chr9.25297_chr9_107994308_108038497_-_2.R 

fitVsDatCorrelation=0.853368272311004
cont.fitVsDatCorrelation=0.265832410073402

fstatistic=5265.53729643506,50,646
cont.fstatistic=1530.64623015465,50,646

residuals=-0.682479381092793,-0.134192482884897,-0.0156659543102024,0.125610891349623,1.12630634647659
cont.residuals=-0.745918169209095,-0.321647016080512,-0.0574076585141225,0.332129568522889,1.27870485711180

predictedValues:
Include	Exclude	Both
chr9.25297_chr9_107994308_108038497_-_2.R.tl.Lung	62.7429888204979	132.709744203002	50.6779550325154
chr9.25297_chr9_107994308_108038497_-_2.R.tl.cerebhem	69.3946522529274	113.444227864719	74.5252334168975
chr9.25297_chr9_107994308_108038497_-_2.R.tl.cortex	74.4001581274982	109.411945424063	89.938952343573
chr9.25297_chr9_107994308_108038497_-_2.R.tl.heart	59.3860837654282	104.324609663626	49.6176929583551
chr9.25297_chr9_107994308_108038497_-_2.R.tl.kidney	63.7122212911986	149.817965953945	51.3399884754594
chr9.25297_chr9_107994308_108038497_-_2.R.tl.liver	66.4543001611227	131.711515578961	52.9095864811218
chr9.25297_chr9_107994308_108038497_-_2.R.tl.stomach	62.8877766240127	113.911696704106	51.7316247442087
chr9.25297_chr9_107994308_108038497_-_2.R.tl.testicle	60.1883575627612	110.227614969211	54.2726843352909


diffExp=-69.9667553825041,-44.0495756117914,-35.0117872965646,-44.9385258981976,-86.1057446627466,-65.2572154178381,-51.0239200800931,-50.0392574064499
diffExpScore=0.99776482759495
diffExp1.5=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	72.3250095953795	75.5511981224321	70.5413612753385
cerebhem	65.1494915197551	71.533149118833	72.8801222434355
cortex	74.0974804835211	72.1042879567904	89.7813827148249
heart	76.3789740088392	68.6960636389088	71.1117778369654
kidney	74.9547827784537	69.6108359447546	84.315782386105
liver	79.2245604253093	64.7976634067327	65.644122027706
stomach	75.5741613188138	70.4773396067039	68.1370212758946
testicle	74.0392032297537	66.6643635575447	72.7202692409104
cont.diffExp=-3.22618852705261,-6.38365759907789,1.99319252673067,7.6829103699304,5.34394683369904,14.4268970185766,5.0968217121099,7.374839672209
cont.diffExpScore=1.54699397859229

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

tran.correlation=-0.0545087909125798
cont.tran.correlation=-0.55127611598557

tran.covariance=-0.000176219659980423
cont.tran.covariance=-0.00147360004480554

tran.mean=92.7953661854424
cont.tran.mean=71.9486602945328

weightedLogRatios:
wLogRatio
Lung	-3.38122107784591
cerebhem	-2.20465985613474
cortex	-1.73636183989432
heart	-2.45988927124143
kidney	-3.91772772039059
liver	-3.10482976107366
stomach	-2.63672071068324
testicle	-2.66230946049902

cont.weightedLogRatios:
wLogRatio
Lung	-0.187784920903912
cerebhem	-0.394790637884677
cortex	0.117027436998817
heart	0.454032758177596
kidney	0.316562105749956
liver	0.858699189301424
stomach	0.299555730040862
testicle	0.446151595850234

varWeightedLogRatios=0.473733110092352
cont.varWeightedLogRatios=0.154510467061683

