chr4.16851_chr4_56896373_56912645_-_2.R 

fitVsDatCorrelation=0.620716892042267
cont.fitVsDatCorrelation=0.231208332256680

fstatistic=10425.0999205959,62,922
cont.fstatistic=6765.12965299536,62,922

residuals=-0.404675588911397,-0.0764876850188295,-0.00838347210519196,0.0627445999932847,2.75317704576913
cont.residuals=-0.475245456734204,-0.116003466320097,-0.0167613996955519,0.0861057093892327,2.62523921696282

predictedValues:
Include	Exclude	Both
chr4.16851_chr4_56896373_56912645_-_2.R.tl.Lung	49.3260871535612	44.8559716648896	47.7679818960383
chr4.16851_chr4_56896373_56912645_-_2.R.tl.cerebhem	55.3826028874103	51.1254575963757	51.5246243623835
chr4.16851_chr4_56896373_56912645_-_2.R.tl.cortex	49.1136620866967	46.3875446216047	48.0548513393272
chr4.16851_chr4_56896373_56912645_-_2.R.tl.heart	51.3812395468915	50.2156643246417	49.3763341110233
chr4.16851_chr4_56896373_56912645_-_2.R.tl.kidney	47.8658545493594	45.0237110837180	49.2064833887007
chr4.16851_chr4_56896373_56912645_-_2.R.tl.liver	52.4615317878328	49.137820358812	49.9515526296253
chr4.16851_chr4_56896373_56912645_-_2.R.tl.stomach	54.3658690006159	46.3340315088487	49.7139833083278
chr4.16851_chr4_56896373_56912645_-_2.R.tl.testicle	52.0155664290955	53.14090754053	49.3147043784888


diffExp=4.47011548867165,4.25714529103463,2.72611746509202,1.16557522224987,2.84214346564131,3.32371142902079,8.03183749176716,-1.12534111143450
diffExpScore=1.04685729060292
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	52.8846138614061	49.6447202789465	47.8709484653022
cerebhem	50.1807682611709	45.9605047826604	49.1169203071618
cortex	49.0976748227851	46.3304280869867	49.2988128299378
heart	50.1528321406802	48.1083914018982	49.4908192042898
kidney	51.6932776132754	51.4116790508726	49.0719317646336
liver	50.7958039251785	47.1562297703267	49.0549081763614
stomach	50.677709111785	48.9073768692237	48.3386706789427
testicle	50.2811158444998	57.717711986347	49.1382058474121
cont.diffExp=3.2398935824596,4.22026347851056,2.76724673579836,2.04444073878201,0.281598562402884,3.63957415485181,1.77033224256125,-7.43659614184727
cont.diffExpScore=2.20356459952003

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=0.574733859530945
cont.tran.correlation=0.206079688547064

tran.covariance=0.00189788748654194
cont.tran.covariance=0.000386081603272921

tran.mean=49.8833451338052
cont.tran.mean=50.0625523630027

weightedLogRatios:
wLogRatio
Lung	0.365826718797043
cerebhem	0.317874170929108
cortex	0.220749043988005
heart	0.0901276935474549
kidney	0.234923524357897
liver	0.257049924773952
stomach	0.625980205808324
testicle	-0.0848078540063207

cont.weightedLogRatios:
wLogRatio
Lung	0.248867235614035
cerebhem	0.340127207300099
cortex	0.224207939290756
heart	0.162072982950766
kidney	0.0215359633278705
liver	0.289259836477525
stomach	0.138949901198696
testicle	-0.549889309487798

varWeightedLogRatios=0.0425822638467006
cont.varWeightedLogRatios=0.0806066713510134

