_chr9_37408585_37411647_-_2.R.tl.stomach	61.0608669836073	79.4463546512016	55.030213727181
chr9.24782_chr9_37408585_37411647_-_2.R.tl.testicle	72.916837572231	63.8340723589075	57.4430282983969


diffExp=-7.51403527810957,-3.47886039178791,-15.1495536126012,-16.2796168045075,-6.1314782212325,-8.88704549159819,-18.3854876675944,9.08276521332353
diffExpScore=1.25339077549469
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,-1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,-1,-1,0,0,-1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	62.5914729245265	60.7665148498363	64.0665592025399
cerebhem	62.9383105784697	68.1762942156831	59.9647666670191
cortex	58.2978395199904	87.0707011388498	57.3357984587667
heart	59.9976436279166	57.8095473095876	69.7315723179874
kidney	54.4304295414244	62.6606247571433	61.9284746321414
liver	60.6989664294228	65.4589222703646	55.231956979402
stomach	61.1990614312417	56.9431056288255	67.2547808980204
testicle	64.679048480658	68.1620029446961	58.2634764448834
cont.diffExp=1.82495807469022,-5.23798363721339,-28.7728616188594,2.18809631832895,-8.2301952157189,-4.75995584094176,4.25595580241612,-3.48295446403804
cont.diffExpScore=1.35955204801511

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,-1,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,-1,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,-1,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.0221297528173217
cont.tran.correlation=-0.117681611782078

tran.covariance=0.000455876211226143
cont.tran.covariance=-0.000676442351300958

tran.mean=63.3778730672511
cont.tran.mean=63.2425303530398

weightedLogRatios:
wLogRatio
Lung	-0.507314360506804
cerebhem	-0.248499465290674
cortex	-1.03658808955480
heart	-1.05039638184990
kidney	-0.40891467959964
liver	-0.577003890779339
stomach	-1.11692897000951
testicle	0.561770013832477

cont.weightedLogRatios:
wLogRatio
Lung	0.121965722288075
cerebhem	-0.334326941302108
cortex	-1.71138624299871
heart	0.151418849989025
kidney	-0.572720060144142
liver	-0.312831425723391
stomach	0.293945751714085
testicle	-0.220062254961

varWeightedLogRatios=0.308275991989199
cont.varWeightedLogRatios=0.400642020976448

