chr18.11592_chr18_81312855_81316552_-_2.R 

fitVsDatCorrelation=0.860309717218863
cont.fitVsDatCorrelation=0.259092937230977

fstatistic=11124.3045656151,61,899
cont.fstatistic=3088.2337510202,61,899

residuals=-0.489431287900413,-0.0868752950403963,-0.00195101359464783,0.0776399156428731,1.17433516483807
cont.residuals=-0.549675408038142,-0.211557996796185,-0.075959237804446,0.168307317238263,1.14474409381166

predictedValues:
Include	Exclude	Both
chr18.11592_chr18_81312855_81316552_-_2.R.tl.Lung	52.6232696270131	49.2673502119166	88.4241506324972
chr18.11592_chr18_81312855_81316552_-_2.R.tl.cerebhem	73.0187460800128	73.187184808136	79.1233056494828
chr18.11592_chr18_81312855_81316552_-_2.R.tl.cortex	61.435233695704	48.0264092143392	84.382575526424
chr18.11592_chr18_81312855_81316552_-_2.R.tl.heart	54.9061989399972	49.6342200159889	78.5724666083022
chr18.11592_chr18_81312855_81316552_-_2.R.tl.kidney	51.2263288724259	48.0906618174422	79.6279619600417
chr18.11592_chr18_81312855_81316552_-_2.R.tl.liver	53.1803443034987	52.1021305870729	76.3683413584383
chr18.11592_chr18_81312855_81316552_-_2.R.tl.stomach	56.3268266511922	51.5373377442331	84.3530269287585
chr18.11592_chr18_81312855_81316552_-_2.R.tl.testicle	55.2006104164398	63.3109200627775	88.0759846280726


diffExp=3.35591941509649,-0.168438728123235,13.4088244813648,5.27197892400834,3.13566705498374,1.07821371642576,4.7894889069591,-8.1103096463377
diffExpScore=1.65473975998526
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	61.2532728964438	56.6247997067984	62.7017376335587
cerebhem	64.6730162955864	66.1714857517444	66.5672835398554
cortex	64.0448484532788	56.9270334466227	66.1606294939077
heart	65.2818273301707	69.801647803107	64.9084847250856
kidney	64.8651001857494	63.1192679054938	63.9239704280956
liver	66.5857702511056	63.7354761707274	60.937680588935
stomach	66.0936898135828	58.0692012233911	67.4084412283932
testicle	61.4519258053981	63.7870305795667	64.5369623650255
cont.diffExp=4.62847318964548,-1.49846945615805,7.11781500665611,-4.5198204729364,1.74583228025553,2.85029408037823,8.02448859019164,-2.33510477416866
cont.diffExpScore=1.92319520446655

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=0.748249502891842
cont.tran.correlation=0.297766346334581

tran.covariance=0.0124651060063074
cont.tran.covariance=0.00070816664630549

tran.mean=55.8171108155119
cont.tran.mean=63.280337101173

weightedLogRatios:
wLogRatio
Lung	0.258988325825021
cerebhem	-0.0098890263614629
cortex	0.98366598860149
heart	0.399255884975464
kidney	0.246640947427904
liver	0.0811833633923075
stomach	0.354279012420944
testicle	-0.55923560810378

cont.weightedLogRatios:
wLogRatio
Lung	0.320231239820022
cerebhem	-0.0957636001494637
cortex	0.483114130391487
heart	-0.281980196788109
kidney	0.113463654276755
liver	0.182725123606423
stomach	0.534106452767976
testicle	-0.154284589813166

varWeightedLogRatios=0.187843283970946
cont.varWeightedLogRatios=0.0897532905472558

