chr1.1488_chr1_160367117_160371795_+_2.R 

fitVsDatCorrelation=0.915848378526855
cont.fitVsDatCorrelation=0.266584688627369

fstatistic=9103.2251040072,51,669
cont.fstatistic=1569.07755590157,51,669

residuals=-0.526672389984958,-0.0935925671344411,-0.000839426943314173,0.0945102491513625,0.577105260846557
cont.residuals=-0.62956277122177,-0.27621568083614,-0.0928568442912186,0.172090502646035,1.14694540336499

predictedValues:
Include	Exclude	Both
chr1.1488_chr1_160367117_160371795_+_2.R.tl.Lung	60.2385808865812	47.2081404783799	53.9984000712739
chr1.1488_chr1_160367117_160371795_+_2.R.tl.cerebhem	60.6754583262607	63.62270119856	60.2306825503763
chr1.1488_chr1_160367117_160371795_+_2.R.tl.cortex	56.7095580383673	52.5248207242711	58.23868898179
chr1.1488_chr1_160367117_160371795_+_2.R.tl.heart	54.4893056385578	51.5651392577183	57.9647499318792
chr1.1488_chr1_160367117_160371795_+_2.R.tl.kidney	62.7799914769095	48.360536911653	50.8242228112792
chr1.1488_chr1_160367117_160371795_+_2.R.tl.liver	66.843603396884	52.3506832108596	53.5941349244055
chr1.1488_chr1_160367117_160371795_+_2.R.tl.stomach	57.5718026793087	55.3662187645959	60.0001955505506
chr1.1488_chr1_160367117_160371795_+_2.R.tl.testicle	55.8296056523011	58.7254324992947	62.1160912207362


diffExp=13.0304404082012,-2.94724287229938,4.1847373140962,2.92416638083948,14.4194545652565,14.4929201860244,2.20558391471280,-2.89582684699356
diffExpScore=1.23023410571304
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=1,0,0,0,1,1,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	51.9496692934951	61.1447877272916	64.0737981386383
cerebhem	61.8765858004088	50.6796009822102	61.4821391744825
cortex	62.2779440234035	54.4334072883319	57.1666121139616
heart	60.7316078505962	58.5792661347815	68.0519044003123
kidney	64.0336221988636	52.1168417121833	57.9707778133637
liver	59.978048785794	57.520181502106	63.526148702441
stomach	63.6541739807943	57.3579411251757	57.4676742342779
testicle	60.6707884100183	62.210100207188	54.1283482835638
cont.diffExp=-9.1951184337965,11.1969848181986,7.84453673507164,2.15234171581467,11.9167804866802,2.45786728368807,6.29623285561864,-1.53931179716974
cont.diffExpScore=1.63705759850080

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=0,1,0,0,1,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.182579099925826
cont.tran.correlation=-0.585014980893168

tran.covariance=-0.00129261081251133
cont.tran.covariance=-0.00273382284750268

tran.mean=56.5538486962814
cont.tran.mean=58.7009104389151

weightedLogRatios:
wLogRatio
Lung	0.969243971849886
cerebhem	-0.195854844016850
cortex	0.306598173734774
heart	0.219003121413750
kidney	1.04619954420226
liver	0.997152925952402
stomach	0.157561760814356
testicle	-0.20468088626996

cont.weightedLogRatios:
wLogRatio
Lung	-0.657052979141814
cerebhem	0.803530335472657
cortex	0.547172737836572
heart	0.147524477326903
kidney	0.835304379175544
liver	0.170428009508126
stomach	0.427174328288618
testicle	-0.103176369149188

varWeightedLogRatios=0.274001823147485
cont.varWeightedLogRatios=0.247048564995842

