chr9.25321_chr9_41603294_41612569_+_2.R 

fitVsDatCorrelation=0.764283818485378
cont.fitVsDatCorrelation=0.273112931226274

fstatistic=10577.8347366459,55,761
cont.fstatistic=4745.96097769374,55,761

residuals=-0.551264158316823,-0.087925994769909,-0.00882131510732778,0.0644572794909865,0.964332472862712
cont.residuals=-0.463144795580237,-0.14445536172039,-0.044474921073744,0.108809021750716,0.94031033117379

predictedValues:
Include	Exclude	Both
chr9.25321_chr9_41603294_41612569_+_2.R.tl.Lung	50.323100614453	45.2019219695344	56.4076144910412
chr9.25321_chr9_41603294_41612569_+_2.R.tl.cerebhem	58.3685227484077	47.8681995837994	60.6282141239278
chr9.25321_chr9_41603294_41612569_+_2.R.tl.cortex	51.7587804288002	47.3562005600122	61.9498068594281
chr9.25321_chr9_41603294_41612569_+_2.R.tl.heart	49.1818269963281	45.6672967447879	57.4927762701033
chr9.25321_chr9_41603294_41612569_+_2.R.tl.kidney	50.301496669501	44.286154164492	57.8036556992563
chr9.25321_chr9_41603294_41612569_+_2.R.tl.liver	50.0191404828601	49.7351782228441	58.9080368572195
chr9.25321_chr9_41603294_41612569_+_2.R.tl.stomach	54.4194507514498	44.5346491684852	59.8423987001893
chr9.25321_chr9_41603294_41612569_+_2.R.tl.testicle	53.6000305576302	47.4796139374854	58.9254652199502


diffExp=5.12117864491857,10.5003231646083,4.40257986878792,3.51453025154024,6.015342505009,0.283962260015912,9.88480158296458,6.12041662014482
diffExpScore=0.97865215463957
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=0,1,0,0,0,0,1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	52.354552742249	54.6127369131176	57.4550867145616
cerebhem	53.2892446366777	55.7952144189913	54.0450241575314
cortex	52.3383570082634	53.3011478478501	53.4134535688025
heart	55.0213208160103	54.352824946882	54.4948962172856
kidney	54.4104543050583	53.7391574384869	53.4630978365957
liver	52.4863199698398	57.4223267822158	55.4991871114422
stomach	52.0328762993506	55.3087338208056	56.8970460777125
testicle	53.9499178139066	55.5783821919074	56.2598777002527
cont.diffExp=-2.25818417086865,-2.50596978231354,-0.96279083958676,0.66849586912835,0.67129686657136,-4.93600681237599,-3.27585752145504,-1.62846437800078
cont.diffExpScore=1.11029962847365

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=0.175588945355690
cont.tran.correlation=-0.245549115172972

tran.covariance=0.000412253062823612
cont.tran.covariance=-0.000118640587222474

tran.mean=49.3813477250544
cont.tran.mean=54.1245979969758

weightedLogRatios:
wLogRatio
Lung	0.414788401249814
cerebhem	0.78687848239589
cortex	0.34688643307084
heart	0.286072484060796
kidney	0.490901057221536
liver	0.0222580887897263
stomach	0.781068397311173
testicle	0.475409022320186

cont.weightedLogRatios:
wLogRatio
Lung	-0.168032594480673
cerebhem	-0.183755104096274
cortex	-0.0723090574943107
heart	0.0489163533443669
kidney	0.049537732380278
liver	-0.360016233067080
stomach	-0.243145935755029
testicle	-0.119039582584954

varWeightedLogRatios=0.0638738967121329
cont.varWeightedLogRatios=0.0196758717545582

