chr11.4548_chr11_23286870_23292303_+_2.R 

fitVsDatCorrelation=0.861640523791348
cont.fitVsDatCorrelation=0.266233977918450

fstatistic=8031.59722778807,54,738
cont.fstatistic=2216.68562531025,54,738

residuals=-0.620006266846733,-0.107078146350640,-0.00337683046605039,0.0901784013643728,1.19593292169364
cont.residuals=-0.67096922853023,-0.241208951196952,-0.07791909521588,0.200019357520238,1.21835288546859

predictedValues:
Include	Exclude	Both
chr11.4548_chr11_23286870_23292303_+_2.R.tl.Lung	86.186321363192	60.0641772849787	56.1628132223245
chr11.4548_chr11_23286870_23292303_+_2.R.tl.cerebhem	84.6302980450498	62.2266096653631	64.076785896408
chr11.4548_chr11_23286870_23292303_+_2.R.tl.cortex	76.1385426862035	61.2792063909598	60.7253821720059
chr11.4548_chr11_23286870_23292303_+_2.R.tl.heart	76.9420340175866	55.8446426261968	57.1612677432162
chr11.4548_chr11_23286870_23292303_+_2.R.tl.kidney	88.2754191657079	52.3034835827886	51.8187388947558
chr11.4548_chr11_23286870_23292303_+_2.R.tl.liver	82.1411074033245	52.6586270632384	51.2059893669322
chr11.4548_chr11_23286870_23292303_+_2.R.tl.stomach	78.637112747397	52.8560480806074	55.2630476739377
chr11.4548_chr11_23286870_23292303_+_2.R.tl.testicle	79.0195030438051	60.9831159116324	60.704896017709


diffExp=26.1221440782133,22.4036883796867,14.8593362952437,21.0973913913898,35.9719355829192,29.4824803400861,25.7810646667896,18.0363871321727
diffExpScore=0.994865328552707
diffExp1.5=0,0,0,0,1,1,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,0,0,0,1,1,1,0
diffExp1.4Score=0.8
diffExp1.3=1,1,0,1,1,1,1,0
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	72.6906995592516	77.3561308195038	76.2965358976664
cerebhem	67.365759571098	72.6774526356409	66.8094814171877
cortex	65.5962366814251	65.5688298525975	75.0646126369042
heart	70.1709403288546	66.5552445149168	72.8030534569672
kidney	70.9352696483799	62.9694886496398	79.9007697170444
liver	72.0236546728431	66.8357883908296	68.2807516610982
stomach	67.3275004431181	68.9724311575618	68.1355516067222
testicle	72.3917196955394	70.6933561318671	69.753862888574
cont.diffExp=-4.66543126025216,-5.31169306454282,0.0274068288276084,3.61569581393788,7.96578099874004,5.18786628201356,-1.64493071444369,1.69836356367232
cont.diffExpScore=3.82534547730452

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=-0.144230041812863
cont.tran.correlation=0.249909990646887

tran.covariance=-0.000623409038236016
cont.tran.covariance=0.000598044382413839

tran.mean=69.386640567377
cont.tran.mean=69.3831564220667

weightedLogRatios:
wLogRatio
Lung	1.54404095987825
cerebhem	1.31753650231831
cortex	0.917089165594153
heart	1.34050261530051
kidney	2.20809477851663
liver	1.86119094287378
stomach	1.65511616572779
testicle	1.09861186553736

cont.weightedLogRatios:
wLogRatio
Lung	-0.268564671118448
cerebhem	-0.322405462827497
cortex	0.00174819788134198
heart	0.223483288720178
kidney	0.500556484056556
liver	0.316936403667830
stomach	-0.101902309351770
testicle	0.101376466038152

varWeightedLogRatios=0.173867101701913
cont.varWeightedLogRatios=0.0817479949279537