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.56387583695113	0.0716583761415309	49.7342533957537	2.47112383961180e-260	***
df.mm.trans1	0.40427189718054	0.0614496775807682	6.57890998124723	8.03601567178378e-11	***
df.mm.trans2	0.335914121149217	0.0540996800082255	6.20917020392992	8.12835473043367e-10	***
df.mm.exp2	0.834454196990983	0.0689096780132938	12.109390452093	2.24794667534905e-31	***
df.mm.exp3	0.176098756778141	0.0689096780132938	2.55550108279665	0.0107670572819294	*  
df.mm.exp4	0.168010555619992	0.0689096780132938	2.43812713197673	0.0149563343768715	*  
df.mm.exp5	0.0537014659661196	0.0689096780132938	0.779302233218383	0.436006792449434	   
df.mm.exp6	0.213051597160128	0.0689096780132938	3.09175145353352	0.00205115779906377	** 
df.mm.exp7	0.160191897503062	0.0689096780132938	2.32466472230734	0.0203118204074576	*  
df.mm.exp8	0.302557060131373	0.0689096780132938	4.39063232994652	1.26454448636858e-05	***
df.mm.trans1:exp2	-0.506896403457377	0.0630242199265237	-8.0428826258911	2.75798385994494e-15	***
df.mm.trans2:exp2	-0.43869545642341	0.0450646616649267	-9.73479973477402	2.32538772890963e-21	***
df.mm.trans1:exp3	-0.0212736577803053	0.0630242199265237	-0.337547339818042	0.735783128635556	   
df.mm.trans2:exp3	-0.201609299350841	0.0450646616649267	-4.47377816458236	8.66981583841694e-06	***
df.mm.trans1:exp4	-0.125542710446927	0.0630242199265237	-1.99197563402277	0.0466758443499864	*  
df.mm.trans2:exp4	-0.160591634242972	0.0450646616649267	-3.56358237940481	0.000385074741038511	***
df.mm.trans1:exp5	-0.0806062405700475	0.0630242199265237	-1.27897244367359	0.201236793321887	   
df.mm.trans2:exp5	-0.0778750428576123	0.0450646616649267	-1.72807339455122	0.0843184584448778	.  
df.mm.trans1:exp6	-0.202521147327033	0.0630242199265237	-3.21338602148732	0.00135850231767137	** 
df.mm.trans2:exp6	-0.157107349197175	0.0450646616649267	-3.48626492228721	0.000513521626576582	***
df.mm.trans1:exp7	-0.0921793914174318	0.0630242199265237	-1.46260265537437	0.143925629529503	   
df.mm.trans2:exp7	-0.115146941925845	0.0450646616649267	-2.55514937140784	0.0107778630397079	*  
df.mm.trans1:exp8	-0.254741458905675	0.0630242199265237	-4.04196131586656	5.7544727621059e-05	***
df.mm.trans2:exp8	-0.0517608271656689	0.0450646616649267	-1.14859016473996	0.251030529100422	   
df.mm.trans1:probe2	0.0619239120882878	0.0451474251169558	1.37159343922432	0.170532250740612	   
df.mm.trans1:probe3	0.166907931230500	0.0451474251169558	3.6969534984137	0.000231416760877715	***
df.mm.trans1:probe4	0.126082531677540	0.0451474251169558	2.79268488404199	0.00533822880835104	** 
df.mm.trans1:probe5	0.0679479808601782	0.0451474251169558	1.50502449883148	0.132669019470004	   
df.mm.trans1:probe6	0.0785164510994893	0.0451474251169558	1.73911249414756	0.0823572724923325	.  
df.mm.trans1:probe7	0.0681848030336985	0.0451474251169558	1.5102700288458	0.131325920092340	   
df.mm.trans1:probe8	-0.0324878680699364	0.0451474251169558	-0.719595148245456	0.471961321809045	   
df.mm.trans1:probe9	0.108179447725428	0.0451474251169558	2.39613770763640	0.0167723572567518	*  
df.mm.trans1:probe10	-0.192686109182893	0.0451474251169558	-4.26793130912192	2.18235176406002e-05	***
df.mm.trans1:probe11	-0.0179408431206226	0.0451474251169558	-0.397383529052794	0.691178989045926	   
df.mm.trans1:probe12	-0.168659609332270	0.0451474251169558	-3.73575256828826	0.000198953485505842	***
df.mm.trans1:probe13	0.0513639153541639	0.0451474251169558	1.13769312914533	0.255551761529382	   
df.mm.trans1:probe14	-0.108816922979723	0.0451474251169558	-2.41025756613648	0.0161411324438014	*  
df.mm.trans1:probe15	0.104152618263943	0.0451474251169558	2.30694481455215	0.0212843961183001	*  