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.96137840049733	0.110303213378667	44.9794548003339	3.49280027108807e-201	***
df.mm.trans1	-1.02611635607567	0.0965887800215366	-10.6235564404776	2.08740635486113e-24	***
df.mm.trans2	0.0519191757971414	0.0879797977430425	0.590126109959683	0.555312466154879	   
df.mm.exp2	-0.441736836977617	0.117478274587652	-3.7601576847133	0.000185210354032523	***
df.mm.exp3	-0.596273196027711	0.117478274587652	-5.07560396269547	5.05725456548946e-07	***
df.mm.exp4	-0.274500483914931	0.117478274587652	-2.33660636299288	0.0197644211491389	*  
df.mm.exp5	0.123607242314230	0.117478274587652	1.05217107374186	0.293114475417743	   
df.mm.exp6	0.00682379258484382	0.117478274587652	0.058085570364353	0.953698430452667	   
df.mm.exp7	-0.171014123961388	0.117478274587652	-1.45570850918305	0.145959005252386	   
df.mm.exp8	-0.29571486165108	0.117478274587652	-2.51718764758028	0.0120704826584764	*  
df.mm.trans1:exp2	0.54249980482194	0.109476960446026	4.95537876290787	9.23200829426446e-07	***
df.mm.trans2:exp2	0.284883799719546	0.0913965240330212	3.11700912845021	0.00190804781104761	** 
df.mm.trans1:exp3	0.76668442340894	0.109476960446026	7.00315774465561	6.29556709384984e-12	***
df.mm.trans2:exp3	0.403228901443875	0.0913965240330213	4.41186254849462	1.20064499889012e-05	***
df.mm.trans1:exp4	0.219513562967220	0.109476960446026	2.00511196212326	0.0453677772222614	*  
df.mm.trans2:exp4	0.0338433998846922	0.0913965240330212	0.370291980387183	0.711286323283037	   
df.mm.trans1:exp5	-0.108277680958784	0.109476960446026	-0.989045370986227	0.323011243703403	   
df.mm.trans2:exp5	-0.00235061444441301	0.0913965240330212	-0.0257188604192840	0.979489522917943	   
df.mm.trans1:exp6	0.0506438633886987	0.109476960446026	0.462598369395423	0.643808053345147	   
df.mm.trans2:exp6	-0.0143741186597092	0.0913965240330213	-0.157272049585998	0.875079589413262	   
df.mm.trans1:exp7	0.173319098656180	0.109476960446026	1.58315592568566	0.113875345271265	   
df.mm.trans2:exp7	0.0182733129321313	0.0913965240330213	0.199934440893280	0.841594774566122	   
df.mm.trans1:exp8	0.254146959469865	0.109476960446026	2.32146525108509	0.0205719439609547	*  
df.mm.trans2:exp8	0.110097947576038	0.0913965240330212	1.20461854256361	0.228791733212040	   
df.mm.trans1:probe2	-0.125865605174675	0.0639207735879328	-1.96908763942801	0.0493700162703892	*  
df.mm.trans1:probe3	-0.175203845774197	0.0639207735879328	-2.74095315090606	0.00629554677652173	** 
df.mm.trans1:probe4	0.00729997463736009	0.0639207735879328	0.114203477642802	0.909111979585045	   
df.mm.trans1:probe5	0.2294700851508	0.0639207735879328	3.58991408067252	0.00035586442416452	***
df.mm.trans1:probe6	0.00961091170166802	0.0639207735879328	0.150356623710862	0.880530195073502	   
df.mm.trans1:probe7	0.629353595123643	0.0639207735879328	9.8458382118588	2.09636853208489e-21	***
df.mm.trans1:probe8	0.683051562738218	0.0639207735879328	10.6859088899883	1.18056514093165e-24	***
df.mm.trans1:probe9	0.628376501634622	0.0639207735879328	9.83055220334895	2.39244964293970e-21	***
df.mm.trans1:probe10	0.773729448983847	0.0639207735879328	12.1045069631309	1.53202928687697e-30	***
df.mm.trans1:probe11	0.696792790791202	0.0639207735879328	10.9008816958802	1.62624013796553e-25	***
