chr6.19858_chr6_143066722_143070690_-_2.R 

fitVsDatCorrelation=0.932797054904156
cont.fitVsDatCorrelation=0.248433647158525

fstatistic=4310.81613627202,55,761
cont.fstatistic=584.841201301386,55,761

residuals=-1.77087640773826,-0.140051767984578,0.01050671182271,0.155700086202133,1.23206711555237
cont.residuals=-1.58383700574271,-0.606554067720039,-0.152346455177025,0.561311855182088,2.81899312842684

predictedValues:
Include	Exclude	Both
chr6.19858_chr6_143066722_143070690_-_2.R.tl.Lung	153.268535094502	80.176572502844	77.9798214510543
chr6.19858_chr6_143066722_143070690_-_2.R.tl.cerebhem	277.184013742654	95.726572492251	132.358186623518
chr6.19858_chr6_143066722_143070690_-_2.R.tl.cortex	352.10306972712	96.2639370827919	136.931433026057
chr6.19858_chr6_143066722_143070690_-_2.R.tl.heart	562.029053980104	161.633310479941	244.514324797938
chr6.19858_chr6_143066722_143070690_-_2.R.tl.kidney	458.302038443101	101.752495161233	194.493771536428
chr6.19858_chr6_143066722_143070690_-_2.R.tl.liver	94.5694487771992	88.8956320172356	59.7391866372724
chr6.19858_chr6_143066722_143070690_-_2.R.tl.stomach	185.821006246622	97.8463976137177	92.4742457573088
chr6.19858_chr6_143066722_143070690_-_2.R.tl.testicle	131.000860312793	88.6908506179263	79.689128731617


diffExp=73.0919625916581,181.457441250403,255.839132644328,400.395743500163,356.549543281868,5.67381675996351,87.974608632904,42.3100096948669
diffExpScore=0.999287897519872
diffExp1.5=1,1,1,1,1,0,1,0
diffExp1.5Score=0.857142857142857
diffExp1.4=1,1,1,1,1,0,1,1
diffExp1.4Score=0.875
diffExp1.3=1,1,1,1,1,0,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	182.058165725486	161.577852630852	187.490298140437
cerebhem	169.234147254706	189.778976504306	168.891492349628
cortex	142.758202039317	168.056449526095	192.107201185737
heart	168.489377017149	244.441615371246	165.784207547527
kidney	171.533952427023	264.44681083085	147.730568384031
liver	180.013651902239	175.101821555803	156.212702088477
stomach	151.291652213706	177.086865744102	209.329305858359
testicle	144.932284694030	270.481329988893	174.268655227081
cont.diffExp=20.4803130946334,-20.5448292495999,-25.2982474867781,-75.9522383540967,-92.9128584038265,4.91183034643529,-25.7952135303960,-125.549045294864
cont.diffExpScore=1.14571282792494

cont.diffExp1.5=0,0,0,0,-1,0,0,-1
cont.diffExp1.5Score=0.666666666666667
cont.diffExp1.4=0,0,0,-1,-1,0,0,-1
cont.diffExp1.4Score=0.75
cont.diffExp1.3=0,0,0,-1,-1,0,0,-1
cont.diffExp1.3Score=0.75
cont.diffExp1.2=0,0,0,-1,-1,0,0,-1
cont.diffExp1.2Score=0.75

tran.correlation=0.804705759653481
cont.tran.correlation=-0.167798283294892

tran.covariance=0.0977307420248052
cont.tran.covariance=-0.00303646437241929

tran.mean=189.078987143252
cont.tran.mean=185.080197214113

weightedLogRatios:
wLogRatio
Lung	3.05073341462245
cerebhem	5.414898989313
cortex	6.76362929070889
heart	7.11399325673576
kidney	8.08934889237085
liver	0.279559947842662
stomach	3.14541065144783
testicle	1.82549102133247

cont.weightedLogRatios:
wLogRatio
Lung	0.613958910168754
cerebhem	-0.594490306439545
cortex	-0.82270880379624
heart	-1.97695935191297
kidney	-2.32064785083160
liver	0.143282830193783
stomach	-0.802571078201046
testicle	-3.2995224510405