df.mm.trans1:probe16	-0.109391054215141	0.0451474251169558	-2.42297437631848	0.0155906293847865	*  
df.mm.trans1:probe17	-0.0475661997418991	0.0451474251169558	-1.05357502933284	0.292360607048151	   
df.mm.trans1:probe18	-0.129604088278740	0.0451474251169558	-2.87068615636433	0.00419194916052932	** 
df.mm.trans1:probe19	-0.0764396196676085	0.0451474251169558	-1.69311138940015	0.0907807131711092	.  
df.mm.trans1:probe20	-0.0261141912555638	0.0451474251169558	-0.578420390263988	0.563125199332388	   
df.mm.trans1:probe21	-0.0931900969541125	0.0451474251169558	-2.06412872301578	0.0392919433392322	*  
df.mm.trans2:probe2	-0.108297220460448	0.0451474251169558	-2.39874633337120	0.0166541300743951	*  
df.mm.trans2:probe3	-0.103253404985787	0.0451474251169558	-2.28702754848822	0.0224258470407592	*  
df.mm.trans2:probe4	0.105651589764388	0.0451474251169558	2.34014651977812	0.0194941027114299	*  
df.mm.trans2:probe5	0.0114651882268237	0.0451474251169558	0.253949991547087	0.799592261551821	   
df.mm.trans2:probe6	0.0463949323202169	0.0451474251169558	1.02763185718852	0.304399440759471	   
df.mm.trans3:probe2	-0.0956356942957595	0.0451474251169558	-2.11829786633484	0.0344236656718822	*  
df.mm.trans3:probe3	0.176683971264331	0.0451474251169558	3.91348943614449	9.7838025574869e-05	***
df.mm.trans3:probe4	0.568980963813846	0.0451474251169558	12.6027334303093	1.18643536711137e-33	***
df.mm.trans3:probe5	0.116831208877498	0.0451474251169558	2.58777125328506	0.00981573575577764	** 
df.mm.trans3:probe6	0.0787923327062023	0.0451474251169558	1.74522317722635	0.0812876896634197	.  
df.mm.trans3:probe7	0.303391709972430	0.0451474251169558	6.72002244173358	3.22276192894774e-11	***
df.mm.trans3:probe8	0.150310094301257	0.0451474251169558	3.32931709642075	0.000905993240746071	***
df.mm.trans3:probe9	0.428615038144832	0.0451474251169558	9.49367626247768	1.94563582978259e-20	***
df.mm.trans3:probe10	0.281796139401515	0.0451474251169558	6.24168795167195	6.66266974689818e-10	***
df.mm.trans3:probe11	-0.266209930588388	0.0451474251169558	-5.89645876589335	5.25170870038439e-09	***
df.mm.trans3:probe12	0.466179186807279	0.0451474251169558	10.3257092868448	1.06943068777070e-23	***
df.mm.trans3:probe13	0.105347544862451	0.0451474251169558	2.33341202935815	0.0198461967358191	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0341929307717	0.135769431052520	29.7135584902845	1.04796955996252e-135	***
df.mm.trans1	0.102449202645707	0.116427251254252	0.879941779454885	0.379126005145203	   
df.mm.trans2	0.0095585993137585	0.102501384629943	0.0932533677302761	0.925723055376822	   
df.mm.exp2	0.150305309388185	0.130561537696567	1.15122196046361	0.249947018920771	   
df.mm.exp3	-0.00380680649079451	0.130561537696567	-0.0291571818006753	0.976745701950372	   
df.mm.exp4	0.238317799967277	0.130561537696567	1.82532929813635	0.0682830154818655	.  
df.mm.exp5	0.146566225508779	0.130561537696567	1.12258348128075	0.261914149379396	   
df.mm.exp6	0.230305346927106	0.130561537696567	1.76396012937861	0.0780782931917568	.  
df.mm.exp7	0.0288632821422709	0.130561537696567	0.221070329374881	0.825087871803897	   
df.mm.exp8	0.0934917736121056	0.130561537696567	0.716074391137966	0.474131371983645	   
df.mm.trans1:exp2	-0.0959785367165858	0.119410499409765	-0.803769661721529	0.421742487492853	   
df.mm.trans2:exp2	0.00549728532869972	0.0853829489903187	0.0643838775037278	0.948678880364347	   
df.mm.trans1:exp3	0.0483731184761321	0.119410499409765	0.405099373298294	0.685500843388997	   
df.mm.trans2:exp3	0.00913009269657401	0.0853829489903187	0.106931100466081	0.914867497163264	   
df.mm.trans1:exp4	-0.174621380554301	0.119410499409765	-1.46236203196066	0.143991508439573	   
df.mm.trans2:exp4	-0.0291072295933905	0.0853829489903187	-0.340902134882819	0.733256909418016	   