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.69242916000323	0.0695536594408843	53.0874894245001	4.5750358421866e-258	***
df.mm.trans1	0.236983593531321	0.0579753477833676	4.08766143873498	4.82070555385085e-05	***
df.mm.trans2	0.109465631409120	0.0530767487622917	2.06240272740462	0.0395082862807879	*  
df.mm.exp2	0.133468100610856	0.0682850971877873	1.95457143809596	0.0509994874159738	.  
df.mm.exp3	-0.0190323175296106	0.0682850971877873	-0.278718465864826	0.780536609693626	   
df.mm.exp4	-0.0317523678736803	0.0682850971877873	-0.464997037147941	0.642066599534548	   
df.mm.exp5	-0.0453447861398958	0.0682850971877873	-0.664050986340335	0.506858815667466	   
df.mm.exp6	0.0461410276269158	0.0682850971877873	0.675711531903158	0.499429090183376	   
df.mm.exp7	0.00427506392058201	0.0682850971877873	0.0626061043572272	0.950096607867873	   
df.mm.exp8	0.0685770552088131	0.0682850971877873	1.00427557451113	0.315565018973572	   
df.mm.trans1:exp2	0.0148444214515521	0.0584758636689253	0.25385553149924	0.799675740574029	   
df.mm.trans2:exp2	-0.0761563158304883	0.0466476019288015	-1.63258801485071	0.102969343952007	   
df.mm.trans1:exp3	0.0471621769763264	0.0584758636689253	0.806523820551774	0.420192850656991	   
df.mm.trans2:exp3	0.0655904728412367	0.0466476019288015	1.40608456017413	0.160107189268092	   
df.mm.trans1:exp4	0.0088123247165556	0.0584758636689253	0.150700206267130	0.880252145899727	   
df.mm.trans2:exp4	0.0419951948855855	0.0466476019288015	0.900264818536288	0.368264134785536	   
df.mm.trans1:exp5	0.0449153892356198	0.0584758636689253	0.768101339895016	0.442665255006712	   
df.mm.trans2:exp5	0.0248772598769894	0.0466476019288015	0.533302010143194	0.5939802485324	   
df.mm.trans1:exp6	-0.0521995142226492	0.0584758636689253	-0.892667691377571	0.372317341066510	   
df.mm.trans2:exp6	0.0494318590361545	0.0466476019288015	1.05968703625114	0.289623219424639	   
df.mm.trans1:exp7	0.0739823481853028	0.0584758636689253	1.26517752015038	0.206194955436065	   
df.mm.trans2:exp7	-0.0191471521101821	0.0466476019288015	-0.41046380346425	0.681581288633027	   
df.mm.trans1:exp8	-0.00549164545533531	0.0584758636689253	-0.0939130285689757	0.925202964237934	   
df.mm.trans2:exp8	-0.0194162239399410	0.0466476019288015	-0.41623198486336	0.67735761645638	   
df.mm.trans1:probe2	-0.0578805336857883	0.0431872874718733	-1.34022155763972	0.180573320013897	   
df.mm.trans1:probe3	0.0215388092106618	0.0431872874718733	0.498730308651347	0.618113511781201	   
df.mm.trans1:probe4	-0.0705682247223926	0.0431872874718733	-1.63400456137357	0.102671487466398	   
df.mm.trans1:probe5	0.135356479562081	0.0431872874718733	3.13417413979138	0.00178953803033466	** 
df.mm.trans1:probe6	0.0794760353797802	0.0431872874718733	1.84026457858787	0.0661186306215715	.  
df.mm.trans1:probe7	-0.0798538854439454	0.0431872874718733	-1.84901368246274	0.0648434932052698	.  
df.mm.trans1:probe8	-0.144861508313658	0.0431872874718733	-3.35426271927827	0.000835172771707471	***
df.mm.trans1:probe9	0.065427775642812	0.0431872874718733	1.51497765830798	0.130193285424118	   
df.mm.trans1:probe10	-0.106339300870162	0.0431872874718733	-2.46228247003051	0.0140261615120363	*  
df.mm.trans1:probe11	-0.0533703717214806	0.0431872874718733	-1.23578892877306	0.216918360508981	   
df.mm.trans1:probe12	-0.095483978016821	0.0431872874718733	-2.21092788193765	0.0273374643007187	*  
df.mm.trans2:probe2	0.0256447492194932	0.0431872874718733	0.593803193502137	0.552820212193665	   
df.mm.trans2:probe3	-0.0190318045101562	0.0431872874718733	-0.440680710094403	0.659569316787407	   