varWeightedLogRatios=7.79504092226728
cont.varWeightedLogRatios=1.71325883784939

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.25031792291021	0.148196279209998	35.4281359214855	3.6254721280108e-163	***
df.mm.trans1	-0.00794097936062478	0.129767488824324	-0.0611939048260004	0.951220851150448	   
df.mm.trans2	-0.921628813459731	0.116371545464525	-7.91970932224737	8.43323658202475e-15	***
df.mm.exp2	0.240692957782925	0.153466249476395	1.56837714223248	0.117208830744416	   
df.mm.exp3	0.451564617148081	0.153466249476396	2.94243599937285	0.00335536016965134	** 
df.mm.exp4	0.857637125576058	0.153466249476395	5.58844129248086	3.19256303084901e-08	***
df.mm.exp5	0.419698867056875	0.153466249476396	2.73479588175789	0.00638710824909789	** 
df.mm.exp6	-0.113163496898795	0.153466249476396	-0.737383609001278	0.461116389373816	   
df.mm.exp7	0.221279795453444	0.153466249476396	1.44187921584334	0.149747841353634	   
df.mm.exp8	-0.0777453286007186	0.153466249476396	-0.506595612168632	0.612585328221295	   
df.mm.trans1:exp2	0.351797123102874	0.143964103029016	2.44364473991110	0.0147658829199774	*  
df.mm.trans2:exp2	-0.063428392137995	0.114843625194770	-0.552302246036063	0.580903459719804	   
df.mm.trans1:exp3	0.380167813027904	0.143964103029016	2.64071254589960	0.00844282045249776	** 
df.mm.trans2:exp3	-0.268702212370803	0.114843625194770	-2.33972248712191	0.0195557321173038	*  
df.mm.trans1:exp4	0.441724906168963	0.143964103029016	3.068298950051	0.00222917214079208	** 
df.mm.trans2:exp4	-0.156538230291371	0.114843625194770	-1.36305545933341	0.173268189075044	   
df.mm.trans1:exp5	0.675638057998114	0.143964103029016	4.69310087572274	3.19081124270178e-06	***
df.mm.trans2:exp5	-0.181386879333877	0.114843625194770	-1.57942488341214	0.114654173618693	   
df.mm.trans1:exp6	-0.369693545104604	0.143964103029016	-2.56795643723833	0.0104197771680934	*  
df.mm.trans2:exp6	0.216395145779007	0.114843625194770	1.88425909937979	0.0599108740602315	.  
df.mm.trans1:exp7	-0.0286874313722769	0.143964103029016	-0.199267947833461	0.842106379272657	   
df.mm.trans2:exp7	-0.0221122764510103	0.114843625194770	-0.192542480381552	0.847368665585825	   
df.mm.trans1:exp8	-0.0792422952323612	0.143964103029016	-0.550430930802173	0.582185367783905	   
df.mm.trans2:exp8	0.178670704086264	0.114843625194770	1.55577380793445	0.120177644105062	   
df.mm.trans1:probe2	-0.139201407780617	0.0881596484246388	-1.57896963370504	0.114758570214198	   
df.mm.trans1:probe3	-0.590737865850028	0.0881596484246388	-6.70077383934903	4.03425652074386e-11	***
df.mm.trans1:probe4	1.38103999544972	0.0881596484246388	15.6652166850492	3.78971037845874e-48	***
df.mm.trans1:probe5	0.258861073155021	0.0881596484246388	2.93627615105909	0.00342194562362014	** 
df.mm.trans1:probe6	-0.414918667204372	0.0881596484246388	-4.70644648224811	2.99449576764845e-06	***
df.mm.trans1:probe7	-0.627184588086919	0.0881596484246388	-7.11419112138422	2.60202087809846e-12	***
df.mm.trans1:probe8	-0.83111575953176	0.0881596484246388	-9.42739421473783	4.94040347809260e-20	***
df.mm.trans1:probe9	-0.71322339490613	0.0881596484246388	-8.09013429217351	2.35109981228898e-15	***
df.mm.trans1:probe10	0.0660747796546674	0.0881596484246388	0.74949005395762	0.453793510806268	   
df.mm.trans1:probe11	-0.326624507423569	0.0881596484246388	-3.70492071214163	0.000226765840537650	***
df.mm.trans1:probe12	-0.655484708618338	0.0881596484246388	-7.4352010282648	2.81732245791512e-13	***
df.mm.trans1:probe13	-1.25387684278047	0.0881596484246388	-14.2227976765620	7.00702674271166e-41	***
