chr6.20429_chr6_141103079_141110619_+_2.R 

fitVsDatCorrelation=0.784143370644939
cont.fitVsDatCorrelation=0.272488099678248

fstatistic=6202.63883825276,48,600
cont.fstatistic=2573.04526427276,48,600

residuals=-0.610237583677833,-0.0814077649867066,0.00148518806204587,0.0813461056778102,1.38310825445102
cont.residuals=-0.596242767928671,-0.175569940226142,-0.0395250561897682,0.132968759683594,2.34567789451164

predictedValues:
Include	Exclude	Both
chr6.20429_chr6_141103079_141110619_+_2.R.tl.Lung	49.9230180482043	57.5353001568433	64.7421840297408
chr6.20429_chr6_141103079_141110619_+_2.R.tl.cerebhem	64.4933476229168	60.8593624256755	98.039506792303
chr6.20429_chr6_141103079_141110619_+_2.R.tl.cortex	55.7009179006494	52.1280428004168	83.4103046468863
chr6.20429_chr6_141103079_141110619_+_2.R.tl.heart	51.5382799969395	54.1639456114252	62.7091858566157
chr6.20429_chr6_141103079_141110619_+_2.R.tl.kidney	48.0533429817648	58.7546194597109	62.374752248606
chr6.20429_chr6_141103079_141110619_+_2.R.tl.liver	53.0729436386888	56.8602496003856	62.9492401740584
chr6.20429_chr6_141103079_141110619_+_2.R.tl.stomach	50.0046115343935	55.8638218599477	69.3402566524195
chr6.20429_chr6_141103079_141110619_+_2.R.tl.testicle	50.9662687245884	54.9365520131998	64.0394276916648


diffExp=-7.61228210863904,3.63398519724131,3.57287510023251,-2.62566561448572,-10.7012764779461,-3.78730596169674,-5.8592103255542,-3.97028328861136
diffExpScore=1.47316107244811
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,-1,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	58.9694238477673	63.8648854293326	62.1212633302977
cerebhem	65.7190405741206	60.6869293504346	64.4549321210765
cortex	70.6718280089231	61.8411667374274	60.6658004350032
heart	65.2478775748677	56.7907892921342	64.5022094589832
kidney	67.6273410564555	63.2542517943221	59.8314298121391
liver	62.0616619483451	63.5732228417686	70.7231535074717
stomach	62.6726070334891	66.9925437896639	60.2490393468986
testicle	61.7998930608445	69.8806312184339	68.1999957879525
cont.diffExp=-4.89546158156532,5.03211122368604,8.83066127149572,8.45708828273344,4.3730892621334,-1.51156089342346,-4.31993675617471,-8.08073815758941
cont.diffExpScore=5.12091768399701

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=0.325851859759476
cont.tran.correlation=-0.442837227434046

tran.covariance=0.00121475244669788
cont.tran.covariance=-0.00157846388865814

tran.mean=54.6784140234844
cont.tran.mean=63.8533808473956

weightedLogRatios:
wLogRatio
Lung	-0.565032071627471
cerebhem	0.239963861303297
cortex	0.264302519619104
heart	-0.19713124184534
kidney	-0.798771704249976
liver	-0.276139599945219
stomach	-0.439607707111757
testicle	-0.297709506067597

cont.weightedLogRatios:
wLogRatio
Lung	-0.328324866110858
cerebhem	0.330236648543929
cortex	0.559446381171048
heart	0.570378766442372
kidney	0.279472295291865
liver	-0.0996284837370292
stomach	-0.278042635737513
testicle	-0.51432401512069

varWeightedLogRatios=0.135151174729674
cont.varWeightedLogRatios=0.178762177397692

