chr17.10660_chr17_31540823_31551824_-_2.R 

fitVsDatCorrelation=0.88194618189773
cont.fitVsDatCorrelation=0.269467780238251

fstatistic=8219.78286337328,53,715
cont.fstatistic=1958.92634893173,53,715

residuals=-0.61012708702388,-0.0948597137674248,-0.00394959990953235,0.0953120417099,0.886720243217023
cont.residuals=-0.690249259691196,-0.272851163649909,-0.0409113113906036,0.209998033921521,1.58769596089562

predictedValues:
Include	Exclude	Both
chr17.10660_chr17_31540823_31551824_-_2.R.tl.Lung	72.3920863567295	51.1584341441023	95.8462375858475
chr17.10660_chr17_31540823_31551824_-_2.R.tl.cerebhem	83.4923411854084	61.5174081544072	86.4072644803059
chr17.10660_chr17_31540823_31551824_-_2.R.tl.cortex	105.409311281078	48.2734326207322	144.197820144737
chr17.10660_chr17_31540823_31551824_-_2.R.tl.heart	72.2770445730172	47.3101040925045	84.3897486714389
chr17.10660_chr17_31540823_31551824_-_2.R.tl.kidney	63.3890477626317	46.8599699476232	87.0193335517111
chr17.10660_chr17_31540823_31551824_-_2.R.tl.liver	63.2699594046361	50.7816970043152	75.0354011054833
chr17.10660_chr17_31540823_31551824_-_2.R.tl.stomach	60.8443423734288	49.2045043111596	72.8852882696871
chr17.10660_chr17_31540823_31551824_-_2.R.tl.testicle	68.9226884477395	50.7828572410344	75.2973586335131


diffExp=21.2336522126272,21.9749330310012,57.1358786603454,24.9669404805128,16.5290778150085,12.4882624003208,11.6398380622692,18.1398312067051
diffExpScore=0.994597760419962
diffExp1.5=0,0,1,1,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,0,1,1,0,0,0,0
diffExp1.4Score=0.75
diffExp1.3=1,1,1,1,1,0,0,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	66.02725972412	72.1126529214226	76.740855909992
cerebhem	77.484215284054	82.8351336000653	65.4809433684261
cortex	68.7687804226814	70.1314366408726	61.9442961554453
heart	71.7941170803447	72.8659444262726	64.962165332492
kidney	73.2054297169383	67.4964657568139	77.9221832143723
liver	75.5974338877893	66.8448455301516	69.2657454567276
stomach	69.563829658179	70.2993293941954	78.4197299130563
testicle	68.2453512736824	71.248076961683	57.9249955518782
cont.diffExp=-6.08539319730258,-5.3509183160112,-1.36265621819112,-1.07182734592787,5.70896396012445,8.75258835763768,-0.73549973601638,-3.00272568800061
cont.diffExpScore=7.7325663269337

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=0.18981867985472
cont.tran.correlation=0.340680310810931

tran.covariance=0.00352958545320646
cont.tran.covariance=0.00107627361821889

tran.mean=62.2428268062842
cont.tran.mean=71.5325188924541

weightedLogRatios:
wLogRatio
Lung	1.42635061092869
cerebhem	1.30482855498558
cortex	3.33268331045491
heart	1.72420869356469
kidney	1.20797368890095
liver	0.887738020833269
stomach	0.849791615616797
testicle	1.24622355161409

cont.weightedLogRatios:
wLogRatio
Lung	-0.373290320104761
cerebhem	-0.292719122025599
cortex	-0.083205219615883
heart	-0.0634425835774426
kidney	0.345293003744083
liver	0.524664766990455
stomach	-0.0446731866080542
testicle	-0.182767813463045

varWeightedLogRatios=0.62854004166725
cont.varWeightedLogRatios=0.0946085554602141

