chr12.5356_chr12_30117113_30121460_+_2.R 

fitVsDatCorrelation=0.921765567683456
cont.fitVsDatCorrelation=0.290800702033561

fstatistic=6006.79351410354,52,692
cont.fstatistic=975.415328035608,52,692

residuals=-0.903434177405849,-0.0931790446574104,-0.00398595661425579,0.0848030646247143,1.16725178840196
cont.residuals=-0.83432034036692,-0.292994213082887,-0.126012577918435,0.122822989042738,2.32432568003967

predictedValues:
Include	Exclude	Both
chr12.5356_chr12_30117113_30121460_+_2.R.tl.Lung	49.6444827533799	73.3523739839674	60.78740440303
chr12.5356_chr12_30117113_30121460_+_2.R.tl.cerebhem	56.5794692744385	70.930549981914	62.034338160651
chr12.5356_chr12_30117113_30121460_+_2.R.tl.cortex	48.4939674804018	66.9753767168197	57.3590405597118
chr12.5356_chr12_30117113_30121460_+_2.R.tl.heart	50.3088239756682	76.3348314153939	61.3970284579444
chr12.5356_chr12_30117113_30121460_+_2.R.tl.kidney	50.4061170520319	75.9784249167064	61.3481836767029
chr12.5356_chr12_30117113_30121460_+_2.R.tl.liver	51.9696505713302	82.8832298129202	67.8112763170968
chr12.5356_chr12_30117113_30121460_+_2.R.tl.stomach	225.091119519017	73.557938072441	314.805771147911
chr12.5356_chr12_30117113_30121460_+_2.R.tl.testicle	51.3738150016477	73.071979955633	59.3122310391272


diffExp=-23.7078912305875,-14.3510807074755,-18.4814092364179,-26.0260074397257,-25.5723078646745,-30.91357924159,151.533181446576,-21.6981649539854
diffExpScore=30.5643240673485
diffExp1.5=0,0,0,-1,-1,-1,1,0
diffExp1.5Score=1.33333333333333
diffExp1.4=-1,0,0,-1,-1,-1,1,-1
diffExp1.4Score=1.2
diffExp1.3=-1,0,-1,-1,-1,-1,1,-1
diffExp1.3Score=1.16666666666667
diffExp1.2=-1,-1,-1,-1,-1,-1,1,-1
diffExp1.2Score=1.14285714285714

cont.predictedValues:
Include	Exclude	Both
Lung	58.6961422884554	72.0415474378	70.3667854369787
cerebhem	63.3779810112732	67.0833171814078	83.9952654066163
cortex	60.6233622369088	71.9879659365489	66.7287501523829
heart	64.5034606575995	66.7873058781456	55.8526701084484
kidney	61.2185264122357	66.5421827134128	74.0535478816652
liver	66.7086798779734	70.9639522489636	82.1441032808875
stomach	62.4505970355569	62.0477499016245	94.8957176407085
testicle	60.799674180433	84.2355329270935	53.9369093641356
cont.diffExp=-13.3454051493445,-3.70533617013459,-11.3646036996400,-2.28384522054608,-5.32365630117717,-4.25527237099016,0.40284713393239,-23.4358587466604
cont.diffExpScore=0.996978660916853

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,-1
cont.diffExp1.3Score=0.5
cont.diffExp1.2=-1,0,0,0,0,0,0,-1
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.0474060231453727
cont.tran.correlation=-0.303657069225977

tran.covariance=-0.000995603860198263
cont.tran.covariance=-0.00111795019214317

tran.mean=73.559509405232
cont.tran.mean=66.2542486203395

weightedLogRatios:
wLogRatio
Lung	-1.60062083845992
cerebhem	-0.937828614623144
cortex	-1.30538858124268
heart	-1.72060402732563
kidney	-1.69275492345712
liver	-1.95299919553601
stomach	5.43254746831404
testicle	-1.44988292653062

cont.weightedLogRatios:
wLogRatio
Lung	-0.85528882134515
cerebhem	-0.237362380316233
cortex	-0.720021376841483
heart	-0.145582777978344
kidney	-0.346565011219147
liver	-0.261647838870648
stomach	0.0267348726652284
testicle	-1.39235421013387

varWeightedLogRatios=6.14064269302532
cont.varWeightedLogRatios=0.217536119655992