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.71552141527379	0.0902722807376782	52.236648689276	4.0252077585657e-250	***
df.mm.trans1	-0.0877565557930809	0.0795218414868553	-1.10355286236155	0.27014684921283	   
df.mm.trans2	-0.624371643942446	0.071754985938201	-8.70143915128331	2.12015841421381e-17	***
df.mm.exp2	-0.114677332770462	0.095556282962989	-1.20010248635225	0.230484638537660	   
df.mm.exp3	-0.182036823442918	0.0955562829629889	-1.90502202260656	0.0571656523390127	.  
df.mm.exp4	-0.203920842440420	0.095556282962989	-2.13403908269855	0.0331686002032427	*  
df.mm.exp5	-0.0338974125517896	0.095556282962989	-0.354737663507891	0.722887476672649	   
df.mm.exp6	-0.0872580734240284	0.0955562829629889	-0.91315893333592	0.361457172899239	   
df.mm.exp7	-0.203358805520041	0.0955562829629889	-2.12815734574781	0.0336547824816799	*  
df.mm.exp8	-0.149402861329739	0.095556282962989	-1.56350641419995	0.118362208938616	   
df.mm.trans1:exp2	0.0964581879924554	0.090132720312964	1.07017948262881	0.284888405771145	   
df.mm.trans2:exp2	0.150046437155586	0.0737449529833342	2.03466720209986	0.042241344136828	*  
df.mm.trans1:exp3	0.0580799540942132	0.090132720312964	0.644382571529459	0.519527610543031	   
df.mm.trans2:exp3	0.202063786251215	0.0737449529833342	2.74003546109629	0.00629167600242043	** 
df.mm.trans1:exp4	0.0904616956316418	0.090132720312964	1.00364989892167	0.315876350313147	   
df.mm.trans2:exp4	0.131080827024073	0.0737449529833342	1.77748878697768	0.0758996037301621	.  
df.mm.trans1:exp5	0.0578476225667651	0.090132720312964	0.641804911311933	0.521199149691648	   
df.mm.trans2:exp5	-0.104453222895038	0.0737449529833342	-1.41641181761474	0.157076905318348	   
df.mm.trans1:exp6	0.0391851835661652	0.090132720312964	0.434749815939252	0.6638711271178	   
df.mm.trans2:exp6	-0.0443254565684381	0.0737449529833342	-0.6010642732182	0.547981739191008	   
df.mm.trans1:exp7	0.111691085679546	0.090132720312964	1.23918467446367	0.215671056778335	   
df.mm.trans2:exp7	0.0755173378989793	0.0737449529833342	1.02403398258381	0.306154764743662	   
df.mm.trans1:exp8	0.0625860771045054	0.090132720312964	0.694376879863278	0.487664415137932	   
df.mm.trans2:exp8	0.164586286855181	0.0737449529833342	2.23183119924663	0.025925574467968	*  
df.mm.trans1:probe2	-0.455752257465273	0.0526261706985362	-8.66018278388532	2.94657163141932e-17	***
df.mm.trans1:probe3	0.269219728807808	0.0526261706985362	5.11570051999424	3.98890370586575e-07	***
df.mm.trans1:probe4	-0.482100219712843	0.0526261706985362	-9.16084551305292	4.99700013591442e-19	***
df.mm.trans1:probe5	-0.663824554285725	0.0526261706985362	-12.6139627009607	3.57292309433426e-33	***
df.mm.trans1:probe6	0.521863893335394	0.0526261706985362	9.91643295357435	7.63229617501504e-22	***
df.mm.trans1:probe7	-0.200938350313169	0.0526261706985362	-3.81822100384663	0.000145704504761789	***
df.mm.trans1:probe8	-0.625484867666763	0.0526261706985362	-11.8854337939538	6.23244701253232e-30	***
df.mm.trans1:probe9	-0.326528099263393	0.0526261706985362	-6.20467145774061	9.13136513940085e-10	***
df.mm.trans1:probe10	-0.0946831278112842	0.0526261706985362	-1.79916430465114	0.0724011083967712	.  
df.mm.trans1:probe11	-0.456242736037717	0.0526261706985362	-8.66950283445964	2.73571594256229e-17	***
df.mm.trans1:probe12	-0.488664832729757	0.0526261706985362	-9.28558597829632	1.76018150976789e-19	***
df.mm.trans1:probe13	-0.485523040065328	0.0526261706985362	-9.22588578307547	2.90417930651448e-19	***