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98792049965665	0.0869409701914204	45.8692891380937	5.23077646471327e-212	***
df.mm.trans1	-0.109637212182808	0.0763810434708894	-1.43539819830550	0.151625487301354	   
df.mm.trans2	0.173369214225702	0.0692152138085654	2.50478478192966	0.0124814081931252	*  
df.mm.exp2	-0.090746332019911	0.092286951744754	-0.983306202060894	0.325800419512328	   
df.mm.exp3	0.0617316413466155	0.092286951744754	0.66890974487219	0.50377613658873	   
df.mm.exp4	0.114509016228421	0.092286951744754	1.24079313557921	0.215102664107206	   
df.mm.exp5	0.0356625066935289	0.092286951744754	0.386430649396285	0.699296543302923	   
df.mm.exp6	0.137474566739974	0.092286951744754	1.48964251327966	0.136773870325016	   
df.mm.exp7	0.355973016558934	0.092286951744754	3.85724102734999	0.000125379209566617	***
df.mm.exp8	0.271715229039901	0.092286951744754	2.9442431882614	0.00334596556855224	** 
df.mm.trans1:exp2	0.0475885868608667	0.0865728345779316	0.549694220974457	0.582706476860385	   
df.mm.trans2:exp2	-0.0143086856274777	0.0714851654359359	-0.200163006411462	0.841411906405889	   
df.mm.trans1:exp3	-0.14716374334689	0.0865728345779316	-1.69988361897075	0.0896021895637512	.  
df.mm.trans2:exp3	-0.0170533226707132	0.0714851654359359	-0.238557504437703	0.81151934640109	   
df.mm.trans1:exp4	-0.121707442004205	0.0865728345779316	-1.40583870907733	0.160220854991122	   
df.mm.trans2:exp4	0.0069971802408878	0.0714851654359359	0.0978829691197761	0.922053567813432	   
df.mm.trans1:exp5	-0.00695575192123303	0.0865728345779316	-0.0803456644933066	0.93598557062225	   
df.mm.trans2:exp5	-0.031310263958228	0.0714851654359359	-0.437996663605512	0.661525384587035	   
df.mm.trans1:exp6	-0.0937253842893746	0.0865728345779316	-1.08261886937529	0.279354720379830	   
df.mm.trans2:exp6	-0.0784942731607667	0.0714851654359359	-1.09804982169500	0.272564645747151	   
df.mm.trans1:exp7	-0.289949354277638	0.0865728345779316	-3.34919557262076	0.000854412808910189	***
df.mm.trans2:exp7	-0.150244782623047	0.0714851654359359	-2.10176169708517	0.0359350562981345	*  
df.mm.trans1:exp8	-0.0282431724298327	0.0865728345779316	-0.326235967293050	0.744344466235344	   
df.mm.trans2:exp8	-0.284781907423605	0.0714851654359359	-3.98379028273807	7.50125008901783e-05	***
df.mm.trans1:probe2	-0.087585236669541	0.0505476452339925	-1.73272634687721	0.0835898401085118	.  
df.mm.trans1:probe3	-0.0464034024986918	0.0505476452339925	-0.91801313956137	0.358931832883019	   
df.mm.trans1:probe4	0.0804944213949592	0.0505476452339925	1.59244651303416	0.111741068088870	   
df.mm.trans1:probe5	0.0479495367970327	0.0505476452339925	0.948600801779535	0.343154833661408	   
df.mm.trans1:probe6	-0.143601347835858	0.0505476452339925	-2.84091073226272	0.00463074792541347	** 
df.mm.trans1:probe7	0.00322355388818634	0.0505476452339925	0.0637725827437466	0.949169726177049	   
df.mm.trans1:probe8	0.00608431111048279	0.0505476452339925	0.120367844680353	0.904226701043238	   
df.mm.trans1:probe9	0.192135537339991	0.0505476452339925	3.80107790284923	0.000156768483780642	***
df.mm.trans1:probe10	0.199719044734134	0.0505476452339925	3.95110482020685	8.57718387801634e-05	***
df.mm.trans1:probe11	-0.104533537061253	0.0505476452339925	-2.06801991620681	0.0390091915446551	*  
df.mm.trans1:probe12	-0.0706430626316982	0.0505476452339925	-1.39755397713743	0.162694919811354	   
df.mm.trans1:probe13	-0.0323241475815650	0.0505476452339925	-0.639478801276137	0.522723131386686	   
df.mm.trans1:probe14	-0.127160926471463	0.0505476452339925	-2.51566469383126	0.0121061335449538	*  
df.mm.trans1:probe15	1.21739138778245	0.0505476452339925	24.0840375876456	1.48200096672064e-93	***
df.mm.trans1:probe16	0.829146153304518	0.0505476452339925	16.4032597258741	2.51497379720820e-51	***
df.mm.trans1:probe17	0.644914078271854	0.0505476452339925	12.7585385092906	1.24246696474699e-33	***
df.mm.trans1:probe18	0.309758315207022	0.0505476452339925	6.12804639609037	1.49325095253795e-09	***
df.mm.trans1:probe19	0.566563844484766	0.0505476452339925	11.2085111356238	6.56139954747618e-27	***
df.mm.trans1:probe20	0.70397738470204	0.0505476452339925	13.9270065191608	4.79229902946032e-39	***
df.mm.trans2:probe2	0.0950155351496148	0.0505476452339925	1.87972228399115	0.0605658583073169	.  
df.mm.trans2:probe3	-0.154630631224424	0.0505476452339925	-3.05910652234375	0.00230569895056964	** 
df.mm.trans2:probe4	-0.00207553514819791	0.0505476452339925	-0.0410609661160269	0.967259136639227	   
df.mm.trans2:probe5	0.0214170256608259	0.0505476452339925	0.423699770022587	0.671916446091783	   
df.mm.trans2:probe6	0.12897828548382	0.0505476452339925	2.55161808006605	0.0109362921675117	*  
df.mm.trans3:probe2	0.0232257418934951	0.0505476452339925	0.459482173422318	0.646032259285594	   
df.mm.trans3:probe3	0.00236896923232214	0.0505476452339925	0.0468660651026537	0.962633496135033	   
df.mm.trans3:probe4	0.196087467100070	0.0505476452339925	3.87926017507546	0.000114776062926176	***