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.44424299501956	0.0909037115708977	37.8889149353744	3.87803405652867e-173	***
df.mm.trans1	0.851268757294677	0.0807242551604838	10.5453900516309	2.89582917757035e-24	***
df.mm.trans2	0.525282371381389	0.0734292074864207	7.15358900582619	2.09692197326631e-12	***
df.mm.exp2	0.43072420500507	0.0989934834846615	4.35103594542976	1.55230315865580e-05	***
df.mm.exp3	-0.0907329158380703	0.0989934834846616	-0.91655443009164	0.35968520617312	   
df.mm.exp4	0.0475053905930672	0.0989934834846616	0.479884017824545	0.631456629810698	   
df.mm.exp5	-0.123954571230607	0.0989934834846615	-1.25214879674189	0.210925081409988	   
df.mm.exp6	0.102707476511415	0.0989934834846616	1.03751755061058	0.299845462368384	   
df.mm.exp7	0.0611380546164197	0.0989934834846616	0.617596759547235	0.537037822788197	   
df.mm.exp8	0.184820062016580	0.0989934834846615	1.86699220504971	0.0623117130556466	.  
df.mm.trans1:exp2	-0.288066288637889	0.0939989568727906	-3.06456899333182	0.00226181228696502	** 
df.mm.trans2:exp2	-0.246331380032593	0.0791542052904257	-3.11204413118388	0.00193194622267974	** 
df.mm.trans1:exp3	0.466486901416153	0.0939989568727907	4.96268168217497	8.69827289195216e-07	***
df.mm.trans2:exp3	0.0326869066963137	0.0791542052904257	0.412952243994893	0.679765408067822	   
df.mm.trans1:exp4	-0.0490958032432564	0.0939989568727906	-0.52230157521533	0.601622151406662	   
df.mm.trans2:exp4	-0.125708869937887	0.0791542052904257	-1.58815150094233	0.112694166415166	   
df.mm.trans1:exp5	-0.00885131942481974	0.0939989568727906	-0.0941640175517936	0.925005248545687	   
df.mm.trans2:exp5	0.036190993779966	0.0791542052904257	0.457221364893718	0.647650829831014	   
df.mm.trans1:exp6	-0.237393823926319	0.0939989568727906	-2.5254942376391	0.0117685688650433	*  
df.mm.trans2:exp6	-0.110098851247020	0.0791542052904257	-1.3909412752368	0.164675930831111	   
df.mm.trans1:exp7	-0.234916205065496	0.0939989568727906	-2.49913629768689	0.012672836245411	*  
df.mm.trans2:exp7	-0.100080253476358	0.0791542052904257	-1.26437064346932	0.206509020241141	   
df.mm.trans1:exp8	-0.233931631832433	0.0939989568727906	-2.48866199812211	0.0130489579944834	*  
df.mm.trans2:exp8	-0.192188589475833	0.0791542052904257	-2.4280275289313	0.0154269265282425	*  
df.mm.trans1:probe2	0.195898845773056	0.0514853490611827	3.80494352947397	0.000153974776292561	***
df.mm.trans1:probe3	0.319412738204731	0.0514853490611827	6.20395401855305	9.31759478790025e-10	***
df.mm.trans1:probe4	0.312099477772365	0.0514853490611827	6.06190855191602	2.17677673399206e-09	***
df.mm.trans1:probe5	-0.340061394328988	0.0514853490611827	-6.60501289259738	7.75921683460103e-11	***
df.mm.trans1:probe6	-0.427112900630249	0.0514853490611827	-8.29581440970108	5.35507897640534e-16	***
df.mm.trans1:probe7	0.62426398493041	0.0514853490611827	12.1250801696724	6.65593392046065e-31	***
df.mm.trans1:probe8	-0.388548861722099	0.0514853490611827	-7.54678503316285	1.36098996995743e-13	***
df.mm.trans1:probe9	0.0618454442460561	0.0514853490611827	1.20122414189252	0.23006202322282	   
df.mm.trans1:probe10	0.147334032874095	0.0514853490611827	2.86166910705044	0.00433728534420124	** 
df.mm.trans1:probe11	0.0229527020589714	0.0514853490611827	0.445810361151393	0.655869196653503	   
df.mm.trans1:probe12	0.0227888386395972	0.0514853490611827	0.442627641749423	0.658168957241133	   
