chr9.25028_chr9_37018877_37025734_+_2.R 

fitVsDatCorrelation=0.842643554861518
cont.fitVsDatCorrelation=0.304427754276281

fstatistic=4917.93399899313,62,922
cont.fstatistic=1561.49676835399,62,922

residuals=-1.14407630483354,-0.114240999136391,-0.0109273083967856,0.0991847693523912,1.58224569877895
cont.residuals=-0.687934972243851,-0.265641210412197,-0.0623290034343606,0.146295211070334,1.98195778086013

predictedValues:
Include	Exclude	Both
chr9.25028_chr9_37018877_37025734_+_2.R.tl.Lung	53.8929719279994	78.332317064974	57.3339840942
chr9.25028_chr9_37018877_37025734_+_2.R.tl.cerebhem	62.4681515515819	120.389481910681	166.972348344316
chr9.25028_chr9_37018877_37025734_+_2.R.tl.cortex	53.0866606134931	74.5143911620094	192.202127759025
chr9.25028_chr9_37018877_37025734_+_2.R.tl.heart	51.7176318330513	70.9511959559638	59.649558459145
chr9.25028_chr9_37018877_37025734_+_2.R.tl.kidney	55.165181324505	74.8940586279692	59.7609832617017
chr9.25028_chr9_37018877_37025734_+_2.R.tl.liver	53.6101108696754	74.1130802188111	55.511561836423
chr9.25028_chr9_37018877_37025734_+_2.R.tl.stomach	53.3884884751189	76.9252660255253	60.827159057683
chr9.25028_chr9_37018877_37025734_+_2.R.tl.testicle	53.7197004666578	75.1728990692201	54.2328570588004


diffExp=-24.4393451369745,-57.9213303590995,-21.4277305485164,-19.2335641229125,-19.7288773034642,-20.5029693491357,-23.5367775504064,-21.4531986025623
diffExpScore=0.995220885715216
diffExp1.5=0,-1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=-1,-1,-1,0,0,0,-1,0
diffExp1.4Score=0.8
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	64.2262359891965	63.2534543594439	68.471024353307
cerebhem	77.0186801059828	86.1732629081933	64.1400916753297
cortex	62.0075498091106	63.4338528088132	63.6007679695585
heart	65.7127560989918	83.2543937127005	65.1724837611845
kidney	69.786319837888	62.1613468669788	68.2664788464044
liver	66.2508181797493	64.3601285857211	84.6713422360562
stomach	72.2840212725518	57.396009760129	70.653073731054
testicle	64.9984039501711	62.2984244665607	63.0922399083723
cont.diffExp=0.972781629752603,-9.15458280221044,-1.42630299970263,-17.5416376137087,7.62497297090911,1.89068959402819,14.8880115124228,2.69997948361038
cont.diffExpScore=53.7229626227605

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

tran.correlation=0.971269598055053
cont.tran.correlation=0.38427941043031

tran.covariance=0.00948125425212195
cont.tran.covariance=0.00360767156416075

tran.mean=67.6463491935773
cont.tran.mean=67.7884786695114

weightedLogRatios:
wLogRatio
Lung	-1.56090234583758
cerebhem	-2.92786371938850
cortex	-1.40423038468953
heart	-1.29762506575003
kidney	-1.27286851852092
liver	-1.34194389867466
stomach	-1.51947363062742
testicle	-1.39504546626409

cont.weightedLogRatios:
wLogRatio
Lung	0.0634104592532876
cerebhem	-0.49419554696128
cortex	-0.0941186922154227
heart	-1.01826479264266
kidney	0.484523036257458
liver	0.120995816387936
stomach	0.96063356715936
testicle	0.176203922071300

varWeightedLogRatios=0.302257843137025
cont.varWeightedLogRatios=0.356039914708977

