chr4.16172_chr4_114755521_114756163_-_2.R 

fitVsDatCorrelation=0.869277079714879
cont.fitVsDatCorrelation=0.223550121691092

fstatistic=10928.1959933735,65,991
cont.fstatistic=2799.53203858073,65,991

residuals=-0.872173605992233,-0.0921516628098823,1.0487999932438e-05,0.0863080858085055,1.08777973007834
cont.residuals=-0.621110169168213,-0.211598296148767,-0.0528531239158201,0.148180652837808,1.22145449989207

predictedValues:
Include	Exclude	Both
chr4.16172_chr4_114755521_114756163_-_2.R.tl.Lung	64.8000962248711	59.620433469577	78.2851518385403
chr4.16172_chr4_114755521_114756163_-_2.R.tl.cerebhem	71.0471430500503	70.4815687844105	81.9119061511526
chr4.16172_chr4_114755521_114756163_-_2.R.tl.cortex	66.1172956028243	55.7887521963598	86.1346397475116
chr4.16172_chr4_114755521_114756163_-_2.R.tl.heart	71.7720690729425	54.0980793422305	91.4019250411914
chr4.16172_chr4_114755521_114756163_-_2.R.tl.kidney	67.3655073390211	61.1984120027335	91.6807358909076
chr4.16172_chr4_114755521_114756163_-_2.R.tl.liver	68.8433364818367	57.2058847617603	89.0584932692463
chr4.16172_chr4_114755521_114756163_-_2.R.tl.stomach	65.1723682316068	59.9415938564208	78.2660416702942
chr4.16172_chr4_114755521_114756163_-_2.R.tl.testicle	70.8599015198145	61.6771053580638	89.7882198481835


diffExp=5.17966275529403,0.565574265639853,10.3285434064645,17.6739897307119,6.16709533628757,11.6374517200764,5.23077437518602,9.18279616175067
diffExpScore=0.985067023919519
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=0,0,0,1,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,1,0,1,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	70.184162563974	77.3455416996721	71.6915028780205
cerebhem	70.9070909523395	67.346783066793	70.8603335944003
cortex	72.3605657128698	65.4000155792018	64.0613127167915
heart	73.8340079596011	69.7211332817916	67.9257008517472
kidney	72.9148206792646	66.2327247947362	73.1163265175827
liver	70.6734726026774	73.5295755626009	74.2365683039717
stomach	70.3237325445845	69.8005942334864	67.0640715035571
testicle	75.6816771070626	60.5228664971851	70.5689963228813
cont.diffExp=-7.16137913569804,3.56030788554651,6.96055013366798,4.11287467780953,6.68209588452841,-2.85610295992353,0.523138311098137,15.1588106098775
cont.diffExpScore=1.68029890015188

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=0,0,0,0,0,0,0,1
cont.diffExp1.2Score=0.5

tran.correlation=0.212572566869893
cont.tran.correlation=-0.766760610960765

tran.covariance=0.000612487903793682
cont.tran.covariance=-0.00157132240361196

tran.mean=64.1243467059077
cont.tran.mean=70.423672802365

weightedLogRatios:
wLogRatio
Lung	0.344036121862491
cerebhem	0.0340424228223567
cortex	0.697522492854408
heart	1.16814436018231
kidney	0.399613675964269
liver	0.76649159936762
stomach	0.345971465049555
testicle	0.58171975359826

cont.weightedLogRatios:
wLogRatio
Lung	-0.417760226815765
cerebhem	0.218198883307333
cortex	0.427928335749803
heart	0.244920362431476
kidney	0.407655667502603
liver	-0.169478538179810
stomach	0.0317292753548678
testicle	0.942065549172527

varWeightedLogRatios=0.117732784921743
cont.varWeightedLogRatios=0.170518714448469