df.mm.trans1:probe14	-0.446016245493756	0.0526261706985362	-8.47517954609914	1.26970979533477e-16	***
df.mm.trans1:probe15	-0.328219860856592	0.0526261706985362	-6.23681823130866	7.51596250625733e-10	***
df.mm.trans1:probe16	-0.306590579386494	0.0526261706985362	-5.82581965050749	8.48580972795783e-09	***
df.mm.trans1:probe17	-0.0823701232001647	0.0526261706985362	-1.56519317493218	0.117966290132537	   
df.mm.trans1:probe18	-0.102638062199733	0.0526261706985362	-1.95032359066527	0.0515157853386713	.  
df.mm.trans1:probe19	-0.0138775659803114	0.0526261706985362	-0.263700850662453	0.792084052886853	   
df.mm.trans1:probe20	-0.102797954634989	0.0526261706985362	-1.95336185913767	0.0511542375314159	.  
df.mm.trans1:probe21	0.149375919151365	0.0526261706985362	2.83843413207945	0.00465807557021854	** 
df.mm.trans1:probe22	0.097951695124391	0.0526261706985362	1.86127346573433	0.0631029707904893	.  
df.mm.trans2:probe2	0.0156470281875803	0.0526261706985362	0.297324087614368	0.766302763093191	   
df.mm.trans2:probe3	-0.0202420973990168	0.0526261706985362	-0.384639374864868	0.700615430253523	   
df.mm.trans2:probe4	0.00073768765572093	0.0526261706985362	0.0140175058517311	0.988819803224779	   
df.mm.trans2:probe5	0.0651013649092618	0.0526261706985362	1.23705304879940	0.216460871560448	   
df.mm.trans2:probe6	-0.0143417359872107	0.0526261706985362	-0.272520987121899	0.785297687270162	   
df.mm.trans3:probe2	-0.066339456607156	0.0526261706985362	-1.26057920853058	0.207858893018165	   
df.mm.trans3:probe3	0.372860216991724	0.0526261706985362	7.08507216167439	3.24946224609075e-12	***
df.mm.trans3:probe4	-0.106973577865010	0.0526261706985362	-2.03270685374008	0.0424396449489539	*  
df.mm.trans3:probe5	-0.0752390029456336	0.0526261706985362	-1.42968796602422	0.153229774809433	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.35910245206914	0.171450133934187	25.4248996605766	6.55826552892029e-103	***
df.mm.trans1	-0.156437134219562	0.151032302077685	-1.03578593497898	0.300641290093754	   
df.mm.trans2	0.0191949664406462	0.136281058249761	0.140848381184917	0.888028156121091	   
df.mm.exp2	-0.00568268915722182	0.18148580470527	-0.0313120310784109	0.975029161615338	   
df.mm.exp3	-0.251736235272436	0.18148580470527	-1.38708498816892	0.165834612175333	   
df.mm.exp4	-0.138797025920354	0.18148580470527	-0.764781720232932	0.444646009125146	   
df.mm.exp5	-0.276373163977022	0.18148580470527	-1.52283625943003	0.128228034427973	   
df.mm.exp6	-0.0444003585385457	0.18148580470527	-0.244649208849426	0.806796063202171	   
df.mm.exp7	-0.078229218522286	0.18148580470527	-0.431048690829176	0.666558839323428	   
df.mm.exp8	-0.00453502704237176	0.18148580470527	-0.024988329251076	0.980071027570591	   
df.mm.trans1:exp2	-0.0703938875646451	0.171185073017219	-0.411215103769967	0.681034196469547	   
df.mm.trans2:exp2	-0.0567059516053137	0.140060514286816	-0.40486750954799	0.68569222416607	   
df.mm.trans1:exp3	0.149041114593511	0.171185073017219	0.870643169796233	0.384232126046555	   
df.mm.trans2:exp3	0.086416828980248	0.140060514286816	0.616996370606521	0.537427417506386	   
df.mm.trans1:exp4	0.103517848497318	0.171185073017219	0.604713055132474	0.545555544391655	   
df.mm.trans2:exp4	-0.0115904613443754	0.140060514286815	-0.0827532399362786	0.934070190078283	   
df.mm.trans1:exp5	0.251927482756176	0.171185073017219	1.4716673499379	0.141537007083948	   
df.mm.trans2:exp5	0.0706036312709363	0.140060514286816	0.504093759975451	0.614345944392359	   
