chr14.7772_chr14_52092208_52119909_-_2.R 

fitVsDatCorrelation=0.837898303881995
cont.fitVsDatCorrelation=0.2691102184105

fstatistic=10295.0987517407,62,922
cont.fstatistic=3296.55610223848,62,922

residuals=-0.589303700191424,-0.0983899427164497,-0.00953901337482949,0.0853738176325733,0.733902180216703
cont.residuals=-0.573012434958951,-0.206768761777354,-0.029920011389685,0.170615741567771,1.10543484188294

predictedValues:
Include	Exclude	Both
chr14.7772_chr14_52092208_52119909_-_2.R.tl.Lung	81.6311490369001	45.2006626420612	82.4167149857774
chr14.7772_chr14_52092208_52119909_-_2.R.tl.cerebhem	71.4503263356034	54.1128004056329	85.3086501708187
chr14.7772_chr14_52092208_52119909_-_2.R.tl.cortex	84.0076699941147	47.1693554038	84.1663384787577
chr14.7772_chr14_52092208_52119909_-_2.R.tl.heart	64.4802921268106	46.9985733715148	60.9581496773475
chr14.7772_chr14_52092208_52119909_-_2.R.tl.kidney	62.7699580341255	45.7163613888296	60.2642652867684
chr14.7772_chr14_52092208_52119909_-_2.R.tl.liver	64.6899489803023	50.3706743937975	58.6126248756909
chr14.7772_chr14_52092208_52119909_-_2.R.tl.stomach	72.0392299364825	47.8020910198669	72.5846267808918
chr14.7772_chr14_52092208_52119909_-_2.R.tl.testicle	64.53104882191	48.8491306690537	64.7355801594587


diffExp=36.4304863948389,17.3375259299706,36.8383145903147,17.4817187552958,17.0535966452959,14.3192745865048,24.2371389166156,15.6819181528562
diffExpScore=0.99445614733176
diffExp1.5=1,0,1,0,0,0,1,0
diffExp1.5Score=0.75
diffExp1.4=1,0,1,0,0,0,1,0
diffExp1.4Score=0.75
diffExp1.3=1,1,1,1,1,0,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	67.5402131868043	66.4568021241953	62.5840822571043
cerebhem	68.6326141221305	68.4985754964012	60.6288179711278
cortex	65.5860435439319	60.9001985795426	65.6045510764876
heart	67.5645329102721	64.3579879739564	61.8219995275624
kidney	68.9721592654301	67.597999796068	60.7295239279118
liver	65.2522133677671	54.16989211601	72.9313164407039
stomach	65.7765366505585	62.7344649387828	61.5066391196445
testicle	66.208293157193	69.4983931147061	62.3356140246597
cont.diffExp=1.08341106260899,0.134038625729332,4.68584496438928,3.20654493631572,1.37415946936207,11.0823212517571,3.04207171177563,-3.29009995751319
cont.diffExpScore=1.25002808901857

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

tran.correlation=-0.19596253804053
cont.tran.correlation=0.692860278242378

tran.covariance=-0.00120142648508778
cont.tran.covariance=0.0012056255990136

tran.mean=59.4887045350504
cont.tran.mean=65.6091825214844

weightedLogRatios:
wLogRatio
Lung	2.42744410729963
cerebhem	1.14786812902059
cortex	2.39080024073208
heart	1.26757486822912
kidney	1.26204729802081
liver	1.01192239967493
stomach	1.67015343654797
testicle	1.12141881030900

cont.weightedLogRatios:
wLogRatio
Lung	0.0679933088264165
cerebhem	0.00826489432265222
cortex	0.307350629260083
heart	0.20366700044307
kidney	0.0849988274795176
liver	0.760396137678926
stomach	0.197107706915718
testicle	-0.204518305792781

varWeightedLogRatios=0.326802972568528
cont.varWeightedLogRatios=0.0789744996992747