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9293748152332	0.108971244598864	36.0588229463442	4.77144392123916e-161	***
df.mm.trans1	0.00687188826077471	0.0978721134978524	0.0702129341564234	0.94404446488089	   
df.mm.trans2	0.468130004169186	0.0900067006132026	5.20105726551339	2.61289558003114e-07	***
df.mm.exp2	0.0768798141024482	0.123286889616311	0.623584667775386	0.53310586633021	   
df.mm.exp3	-0.0563455486510145	0.123286889616311	-0.457027903180711	0.647794422264349	   
df.mm.exp4	0.0431688637069727	0.123286889616311	0.350149669939125	0.726332924037851	   
df.mm.exp5	0.0412168626486661	0.123286889616311	0.334316672088489	0.738241906512107	   
df.mm.exp6	0.0585846267044334	0.123286889616311	0.4751894291985	0.634801906635299	   
df.mm.exp7	-0.130156712122426	0.123286889616311	-1.05572224692743	0.291463562625407	   
df.mm.exp8	0.0549785196465213	0.123286889616311	0.445939708736455	0.655780288111051	   
df.mm.trans1:exp2	0.0538791101111115	0.118038210149620	0.456454821221169	0.64820617984569	   
df.mm.trans2:exp2	-0.110453454267893	0.102739074680259	-1.07508710402194	0.282710432278991	   
df.mm.trans1:exp3	0.0328976954863614	0.11803821014962	0.278703781128685	0.78055542400454	   
df.mm.trans2:exp3	-0.0346042804144289	0.102739074680259	-0.336817131379886	0.736356879354243	   
df.mm.trans1:exp4	-0.0298756365597742	0.118038210149620	-0.253101402689054	0.800265024501935	   
df.mm.trans2:exp4	-0.00331439270242393	0.102739074680259	-0.0322602934933846	0.974273776192525	   
df.mm.trans1:exp5	-0.0259915863807383	0.11803821014962	-0.220196378340476	0.825783160754743	   
df.mm.trans2:exp5	-0.00604231455774407	0.102739074680259	-0.0588122345519339	0.953118642537858	   
df.mm.trans1:exp6	-0.0128119828924749	0.11803821014962	-0.108540979029037	0.9135980401527	   
df.mm.trans2:exp6	0.063573251579743	0.102739074680259	0.618783571660472	0.536262579097076	   
df.mm.trans1:exp7	1.64177474646178	0.118038210149620	13.9088414199160	5.84475285430121e-39	***
df.mm.trans2:exp7	0.132955211693410	0.102739074680259	1.29410559815911	0.196060662334361	   
df.mm.trans1:exp8	-0.0207371742821398	0.11803821014962	-0.175681876706317	0.86059526903298	   
df.mm.trans2:exp8	-0.0588084067319132	0.102739074680259	-0.572405454447925	0.567233233973736	   
df.mm.trans1:probe2	-0.0728098528074911	0.05901910507481	-1.23366582253663	0.21774629917937	   
df.mm.trans1:probe3	0.0313267456301574	0.05901910507481	0.530789912697745	0.59573467201375	   
df.mm.trans1:probe4	0.236630880566670	0.05901910507481	4.00939458954397	6.74911413555415e-05	***
df.mm.trans1:probe5	-0.0108151831104142	0.05901910507481	-0.183248849617516	0.854656443647621	   
df.mm.trans1:probe6	-0.0167918345014062	0.05901910507481	-0.284515234179197	0.776100679403961	   
df.mm.trans1:probe7	-0.110304483802025	0.0590191050748099	-1.86896232435595	0.0620502775393223	.  
df.mm.trans1:probe8	0.0448113425923173	0.05901910507481	0.759268418853801	0.44795055881399	   
df.mm.trans1:probe9	-0.087043324696409	0.05901910507481	-1.47483301527661	0.140712244037551	   
df.mm.trans1:probe10	0.008889430623554	0.05901910507481	0.150619542812216	0.880319736965071	   
df.mm.trans1:probe11	-0.0378535705539355	0.05901910507481	-0.641378253803646	0.521489326095313	   
df.mm.trans1:probe12	-0.173147924062982	0.05901910507481	-2.93376058216247	0.00345960358582205	** 