df.mm.trans2:probe4	0.0604832484643329	0.0431872874718733	1.40048731941602	0.161775028640261	   
df.mm.trans2:probe5	0.0569375518214282	0.0431872874718733	1.31838684841020	0.187770836116880	   
df.mm.trans2:probe6	0.0793522063823765	0.0431872874718733	1.83739732285933	0.0665409957451689	.  
df.mm.trans3:probe2	0.0184778051207439	0.0431872874718733	0.427852875288312	0.668879287427918	   
df.mm.trans3:probe3	-0.182702094014090	0.0431872874718733	-4.23046004297164	2.6163595732069e-05	***
df.mm.trans3:probe4	-0.0304591759999495	0.0431872874718733	-0.705281062622576	0.480851156884243	   
df.mm.trans3:probe5	-0.252067555882964	0.0431872874718733	-5.83661467618519	7.88285483003546e-09	***
df.mm.trans3:probe6	-0.105438041070443	0.0431872874718733	-2.44141383362202	0.0148566954339479	*  
df.mm.trans3:probe7	-0.166519277216997	0.0431872874718733	-3.85574753509229	0.000125128433734474	***
df.mm.trans3:probe8	-0.106546321354414	0.0431872874718733	-2.46707602147518	0.0138412877889014	*  
df.mm.trans3:probe9	-0.188868642267952	0.0431872874718733	-4.37324623341897	1.39458110690402e-05	***
df.mm.trans3:probe10	0.328836406185718	0.0431872874718733	7.61419448720599	7.87281183012089e-14	***
df.mm.trans3:probe11	0.0916476248413235	0.0431872874718733	2.12209726996656	0.0341520212628179	*  
df.mm.trans3:probe12	-0.0069055818464224	0.0431872874718733	-0.159898485194742	0.873003472424562	   
df.mm.trans3:probe13	-0.254962341710862	0.0431872874718733	-5.90364333200857	5.35443945291421e-09	***
df.mm.trans3:probe14	0.435712967651574	0.0431872874718733	10.0889172059102	1.50225699420412e-22	***
df.mm.trans3:probe15	0.165288337680152	0.0431872874718733	3.82724517690073	0.000140226273824161	***
df.mm.trans3:probe16	0.154118260346899	0.0431872874718733	3.56860246078831	0.000381384629344322	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01894907518598	0.103754751999390	38.7350843960341	3.76420531023398e-182	***
df.mm.trans1	-0.0593822813379349	0.086483125110825	-0.68663431463466	0.492522362963779	   
df.mm.trans2	-0.00698893327445187	0.0791757752077167	-0.088271106359445	0.929684426220089	   
df.mm.exp2	0.100302653093272	0.101862409266831	0.984687617495152	0.325090493924921	   
df.mm.exp3	0.0483222273955704	0.101862409266831	0.474387242000031	0.635359879034272	   
df.mm.exp4	0.0978078092931663	0.101862409266831	0.960195326196893	0.337261890478405	   
df.mm.exp5	0.0944040875766616	0.101862409266831	0.926780431134	0.354334370202521	   
df.mm.exp6	0.0873148943760994	0.101862409266831	0.85718465727014	0.391612695852791	   
df.mm.exp7	0.0162606698682604	0.101862409266831	0.159633666485005	0.873212015674007	   
df.mm.exp8	0.0685663639647005	0.101862409266831	0.67312725526734	0.501070694556923	   
df.mm.trans1:exp2	-0.0826070323774866	0.0872297558703747	-0.947005199696334	0.343936733218663	   
df.mm.trans2:exp2	-0.0788816828274714	0.0695852728439497	-1.1335973777723	0.257320460901324	   
df.mm.trans1:exp3	-0.0486316224473148	0.0872297558703747	-0.557511848589632	0.577341713737079	   
df.mm.trans2:exp3	-0.0726314931918005	0.0695852728439497	-1.04377679677541	0.296920118597382	   
df.mm.trans1:exp4	-0.04812594889113	0.0872297558703746	-0.551714818079352	0.581305723222375	   
df.mm.trans2:exp4	-0.102578352476237	0.0695852728439496	-1.47413882685029	0.140857754015714	   
df.mm.trans1:exp5	-0.0558866783659325	0.0872297558703746	-0.640683649842851	0.521921111171702	   
df.mm.trans2:exp5	-0.110529295335271	0.0695852728439497	-1.58840068908175	0.112611120054624	   
df.mm.trans1:exp6	-0.0848012316496608	0.0872297558703746	-0.972159451823725	0.331280122947283	   