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97465971814572	0.0784469940291762	50.6668198996567	3.4728748272786e-231	***
df.mm.trans1	-0.145906143128847	0.0677976496771184	-2.1520826138327	0.0317479622651967	*  
df.mm.trans2	-0.124104069037852	0.0613493259564288	-2.02290843628816	0.0434798478070153	*  
df.mm.exp2	0.196402796506492	0.0806322946284625	2.43578329764118	0.0151196127030336	*  
df.mm.exp3	-0.0292462440349769	0.0806322946284625	-0.362711295390237	0.716935121390758	   
df.mm.exp4	-0.0829097152242567	0.0806322946284625	-1.02824452170545	0.304206435259211	   
df.mm.exp5	0.126022522574952	0.0806322946284626	1.56292863989099	0.118542268199249	   
df.mm.exp6	0.214956008859631	0.0806322946284625	2.66587984194305	0.00786414159759902	** 
df.mm.exp7	0.00872977458359266	0.0806322946284625	0.108266478385834	0.913816775132018	   
df.mm.exp8	0.00224716479146033	0.0806322946284626	0.0278692898647473	0.977774713325717	   
df.mm.trans1:exp2	-0.18917651668597	0.0739140980467842	-2.55941047357745	0.0107037323022318	*  
df.mm.trans2:exp2	0.102001201754034	0.0596640034186173	1.70959365630175	0.0878047605025545	.  
df.mm.trans1:exp3	-0.0311240128872314	0.0739140980467842	-0.421083578230656	0.673829296217288	   
df.mm.trans2:exp3	0.135965732126796	0.0596640034186173	2.27885700483132	0.0229898191897038	*  
df.mm.trans1:exp4	-0.0173988547314744	0.0739140980467842	-0.235392911382910	0.813975823771316	   
df.mm.trans2:exp4	0.17118921813051	0.0596640034186172	2.8692211102464	0.00424463327378145	** 
df.mm.trans1:exp5	-0.0846991324768701	0.0739140980467842	-1.14591308985817	0.252240837063667	   
df.mm.trans2:exp5	-0.101904739981433	0.0596640034186173	-1.70797690638431	0.0881045360750327	.  
df.mm.trans1:exp6	-0.110913421354831	0.0739140980467842	-1.50057193804392	0.133938094864484	   
df.mm.trans2:exp6	-0.111557366282924	0.0596640034186173	-1.86975998744520	0.0619537224180553	.  
df.mm.trans1:exp7	-0.05400988911605	0.0739140980467842	-0.730711603649201	0.465211148189706	   
df.mm.trans2:exp7	0.150673518109484	0.0596640034186173	2.52536721433730	0.0117876281943329	*  
df.mm.trans1:exp8	-0.0782558944141678	0.0739140980467842	-1.05874111275274	0.290099814590513	   
df.mm.trans2:exp8	0.216059385067544	0.0596640034186173	3.62126864923258	0.000315335719687653	***
df.mm.trans1:probe2	0.137210920689309	0.0469715895498099	2.92114705941144	0.00360494018307250	** 
df.mm.trans1:probe3	0.0703007310017723	0.0469715895498099	1.49666493460315	0.134952074454833	   
df.mm.trans1:probe4	0.409359882769720	0.0469715895498099	8.71505279453282	2.27734204034099e-17	***
df.mm.trans1:probe5	0.0444700370839213	0.0469715895498099	0.946743286955705	0.344111435552928	   
df.mm.trans1:probe6	0.990860864533294	0.0469715895498099	21.0948974482237	4.07373339789487e-76	***
df.mm.trans1:probe7	0.879589414310886	0.0469715895498099	18.7259878309663	3.13299414221066e-63	***
df.mm.trans1:probe8	1.04371287248955	0.0469715895498099	22.2200884086064	2.37819198272607e-82	***
df.mm.trans1:probe9	0.895327710520324	0.0469715895498099	19.0610477333516	4.99209760217390e-65	***
df.mm.trans1:probe10	1.05659332338903	0.0469715895498099	22.4943063140026	7.06793949043001e-84	***
df.mm.trans1:probe11	0.899691689193686	0.0469715895498099	19.1539545034904	1.57818740077544e-65	***