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.82532580724026	0.110402052575184	34.6490460821390	3.99651000914331e-169	***
df.mm.trans1	-0.0467365070185407	0.0948164687100246	-0.492915499326114	0.622189663050118	   
df.mm.trans2	0.524134729480254	0.0832549699395189	6.29553682934502	4.73245066198026e-10	***
df.mm.exp2	-0.491505990386804	0.105931310349679	-4.63985566462212	3.99081644158948e-06	***
df.mm.exp3	-1.27469639785328	0.105931310349679	-12.0332354395080	4.41666679970874e-31	***
df.mm.exp4	-0.179762513565916	0.105931310349679	-1.69697243404731	0.0900393786560017	.  
df.mm.exp5	-0.063013241384573	0.105931310349679	-0.594850013433858	0.552089684699524	   
df.mm.exp6	-0.0283283882815835	0.105931310349679	-0.267422239827596	0.78920388505445	   
df.mm.exp7	-0.0866736453727272	0.105931310349679	-0.818206110040719	0.413450912023039	   
df.mm.exp8	0.0112168418723814	0.105931310349679	0.105887879941771	0.91569434304167	   
df.mm.trans1:exp2	0.639162763609811	0.0972459777153844	6.57263959523844	8.25987184233825e-11	***
df.mm.trans2:exp2	0.92127790814636	0.0689275994572826	13.3658783332113	2.25499705365748e-37	***
df.mm.trans1:exp3	1.25962200352299	0.0972459777153844	12.9529470844502	2.24480005026710e-35	***
df.mm.trans2:exp3	1.22472842279048	0.0689275994572826	17.7683312988362	5.72556356372916e-61	***
df.mm.trans1:exp4	0.138561199730088	0.0972459777153844	1.42485275982955	0.154538165848501	   
df.mm.trans2:exp4	0.0807945215974833	0.0689275994572826	1.17216502872054	0.241433589777327	   
df.mm.trans1:exp5	0.0863451440726727	0.0972459777153844	0.887904529330603	0.374823673032515	   
df.mm.trans2:exp5	0.0181275529490108	0.0689275994572826	0.262994113994137	0.79261387274387	   
df.mm.trans1:exp6	0.0230659957300443	0.0972459777153844	0.23719228570618	0.812560312418831	   
df.mm.trans2:exp6	-0.0270398255761238	0.0689275994572826	-0.392293156718472	0.694932284736089	   
df.mm.trans1:exp7	0.0772687180058815	0.0972459777153844	0.79456980968435	0.427068257582406	   
df.mm.trans2:exp7	0.0685477730669657	0.0689275994572826	0.994489487617333	0.320245541268689	   
df.mm.trans1:exp8	-0.0144371245254271	0.0972459777153844	-0.14845986296401	0.882012321982719	   
df.mm.trans2:exp8	-0.0523863123418443	0.0689275994572826	-0.760019393600242	0.447437283686951	   
df.mm.trans1:probe2	0.381864090779345	0.0696621949775657	5.4816545890109	5.44004017545486e-08	***
df.mm.trans1:probe3	0.135225909815962	0.0696621949775657	1.94116636519292	0.0525425277790149	.  
df.mm.trans1:probe4	0.138961370447486	0.0696621949775657	1.99478885918306	0.0463599165339221	*  
df.mm.trans1:probe5	0.115404963752911	0.0696621949775657	1.65663691461454	0.097933137055968	.  
df.mm.trans1:probe6	0.289557045862491	0.0696621949775657	4.15658803107972	3.53118530133031e-05	***
df.mm.trans1:probe7	0.0509584388625915	0.0696621949775657	0.731507798153681	0.464654916468141	   
df.mm.trans1:probe8	0.129545582066348	0.0696621949775657	1.859625326306	0.0632568872450446	.  
df.mm.trans1:probe9	0.0058861600348812	0.0696621949775657	0.0844957589518506	0.932680617387393	   
df.mm.trans1:probe10	0.0476581321464092	0.0696621949775657	0.684131933565361	0.494063770277452	   
df.mm.trans1:probe11	0.646942878370553	0.0696621949775657	9.2868575068428	1.11002721086093e-19	***
df.mm.trans1:probe12	0.518609913895421	0.0696621949775657	7.44463929197804	2.22908494521293e-13	***
df.mm.trans1:probe13	0.479475818928037	0.0696621949775657	6.88286981313824	1.08312376491107e-11	***
df.mm.trans1:probe14	0.408469762960976	0.0696621949775657	5.86357870423868	6.3064385096901e-09	***
df.mm.trans1:probe15	0.725568418767529	0.0696621949775657	10.4155262262579	4.30015538044317e-24	***
df.mm.trans1:probe16	0.539599969727299	0.0696621949775657	7.74595129971249	2.49516974520000e-14	***
df.mm.trans1:probe17	0.450897696962838	0.0696621949775657	6.47263120417103	1.56264085415424e-10	***
df.mm.trans1:probe18	0.490061724277532	0.0696621949775657	7.0348303615089	3.88766893906347e-12	***