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95381362409999	0.0751011553158809	52.6465086651459	4.2194244556415e-280	***
df.mm.trans1	0.52109440666324	0.0644990394381087	8.0791033665434	2.03473223355629e-15	***
df.mm.trans2	-0.140038421753425	0.0566343132433053	-2.47267802386532	0.0135898993132468	*  
df.mm.exp2	0.0122629814257442	0.0720599264761706	0.170177545626548	0.864907843841063	   
df.mm.exp3	0.0503230788051223	0.0720599264761706	0.6983504045312	0.48513417872652	   
df.mm.exp4	0.104754921922643	0.0720599264761706	1.45371952269871	0.146364373370957	   
df.mm.exp5	0.0616591258969494	0.0720599264761706	0.855664568535736	0.392405648981444	   
df.mm.exp6	0.216530478534988	0.0720599264761706	3.00486677025118	0.00272886209747104	** 
df.mm.exp7	0.0579926711458243	0.0720599264761706	0.804783934452137	0.421151846127177	   
df.mm.exp8	0.0840376955637266	0.0720599264761706	1.16621955743345	0.243827260263476	   
df.mm.trans1:exp2	-0.145471427255746	0.0661517164390966	-2.19905748613003	0.0281215173322054	*  
df.mm.trans2:exp2	0.167696035875275	0.0468880988319216	3.57651600412313	0.000366307758704977	***
df.mm.trans1:exp3	-0.0216258922002144	0.0661517164390966	-0.326913546077440	0.743807476030949	   
df.mm.trans2:exp3	-0.00769039394042093	0.0468880988319216	-0.164015904504648	0.869754570340956	   
df.mm.trans1:exp4	-0.340606210755584	0.0661517164390966	-5.14886429393208	3.20398631956502e-07	***
df.mm.trans2:exp4	-0.065749421424146	0.0468880988319216	-1.40226247303897	0.161173471997078	   
df.mm.trans1:exp5	-0.324393459657219	0.0661517164390966	-4.90377993375086	1.11073398783475e-06	***
df.mm.trans2:exp5	-0.0503146217619552	0.0468880988319216	-1.07307873459141	0.28351656366132	   
df.mm.trans1:exp6	-0.449135554682925	0.0661517164390966	-6.78947696083483	2.01369446659276e-11	***
df.mm.trans2:exp6	-0.108233077021537	0.0468880988319216	-2.30832726678720	0.0212014333548406	*  
df.mm.trans1:exp7	-0.182992757806982	0.0661517164390966	-2.76625864992418	0.00578373360632349	** 
df.mm.trans2:exp7	-0.00203503437302591	0.0468880988319216	-0.0434019383110593	0.96539053000559	   
df.mm.trans1:exp8	-0.319102128026603	0.0661517164390966	-4.82379211309488	1.64741089680996e-06	***
df.mm.trans2:exp8	-0.00641286011180822	0.0468880988319216	-0.136769463287394	0.891242879870234	   
df.mm.trans1:probe2	-0.113039636271071	0.0473878084928951	-2.38541599339879	0.0172609130127357	*  
df.mm.trans1:probe3	0.45140081104138	0.0473878084928951	9.5256739106021	1.39751177587248e-20	***
df.mm.trans1:probe4	-0.159243724094838	0.0473878084928951	-3.36043655867128	0.000810138716250534	***
df.mm.trans1:probe5	-0.202148498678559	0.0473878084928951	-4.26583345184378	2.19709364608717e-05	***
df.mm.trans1:probe6	0.0969963261534907	0.0473878084928951	2.04686245763050	0.0409546543782867	*  
df.mm.trans1:probe7	-0.234417694353861	0.0473878084928951	-4.94679331687193	8.96455507997648e-07	***
df.mm.trans1:probe8	-0.227772722478567	0.0473878084928951	-4.80656797017143	1.79201398691590e-06	***
df.mm.trans1:probe9	0.083626521361633	0.0473878084928951	1.76472649867679	0.0779407861323706	.  
df.mm.trans1:probe10	0.147467157516105	0.0473878084928951	3.11192186779886	0.00191598182427033	** 
df.mm.trans1:probe11	-0.214298448164198	0.0473878084928951	-4.52222744582771	6.91686698446125e-06	***
df.mm.trans1:probe12	-0.310459990243433	0.0473878084928951	-6.5514738941764	9.45978125431383e-11	***
df.mm.trans1:probe13	-0.191576227586522	0.0473878084928951	-4.04273237525312	5.72429522342297e-05	***
df.mm.trans1:probe14	-0.307138346068941	0.0473878084928951	-6.48137898411129	1.47838397998769e-10	***
df.mm.trans1:probe15	-0.253598627434377	0.0473878084928951	-5.35155845985997	1.10077429079985e-07	***
df.mm.trans1:probe16	-0.0211129981262044	0.0473878084928951	-0.445536495518038	0.656036683474904	   
df.mm.trans1:probe17	-0.192087397220891	0.0473878084928951	-4.05351931920824	5.47094678533471e-05	***
df.mm.trans1:probe18	-0.179159270355158	0.0473878084928951	-3.7807038572383	0.000166447217011752	***