df.mm.trans2:exp6	-0.0371488306909474	0.0695852728439497	-0.533860530722592	0.593593932142172	   
df.mm.trans1:exp7	-0.0224238154514787	0.0872297558703746	-0.257066126435124	0.797197213668188	   
df.mm.trans2:exp7	-0.00359697087674342	0.0695852728439496	-0.0516915538264815	0.958788032385058	   
df.mm.trans1:exp8	-0.038549096550248	0.0872297558703746	-0.441925993780526	0.658668313489801	   
df.mm.trans2:exp8	-0.0510391801553276	0.0695852728439496	-0.733476755488003	0.463493591472189	   
df.mm.trans1:probe2	-0.0342320196799776	0.0644234442470857	-0.531359663862214	0.59532462302966	   
df.mm.trans1:probe3	-0.0455363891217097	0.0644234442470857	-0.706829472622766	0.479888823337135	   
df.mm.trans1:probe4	-0.0232693636239009	0.0644234442470857	-0.36119403263593	0.718054652418983	   
df.mm.trans1:probe5	0.0119570667808778	0.0644234442470857	0.185601172377845	0.852806991511514	   
df.mm.trans1:probe6	-0.0257137282544662	0.0644234442470857	-0.399136192654422	0.6899048356601	   
df.mm.trans1:probe7	-0.0732741567161954	0.0644234442470857	-1.13738341022508	0.255736024435932	   
df.mm.trans1:probe8	0.024282281502872	0.0644234442470857	0.376916847378436	0.706340476681443	   
df.mm.trans1:probe9	0.00629224802895506	0.0644234442470857	0.0976701587829139	0.922219930075467	   
df.mm.trans1:probe10	0.0372348494176389	0.0644234442470857	0.577970486564342	0.563455022994318	   
df.mm.trans1:probe11	0.00389899645580515	0.0644234442470857	0.0605213909528211	0.951756269604346	   
df.mm.trans1:probe12	0.0755792130375306	0.0644234442470857	1.17316318493713	0.241097436151235	   
df.mm.trans2:probe2	-0.0883844197291771	0.0644234442470857	-1.37192943907490	0.170489697030560	   
df.mm.trans2:probe3	-0.0671508491454287	0.0644234442470857	-1.04233559584120	0.297587127384654	   
df.mm.trans2:probe4	-0.0647008370579902	0.0644234442470857	-1.0043057743054	0.315550476304579	   
df.mm.trans2:probe5	0.00362898906700826	0.0644234442470857	0.0563302553817187	0.955093502839975	   
df.mm.trans2:probe6	-0.0406390936400867	0.0644234442470857	-0.63081218514524	0.528352730926905	   
df.mm.trans3:probe2	0.206424036175122	0.0644234442470857	3.20417572496459	0.00141096153388597	** 
df.mm.trans3:probe3	0.066452530156409	0.0644234442470857	1.03149607930835	0.302635950476192	   
df.mm.trans3:probe4	0.182349051410662	0.0644234442470857	2.83047659965667	0.0047703944834304	** 
df.mm.trans3:probe5	0.186210791211848	0.0644234442470857	2.89041968165606	0.00395677280015513	** 
df.mm.trans3:probe6	0.039275155179156	0.0644234442470857	0.609640723779413	0.54228180415979	   
df.mm.trans3:probe7	0.0859783305968975	0.0644234442470857	1.33458140280643	0.182412549784011	   
df.mm.trans3:probe8	0.0285337895064726	0.0644234442470857	0.44291002817291	0.657956683501462	   
df.mm.trans3:probe9	0.092713100357532	0.0644234442470857	1.43912051646829	0.150527554109928	   
df.mm.trans3:probe10	0.213205758402658	0.0644234442470857	3.30944364888256	0.000978814997822096	***
df.mm.trans3:probe11	0.140948963706820	0.0644234442470857	2.18785203669387	0.0289838450685957	*  
df.mm.trans3:probe12	0.0327106287921788	0.0644234442470857	0.507744178760801	0.611779885412377	   
df.mm.trans3:probe13	0.112010685607152	0.0644234442470857	1.73866341541059	0.082498406460878	.  
df.mm.trans3:probe14	0.0188967555951172	0.0644234442470857	0.293321100974387	0.769356695867452	   
df.mm.trans3:probe15	0.100115783326897	0.0644234442470857	1.55402717903314	0.120593690025892	   
df.mm.trans3:probe16	0.0689855423008845	0.0644234442470857	1.07081425259261	0.284592449746578