df.mm.trans1:probe13	-0.0139051825297429	0.0514853490611827	-0.270080377880291	0.787176351015868	   
df.mm.trans1:probe14	0.0510297102801041	0.0514853490611827	0.991150127378236	0.321947574916079	   
df.mm.trans1:probe15	-0.0408635487976489	0.0514853490611827	-0.793692760033319	0.42763763477347	   
df.mm.trans1:probe16	0.239240993754642	0.0514853490611827	4.64677812459501	4.01412338824258e-06	***
df.mm.trans1:probe17	-0.203081181207802	0.0514853490611827	-3.94444603971647	8.78650815669935e-05	***
df.mm.trans1:probe18	-0.148143824091252	0.0514853490611827	-2.87739768288654	0.00412921897750227	** 
df.mm.trans1:probe19	-0.287914019922404	0.0514853490611827	-5.59215437347548	3.19341045127606e-08	***
df.mm.trans1:probe20	-0.134423124904586	0.0514853490611827	-2.61090052521240	0.0092196667461373	** 
df.mm.trans1:probe21	-0.131738265709204	0.0514853490611827	-2.55875250166125	0.0107096683864881	*  
df.mm.trans1:probe22	-0.229858312096751	0.0514853490611827	-4.46453828687455	9.32165452972053e-06	***
df.mm.trans2:probe2	-0.0604441506223579	0.0514853490611827	-1.17400681406528	0.240783186049149	   
df.mm.trans2:probe3	-0.0473443565446262	0.0514853490611827	-0.919569497108088	0.358107895565389	   
df.mm.trans2:probe4	0.0175438094746474	0.0514853490611827	0.340753433637969	0.7333893071326	   
df.mm.trans2:probe5	-0.140094038271425	0.0514853490611827	-2.72104668271636	0.0066658281152922	** 
df.mm.trans2:probe6	-0.115641235970812	0.0514853490611827	-2.24609987267232	0.0250021498054513	*  
df.mm.trans3:probe2	-0.441003929973451	0.0514853490611827	-8.56561989022125	6.57597238068285e-17	***
df.mm.trans3:probe3	-0.332272389475181	0.0514853490611827	-6.45372704146037	2.01254119651624e-10	***
df.mm.trans3:probe4	-0.258919313037472	0.0514853490611827	-5.02899014493978	6.24076093178442e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.42764413606815	0.185724212652759	23.8398864252899	6.80709797714005e-93	***
df.mm.trans1	-0.0735677343355702	0.164926695209451	-0.446063229740594	0.655686619305702	   
df.mm.trans2	-0.0983266750776291	0.150022276433623	-0.655413831966038	0.512412164975082	   
df.mm.exp2	0.457304407803938	0.202252322377436	2.26105887155418	0.0240553459803936	*  
df.mm.exp3	0.227022519748641	0.202252322377436	1.12247175745641	0.262038693193267	   
df.mm.exp4	0.260755919560269	0.202252322377436	1.28926044702545	0.197724477120848	   
df.mm.exp5	0.0217711894978231	0.202252322377436	0.107643705851716	0.914308523790493	   
df.mm.exp6	0.161983079531549	0.202252322377436	0.80089601754615	0.423457893822763	   
df.mm.exp7	0.00506848533414234	0.202252322377436	0.0250602083306797	0.98001393163951	   
df.mm.exp8	0.302265188862729	0.202252322377436	1.49449551584705	0.135487336320755	   
df.mm.trans1:exp2	-0.29729784882074	0.192048068815803	-1.54803873141721	0.122055455271980	   
df.mm.trans2:exp2	-0.318681637745115	0.161718946362863	-1.97058937689379	0.0491562447150685	*  
df.mm.trans1:exp3	-0.186340333599221	0.192048068815803	-0.970279653152583	0.332235223108917	   
df.mm.trans2:exp3	-0.254880892188893	0.161718946362863	-1.57607316842761	0.115451267524675	   
df.mm.trans1:exp4	-0.177021064554717	0.192048068815803	-0.921753942365862	0.356967847416622	   
df.mm.trans2:exp4	-0.250364064518826	0.161718946362863	-1.54814306022661	0.122030340794318	   
df.mm.trans1:exp5	0.081430722301765	0.192048068815803	0.424012190301517	0.671684457419356	   
df.mm.trans2:exp5	-0.0879254723244174	0.161718946362863	-0.543693081743999	0.586822171561696	   