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95571672035559	0.0697140830325591	56.7420031689742	4.98638873649313e-303	***
df.mm.trans1	-0.0257994553064421	0.05987246630223	-0.430906840820773	0.666636828212112	   
df.mm.trans2	-0.139730521648928	0.052571883872216	-2.65789451237024	0.00799915312388135	** 
df.mm.exp2	0.170934006472778	0.0668910042269026	2.55541097713153	0.0107657320001303	*  
df.mm.exp3	0.0232709007652903	0.0668910042269026	0.347892830048603	0.728000078367338	   
df.mm.exp4	0.120574650844091	0.0668910042269026	1.80255405398141	0.071784741595094	.  
df.mm.exp5	-0.0559879646437241	0.0668910042269027	-0.837002901822283	0.402807827704554	   
df.mm.exp6	0.108101356080527	0.0668910042269027	1.61608212240070	0.106418650397740	   
df.mm.exp7	0.0897727747762416	0.0668910042269026	1.34207545265311	0.179901921660479	   
df.mm.exp8	0.190713507667663	0.0668910042269026	2.85110845429588	0.00445393919635066	** 
df.mm.trans1:exp2	-0.0551215816248791	0.0614065953204623	-0.897649207503141	0.369606932189551	   
df.mm.trans2:exp2	-0.0401081687401528	0.0435247740392112	-0.921502055450527	0.35702942366901	   
df.mm.trans1:exp3	-0.0275867467163877	0.0614065953204623	-0.449247292940129	0.653358854440435	   
df.mm.trans2:exp3	0.0103033604219780	0.0435247740392112	0.236724041638809	0.81292346496417	   
df.mm.trans1:exp4	-0.0797546266532632	0.0614065953204623	-1.29879577652935	0.194338747879773	   
df.mm.trans2:exp4	-0.00770436161141113	0.0435247740392112	-0.177010950234235	0.859538664576815	   
df.mm.trans1:exp5	0.0259372739214228	0.0614065953204623	0.422385800516444	0.672841919903535	   
df.mm.trans2:exp5	0.0597205014850192	0.0435247740392112	1.37210365368508	0.170364903820864	   
df.mm.trans1:exp6	-0.0464742751953851	0.0614065953204623	-0.75682872422498	0.449345830956497	   
df.mm.trans2:exp6	-0.0169290728444119	0.0435247740392112	-0.388952572830376	0.697401037755172	   
df.mm.trans1:exp7	0.0075106818205539	0.0614065953204623	0.122310670073108	0.902679635453598	   
df.mm.trans2:exp7	-0.0573527890215272	0.0435247740392112	-1.31770446343635	0.187929920113248	   
df.mm.trans1:exp8	-0.137623571763520	0.0614065953204623	-2.24118551183802	0.0252513575839368	*  
df.mm.trans2:exp8	-0.0212232163187173	0.0435247740392112	-0.487612326248896	0.625940383433046	   
df.mm.trans1:probe2	0.27854932063621	0.0439886390843061	6.33230139496606	3.76826499442092e-10	***
df.mm.trans1:probe3	-0.0538739878107565	0.0439886390843061	-1.22472504110674	0.220991647186905	   
df.mm.trans1:probe4	-0.204671532822241	0.0439886390843061	-4.65282711815611	3.75312939311605e-06	***
df.mm.trans1:probe5	-0.11513598430563	0.0439886390843061	-2.61740273630578	0.00900531552879227	** 
df.mm.trans1:probe6	0.251748849873536	0.0439886390843061	5.7230424744682	1.41438899440480e-08	***
df.mm.trans1:probe7	-0.0166610077357472	0.0439886390843061	-0.378757062791046	0.70495547391516	   
df.mm.trans1:probe8	-0.194087220502392	0.0439886390843061	-4.41221243808919	1.14399500822262e-05	***
df.mm.trans1:probe9	-0.0829517539366863	0.0439886390843061	-1.88575404157664	0.0596422615258732	.  
df.mm.trans1:probe10	-0.113814188154120	0.0439886390843061	-2.58735415605812	0.0098236348795902	** 
df.mm.trans1:probe11	-0.0397856037782729	0.0439886390843061	-0.90445179952083	0.365992143550683	   
df.mm.trans1:probe12	-0.157547508245426	0.0439886390843061	-3.58154995301126	0.000359415798527556	***
df.mm.trans1:probe13	-0.200344187641786	0.0439886390843061	-4.55445296358947	5.95675536697288e-06	***
df.mm.trans1:probe14	-0.0448223108266649	0.0439886390843061	-1.01895197850429	0.308493058372593	   
df.mm.trans1:probe15	-0.0529836365420144	0.0439886390843061	-1.20448455885323	0.228711443779874	   
df.mm.trans1:probe16	-0.049514810232009	0.0439886390843061	-1.12562723609412	0.260616250933976	   
df.mm.trans1:probe17	-0.0480315588909544	0.0439886390843061	-1.09190827201769	0.275158659523603	   
df.mm.trans1:probe18	-0.0726865946710522	0.0439886390843061	-1.65239471336554	0.0987945815502444	.  