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86562741984394	0.0744108754107837	51.9497640432775	3.77945350708427e-285	***
df.mm.trans1	0.373530230476182	0.0635131983467447	5.88114345048294	5.56573700266686e-09	***
df.mm.trans2	0.247179517185032	0.0556283997141211	4.44340513937678	9.85203218396646e-06	***
df.mm.exp2	0.214103034982496	0.0701852462359455	3.05054190823431	0.00234505185484411	** 
df.mm.exp3	-0.14185649792763	0.0701852462359455	-2.02117261868319	0.0435303506841919	*  
df.mm.exp4	-0.149919960233322	0.0701852462359455	-2.13606089988383	0.0329189571868805	*  
df.mm.exp5	-0.0930054055274692	0.0701852462359455	-1.32514182845221	0.185429553571906	   
df.mm.exp6	-0.109750664436199	0.0701852462359455	-1.56372842331056	0.118200615957018	   
df.mm.exp7	0.0113449238447084	0.0701852462359455	0.161642573804892	0.87162027750251	   
df.mm.exp8	-0.0137837705484538	0.0701852462359454	-0.196391282893219	0.844344184102442	   
df.mm.trans1:exp2	-0.122066479971179	0.0637736773550892	-1.91405741418231	0.0559006035029578	.  
df.mm.trans2:exp2	-0.0467501537898568	0.0438162613520118	-1.06695898616896	0.286250210351869	   
df.mm.trans1:exp3	0.161979780386435	0.0637736773550892	2.53991595128722	0.0112396739729923	*  
df.mm.trans2:exp3	0.0754304146910806	0.0438162613520118	1.72151644991083	0.0854692269169751	.  
df.mm.trans1:exp4	0.252108262168442	0.0637736773550892	3.9531711612726	8.26008049801525e-05	***
df.mm.trans2:exp4	0.0527202847003073	0.0438162613520118	1.20321275876922	0.229181357412552	   
df.mm.trans1:exp5	0.131831443594118	0.0637736773550892	2.06717644428885	0.0389767248272632	*  
df.mm.trans2:exp5	0.119128288278152	0.0438162613520118	2.71881453602573	0.0066660838399814	** 
df.mm.trans1:exp6	0.170277013493130	0.0637736773550892	2.67002030547862	0.00770879243590927	** 
df.mm.trans2:exp6	0.0684090792172774	0.0438162613520118	1.56127148018612	0.118778976715165	   
df.mm.trans1:exp7	-0.005616433178328	0.0637736773550892	-0.0880682032346344	0.92984026098863	   
df.mm.trans2:exp7	-0.00597263020837636	0.0438162613520119	-0.136310813019699	0.891603260947191	   
df.mm.trans1:exp8	0.103181391890685	0.0637736773550892	1.61793072267379	0.105995743425442	   
df.mm.trans2:exp8	0.0476982099466025	0.0438162613520118	1.08859607083781	0.276596710865416	   
df.mm.trans1:probe2	0.0771738060214285	0.0474823969777155	1.62531403074802	0.104413618743704	   
df.mm.trans1:probe3	-0.286625974520098	0.0474823969777155	-6.03646809689531	2.22382362187259e-09	***
df.mm.trans1:probe4	-0.170771719952515	0.0474823969777155	-3.59652694097693	0.000338410933306128	***
df.mm.trans1:probe5	-0.0805709307798211	0.0474823969777155	-1.69685896054563	0.0900373447323163	.  
df.mm.trans1:probe6	-0.259079358551605	0.0474823969777155	-5.45632434422374	6.14136038118751e-08	***
df.mm.trans1:probe7	-0.290639201635108	0.0474823969777155	-6.12098841116869	1.33800883929318e-09	***
df.mm.trans1:probe8	0.101548568430238	0.0474823969777155	2.13865716336724	0.0327073814272757	*  
df.mm.trans1:probe9	-0.453784991924644	0.0474823969777155	-9.55690994575558	9.21298623248752e-21	***
df.mm.trans1:probe10	-0.143388559047077	0.0474823969777155	-3.01982562325934	0.00259401465812336	** 