df.mm.trans1:probe19	0.51749496725603	0.0696621949775657	7.42863424591612	2.49886966787180e-13	***
df.mm.trans1:probe20	0.336461848172993	0.0696621949775657	4.82990592359814	1.59883780903729e-06	***
df.mm.trans1:probe21	0.355014440520633	0.0696621949775657	5.09622817131966	4.20339269977605e-07	***
df.mm.trans1:probe22	0.530718104980177	0.0696621949775657	7.61845223440192	6.35971110106624e-14	***
df.mm.trans2:probe2	0.000406006827292739	0.0696621949775657	0.00582822329132022	0.995351037707538	   
df.mm.trans2:probe3	0.102203302426599	0.0696621949775657	1.46712721957029	0.142682458471669	   
df.mm.trans2:probe4	-0.0327591880183396	0.0696621949775657	-0.470257763610371	0.638282083462164	   
df.mm.trans2:probe5	0.0864211405052789	0.0696621949775657	1.24057446844893	0.215078574460362	   
df.mm.trans2:probe6	0.0622233238813205	0.0696621949775657	0.893215091792029	0.371975074391754	   
df.mm.trans3:probe2	0.0778647466203451	0.0696621949775657	1.11774753358577	0.263965995275933	   
df.mm.trans3:probe3	-0.60285343376042	0.0696621949775657	-8.65395404142183	2.18953878090202e-17	***
df.mm.trans3:probe4	-0.455213097743082	0.0696621949775657	-6.5345787322619	1.05386429044579e-10	***
df.mm.trans3:probe5	-0.514290636897563	0.0696621949775657	-7.38263612082834	3.46604198653698e-13	***
df.mm.trans3:probe6	-0.338278376881909	0.0696621949775657	-4.85598217212148	1.40671358759163e-06	***
df.mm.trans3:probe7	-0.0278647324757862	0.0696621949775657	-0.399997911130418	0.689250707113701	   
df.mm.trans3:probe8	-0.158533819121712	0.0696621949775657	-2.27575113263036	0.0230895813155546	*  
df.mm.trans3:probe9	-0.21078399416071	0.0696621949775657	-3.02580178859699	0.00254858342520222	** 
df.mm.trans3:probe10	-0.329052355247642	0.0696621949775657	-4.72354273869222	2.67829964175803e-06	***
df.mm.trans3:probe11	-0.252772412710889	0.0696621949775657	-3.62854504932399	0.000300682713817143	***
df.mm.trans3:probe12	-0.324944086843854	0.0696621949775657	-4.66456859346021	3.54974941930806e-06	***
df.mm.trans3:probe13	-0.163074185607400	0.0696621949775657	-2.34092804081061	0.0194481583380374	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02429834646986	0.195296900773080	20.6060532990525	6.75493372017885e-78	***
df.mm.trans1	0.092309853189473	0.167726614219471	0.55035900902814	0.582206465744769	   
df.mm.trans2	0.116747032852575	0.147274776364066	0.792715736766589	0.428147386200339	   
df.mm.exp2	0.556187447778628	0.187388333129361	2.96810072692558	0.00307394413092441	** 
df.mm.exp3	0.0414774040506783	0.187388333129361	0.221344644877357	0.824873101076398	   
df.mm.exp4	0.347005742205357	0.187388333129361	1.85180014363971	0.0643740580647991	.  
df.mm.exp5	0.0686016457034053	0.187388333129361	0.366093473151539	0.714379311311692	   
df.mm.exp6	-0.163985939721043	0.187388333129361	-0.875112857788417	0.381740408555060	   
df.mm.exp7	-0.010354605620773	0.187388333129361	-0.0552574722654951	0.955945313907481	   
df.mm.exp8	0.0785501955154632	0.187388333129361	0.419184023912722	0.675179287900345	   
df.mm.trans1:exp2	-0.374551244053180	0.172024320358801	-2.17731564508993	0.0297099934367375	*  
df.mm.trans2:exp2	-0.246977233797062	0.12193021993471	-2.02556211191377	0.0430973049197682	*  
df.mm.trans1:exp3	-0.0766330427893083	0.172024320358801	-0.445477957009045	0.656078962327503	   
df.mm.trans2:exp3	-0.0386294696311707	0.12193021993471	-0.31681620562856	0.751454786305348	   
df.mm.trans1:exp4	-0.324124466287819	0.172024320358801	-1.88417815348303	0.0598552961375507	.  
df.mm.trans2:exp4	-0.0722545779026199	0.12193021993471	-0.592589580674177	0.553601157592436	   
df.mm.trans1:exp5	0.0144245665100906	0.172024320358801	0.0838519023356954	0.93319238318342	   
df.mm.trans2:exp5	-0.086018012629443	0.12193021993471	-0.705469182910546	0.480696494942859	   
df.mm.trans1:exp6	0.195021966909215	0.172024320358801	1.13368834419718	0.257219988595046	   
df.mm.trans2:exp6	0.181330519261980	0.12193021993471	1.48716634284	0.137312772002739	   
df.mm.trans1:exp7	0.128545916963032	0.172024320358801	0.747254322498795	0.455100581819797	   