df.mm.trans1:probe19	-0.27184011044937	0.0473878084928951	-5.73649888219936	1.31010029534787e-08	***
df.mm.trans1:probe20	-0.126345544063517	0.0473878084928951	-2.6662035675792	0.00780560571226147	** 
df.mm.trans1:probe21	-0.190524657619470	0.0473878084928951	-4.02054164728963	6.280902582009e-05	***
df.mm.trans1:probe22	-0.12912588624721	0.0473878084928951	-2.72487566641893	0.00655436217809852	** 
df.mm.trans2:probe2	0.0386145047042332	0.0473878084928951	0.814861584283281	0.415361971002629	   
df.mm.trans2:probe3	-0.0532188728190373	0.0473878084928951	-1.1230498837484	0.261708653504951	   
df.mm.trans2:probe4	-0.0132519933538475	0.0473878084928951	-0.279649846137839	0.779808882565474	   
df.mm.trans2:probe5	-0.097909100829194	0.0473878084928951	-2.06612426155714	0.0390955860192515	*  
df.mm.trans2:probe6	0.075159809826219	0.0473878084928951	1.58605793803459	0.113069050366797	   
df.mm.trans3:probe2	0.120387098392812	0.0473878084928951	2.54046562231005	0.0112335200627483	*  
df.mm.trans3:probe3	-0.0848373902276833	0.0473878084928951	-1.79027882752592	0.0737371209159416	.  
df.mm.trans3:probe4	0.35107189601613	0.0473878084928951	7.4084855827162	2.88453951881052e-13	***
df.mm.trans3:probe5	-0.0145188511940793	0.0473878084928951	-0.306383680862897	0.759381704627954	   
df.mm.trans3:probe6	0.190797791119326	0.0473878084928951	4.02630543988825	6.13159004287639e-05	***
df.mm.trans3:probe7	0.0413410817712553	0.0473878084928951	0.872399106142535	0.383217815997073	   
df.mm.trans3:probe8	0.361991879018834	0.0473878084928951	7.63892424088587	5.47746112088062e-14	***
df.mm.trans3:probe9	0.419465672205875	0.0473878084928951	8.851763471374	4.33798706926214e-18	***
df.mm.trans3:probe10	0.113777339368183	0.0473878084928951	2.40098335387765	0.0165482734662103	*  
df.mm.trans3:probe11	-0.105598052420592	0.0473878084928951	-2.22838016314733	0.0260956453874808	*  
df.mm.trans3:probe12	-0.192908880992104	0.0473878084928951	-4.07085465918996	5.08595945769791e-05	***
df.mm.trans3:probe13	-0.201027014717281	0.0473878084928951	-4.24216736562994	2.43717186011221e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.2079150842701	0.132515816106059	31.7540593109451	4.22529207981946e-150	***
df.mm.trans1	0.0366691554020255	0.113808406984526	0.322200761557195	0.747373648239882	   
df.mm.trans2	-0.0231153737105098	0.099931115672941	-0.231313075560597	0.817122911609868	   
df.mm.exp2	0.0780461243912525	0.127149574801722	0.613813491023921	0.539489989806702	   
df.mm.exp3	-0.163810058934511	0.127149574801722	-1.28832565260212	0.197955832899116	   
df.mm.exp4	-0.0194793838192109	0.127149574801722	-0.153200542350119	0.878273651860731	   
df.mm.exp5	0.0680870101981683	0.127149574801722	0.535487517786382	0.592441923435694	   
df.mm.exp6	-0.391897249661878	0.127149574801722	-3.08217507036893	0.00211601247586318	** 
df.mm.exp7	-0.0667353037486817	0.127149574801722	-0.524856680431287	0.599808999253982	   
df.mm.exp8	0.0288120977344036	0.127149574801722	0.226600032122272	0.820784995120862	   
df.mm.trans1:exp2	-0.0620014495219668	0.116724551757858	-0.53117744800243	0.595423752745498	   
df.mm.trans2:exp2	-0.0477853194803525	0.0827339427235118	-0.57757817296398	0.563689962073296	   
df.mm.trans1:exp3	0.134449809990207	0.116724551757858	1.15185544056849	0.249679067800233	   
df.mm.trans2:exp3	0.0764943499247996	0.0827339427235117	0.924582431426431	0.355425070782586	   
df.mm.trans1:exp4	0.0198393966897137	0.116724551757858	0.169967640834209	0.86507287160912	   
df.mm.trans2:exp4	-0.0126117010712026	0.0827339427235118	-0.152436843404763	0.878875750835483	   
df.mm.trans1:exp5	-0.0471072473362483	0.116724551757858	-0.403576168225267	0.686617991140747	   
df.mm.trans2:exp5	-0.0510607608706405	0.0827339427235118	-0.617168228538077	0.537276147195955	   
df.mm.trans1:exp6	0.357434045229479	0.116724551757858	3.06220105236271	0.00226090495376072	** 
df.mm.trans2:exp6	0.187470363279759	0.0827339427235118	2.26594257578495	0.0236860261103736	*  
df.mm.trans1:exp7	0.0402753212253482	0.116724551757858	0.345045841845665	0.730138605160661	   