df.mm.trans1:probe11	0.0552973172400097	0.0474823969777155	1.16458563088047	0.244467032126333	   
df.mm.trans1:probe12	-0.0813914369311817	0.0474823969777155	-1.71413917813333	0.0868158556742172	.  
df.mm.trans1:probe13	-0.105473536331186	0.0474823969777155	-2.22131870007925	0.0265547142534392	*  
df.mm.trans1:probe14	-0.033725899215593	0.0474823969777155	-0.710282154277538	0.477696291079203	   
df.mm.trans1:probe15	-0.0268700579232112	0.0474823969777155	-0.56589514501178	0.57159317227788	   
df.mm.trans1:probe16	-0.175433064772529	0.0474823969777155	-3.69469689693347	0.000232193578586189	***
df.mm.trans1:probe17	-0.128882934532900	0.0474823969777155	-2.71433084124603	0.00675630303867463	** 
df.mm.trans1:probe18	-0.0481033208282327	0.0474823969777155	-1.01307692724124	0.31127069504952	   
df.mm.trans1:probe19	-0.190506616257794	0.0474823969777155	-4.01215246878128	6.46956800371964e-05	***
df.mm.trans1:probe20	-0.197334890887127	0.0474823969777155	-4.15595891209410	3.51911027721035e-05	***
df.mm.trans1:probe21	-0.139758554029581	0.0474823969777155	-2.94337613358425	0.00332224377081595	** 
df.mm.trans2:probe2	-0.115136180504878	0.0474823969777155	-2.42481820281554	0.0154939540261264	*  
df.mm.trans2:probe3	-0.0966298358713751	0.0474823969777155	-2.03506650931556	0.0421098897677291	*  
df.mm.trans2:probe4	-0.0964259658088504	0.0474823969777155	-2.03077291683706	0.0425445968880608	*  
df.mm.trans2:probe5	-0.100002314928921	0.0474823969777155	-2.10609238989881	0.0354473317696672	*  
df.mm.trans2:probe6	-0.162403002998684	0.0474823969777155	-3.42027811011528	0.000651085478694747	***
df.mm.trans3:probe2	0.0513927886864826	0.0474823969777155	1.08235455574415	0.279358302298112	   
df.mm.trans3:probe3	-0.555686177201051	0.0474823969777155	-11.7029933737727	1.00344201923327e-29	***
df.mm.trans3:probe4	-0.605562761038043	0.0474823969777155	-12.7534159937681	1.3287517487944e-34	***
df.mm.trans3:probe5	0.10745828884431	0.0474823969777155	2.26311845408189	0.0238440580201874	*  
df.mm.trans3:probe6	-0.0910462142548642	0.0474823969777155	-1.91747300157560	0.0554650900571569	.  
df.mm.trans3:probe7	-0.313561959151795	0.0474823969777155	-6.6037516871559	6.52703611405434e-11	***
df.mm.trans3:probe8	-0.088929268270767	0.0474823969777155	-1.87288919538968	0.0613779103068383	.  
df.mm.trans3:probe9	0.417684522918449	0.0474823969777155	8.79661831550915	6.15455869744447e-18	***
df.mm.trans3:probe10	0.529143610390831	0.0474823969777155	11.1439953345062	2.95393341478571e-27	***
df.mm.trans3:probe11	-0.539572225485414	0.0474823969777155	-11.3636265190792	3.24504591064676e-28	***
df.mm.trans3:probe12	-0.559687245990653	0.0474823969777155	-11.7872576284076	4.18314310465047e-30	***
df.mm.trans3:probe13	-0.216485414981719	0.0474823969777155	-4.5592773061419	5.7741611577526e-06	***
df.mm.trans3:probe14	-0.0458440057158263	0.0474823969777155	-0.965494765088246	0.334532603253528	   
df.mm.trans3:probe15	-0.292139893703960	0.0474823969777155	-6.152593640988	1.10473970955861e-09	***
df.mm.trans3:probe16	-0.171045015445618	0.0474823969777155	-3.60228266331823	0.000331098699527433	***