df.mm.trans3:probe5	-0.0194272105427824	0.0505476452339925	-0.384334630284972	0.700848517633547	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97036689002312	0.178213525679872	22.2787068202396	2.61556277395978e-83	***
df.mm.trans1	0.100647367866405	0.156567554078182	0.642836687709558	0.520543008378537	   
df.mm.trans2	0.165402400325102	0.141878877775945	1.16580003252003	0.244096957469142	   
df.mm.exp2	0.186749376520121	0.189171837034594	0.987194391340455	0.323892389952362	   
df.mm.exp3	0.399614878760892	0.189171837034594	2.11244382369568	0.0350060241809928	*  
df.mm.exp4	-0.176939378384457	0.189171837034594	-0.935336787748696	0.349941040219119	   
df.mm.exp5	-0.0750688817557508	0.189171837034594	-0.396829057287333	0.691615871744695	   
df.mm.exp6	0.192062341106578	0.189171837034594	1.01527978010520	0.310327320401563	   
df.mm.exp7	-0.136049049308186	0.189171837034594	-0.719182365836555	0.472271351226027	   
df.mm.exp8	0.242603752733914	0.189171837034594	1.28245174618434	0.200113643522766	   
df.mm.trans1:exp2	-0.181223380587229	0.177458913148361	-1.02121317758619	0.307510324991019	   
df.mm.trans2:exp2	-0.0716913588965985	0.146531874881289	-0.489254361582956	0.624816664190921	   
df.mm.trans1:exp3	-0.470678897632018	0.177458913148361	-2.65232604709190	0.00817712836339229	** 
df.mm.trans2:exp3	-0.0399333280209765	0.146531874881289	-0.272523149337494	0.785301092049655	   
df.mm.trans1:exp4	0.134615613402545	0.177458913148361	0.758573412934192	0.448366062317752	   
df.mm.trans2:exp4	0.127054424132581	0.146531874881289	0.867077038600048	0.38620035595806	   
df.mm.trans1:exp5	-0.0646368077639691	0.177458913148361	-0.364235341112062	0.715793544545966	   
df.mm.trans2:exp5	0.105763243163343	0.146531874881289	0.721776359232599	0.470675794214937	   
df.mm.trans1:exp6	-0.222764724272717	0.177458913148361	-1.25530310267638	0.209792477098502	   
df.mm.trans2:exp6	-0.117678430767287	0.146531874881289	-0.803091005712055	0.422197882456684	   
df.mm.trans1:exp7	0.113551849053289	0.177458913148361	0.639876842694035	0.52246445586198	   
df.mm.trans2:exp7	0.0710627771433836	0.146531874881289	0.484964634493036	0.627854912081102	   
df.mm.trans1:exp8	-0.209795482881369	0.177458913148361	-1.18222003707401	0.237524467374955	   
df.mm.trans2:exp8	-0.127755379375237	0.146531874881289	-0.871860675219892	0.383586776963488	   
df.mm.trans1:probe2	0.0537283466863212	0.103613682388538	0.518544901095656	0.604243907093224	   
df.mm.trans1:probe3	-0.0137461030352386	0.103613682388538	-0.132666870999647	0.894495411013933	   
df.mm.trans1:probe4	0.187605657609597	0.103613682388538	1.81062629263672	0.0706322894949782	.  
df.mm.trans1:probe5	0.122591183584200	0.103613682388538	1.18315632412810	0.237153508919705	   
df.mm.trans1:probe6	0.0445192443724232	0.103613682388538	0.429665690342726	0.667572596918203	   
df.mm.trans1:probe7	0.0247180139866402	0.103613682388538	0.238559362208080	0.811517906276832	   
df.mm.trans1:probe8	-0.0479941717004963	0.103613682388538	-0.463203030662729	0.643364483441766	   
df.mm.trans1:probe9	0.143071332920008	0.103613682388538	1.38081505860885	0.167781615398087	   
df.mm.trans1:probe10	0.0397776647293478	0.103613682388538	0.383903590842247	0.701167831488292	   
df.mm.trans1:probe11	0.126280759595926	0.103613682388538	1.21876528934073	0.223348598543483	   
df.mm.trans1:probe12	0.124803202672078	0.103613682388538	1.20450504021353	0.228806232012250	   
df.mm.trans1:probe13	0.00443409259173859	0.103613682388538	0.0427944697024791	0.965877717172173	   
df.mm.trans1:probe14	0.200453616337509	0.103613682388538	1.93462496184465	0.0534435983137745	.  
df.mm.trans1:probe15	0.0355207675899882	0.103613682388538	0.342819276095119	0.731838532953393	   
df.mm.trans1:probe16	0.130746795114903	0.103613682388538	1.26186804774121	0.207421420615211	   
df.mm.trans1:probe17	0.177408656007560	0.103613682388538	1.71221263367806	0.0873055015017172	.  
df.mm.trans1:probe18	0.0648821761796882	0.103613682388538	0.626193130907059	0.531394763065243	   
df.mm.trans1:probe19	0.109733865352636	0.103613682388538	1.05906732415076	0.289938646700396	   
df.mm.trans1:probe20	0.111834791286047	0.103613682388538	1.07934385409333	0.28081052504347	   
df.mm.trans2:probe2	-0.00596725038783707	0.103613682388538	-0.0575913359150835	0.95409077648485	   
df.mm.trans2:probe3	0.00947376395618269	0.103613682388538	0.0914335224633488	0.927174586883758	   
df.mm.trans2:probe4	-0.0755352204214779	0.103613682388538	-0.729008164561032	0.46624331970009	   
df.mm.trans2:probe5	-0.126268645574324	0.103613682388538	-1.21864837407122	0.223392961175409	   
df.mm.trans2:probe6	-0.117737000183281	0.103613682388538	-1.13630745929656	0.256221265327357	   
df.mm.trans3:probe2	-0.161334884738715	0.103613682388538	-1.55708088950772	0.119908480708997	   
df.mm.trans3:probe3	-0.149053267000893	0.103613682388538	-1.4385481102965	0.150730706066477	   
df.mm.trans3:probe4	-0.0707605350929386	0.103613682388538	-0.682926554309648	0.494881816076692	   
df.mm.trans3:probe5	-0.00132195180573828	0.103613682388538	-0.0127584675620458	0.98982416918343