df.mm.trans1:probe14	-0.983374022352459	0.0881596484246388	-11.1544685116692	7.2436112944534e-27	***
df.mm.trans1:probe15	-0.333993078833354	0.0881596484246388	-3.78850284457361	0.000163520356188624	***
df.mm.trans1:probe16	-0.256892852353563	0.0881596484246388	-2.91395050847057	0.00367350093918794	** 
df.mm.trans1:probe17	0.116723774300752	0.0881596484246388	1.32400453480177	0.185899082283138	   
df.mm.trans1:probe18	-0.162571703092898	0.0881596484246388	-1.84406024749371	0.0655629111269717	.  
df.mm.trans1:probe19	-0.0545155115791752	0.0881596484246388	-0.618372606439969	0.536514780603363	   
df.mm.trans1:probe20	0.00259431255647516	0.0881596484246388	0.0294274376410751	0.976531405707267	   
df.mm.trans1:probe21	-0.424712014388524	0.0881596484246388	-4.81753298677885	1.75443225037202e-06	***
df.mm.trans1:probe22	0.0579409702823219	0.0881596484246388	0.657227782978868	0.511233182491788	   
df.mm.trans2:probe2	0.0733105133652743	0.0881596484246388	0.831565400671284	0.405915143519964	   
df.mm.trans2:probe3	0.117314313345884	0.0881596484246388	1.33070305340621	0.183685314712101	   
df.mm.trans2:probe4	0.208240735143077	0.0881596484246388	2.36208672407634	0.0184232311106341	*  
df.mm.trans2:probe5	0.0176138387936707	0.0881596484246388	0.199794793972295	0.841694447947224	   
df.mm.trans2:probe6	0.250027591164241	0.0881596484246388	2.83607745303080	0.00468840350301825	** 
df.mm.trans3:probe2	0.780848666595595	0.0881596484246388	8.85721166711645	5.71791510191612e-18	***
df.mm.trans3:probe3	-0.273624899887191	0.0881596484246388	-3.10374309308972	0.00198162565742214	** 
df.mm.trans3:probe4	-0.210696713546075	0.0881596484246388	-2.38994502939953	0.0170932598570938	*  
df.mm.trans3:probe5	-0.0828556328835109	0.0881596484246388	-0.939836244405376	0.347599775794477	   
df.mm.trans3:probe6	0.000540362436175944	0.0881596484246388	0.00612936242183247	0.995111113356806	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.10420899303547	0.398305603454585	12.8148058896627	3.50497737024624e-34	***
df.mm.trans1	0.0304364588374141	0.348774734564805	0.0872668109844372	0.930482391277598	   
df.mm.trans2	0.0203835098740872	0.312770596456806	0.0651707996371782	0.948055125922298	   
df.mm.exp2	0.192300691351093	0.412469647911942	0.46621779887219	0.641193029523835	   
df.mm.exp3	-0.228187532659592	0.412469647911943	-0.553222603929169	0.580273472510897	   
df.mm.exp4	0.459575990469888	0.412469647911942	1.11420559742132	0.265542766436539	   
df.mm.exp5	0.671445014140029	0.412469647911943	1.62786526848486	0.103967370430234	   
df.mm.exp6	0.251595567372419	0.412469647911943	0.609973530527831	0.542061433510898	   
df.mm.exp7	-0.203645026684747	0.412469647911943	-0.493721241588722	0.621645521983262	   
df.mm.exp8	0.360285389486132	0.412469647911943	0.873483397651234	0.382675194594288	   
df.mm.trans1:exp2	-0.26534367653136	0.386930827402999	-0.685765149063703	0.493070070934622	   
df.mm.trans2:exp2	-0.0314276640271169	0.308664020985953	-0.10181835876669	0.918927674351638	   
df.mm.trans1:exp3	-0.0149863910031105	0.386930827402999	-0.0387314474364735	0.969114658893825	   
df.mm.trans2:exp3	0.267500378565288	0.308664020985953	0.866639324242657	0.386412819519942	   
df.mm.trans1:exp4	-0.537029514989922	0.386930827402999	-1.38792124316988	0.165567235062752	   
df.mm.trans2:exp4	-0.0455865881509949	0.308664020985953	-0.147689996408974	0.882626547643184	   
df.mm.trans1:exp5	-0.730990021709212	0.386930827402999	-1.88920078199886	0.0592448768431863	.  
df.mm.trans2:exp5	-0.178791962473798	0.308664020985953	-0.579244584136142	0.562595569203209	   