df.mm.trans1:probe12	0.0102961479493804	0.0469715895498099	0.219199478835225	0.826561520004915	   
df.mm.trans1:probe13	0.0443715984712724	0.0469715895498099	0.94464758158162	0.345179860829476	   
df.mm.trans1:probe14	0.0162991556522708	0.0469715895498099	0.347000299723447	0.728700218664032	   
df.mm.trans1:probe15	0.011021359290781	0.0469715895498099	0.234638840124703	0.814560855120691	   
df.mm.trans1:probe16	-0.0426010125552368	0.0469715895498099	-0.906952755134283	0.364758333865423	   
df.mm.trans1:probe17	0.00292211811252747	0.0469715895498099	0.0622103305537228	0.950413916962714	   
df.mm.trans2:probe2	0.0646261224252734	0.0469715895498099	1.37585555534037	0.169326779509080	   
df.mm.trans2:probe3	-0.0529044147521066	0.0469715895498099	-1.12630667301573	0.260439587534245	   
df.mm.trans2:probe4	0.0266829084460631	0.0469715895498099	0.568064838805761	0.570181545936642	   
df.mm.trans2:probe5	0.0960628246861741	0.0469715895498099	2.04512611999870	0.0412331489716077	*  
df.mm.trans2:probe6	-0.0823283879294386	0.0469715895498099	-1.75272731279693	0.0801068722813991	.  
df.mm.trans3:probe2	0.357168459972022	0.0469715895498099	7.60392533859796	9.75800941451941e-14	***
df.mm.trans3:probe3	0.267415195807327	0.0469715895498099	5.69312638491302	1.86769216889488e-08	***
df.mm.trans3:probe4	0.392422477090418	0.0469715895498099	8.35446449335683	3.79122181753268e-16	***
df.mm.trans3:probe5	0.234609575601799	0.0469715895498099	4.99471228992607	7.52797385649036e-07	***
df.mm.trans3:probe6	0.335852883133542	0.0469715895498099	7.15012811685657	2.27821320369783e-12	***
df.mm.trans3:probe7	0.402665791250987	0.0469715895498099	8.57253916910753	6.9972977524552e-17	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81187684356501	0.188302721831033	20.2433443685720	1.92946860040421e-71	***
df.mm.trans1	0.0431817976002730	0.162740231489305	0.265341871552585	0.790827851205712	   
df.mm.trans2	0.279909540685776	0.147261793814537	1.90076144962825	0.0577627962422403	.  
df.mm.exp2	0.0284341613962449	0.193548277201982	0.146909917294545	0.883247383786898	   
df.mm.exp3	0.179130923173661	0.193548277201982	0.92551029522585	0.35503425782051	   
df.mm.exp4	0.0530899269853952	0.193548277201982	0.274298111834867	0.783940210945943	   
df.mm.exp5	0.149472935748918	0.193548277201982	0.77227727319387	0.440222951195468	   
df.mm.exp6	0.0911784411549622	0.193548277201982	0.471088880113413	0.637730837952154	   
df.mm.exp7	0.248069141476487	0.193548277201982	1.28169129202637	0.200394982044843	   
df.mm.exp8	0.341137202079603	0.193548277201982	1.76254321149860	0.0784341410984362	.  
df.mm.trans1:exp2	0.14643233659823	0.177422041674647	0.825333398353937	0.409476534225422	   
df.mm.trans2:exp2	-0.216155300260572	0.143216376587778	-1.50929178220124	0.131696353296286	   
df.mm.trans1:exp3	0.00220105975658239	0.177422041674647	0.0124057853004456	0.99010556787795	   
df.mm.trans2:exp3	-0.295397474238959	0.143216376587778	-2.06259564218138	0.0395365254859969	*  
df.mm.trans1:exp4	0.103099006302805	0.177422041674647	0.58109468998145	0.561372349083555	   
df.mm.trans2:exp4	-0.0959537342442162	0.143216376587778	-0.669991355251232	0.503094488284309	   
df.mm.trans1:exp5	0.059660004858172	0.177422041674647	0.336260389605793	0.736779958311068	   