df.mm.trans1:probe12	0.601127585939047	0.0639207735879328	9.40426018330496	8.9688062359077e-20	***
df.mm.trans1:probe13	0.0449055255336217	0.0639207735879328	0.702518493019289	0.482608932615361	   
df.mm.trans1:probe14	0.117006708069796	0.0639207735879328	1.83049580757711	0.0676363768374291	.  
df.mm.trans1:probe15	0.113898017021020	0.0639207735879328	1.78186230591117	0.0752413397728027	.  
df.mm.trans1:probe16	0.0574688070161155	0.0639207735879328	0.899063071210463	0.368954057607669	   
df.mm.trans1:probe17	0.19882504325068	0.0639207735879328	3.11049181808111	0.00195001561027313	** 
df.mm.trans1:probe18	0.197203187637639	0.0639207735879328	3.08511891468829	0.00212162168412637	** 
df.mm.trans2:probe2	-0.366335940700556	0.0639207735879328	-5.73109366701587	1.53143197431248e-08	***
df.mm.trans2:probe3	-0.150242604626443	0.0639207735879328	-2.35045034959975	0.0190504980303192	*  
df.mm.trans2:probe4	-0.335035758501695	0.0639207735879328	-5.24142214957399	2.16079075314387e-07	***
df.mm.trans2:probe5	-0.228017723938679	0.0639207735879328	-3.56719280978359	0.000387520357864097	***
df.mm.trans2:probe6	-0.296833252924973	0.0639207735879328	-4.64376815647629	4.14607512052673e-06	***
df.mm.trans3:probe2	-0.189761554359372	0.0639207735879328	-2.96869927736913	0.00310140030787979	** 
df.mm.trans3:probe3	0.115550185350268	0.0639207735879328	1.80770943254169	0.0711167364450687	.  
df.mm.trans3:probe4	-0.281755248748846	0.0639207735879328	-4.40788233517306	1.22225725863299e-05	***
df.mm.trans3:probe5	0.0475155369491952	0.0639207735879328	0.743350467807314	0.457539776774746	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27916812274011	0.203975983815285	20.9787840837929	6.4532091625106e-75	***
df.mm.trans1	-0.00369909755593749	0.178614845632607	-0.0207099109978023	0.983483457796902	   
df.mm.trans2	0.0433361224894026	0.162694859477029	0.266364423735965	0.79004355733572	   
df.mm.exp2	-0.191751773449368	0.217244320468482	-0.88265494368672	0.377751059195229	   
df.mm.exp3	-0.263663897602775	0.217244320468482	-1.21367452568698	0.225315577933473	   
df.mm.exp4	-0.0486349343163423	0.217244320468482	-0.223872063543306	0.822927634880476	   
df.mm.exp5	-0.224545093136185	0.217244320468482	-1.03360627634342	0.301707045651065	   
df.mm.exp6	0.00952653129850834	0.217244320468482	0.0438516932362817	0.965036172286552	   
df.mm.exp7	0.00910367263597274	0.217244320468482	0.0419052273327142	0.96658719753198	   
df.mm.exp8	-0.132136203601704	0.217244320468482	-0.608237781852045	0.543243564327304	   
df.mm.trans1:exp2	0.0872662886952644	0.202448052310360	0.431055214902647	0.666572020157826	   
df.mm.trans2:exp2	0.137102192486676	0.169013171383622	0.811192354798694	0.417553981505886	   
df.mm.trans1:exp3	0.287875444222344	0.202448052310360	1.42197191297756	0.155517023309903	   
df.mm.trans2:exp3	0.216966865218340	0.169013171383622	1.28372755473521	0.199697560421178	   
df.mm.trans1:exp4	0.103172399770908	0.202448052310360	0.509624066981594	0.610488907406323	   
df.mm.trans2:exp4	-0.0464837131912729	0.169013171383622	-0.275030122272336	0.783381035000272	   
df.mm.trans1:exp5	0.260260145037673	0.20244805231036	1.28556507245960	0.199055545818219	   