df.mm.trans3:probe17	0.232234329297974	0.0474823969777155	4.89095631391495	1.17032378449719e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31607854132598	0.146720668870702	29.4169769981728	3.17374226374184e-137	***
df.mm.trans1	-0.0654177148123105	0.125233023964686	-0.522367924540073	0.601530931035347	   
df.mm.trans2	0.0382824710986991	0.109686063619134	0.349018551997896	0.727149523741681	   
df.mm.exp2	-0.116518615161349	0.138388726321859	-0.841966092601757	0.400010068860237	   
df.mm.exp3	-0.0246900479643175	0.138388726321859	-0.178410833169274	0.858436806461343	   
df.mm.exp4	0.000875052710209498	0.138388726321859	0.00632315025556589	0.994956162262732	   
df.mm.exp5	-0.136618481104853	0.138388726321859	-0.987208168872889	0.323781562928053	   
df.mm.exp6	-0.0785322445965318	0.138388726321859	-0.567475737972217	0.570519532871523	   
df.mm.exp7	-0.033929986254406	0.138388726321859	-0.245178831803778	0.806368727100469	   
df.mm.exp8	-0.154066952426454	0.138388726321859	-1.11329120891056	0.265853339971798	   
df.mm.trans1:exp2	0.126766376053124	0.125746627038432	1.00810955362151	0.313647949511872	   
df.mm.trans2:exp2	-0.0219091848805585	0.086395317048629	-0.253592273620880	0.799863166599616	   
df.mm.trans1:exp3	0.05522884541643	0.125746627038432	0.439207370544821	0.660607032644366	   
df.mm.trans2:exp3	-0.143070392703115	0.086395317048629	-1.65599707936237	0.0980389894499296	.  
df.mm.trans1:exp4	0.0498217041380337	0.125746627038432	0.396207081743884	0.692037558663275	   
df.mm.trans2:exp4	-0.104654514776233	0.086395317048629	-1.21134476209314	0.226052072559884	   
df.mm.trans1:exp5	0.174787719877897	0.125746627038432	1.38999927071186	0.164841234081149	   
df.mm.trans2:exp5	-0.0184897831920011	0.086395317048629	-0.214013719998201	0.830580382593933	   
df.mm.trans1:exp6	0.0854798553006817	0.125746627038432	0.679778514254354	0.496803467491476	   
df.mm.trans2:exp6	0.0279370211409012	0.086395317048629	0.323362678618060	0.746488812250468	   
df.mm.trans1:exp7	0.0359166365619479	0.125746627038432	0.285627037542492	0.77522348145522	   
df.mm.trans2:exp7	-0.0687104279844282	0.086395317048629	-0.795302689215822	0.426627888202098	   
df.mm.trans1:exp8	0.229480356352672	0.125746627038432	1.82494243986788	0.0683106360928775	.  
df.mm.trans2:exp8	-0.0911947326586153	0.0863953170486289	-1.05555180273585	0.291430291289993	   
df.mm.trans1:probe2	-0.0252299475863362	0.0936240704829152	-0.269481421350295	0.787615336061963	   
df.mm.trans1:probe3	0.0490131286973434	0.0936240704829152	0.523509909839772	0.600736498389806	   
df.mm.trans1:probe4	-0.0556469886073053	0.0936240704829152	-0.594366259876085	0.552402832408624	   
df.mm.trans1:probe5	-0.00365263297175735	0.0936240704829152	-0.0390138236130621	0.968887225138602	   
df.mm.trans1:probe6	0.0576090500974288	0.0936240704829152	0.615323065962417	0.538482661609166	   
df.mm.trans1:probe7	0.00389801837789112	0.0936240704829152	0.041634788551545	0.96679822657801	   
df.mm.trans1:probe8	0.0138258472221247	0.0936240704829152	0.14767406662422	0.882630038262278	   
df.mm.trans1:probe9	-0.0503103128939973	0.0936240704829152	-0.537365152299996	0.591136044768424	   
df.mm.trans1:probe10	0.0160597663165084	0.0936240704829152	0.171534587565695	0.863838455781616	   