df.mm.trans2:exp7	-0.0868203503390174	0.12193021993471	-0.712049485234318	0.476614264773161	   
df.mm.trans1:exp8	-0.0665992680189553	0.172024320358801	-0.387150304561850	0.698734282910622	   
df.mm.trans2:exp8	-0.0937637999980217	0.12193021993471	-0.768995578358092	0.442092874938632	   
df.mm.trans1:probe2	0.0444721987172835	0.123229690597498	0.360888666535257	0.718265345983495	   
df.mm.trans1:probe3	0.0719481260724699	0.123229690597498	0.58385382389275	0.559461467355665	   
df.mm.trans1:probe4	0.091270582762616	0.123229690597498	0.74065415826394	0.459091735126143	   
df.mm.trans1:probe5	0.130951772120839	0.123229690597498	1.06266413139479	0.288212543087678	   
df.mm.trans1:probe6	-0.0424886653062232	0.123229690597498	-0.344792436791899	0.730329053586044	   
df.mm.trans1:probe7	0.0717604184326007	0.123229690597498	0.582330590011702	0.560486394766992	   
df.mm.trans1:probe8	-0.0475822379584067	0.123229690597498	-0.386126409371774	0.699492134288761	   
df.mm.trans1:probe9	-0.110807625749701	0.123229690597498	-0.899195844868496	0.368783127566184	   
df.mm.trans1:probe10	0.362180331833384	0.123229690597498	2.93906711992294	0.00337412409770198	** 
df.mm.trans1:probe11	0.0363079452379528	0.123229690597498	0.294636341793184	0.768338021896204	   
df.mm.trans1:probe12	0.159489285620439	0.123229690597498	1.29424398330573	0.195905229257844	   
df.mm.trans1:probe13	0.126863150944515	0.123229690597498	1.02948526714137	0.303521685525416	   
df.mm.trans1:probe14	0.0640535048629487	0.123229690597498	0.5197895454608	0.603335033566696	   
df.mm.trans1:probe15	0.107668388960985	0.123229690597498	0.873721166051286	0.382497628868391	   
df.mm.trans1:probe16	-0.105957466298781	0.123229690597498	-0.85983715275945	0.390102326893731	   
df.mm.trans1:probe17	0.224158147374151	0.123229690597498	1.81902710529650	0.069231557885917	.  
df.mm.trans1:probe18	0.00951254700065166	0.123229690597498	0.0771936288611015	0.93848625775399	   
df.mm.trans1:probe19	0.0218831286053381	0.123229690597498	0.177580001209403	0.85909183651091	   
df.mm.trans1:probe20	0.187504692259786	0.123229690597498	1.52158697591985	0.128455470157436	   
df.mm.trans1:probe21	0.0303821501312436	0.123229690597498	0.246548944365039	0.80531220192864	   
df.mm.trans1:probe22	0.16955519827108	0.123229690597498	1.37592813427483	0.169178005774099	   
df.mm.trans2:probe2	0.00280094611800019	0.123229690597498	0.0227294745642820	0.98187098263848	   
df.mm.trans2:probe3	0.1128644922571	0.123229690597498	0.915887167368991	0.359965560156966	   
df.mm.trans2:probe4	0.148646766078654	0.123229690597498	1.20625772375080	0.228027563028527	   
df.mm.trans2:probe5	-0.0561602652221772	0.123229690597498	-0.455736478359033	0.648686799517609	   
df.mm.trans2:probe6	-0.0921690769551795	0.123229690597498	-0.747945373458972	0.45468383455597	   
df.mm.trans3:probe2	0.216047543692725	0.123229690597498	1.75321014477262	0.079898245019439	.  
df.mm.trans3:probe3	0.0200188565901294	0.123229690597498	0.162451569042045	0.870985859385565	   
df.mm.trans3:probe4	0.00396338656941857	0.123229690597498	0.0321625945030088	0.974349346902486	   
df.mm.trans3:probe5	-0.0813062203468455	0.123229690597498	-0.659794080084269	0.509550758402604	   
df.mm.trans3:probe6	-0.031999448469373	0.123229690597498	-0.259673203058603	0.795173835683639	   
df.mm.trans3:probe7	0.038240534377852	0.123229690597498	0.310319162471616	0.756388391717656	   
df.mm.trans3:probe8	-0.121920999038939	0.123229690597498	-0.989380062936015	0.322736794806556	   
df.mm.trans3:probe9	-0.0901246978045662	0.123229690597498	-0.731355384952949	0.464747937368673	   
df.mm.trans3:probe10	-0.00343000931199943	0.123229690597498	-0.0278342767507448	0.977800351003643	   
df.mm.trans3:probe11	-0.0885121453418592	0.123229690597498	-0.718269638694167	0.472773012949081	   
df.mm.trans3:probe12	-0.0386975891124737	0.123229690597498	-0.314028128487887	0.75357071227668	   
df.mm.trans3:probe13	0.0874412628095638	0.123229690597498	0.709579504627426	0.478144336045883