df.mm.trans1:exp6	-0.0266284230830041	0.192048068815803	-0.138654990113667	0.889761828748574	   
df.mm.trans2:exp6	-0.237838404141268	0.161718946362863	-1.47068979541587	0.141814954258681	   
df.mm.trans1:exp7	0.0471085752242809	0.192048068815803	0.245295750770937	0.806297828959522	   
df.mm.trans2:exp7	-0.0305357459396321	0.161718946362863	-0.188819842241097	0.850287601352314	   
df.mm.trans1:exp8	-0.269223553201234	0.192048068815803	-1.40185504004964	0.161392496468266	   
df.mm.trans2:exp8	-0.314326880265649	0.161718946362863	-1.9436614406352	0.0523282964434847	.  
df.mm.trans1:probe2	-0.268440736622365	0.105189059415720	-2.55198342977339	0.0109180370921461	*  
df.mm.trans1:probe3	-0.173931279910660	0.105189059415720	-1.65351112441516	0.0986658313666153	.  
df.mm.trans1:probe4	-0.302836845895126	0.105189059415720	-2.87897665001717	0.00410884097009326	** 
df.mm.trans1:probe5	-0.327148309396957	0.105189059415720	-3.11009824799391	0.00194454612066842	** 
df.mm.trans1:probe6	-0.235193404166758	0.105189059415720	-2.23591127702022	0.0256653822356882	*  
df.mm.trans1:probe7	-0.136561470718219	0.105189059415720	-1.29824785464153	0.194620777734634	   
df.mm.trans1:probe8	-0.23031425179137	0.105189059415720	-2.18952667768556	0.0288810710817431	*  
df.mm.trans1:probe9	-0.134251261984660	0.105189059415720	-1.27628541152824	0.202269048544921	   
df.mm.trans1:probe10	-0.175655092066286	0.105189059415720	-1.66989887581442	0.0953770635838263	.  
df.mm.trans1:probe11	-0.219820223263232	0.105189059415720	-2.08976317959529	0.0369918837890514	*  
df.mm.trans1:probe12	-0.111801525036879	0.105189059415720	-1.06286267467252	0.288203072128884	   
df.mm.trans1:probe13	-0.182625337010443	0.105189059415720	-1.7361628483499	0.0829658030290098	.  
df.mm.trans1:probe14	-0.173197360257305	0.105189059415720	-1.64653397624566	0.100093296799820	   
df.mm.trans1:probe15	-0.278126289456277	0.105189059415720	-2.64406099836950	0.00837142533878926	** 
df.mm.trans1:probe16	-0.0758311847388795	0.105189059415720	-0.720903724779833	0.471204386153301	   
df.mm.trans1:probe17	-0.156241447503712	0.105189059415720	-1.48533933444758	0.137894706640128	   
df.mm.trans1:probe18	-0.248674858563053	0.105189059415720	-2.36407531300628	0.0183415101626467	*  
df.mm.trans1:probe19	-0.158462330969455	0.105189059415720	-1.50645258974313	0.132392685725035	   
df.mm.trans1:probe20	-0.146270287428953	0.105189059415720	-1.39054658575161	0.164795611721835	   
df.mm.trans1:probe21	-0.305104163577689	0.105189059415720	-2.90053134111487	0.00383965652929598	** 
df.mm.trans1:probe22	-0.223739029870193	0.105189059415720	-2.12701806740138	0.0337603196704915	*  
df.mm.trans2:probe2	-0.173817978932724	0.105189059415720	-1.65243400690346	0.098885131298727	.  
df.mm.trans2:probe3	-0.0532030897768503	0.105189059415720	-0.505785393199356	0.613163205185284	   
df.mm.trans2:probe4	-0.190152201732156	0.105189059415720	-1.80771843372657	0.0710704527370441	.  
df.mm.trans2:probe5	-0.0574099171204829	0.105189059415720	-0.54577840546698	0.585388501297526	   
df.mm.trans2:probe6	-0.0362962205832767	0.105189059415720	-0.345056993425806	0.730153004631621	   
df.mm.trans3:probe2	0.111634859005488	0.105189059415720	1.06127823202880	0.288921816958838	   
df.mm.trans3:probe3	0.185754561493761	0.105189059415720	1.76591142202001	0.0778374282613184	.  
df.mm.trans3:probe4	0.0413486254070698	0.105189059415720	0.393088650442771	0.694371178995456