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.78168784197974	0.0952477241832343	39.7037081400985	5.09492144959242e-170	***
df.mm.trans1	0.127742431639349	0.0753864395587986	1.69450145658776	0.0906889508876492	.  
df.mm.trans2	0.256129546983536	0.0753864395587986	3.39755463293587	0.00072492417874807	***
df.mm.exp2	-0.102710650590295	0.100053111204058	-1.02656128684312	0.30504076468804	   
df.mm.exp3	-0.242540050810483	0.100053111204058	-2.42411303248555	0.0156403179863652	*  
df.mm.exp4	0.00336449583826050	0.100053111204058	0.0336270986256352	0.973185696277161	   
df.mm.exp5	0.0200530311077010	0.100053111204058	0.200423863549859	0.841217012800827	   
df.mm.exp6	0.077467196429088	0.100053111204058	0.774260745086617	0.439081592181753	   
df.mm.exp7	-0.0964613048338266	0.100053111204058	-0.964101002687405	0.335383691830014	   
df.mm.exp8	-0.0146238872519913	0.100053111204058	-0.146161244523080	0.88384319127549	   
df.mm.trans1:exp2	0.358790551513694	0.0764168259347382	4.69517736604383	3.30399877024057e-06	***
df.mm.trans2:exp2	0.158877643717375	0.0764168259347382	2.07909242204146	0.0380330576237214	*  
df.mm.trans1:exp3	0.352054497043196	0.0764168259347382	4.60702852724947	4.99134412212064e-06	***
df.mm.trans2:exp3	0.143844429088342	0.0764168259347382	1.88236592306501	0.0602700202063173	.  
df.mm.trans1:exp4	0.028478156701488	0.0764168259347382	0.372668667576026	0.70952666352049	   
df.mm.trans2:exp4	-0.0637476940696848	0.0764168259347382	-0.834210179367132	0.404494553125229	   
df.mm.trans1:exp5	-0.0582235050599889	0.0764168259347382	-0.761919961314713	0.446407162875802	   
df.mm.trans2:exp5	0.000918072721626282	0.0764168259347382	0.0120140127569593	0.990418428836075	   
df.mm.trans1:exp6	-0.0162821139634132	0.0764168259347383	-0.213069749551212	0.831344932908379	   
df.mm.trans2:exp6	-0.0892693756852255	0.0764168259347382	-1.16819004968178	0.243194077167502	   
df.mm.trans1:exp7	0.0980943567667748	0.0764168259347383	1.28367483949869	0.19975127127943	   
df.mm.trans2:exp7	0.0669796063505479	0.0764168259347383	0.876503381699602	0.381107222073956	   
df.mm.trans1:exp8	0.0353057236692934	0.0764168259347383	0.462015050186006	0.644238002484624	   
df.mm.trans2:exp8	-0.0315958683892199	0.0764168259347383	-0.413467426875378	0.679411917701564	   
df.mm.trans1:probe2	-0.0190691448620703	0.0559313895031548	-0.340938157114704	0.733269488338244	   
df.mm.trans1:probe3	-0.00654169938027425	0.0559313895031548	-0.116959357498266	0.90693139873286	   
df.mm.trans1:probe4	0.105487560626095	0.0559313895031548	1.88601716429993	0.0597753911321928	.  
df.mm.trans1:probe5	-0.00514545925384896	0.0559313895031548	-0.0919959131993088	0.926731992986984	   
df.mm.trans1:probe6	-0.0526412245991396	0.0559313895031548	-0.941174983613993	0.346993961740536	   
df.mm.trans2:probe2	-0.0161411336043508	0.0559313895031548	-0.288588103169516	0.772996271417355	   
df.mm.trans2:probe3	0.00673698041769395	0.0559313895031548	0.120450796548045	0.90416637737711	   
df.mm.trans2:probe4	0.0234161008323599	0.0559313895031548	0.418657591745313	0.675616210448885	   
df.mm.trans2:probe5	0.236066526587611	0.0559313895031548	4.22064477004091	2.81336581353228e-05	***
df.mm.trans2:probe6	0.0561285368491059	0.0559313895031548	1.00352480686967	0.316012248477808	   
df.mm.trans3:probe2	-0.213290748778340	0.0559313895031548	-3.81343554438798	0.000151223631354537	***
df.mm.trans3:probe3	0.242418772777108	0.0559313895031548	4.3342168848395	1.71517639742899e-05	***
df.mm.trans3:probe4	-0.0342800295625073	0.0559313895031548	-0.61289429543984	0.540178468850971	   
df.mm.trans3:probe5	-0.0675373239217923	0.0559313895031548	-1.20750305904671	0.227714241866634	   
df.mm.trans3:probe6	-0.210218698168253	0.0559313895031548	-3.75851020394185	0.000187631847648119	***
df.mm.trans3:probe7	0.0256341257200579	0.0559313895031548	0.458313765271504	0.646892985277045	   
df.mm.trans3:probe8	0.110043432560157	0.0559313895031548	1.96747181748364	0.0495890699276077	*  
df.mm.trans3:probe9	-0.158230441453642	0.0559313895031548	-2.82900966450541	0.00482531206960223	** 
df.mm.trans3:probe10	-0.0658414026764356	0.0559313895031548	-1.177181601625	0.239589787200063	   
df.mm.trans3:probe11	-0.309152262075551	0.0559313895031548	-5.52734814603727	4.85343620534935e-08	***
df.mm.trans3:probe12	0.559275675083663	0.0559313895031548	9.99931666371558	7.07193342672776e-22	***
df.mm.trans3:probe13	0.0792641350307426	0.0559313895031548	1.41716727824671	0.156953064404719	   
df.mm.trans3:probe14	-0.0291552016040236	0.0559313895031548	-0.521267250161542	0.60237294901391	   