df.mm.trans1:probe19	-0.0244813449023146	0.0439886390843061	-0.55653790187496	0.577978240861203	   
df.mm.trans1:probe20	-0.0912139193900514	0.0439886390843061	-2.07357902605798	0.0383956041609221	*  
df.mm.trans1:probe21	-0.0300657941105947	0.0439886390843061	-0.683489981423893	0.494469001686119	   
df.mm.trans1:probe22	-0.0388712727431789	0.0439886390843061	-0.883666181822094	0.377106796528722	   
df.mm.trans2:probe2	-0.113874161905652	0.0439886390843061	-2.58871754789702	0.00978511437590854	** 
df.mm.trans2:probe3	-0.0356887193204616	0.0439886390843061	-0.81131674139913	0.417393186189864	   
df.mm.trans2:probe4	-0.040421899480628	0.0439886390843061	-0.918916800384702	0.35837942319572	   
df.mm.trans2:probe5	0.0224949649284918	0.0439886390843061	0.511381242901815	0.609206566948091	   
df.mm.trans2:probe6	-0.0705701441662381	0.0439886390843061	-1.60428114247834	0.108994558368381	   
df.mm.trans3:probe2	0.160941516106013	0.0439886390843061	3.65870641729928	0.000267867481419107	***
df.mm.trans3:probe3	-0.0563168117721328	0.0439886390843061	-1.28025810628511	0.200776374593259	   
df.mm.trans3:probe4	-0.0351834916983374	0.0439886390843061	-0.799831329878308	0.424014546381424	   
df.mm.trans3:probe5	-0.00574985776577751	0.0439886390843061	-0.130712335854666	0.896031387169966	   
df.mm.trans3:probe6	0.179651388433995	0.0439886390843061	4.08404061079693	4.81049497519876e-05	***
df.mm.trans3:probe7	0.0405159126342423	0.0439886390843061	0.921054014801227	0.357263156799333	   
df.mm.trans3:probe8	0.0386768998091718	0.0439886390843061	0.879247474218194	0.37949620044617	   
df.mm.trans3:probe9	0.0159749281150011	0.0439886390843061	0.363160317016957	0.716568371951633	   
df.mm.trans3:probe10	0.0114736012405381	0.0439886390843061	0.260831011810766	0.794281072930602	   
df.mm.trans3:probe11	0.294508574083295	0.0439886390843061	6.69510537752388	3.74018992408308e-11	***
df.mm.trans3:probe12	0.0932742479213416	0.0439886390843061	2.12041676812455	0.0342374008199761	*  
df.mm.trans3:probe13	0.252427971899782	0.0439886390843061	5.73848105225518	1.29538484567368e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04659405507174	0.0865078057463908	46.777212994338	1.16707112550496e-245	***
df.mm.trans1	-0.0521990836207566	0.0742953999984716	-0.702588365118573	0.482489658023805	   
df.mm.trans2	-0.146357785797163	0.0652361491381224	-2.24350743768282	0.0251008305335978	*  
df.mm.exp2	-0.155284970366378	0.0830046634499853	-1.87079814449155	0.0616896453143637	.  
df.mm.exp3	-0.172785055922015	0.0830046634499853	-2.08163070290776	0.0376515885686247	*  
df.mm.exp4	-0.117751248987694	0.0830046634499853	-1.41861004061110	0.156350664200914	   
df.mm.exp5	-0.0125897778363873	0.0830046634499854	-0.151675548253663	0.879476024504638	   
df.mm.exp6	-0.116156040270192	0.0830046634499853	-1.39939173827483	0.162031882503741	   
df.mm.exp7	-0.0673131555014155	0.0830046634499853	-0.810956309002749	0.417600043932559	   
df.mm.exp8	0.0740611411883872	0.0830046634499853	0.892252773641	0.372490264400433	   
df.mm.trans1:exp2	0.102804378017803	0.0761990918972399	1.34915489749460	0.177618439735709	   
df.mm.trans2:exp2	0.0781753603290316	0.0540096424417019	1.44743339883085	0.148115495567824	   
df.mm.trans1:exp3	0.098484290373052	0.0761990918972399	1.29246015826364	0.196521646771007	   
df.mm.trans2:exp3	0.103691949215919	0.0540096424417019	1.91987846110710	0.055181550081809	.  
df.mm.trans1:exp4	0.0647137919178141	0.0761990918972399	0.849272482211276	0.395950143778333	   
df.mm.trans2:exp4	0.0863158222592116	0.0540096424417019	1.59815578028277	0.110350936765831	   
df.mm.trans1:exp5	-0.0101949192026603	0.0761990918972399	-0.133793185047519	0.893595313525225	   
df.mm.trans2:exp5	0.0475630973315836	0.0540096424417019	0.880640848213785	0.378741732507424	   
df.mm.trans1:exp6	0.075857348268321	0.0761990918972399	0.995515122025605	0.319746984075036	   
df.mm.trans2:exp6	0.0647301212016195	0.0540096424417019	1.19849194098053	0.231033520071831	   
df.mm.trans1:exp7	0.0246868636525276	0.0761990918972399	0.323978449583358	0.746027827637693	   