df.mm.trans2:exp7	0.00909413594513147	0.0827339427235118	0.109920253353852	0.912496539935129	   
df.mm.trans1:exp8	-0.0487295397647461	0.116724551757858	-0.417474636063149	0.67642846675177	   
df.mm.trans2:exp8	0.0159393894492742	0.0827339427235118	0.19265840505803	0.847268928255897	   
df.mm.trans1:probe2	-0.144487979411517	0.083615679272856	-1.72800102406657	0.0843228731869368	.  
df.mm.trans1:probe3	0.0229285631883513	0.083615679272856	0.274213680828095	0.783981838625803	   
df.mm.trans1:probe4	-0.08870219045833	0.083615679272856	-1.06083202611888	0.289044048136793	   
df.mm.trans1:probe5	-0.0691904734600934	0.083615679272856	-0.827482047168569	0.408178009371764	   
df.mm.trans1:probe6	-0.134806789474938	0.083615679272856	-1.61221903173248	0.107256506672687	   
df.mm.trans1:probe7	-0.132002168896083	0.083615679272856	-1.57867722948625	0.114753110862648	   
df.mm.trans1:probe8	-0.0171557124262531	0.083615679272856	-0.205173390630127	0.837481945656041	   
df.mm.trans1:probe9	-0.0361573970322336	0.083615679272856	-0.43242364765398	0.665534652981157	   
df.mm.trans1:probe10	0.0040365672408951	0.083615679272856	0.0482752430644367	0.961507339606002	   
df.mm.trans1:probe11	-0.0407750307486702	0.083615679272856	-0.487648143305905	0.62591501878233	   
df.mm.trans1:probe12	-0.0944003942645552	0.083615679272856	-1.12897957758026	0.259200105069283	   
df.mm.trans1:probe13	-0.00344755039295600	0.083615679272856	-0.0412309081614454	0.96712073997294	   
df.mm.trans1:probe14	-0.0851988920718315	0.083615679272856	-1.01893440097292	0.308501399227263	   
df.mm.trans1:probe15	0.0351475675290929	0.083615679272856	0.420346612438545	0.674330208123278	   
df.mm.trans1:probe16	-0.00985674094797176	0.083615679272856	-0.117881491051542	0.906187234027483	   
df.mm.trans1:probe17	-0.0718323230941287	0.083615679272856	-0.859077193641211	0.390521220207001	   
df.mm.trans1:probe18	-0.0659545634203165	0.083615679272856	-0.78878224746692	0.430442056464294	   
df.mm.trans1:probe19	-0.0876700462373688	0.083615679272856	-1.04848811849369	0.294688534969895	   
df.mm.trans1:probe20	0.0644635231863051	0.083615679272856	0.770950182392787	0.440933976644024	   
df.mm.trans1:probe21	-0.0546913182738311	0.083615679272856	-0.654079698322626	0.51322356570772	   
df.mm.trans1:probe22	-0.105384039883722	0.083615679272856	-1.26033826191653	0.207866239443129	   
df.mm.trans2:probe2	0.0258827025686627	0.083615679272856	0.309543650111385	0.756977955593514	   
df.mm.trans2:probe3	0.0146409391385985	0.083615679272856	0.175098011113704	0.861041064949913	   
df.mm.trans2:probe4	-0.0277222878684649	0.083615679272856	-0.331544132745738	0.740308855122157	   
df.mm.trans2:probe5	0.0233104653490842	0.083615679272856	0.278781031880601	0.780475386183997	   
df.mm.trans2:probe6	0.187184425073260	0.083615679272856	2.23862828958714	0.0254180435045765	*  
df.mm.trans3:probe2	-0.182489218138066	0.083615679272856	-2.18247605861772	0.029326135633472	*  
df.mm.trans3:probe3	-0.0739891926919685	0.083615679272856	-0.884872231325489	0.376456246160896	   
df.mm.trans3:probe4	-0.0994310782389864	0.083615679272856	-1.18914393931456	0.234689188345774	   
df.mm.trans3:probe5	-0.0561484540494616	0.083615679272856	-0.671506283722662	0.502066263238101	   
df.mm.trans3:probe6	-0.185506732639245	0.083615679272856	-2.21856396135821	0.0267592978280404	*  
df.mm.trans3:probe7	-0.0104649741148766	0.083615679272856	-0.125155643126776	0.900427615489194	   
df.mm.trans3:probe8	-0.103948617224516	0.083615679272856	-1.2431713540867	0.214120725389957	   
df.mm.trans3:probe9	-0.120470896915629	0.083615679272856	-1.44076921892253	0.149989392011501	   
df.mm.trans3:probe10	-0.127590617217449	0.083615679272856	-1.52591736773546	0.127373387083705	   
df.mm.trans3:probe11	-0.109266322228915	0.083615679272856	-1.30676833793762	0.191617242475640	   
df.mm.trans3:probe12	-0.0832368119222994	0.083615679272856	-0.995468943697505	0.319769420255922	   
df.mm.trans3:probe13	0.0480902513587398	0.083615679272856	0.575134374042587	0.565340741604847