df.mm.trans1:probe13	-0.102311962912519	0.05901910507481	-1.73353972044871	0.0834452001788344	.  
df.mm.trans1:probe14	-0.120304159667003	0.0590191050748099	-2.03839349164157	0.0418902555854190	*  
df.mm.trans1:probe15	-0.165205416721314	0.0590191050748099	-2.79918539110186	0.00526587980145079	** 
df.mm.trans1:probe16	-0.0822100516248768	0.05901910507481	-1.39293965099388	0.164085358305209	   
df.mm.trans1:probe17	-0.0273071025963278	0.05901910507481	-0.462682423966182	0.643737471349397	   
df.mm.trans1:probe18	0.0699164121252933	0.05901910507481	1.18464033022308	0.236566384130016	   
df.mm.trans1:probe19	0.00359931537720886	0.05901910507481	0.0609855973357531	0.95138828805711	   
df.mm.trans1:probe20	0.0122174019916869	0.05901910507481	0.207007577905505	0.83606483747916	   
df.mm.trans1:probe21	-0.0674106245612569	0.05901910507481	-1.14218310284119	0.253772964117543	   
df.mm.trans1:probe22	-0.117862086522232	0.05901910507481	-1.99701582009479	0.0462146487001095	*  
df.mm.trans2:probe2	-0.23748159723857	0.05901910507481	-4.02380884863552	6.35774825433564e-05	***
df.mm.trans2:probe3	-0.101820403054344	0.05901910507481	-1.72521089442616	0.0849359392052069	.  
df.mm.trans2:probe4	0.00125498778746809	0.05901910507481	0.0212640938197441	0.983041116055792	   
df.mm.trans2:probe5	-0.323257537820664	0.05901910507481	-5.47716773087151	6.05360096503841e-08	***
df.mm.trans2:probe6	-0.258765001331721	0.05901910507481	-4.3844277374881	1.34373403759358e-05	***
df.mm.trans3:probe2	-0.543545510922168	0.05901910507481	-9.20965355596623	3.83684738869327e-19	***
df.mm.trans3:probe3	-0.347436802807031	0.05901910507481	-5.88685311928461	6.13656888689292e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29710539851416	0.268891135056369	15.9808369941736	3.65520661452495e-49	***
df.mm.trans1	-0.264944009667524	0.241503561656831	-1.09706046507091	0.272996566703386	   
df.mm.trans2	0.0662970262958018	0.222095324134851	0.298507078228931	0.765405786472464	   
df.mm.exp2	-0.171604199574118	0.30421559199889	-0.564087456683494	0.572877382673676	   
df.mm.exp3	0.0846477508599341	0.304215591998890	0.278249218929722	0.780904173430112	   
df.mm.exp4	0.249618921908423	0.30421559199889	0.820532965678281	0.41219489913911	   
df.mm.exp5	-0.0883981674395112	0.304215591998890	-0.290577372641155	0.77146162811329	   
df.mm.exp6	-0.0418636029788572	0.304215591998890	-0.137611628331693	0.890587394806177	   
df.mm.exp7	-0.386394186401746	0.30421559199889	-1.27013274981368	0.204464246355172	   
df.mm.exp8	0.457490598322454	0.304215591998890	1.50383678665662	0.133079630747717	   
df.mm.trans1:exp2	0.248346692464380	0.291264254381880	0.852650775809824	0.39414800312351	   
df.mm.trans2:exp2	0.100296586095359	0.253512993332409	0.395627004268966	0.69250214977089	   
df.mm.trans1:exp3	-0.052341422184191	0.29126425438188	-0.179704242442210	0.857437369575342	   
df.mm.trans2:exp3	-0.085391785918689	0.253512993332409	-0.336833961826653	0.736344196717262	   
df.mm.trans1:exp4	-0.15527405150788	0.291264254381880	-0.533103699379116	0.594132959373316	   
df.mm.trans2:exp4	-0.325348891523681	0.253512993332409	-1.28336180030457	0.199794954473577	   
df.mm.trans1:exp5	0.130474024733696	0.29126425438188	0.447957560087789	0.654324031680442	   