df.mm.trans2:exp5	0.142654789957866	0.169013171383622	0.844045400663309	0.398956482495412	   
df.mm.trans1:exp6	0.0815898422965034	0.20244805231036	0.403016187932613	0.68706965820544	   
df.mm.trans2:exp6	-0.163067534390337	0.169013171383622	-0.964821457732487	0.334995280094473	   
df.mm.trans1:exp7	0.0348407870992450	0.20244805231036	0.172097418086457	0.863414841522344	   
df.mm.trans2:exp7	-0.0786229857657629	0.169013171383622	-0.465188512363372	0.641953233955193	   
df.mm.trans1:exp8	0.155560946368061	0.202448052310360	0.76839932314874	0.442530785681941	   
df.mm.trans2:exp8	0.00699618698619521	0.169013171383622	0.0413943299739369	0.96699432309765	   
df.mm.trans1:probe2	0.0665116794306957	0.118204196228379	0.562684587797463	0.573844893868408	   
df.mm.trans1:probe3	0.00118316428676640	0.118204196228379	0.0100094947939110	0.99201680227631	   
df.mm.trans1:probe4	0.0695305216930713	0.118204196228379	0.588223801790703	0.556587667362262	   
df.mm.trans1:probe5	-0.0313766964903405	0.118204196228379	-0.265444861447376	0.790751465216995	   
df.mm.trans1:probe6	-0.00921812695832017	0.118204196228379	-0.0779847691744385	0.93786431676543	   
df.mm.trans1:probe7	0.103338026519222	0.118204196228379	0.874233147523507	0.382316182353668	   
df.mm.trans1:probe8	-0.0463940985175331	0.118204196228379	-0.392491129738715	0.694824829877046	   
df.mm.trans1:probe9	-0.0223419948435601	0.118204196228379	-0.189011858770173	0.850142888285711	   
df.mm.trans1:probe10	-0.0231185241855501	0.118204196228379	-0.195581247732385	0.844999440306576	   
df.mm.trans1:probe11	-0.103854383124335	0.118204196228379	-0.878601491639783	0.379944058222409	   
df.mm.trans1:probe12	0.098471490735321	0.118204196228379	0.833062563574877	0.405117332939046	   
df.mm.trans1:probe13	0.137353463820784	0.118204196228379	1.16200158880491	0.245663945708923	   
df.mm.trans1:probe14	0.0104549584041043	0.118204196228379	0.0884482847284422	0.92954779779587	   
df.mm.trans1:probe15	-0.00354815662126834	0.118204196228379	-0.0300171798843169	0.976062622822198	   
df.mm.trans1:probe16	0.037706233987259	0.118204196228379	0.318992347060233	0.749835432047408	   
df.mm.trans1:probe17	-0.116301181622580	0.118204196228379	-0.983900617181792	0.32553272901855	   
df.mm.trans1:probe18	-0.0372743345023605	0.118204196228379	-0.315338504822145	0.752606491000598	   
df.mm.trans2:probe2	0.172261396344238	0.118204196228379	1.45732048303444	0.145513852352687	   
df.mm.trans2:probe3	0.0810289840077722	0.118204196228379	0.685500063392153	0.493274333127741	   
df.mm.trans2:probe4	0.131489711575562	0.118204196228379	1.11239461686719	0.266382301912288	   
df.mm.trans2:probe5	-0.136576696248681	0.118204196228379	-1.15543018443106	0.248341482144269	   
df.mm.trans2:probe6	-0.222834073323086	0.118204196228379	-1.88516212142380	0.0598565160403872	.  
df.mm.trans3:probe2	-0.0641926773700616	0.118204196228379	-0.543065977505882	0.58727170074099	   
df.mm.trans3:probe3	-0.127367180992087	0.118204196228379	-1.07751826970681	0.281650993771529	   
df.mm.trans3:probe4	-0.115387461866504	0.118204196228379	-0.976170606021191	0.329345326736609	   
df.mm.trans3:probe5	-0.00608230542631968	0.118204196228379	-0.0514559179825413	0.958978134043932