df.mm.trans1:probe11	-0.0237052728633629	0.0936240704829152	-0.253196349411967	0.800169000907695	   
df.mm.trans1:probe12	0.00211804375073225	0.0936240704829152	0.0226228547830417	0.981955667188968	   
df.mm.trans1:probe13	0.00554500129609724	0.0936240704829152	0.059226236025586	0.952783853547495	   
df.mm.trans1:probe14	0.0941368558243003	0.0936240704829152	1.00547706736889	0.314912626318267	   
df.mm.trans1:probe15	-0.0289797769780209	0.0936240704829152	-0.309533401277497	0.756980878959766	   
df.mm.trans1:probe16	-0.0967678153563344	0.0936240704829152	-1.03357838275140	0.301585467858657	   
df.mm.trans1:probe17	0.0934005903750337	0.0936240704829152	0.997613005857054	0.318710575308115	   
df.mm.trans1:probe18	0.102350043793985	0.0936240704829152	1.09320224239408	0.274570677193239	   
df.mm.trans1:probe19	-0.0602276269070612	0.0936240704829152	-0.64329212131459	0.520183390942029	   
df.mm.trans1:probe20	-0.0333392927575525	0.0936240704829152	-0.356097449999638	0.721843379659575	   
df.mm.trans1:probe21	-0.042546205406251	0.0936240704829152	-0.454436612153228	0.649614123976054	   
df.mm.trans2:probe2	-0.103481475948714	0.0936240704829152	-1.10528708498737	0.269303476818986	   
df.mm.trans2:probe3	0.0216047468811267	0.0936240704829152	0.230760602158066	0.817548419988994	   
df.mm.trans2:probe4	-0.0457074896782092	0.0936240704829152	-0.488202333464555	0.625514532038248	   
df.mm.trans2:probe5	-0.0300866606585366	0.0936240704829152	-0.321356041276018	0.748008354445541	   
df.mm.trans2:probe6	0.0178751525636193	0.0936240704829152	0.190924753339807	0.848623652778771	   
df.mm.trans3:probe2	-0.0774839652879977	0.0936240704829152	-0.827607311755765	0.408092170996705	   
df.mm.trans3:probe3	-0.0598211606246736	0.0936240704829152	-0.638950649294723	0.523002684474415	   
df.mm.trans3:probe4	0.0196215748302268	0.0936240704829152	0.209578313878239	0.834039881922274	   
df.mm.trans3:probe5	-0.127089619502982	0.0936240704829152	-1.35744599489694	0.174948582189592	   
df.mm.trans3:probe6	-0.0474397812540365	0.0936240704829152	-0.506704963898076	0.612474564165016	   
df.mm.trans3:probe7	0.0805890120695656	0.0936240704829152	0.860772359649453	0.389571634206930	   
df.mm.trans3:probe8	0.0754787066136234	0.0936240704829152	0.80618911594318	0.420327226639125	   
df.mm.trans3:probe9	-0.0605121866297174	0.0936240704829152	-0.646331507673123	0.518214334395799	   
df.mm.trans3:probe10	-0.0990093738865274	0.0936240704829152	-1.05752050061309	0.290531814454731	   
df.mm.trans3:probe11	-0.0186863437831969	0.0936240704829152	-0.199589098047247	0.841842899773135	   
df.mm.trans3:probe12	-0.0125144416636877	0.0936240704829152	-0.133666925600841	0.89369309847405	   
df.mm.trans3:probe13	0.0265064585996764	0.0936240704829152	0.283115853251791	0.777147157374974	   
df.mm.trans3:probe14	0.0224995736035827	0.0936240704829152	0.240318258835889	0.810133243825684	   
df.mm.trans3:probe15	0.0565995620479309	0.0936240704829152	0.604540710054465	0.545622593318891	   
df.mm.trans3:probe16	-0.0111542985683411	0.0936240704829152	-0.119139218267342	0.905189206284569	   
df.mm.trans3:probe17	-0.0492918453921609	0.0936240704829152	-0.526486886736631	0.598667772534342