df.mm.trans2:exp5	-0.309229402801554	0.143216376587778	-2.15917627696736	0.0311912762942111	*  
df.mm.trans1:exp6	0.0525248488928176	0.177422041674647	0.296044665009191	0.767287842741097	   
df.mm.trans2:exp6	-0.152287193293409	0.143216376587778	-1.06333644881786	0.28801306484333	   
df.mm.trans1:exp7	-0.0448795932120931	0.177422041674647	-0.252953876465881	0.800381560956282	   
df.mm.trans2:exp7	-0.312002460748955	0.143216376587778	-2.17853899241563	0.0297143003252630	*  
df.mm.trans1:exp8	-0.185950216817790	0.177422041674647	-1.04806716833290	0.294986146985008	   
df.mm.trans2:exp8	-0.323864453771974	0.143216376587778	-2.26136466714389	0.0240569628172326	*  
df.mm.trans1:probe2	0.104780201955317	0.112749739749998	0.929316574811147	0.353060235878256	   
df.mm.trans1:probe3	0.144771852955928	0.112749739749998	1.28401052877757	0.199582773396625	   
df.mm.trans1:probe4	0.301447199204417	0.112749739749998	2.6735955211322	0.00768740481653836	** 
df.mm.trans1:probe5	0.113090311836539	0.112749739749998	1.00302060197474	0.316213566275638	   
df.mm.trans1:probe6	0.127271652410222	0.112749739749998	1.1287977488234	0.259387773141203	   
df.mm.trans1:probe7	0.0318166583381373	0.112749739749998	0.282188308449180	0.777886397755696	   
df.mm.trans1:probe8	0.242174344063121	0.112749739749998	2.14789270999736	0.0320807658239350	*  
df.mm.trans1:probe9	0.122118631484088	0.112749739749998	1.08309457525013	0.279156653261001	   
df.mm.trans1:probe10	0.00040410251905947	0.112749739749998	0.00358406609146497	0.997141403570065	   
df.mm.trans1:probe11	0.133706621635852	0.112749739749998	1.18587077834788	0.236094345011001	   
df.mm.trans1:probe12	0.210825778460073	0.112749739749998	1.86985600966831	0.0619403623622745	.  
df.mm.trans1:probe13	0.113308789150870	0.112749739749998	1.00495832098691	0.315280262396457	   
df.mm.trans1:probe14	0.207201181266212	0.112749739749998	1.83770873197261	0.0665486156980655	.  
df.mm.trans1:probe15	0.111197791690236	0.112749739749998	0.986235462155354	0.324374127144572	   
df.mm.trans1:probe16	0.114983516867125	0.112749739749998	1.01981181616986	0.308186361559140	   
df.mm.trans1:probe17	0.206102418119332	0.112749739749998	1.82796358179031	0.0680002185966557	.  
df.mm.trans2:probe2	-0.0312715092114034	0.112749739749998	-0.277353271774659	0.781594538918948	   
df.mm.trans2:probe3	0.160554733894310	0.112749739749998	1.42399205754541	0.154915011512102	   
df.mm.trans2:probe4	0.142478407862602	0.112749739749998	1.26366950538885	0.2067888332661	   
df.mm.trans2:probe5	0.0840342593345656	0.112749739749998	0.745316659008668	0.456342076693136	   
df.mm.trans2:probe6	-0.0768388140483778	0.112749739749998	-0.68149881515251	0.495791756553907	   
df.mm.trans3:probe2	0.0648991937795683	0.112749739749998	0.575603934195061	0.565076477315217	   
df.mm.trans3:probe3	-0.00721089473955656	0.112749739749998	-0.0639548681491007	0.94902526747167	   
df.mm.trans3:probe4	0.0417169839288701	0.112749739749998	0.369996276899351	0.711502363937529	   
df.mm.trans3:probe5	0.0879555530303134	0.112749739749998	0.780095397340504	0.435610628971638	   
df.mm.trans3:probe6	-0.0542209671136784	0.112749739749998	-0.480896605472468	0.630747216881255	   
df.mm.trans3:probe7	0.042331462957018	0.112749739749998	0.37544621434054	0.707447647119585