df.mm.trans1:exp6	-0.262889103373341	0.386930827402999	-0.679421448887387	0.497077459930216	   
df.mm.trans2:exp6	-0.171215011332011	0.308664020985953	-0.554697015820326	0.579264903130858	   
df.mm.trans1:exp7	0.0185282443042294	0.386930827402999	0.0478851592895483	0.96182033080949	   
df.mm.trans2:exp7	0.295298319644658	0.308664020985953	0.956698220613461	0.339023414640112	   
df.mm.trans1:exp8	-0.588344986171599	0.386930827402999	-1.52054306481717	0.12878990752693	   
df.mm.trans2:exp8	0.154930599901560	0.308664020985953	0.501939291164131	0.615855414834365	   
df.mm.trans1:probe2	-0.193408774520134	0.236945773222564	-0.81625754234689	0.414608279544256	   
df.mm.trans1:probe3	0.178338626974447	0.236945773222564	0.752655869522236	0.451889487176137	   
df.mm.trans1:probe4	0.305048687970235	0.236945773222564	1.28741983375117	0.198339393422875	   
df.mm.trans1:probe5	0.236620381132205	0.236945773222564	0.99862672338091	0.318292972636185	   
df.mm.trans1:probe6	0.0316198831627585	0.236945773222564	0.133447762045782	0.893874554894064	   
df.mm.trans1:probe7	-0.138117229216624	0.236945773222564	-0.582906490958545	0.560128933843363	   
df.mm.trans1:probe8	0.227690999033234	0.236945773222564	0.960941383070645	0.33688685907163	   
df.mm.trans1:probe9	0.0991235908080235	0.236945773222564	0.418338717166803	0.675817512237581	   
df.mm.trans1:probe10	-0.186493112170682	0.236945773222564	-0.787070854374381	0.431485435992274	   
df.mm.trans1:probe11	0.118678254414445	0.236945773222564	0.500866729126963	0.616609751447954	   
df.mm.trans1:probe12	0.238277997271836	0.236945773222564	1.00562248497263	0.314916844947052	   
df.mm.trans1:probe13	0.06598882214093	0.236945773222564	0.278497570323597	0.780706085439311	   
df.mm.trans1:probe14	-0.0381544646332084	0.236945773222564	-0.161026145831983	0.872115545153753	   
df.mm.trans1:probe15	0.0973406643249167	0.236945773222564	0.410814098943577	0.68132450250808	   
df.mm.trans1:probe16	0.326522694246867	0.236945773222564	1.37804819139003	0.168593454604441	   
df.mm.trans1:probe17	-0.0718086519582132	0.236945773222564	-0.303059434154848	0.761927393486027	   
df.mm.trans1:probe18	0.110022563737565	0.236945773222564	0.464336469231805	0.642539505202914	   
df.mm.trans1:probe19	0.204252690141290	0.236945773222564	0.862022931928122	0.388946446583939	   
df.mm.trans1:probe20	0.282574977840205	0.236945773222564	1.19257235103655	0.233408613396621	   
df.mm.trans1:probe21	-0.00186046518467443	0.236945773222564	-0.0078518606150737	0.99373724384523	   
df.mm.trans1:probe22	0.0588035937045716	0.236945773222564	0.248173212397156	0.804067342713803	   
df.mm.trans2:probe2	0.203289522805434	0.236945773222564	0.85795800465486	0.391185779210563	   
df.mm.trans2:probe3	-0.100914348239187	0.236945773222564	-0.42589638492685	0.670303775439064	   
df.mm.trans2:probe4	-0.341006116829153	0.236945773222564	-1.43917366489102	0.150512503115104	   
df.mm.trans2:probe5	-0.228938533584569	0.236945773222564	-0.966206446609732	0.334247834868270	   
df.mm.trans2:probe6	-0.00769552535058707	0.236945773222564	-0.0324780022277867	0.97409937457737	   
df.mm.trans3:probe2	0.328478355219328	0.236945773222564	1.38630181392089	0.166060783817618	   
df.mm.trans3:probe3	-0.0745382115510902	0.236945773222564	-0.314579199018149	0.753167401271629	   
df.mm.trans3:probe4	-0.0490840253161289	0.236945773222564	-0.207152989684370	0.83594579664204	   
df.mm.trans3:probe5	0.0583131693822535	0.236945773222564	0.246103437884413	0.805668524633582	   
df.mm.trans3:probe6	0.209019546708216	0.236945773222564	0.882140853856395	0.377979189339564