df.mm.trans2:exp5	0.0089912401835312	0.253512993332409	0.0354665852244575	0.971717918655306	   
df.mm.trans1:exp6	0.169824674968832	0.29126425438188	0.583060476573872	0.560042544895055	   
df.mm.trans2:exp6	0.0267926359507929	0.253512993332409	0.105685454613611	0.915862548535722	   
df.mm.trans1:exp7	0.448395977576642	0.291264254381880	1.53948165911477	0.124143870208724	   
df.mm.trans2:exp7	0.237055434394411	0.253512993332409	0.935081990387696	0.350072230948728	   
df.mm.trans1:exp8	-0.422280173807222	0.29126425438188	-1.44981805166372	0.147562316200541	   
df.mm.trans2:exp8	-0.301116760169000	0.253512993332409	-1.18777643784977	0.235329019940501	   
df.mm.trans1:probe2	0.0252242926957873	0.14563212719094	0.173205550055006	0.862540511136036	   
df.mm.trans1:probe3	-0.0714334783986357	0.14563212719094	-0.490506317366212	0.623931152534867	   
df.mm.trans1:probe4	-0.03990222956168	0.14563212719094	-0.273993316799965	0.78417152248882	   
df.mm.trans1:probe5	0.0954972154542873	0.14563212719094	0.655742776654493	0.512207590346452	   
df.mm.trans1:probe6	-0.0181674293630064	0.14563212719094	-0.124748774281013	0.900758671819543	   
df.mm.trans1:probe7	0.099241304040381	0.14563212719094	0.681451997952792	0.495813515947083	   
df.mm.trans1:probe8	0.068075667397077	0.14563212719094	0.467449516189667	0.640325464549552	   
df.mm.trans1:probe9	-0.107618505399670	0.14563212719094	-0.738975028899837	0.460172732401403	   
df.mm.trans1:probe10	0.0469657033878902	0.14563212719094	0.322495484298688	0.7471747671074	   
df.mm.trans1:probe11	0.140245724722196	0.14563212719094	0.963013638730404	0.335877068788695	   
df.mm.trans1:probe12	-0.0218468020625245	0.14563212719094	-0.150013616390296	0.88079758475254	   
df.mm.trans1:probe13	-0.189875918353040	0.14563212719094	-1.30380515629008	0.192733575592720	   
df.mm.trans1:probe14	0.266627857481929	0.14563212719094	1.83083130504817	0.0675555861871826	.  
df.mm.trans1:probe15	0.161778128316963	0.14563212719094	1.11086840134426	0.267010826061330	   
df.mm.trans1:probe16	0.132217994234635	0.14563212719094	0.907890290315425	0.364252242712924	   
df.mm.trans1:probe17	-0.0592917818465397	0.14563212719094	-0.407133940773945	0.684035519265306	   
df.mm.trans1:probe18	0.109004203836443	0.14563212719094	0.748490088959055	0.454418915638910	   
df.mm.trans1:probe19	0.0745608484250131	0.14563212719094	0.511980768688873	0.608827816293137	   
df.mm.trans1:probe20	0.0687954956823608	0.14563212719094	0.472392301131208	0.636795736737913	   
df.mm.trans1:probe21	-0.0189748816089631	0.14563212719094	-0.130293239376260	0.896372293921281	   
df.mm.trans1:probe22	0.244192008955133	0.14563212719094	1.67677293235558	0.0940385281328341	.  
df.mm.trans2:probe2	-0.0102099331127813	0.14563212719094	-0.0701076974546623	0.944128194660773	   
df.mm.trans2:probe3	0.030639325694111	0.14563212719094	0.210388506197808	0.833426394364744	   
df.mm.trans2:probe4	-0.264559150377476	0.14563212719094	-1.81662628624939	0.0697068491932725	.  
df.mm.trans2:probe5	-0.31230648603145	0.14563212719094	-2.14448893973774	0.0323413618378454	*  
df.mm.trans2:probe6	-0.218998576444809	0.14563212719094	-1.50377928736616	0.133094437938405	   
df.mm.trans3:probe2	0.103847472530654	0.14563212719094	0.713080791537836	0.476036155141158	   
df.mm.trans3:probe3	0.361941344228707	0.14563212719094	2.48531248708715	0.0131788941345852	*