df.mm.trans2:exp7	0.052349350781738	0.0540096424417019	0.969259347314583	0.332669994139914	   
df.mm.trans1:exp8	-0.124544008377907	0.0761990918972399	-1.63445528387483	0.102504648810124	   
df.mm.trans2:exp8	0.0766109060112324	0.0540096424417019	1.41846719488888	0.156392325934805	   
df.mm.trans1:probe2	-0.108087860660164	0.0545852499153722	-1.98016608566858	0.0479818056972653	*  
df.mm.trans1:probe3	-0.0820800400801955	0.0545852499153722	-1.50370366000798	0.133000129946543	   
df.mm.trans1:probe4	-0.0368831261043206	0.0545852499153722	-0.675697668536893	0.499402051382051	   
df.mm.trans1:probe5	-0.0506236936961671	0.0545852499153722	-0.927424419136177	0.353948925082535	   
df.mm.trans1:probe6	-0.054663104648039	0.0545852499153722	-1.00142629616586	0.316883500117081	   
df.mm.trans1:probe7	-0.0974228044221709	0.0545852499153722	-1.78478260286823	0.0746252846608606	.  
df.mm.trans1:probe8	-0.0332780492736628	0.0545852499153722	-0.609652778456752	0.542242049875094	   
df.mm.trans1:probe9	-0.050998350436645	0.0545852499153722	-0.934288118414988	0.350399907352718	   
df.mm.trans1:probe10	-0.0295437607408483	0.0545852499153722	-0.541240734202963	0.588472411107707	   
df.mm.trans1:probe11	-0.0119110401840403	0.0545852499153722	-0.218209868096362	0.827313883113453	   
df.mm.trans1:probe12	-0.0138773097412751	0.0545852499153722	-0.254231862321601	0.799373123360726	   
df.mm.trans1:probe13	-0.0138474920432007	0.0545852499153722	-0.253685603064373	0.79979501555335	   
df.mm.trans1:probe14	-0.108213071414205	0.0545852499153722	-1.98245994260311	0.0477242670507493	*  
df.mm.trans1:probe15	-0.0188090257322027	0.0545852499153722	-0.344580738594471	0.730488169688317	   
df.mm.trans1:probe16	-0.0545716591284805	0.0545852499153722	-0.999751017226948	0.317693320584510	   
df.mm.trans1:probe17	-0.0320171903117843	0.0545852499153722	-0.586553883355358	0.55764693926528	   
df.mm.trans1:probe18	-0.0107835541299884	0.0545852499153722	-0.197554360320911	0.843437290043789	   
df.mm.trans1:probe19	0.0106481580577072	0.0545852499153722	0.195073908688078	0.845378065840318	   
df.mm.trans1:probe20	-0.0424493831803166	0.0545852499153722	-0.777671316814143	0.436962303213328	   
df.mm.trans1:probe21	-0.0780389649253758	0.0545852499153722	-1.42967129483452	0.1531501295143	   
df.mm.trans1:probe22	-0.00243716330506575	0.0545852499153722	-0.044648752343248	0.964396950514865	   
df.mm.trans2:probe2	0.0547154237460513	0.0545852499153722	1.00238478033683	0.316420784732745	   
df.mm.trans2:probe3	-0.000606089188743837	0.0545852499153722	-0.0111035341907110	0.991143245567957	   
df.mm.trans2:probe4	-0.0176490152886051	0.0545852499153722	-0.323329385062224	0.746519120225653	   
df.mm.trans2:probe5	0.0141669832980632	0.0545852499153722	0.259538672444066	0.795277586948496	   
df.mm.trans2:probe6	0.0378324560559047	0.0545852499153722	0.693089362319663	0.48842801872467	   
df.mm.trans3:probe2	0.00501552366873152	0.0545852499153722	0.0918842301996874	0.926809980389179	   
df.mm.trans3:probe3	0.0315712742774693	0.0545852499153722	0.578384716135161	0.563145655439521	   
df.mm.trans3:probe4	0.0221218350861647	0.0545852499153722	0.405271298023951	0.68537211875268	   
df.mm.trans3:probe5	0.00549533362974266	0.0545852499153722	0.100674333052656	0.919830874141017	   
df.mm.trans3:probe6	-0.0117455299633166	0.0545852499153722	-0.215177726245216	0.829676343037698	   
df.mm.trans3:probe7	0.0553993460521943	0.0545852499153722	1.01491421470240	0.310412974208305	   
df.mm.trans3:probe8	0.0221720281738754	0.0545852499153722	0.406190833755464	0.68469664411867	   
df.mm.trans3:probe9	0.0207756417155733	0.0545852499153722	0.380609079335231	0.703581014789492	   
df.mm.trans3:probe10	0.00880105888504679	0.0545852499153722	0.161235112025534	0.871943549844094	   
df.mm.trans3:probe11	0.0167232952392719	0.0545852499153722	0.306370223919454	0.759391946155458	   
df.mm.trans3:probe12	0.0625458606592542	0.0545852499153722	1.14583812946215	0.252159445053452	   
df.mm.trans3:probe13	0.0272555343966341	0.0545852499153722	0.499320502129981	0.617672735528533