df.mm.trans1:exp6	0.0351815133987397	0.171185073017219	0.205517413280543	0.837224569596864	   
df.mm.trans2:exp6	-0.101780784664702	0.140060514286816	-0.726691496050599	0.467645391363384	   
df.mm.trans1:exp7	0.00158454909845456	0.171185073017219	0.00925635086357776	0.992617107567294	   
df.mm.trans2:exp7	-0.0364837397967934	0.140060514286816	-0.260485547854566	0.794561923753372	   
df.mm.trans1:exp8	0.000413503725107116	0.171185073017219	0.00241553610848724	0.998073335688126	   
df.mm.trans2:exp8	-0.0855332117804158	0.140060514286816	-0.610687546136387	0.54159447482983	   
df.mm.trans1:probe2	0.227807150944471	0.0999505489500886	2.27919859708053	0.0229399727883036	*  
df.mm.trans1:probe3	0.0609412669782118	0.0999505489500886	0.60971417984551	0.542238831582031	   
df.mm.trans1:probe4	-0.00100268344348118	0.0999505489500886	-0.0100317952628943	0.991998630825824	   
df.mm.trans1:probe5	0.0384177254920519	0.0999505489500886	0.384367328600028	0.700816938009112	   
df.mm.trans1:probe6	0.126871207343539	0.0999505489500886	1.26933977528121	0.204720049790770	   
df.mm.trans1:probe7	0.251203037240219	0.0999505489500886	2.51327321239286	0.0121736116091946	*  
df.mm.trans1:probe8	0.0778022544250584	0.0999505489500886	0.77840747491952	0.436578327120637	   
df.mm.trans1:probe9	0.0671824986577916	0.0999505489500886	0.672157375457136	0.50169388422583	   
df.mm.trans1:probe10	0.0777115289457848	0.0999505489500886	0.777499771257794	0.437113128843388	   
df.mm.trans1:probe11	0.0134475771771149	0.0999505489500886	0.134542304353226	0.893010444424368	   
df.mm.trans1:probe12	0.193542750539078	0.0999505489500886	1.93638506813730	0.0532020210642967	.  
df.mm.trans1:probe13	0.140479098122261	0.0999505489500886	1.40548600881032	0.160297633148503	   
df.mm.trans1:probe14	0.158937771619897	0.0999505489500886	1.59016406902641	0.112226084440924	   
df.mm.trans1:probe15	0.165692472616547	0.0999505489500886	1.65774449822469	0.0977940198514743	.  
df.mm.trans1:probe16	0.120940399013863	0.0999505489500886	1.21000234900416	0.226665413611201	   
df.mm.trans1:probe17	0.132063031783371	0.0999505489500886	1.32128370649889	0.186816272345764	   
df.mm.trans1:probe18	0.197278098131061	0.0999505489500886	1.97375702488212	0.0487819681708867	*  
df.mm.trans1:probe19	0.0465114597660619	0.0999505489500886	0.465344715508145	0.641821900634386	   
df.mm.trans1:probe20	-0.0101259492062608	0.0999505489500886	-0.101309590718879	0.919332220390076	   
df.mm.trans1:probe21	0.0467054987014338	0.0999505489500886	0.46728606487951	0.640433160794909	   
df.mm.trans1:probe22	0.123393300912031	0.0999505489500886	1.23454350384457	0.217393384889742	   
df.mm.trans2:probe2	-0.0922667852323885	0.0999505489500886	-0.923124347005467	0.356244110765255	   
df.mm.trans2:probe3	-0.0357170489517899	0.0999505489500886	-0.357347201460850	0.72093406136413	   
df.mm.trans2:probe4	-0.178319258783855	0.0999505489500886	-1.78407483157397	0.07482227147286	.  
df.mm.trans2:probe5	0.0791215651840933	0.0999505489500886	0.791607109867936	0.428844148338497	   
df.mm.trans2:probe6	-0.101471894844700	0.0999505489500886	-1.01522098588344	0.310333210667058	   
df.mm.trans3:probe2	0.0735661267265397	0.0999505489500886	0.736025239473929	0.461949207499316	   
df.mm.trans3:probe3	0.119647951165573	0.0999505489500886	1.19707147606883	0.231663072657658	   
df.mm.trans3:probe4	0.241808650726712	0.0999505489500886	2.4192828680457	0.0157919175510564	*  
df.mm.trans3:probe5	0.128813530833394	0.0999505489500886	1.28877261992547	0.197880989264656