df.mm.trans3:probe15	0.143867407952712	0.0559313895031548	2.57221229850901	0.0103445019017617	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10211506939681	0.147673970645260	27.7781863078012	8.15728699625486e-110	***
df.mm.trans1	-0.0137883458001716	0.116880638964561	-0.117969459461565	0.90613133760467	   
df.mm.trans2	0.0586709131180553	0.116880638964561	0.501972898487016	0.615870737227093	   
df.mm.exp2	0.0204503632844509	0.155124338493284	0.131832074083824	0.89516131090547	   
df.mm.exp3	0.172535751331955	0.155124338493284	1.11224165729109	0.266479701328808	   
df.mm.exp4	-0.0538322293195867	0.155124338493284	-0.347026326380868	0.728693222302587	   
df.mm.exp5	0.164943175900995	0.155124338493284	1.06329656263537	0.288075234118131	   
df.mm.exp6	-0.0831526344728511	0.155124338493284	-0.536038607999937	0.592130526226114	   
df.mm.exp7	0.139318740199721	0.155124338493284	0.898110132509942	0.369487086168842	   
df.mm.exp8	0.0435452278421277	0.155124338493284	0.280711771376952	0.779028245384175	   
df.mm.trans1:exp2	0.0879192613837966	0.118478170545904	0.742071395757518	0.458334464257444	   
df.mm.trans2:exp2	-0.0714917069018923	0.118478170545904	-0.603416701764422	0.546459551614127	   
df.mm.trans1:exp3	0.00849220016225779	0.118478170545904	0.0716773404174694	0.942882574737476	   
df.mm.trans2:exp3	-0.204736166738382	0.118478170545904	-1.72804969721453	0.0844938316358425	.  
df.mm.trans1:exp4	0.155006677700446	0.118478170545904	1.30831424038902	0.191267706930817	   
df.mm.trans2:exp4	-0.0635633049948065	0.118478170545904	-0.536498029146887	0.591813253323742	   
df.mm.trans1:exp5	-0.0279498908281579	0.118478170545904	-0.235907515277920	0.813584939520906	   
df.mm.trans2:exp5	-0.174550515050387	0.118478170545904	-1.47327152543056	0.141202238355878	   
df.mm.trans1:exp6	0.134262003132469	0.118478170545904	1.13322144082609	0.257573882776071	   
df.mm.trans2:exp6	0.0785753051203955	0.118478170545904	0.663204915794605	0.507454035185373	   
df.mm.trans1:exp7	-0.0784133471475441	0.118478170545905	-0.66183793002748	0.508329191605771	   
df.mm.trans2:exp7	-0.091507100265354	0.118478170545904	-0.77235409564245	0.440208850071188	   
df.mm.trans1:exp8	0.00333733650829837	0.118478170545904	0.0281683663152556	0.977537233614303	   
df.mm.trans2:exp8	0.0464736037317488	0.118478170545904	0.392254569070532	0.695009423507026	   
df.mm.trans1:probe2	-0.0247801786718048	0.0867171414589177	-0.285758712232742	0.775161558550617	   
df.mm.trans1:probe3	-0.0246432496782344	0.0867171414589177	-0.284179681936461	0.776370728539908	   
df.mm.trans1:probe4	0.00494959305309351	0.0867171414589177	0.0570774470862647	0.954502504110602	   
df.mm.trans1:probe5	-0.107866966110674	0.0867171414589177	-1.24389439384111	0.214024177232384	   
df.mm.trans1:probe6	-0.0851199303235518	0.0867171414589177	-0.98158136778387	0.326701757357759	   
df.mm.trans2:probe2	-0.0501097742208766	0.0867171414589177	-0.577853160030836	0.563580053938649	   
df.mm.trans2:probe3	0.00490740045538253	0.0867171414589177	0.0565908927902959	0.954889929281574	   
df.mm.trans2:probe4	-0.00240639858022877	0.0867171414589177	-0.0277499758380389	0.977870791232022	   
df.mm.trans2:probe5	0.0164374177515827	0.0867171414589177	0.189552117090598	0.849724216973429	   
df.mm.trans2:probe6	-0.0531708608288707	0.0867171414589177	-0.613152831543235	0.540007637958303	   
df.mm.trans3:probe2	-0.0134965673123363	0.0867171414589177	-0.155638978468061	0.87636994567848	   
df.mm.trans3:probe3	-0.0318604861185610	0.0867171414589177	-0.367407015297604	0.713444994523959	   
df.mm.trans3:probe4	0.0634905451273313	0.0867171414589177	0.73215680382419	0.464358753357981	   
df.mm.trans3:probe5	-0.140355740940794	0.0867171414589177	-1.61854667461896	0.10607034680294	   
df.mm.trans3:probe6	0.0578668672203895	0.0867171414589177	0.667305981802963	0.504833262928826	   
df.mm.trans3:probe7	0.0527289534884262	0.0867171414589177	0.608056868588162	0.543379847246835	   
df.mm.trans3:probe8	-0.0479139163009806	0.0867171414589177	-0.552531085491094	0.580790312723836	   
df.mm.trans3:probe9	0.0630319353899983	0.0867171414589177	0.726868233080074	0.467590167498643	   
df.mm.trans3:probe10	0.114408031731772	0.0867171414589177	1.31932429744554	0.187563952669198	   
df.mm.trans3:probe11	-0.0691314038895714	0.0867171414589177	-0.797205751095041	0.425646903546125	   
df.mm.trans3:probe12	-0.0661914871333065	0.0867171414589177	-0.763303379466963	0.445582501596384	   
df.mm.trans3:probe13	-0.0747396078370487	0.0867171414589177	-0.86187813135488	0.389098905992242	   
df.mm.trans3:probe14	-0.0652177090244459	0.0867171414589177	-0.752074018207148	0.452301469906504	   
df.mm.trans3:probe15	-0.111258940016065	0.0867171414589177	-1.28300977343417	0.199984018317066