df.mm.trans1:exp5	-0.0892737770011274	0.119410499409765	-0.747620832693937	0.454884402273542	   
df.mm.trans2:exp5	-0.0379871941040025	0.0853829489903187	-0.444903748971118	0.656496407953043	   
df.mm.trans1:exp6	-0.146831735143815	0.119410499409765	-1.22963839754117	0.219154251565289	   
df.mm.trans2:exp6	-0.112011060227639	0.0853829489903187	-1.31186684873510	0.189899954939905	   
df.mm.trans1:exp7	0.0471927128748346	0.119410499409765	0.395214098493045	0.692778644485886	   
df.mm.trans2:exp7	-0.00367490487221940	0.0853829489903187	-0.043040266419424	0.965678992940606	   
df.mm.trans1:exp8	-0.0902538817442569	0.119410499409765	-0.755828693375987	0.449949903151757	   
df.mm.trans2:exp8	0.0256110670118703	0.0853829489903187	0.299955287498612	0.764280602518714	   
df.mm.trans1:probe2	0.070572508500466	0.0855397589460975	0.825025805192378	0.409575797363845	   
df.mm.trans1:probe3	-0.105188034167099	0.0855397589460975	-1.22969757529224	0.219132092883868	   
df.mm.trans1:probe4	-0.0875038475808572	0.0855397589460975	-1.02296111958881	0.306601414060999	   
df.mm.trans1:probe5	-0.00978702088689708	0.0855397589460975	-0.114414875696158	0.908934469783616	   
df.mm.trans1:probe6	-0.0926276906061633	0.0855397589460975	-1.08286125361345	0.279160360914129	   
df.mm.trans1:probe7	-0.0687487116637584	0.0855397589460975	-0.803704762683282	0.421779956849701	   
df.mm.trans1:probe8	-0.0717313047638666	0.0855397589460975	-0.838572678338593	0.401932054575767	   
df.mm.trans1:probe9	0.0906555645321263	0.0855397589460975	1.05980617258055	0.289517439242848	   
df.mm.trans1:probe10	-0.0447130829089515	0.0855397589460975	-0.522716961794658	0.601300024165722	   
df.mm.trans1:probe11	-0.000651858954152628	0.0855397589460975	-0.00762053765621896	0.99392144017717	   
df.mm.trans1:probe12	-0.0978190277372805	0.0855397589460975	-1.14355042546847	0.253114543408848	   
df.mm.trans1:probe13	-0.0805116924709566	0.0855397589460975	-0.941219538877713	0.346845204461458	   
df.mm.trans1:probe14	-0.0513874141310833	0.0855397589460975	-0.60074303182763	0.548162573055364	   
df.mm.trans1:probe15	0.0625192420805178	0.0855397589460975	0.73087933436794	0.465043297200678	   
df.mm.trans1:probe16	-0.128618364298619	0.0855397589460975	-1.50360915068357	0.133033233473000	   
df.mm.trans1:probe17	-0.0515012142769326	0.0855397589460975	-0.602073409037614	0.547277086181052	   
df.mm.trans1:probe18	-0.0413739690801709	0.0855397589460975	-0.4836811511971	0.628729960230235	   
df.mm.trans1:probe19	-0.0962744879671497	0.0855397589460975	-1.12549402936495	0.260680123625982	   
df.mm.trans1:probe20	0.0731307234223363	0.0855397589460975	0.85493254041573	0.392816268873827	   
df.mm.trans1:probe21	-0.00368522983212127	0.0855397589460975	-0.0430820694087238	0.965645679189643	   
df.mm.trans2:probe2	0.00872276590745912	0.0855397589460975	0.101973234609601	0.918800659473109	   
df.mm.trans2:probe3	0.0122151678088316	0.0855397589460975	0.142801054846658	0.886479302462225	   
df.mm.trans2:probe4	-0.0177707676545948	0.0855397589460975	-0.207748629099983	0.835472297067332	   
df.mm.trans2:probe5	-0.104799158647851	0.0855397589460975	-1.22515143763603	0.220839057672886	   
df.mm.trans2:probe6	-0.0371531934445877	0.0855397589460975	-0.434338299550267	0.664147019470124	   
df.mm.trans3:probe2	0.128297483478538	0.0855397589460975	1.49985790302945	0.134002299291686	   
df.mm.trans3:probe3	-0.0567130976182481	0.0855397589460975	-0.663002775749995	0.507498729491102	   
df.mm.trans3:probe4	0.0384943489184743	0.0855397589460975	0.450017037606236	0.652806646332467	   
df.mm.trans3:probe5	0.1045192869544	0.0855397589460975	1.22187960595332	0.222073446838451	   
df.mm.trans3:probe6	-0.0506810529535469	0.0855397589460975	-0.592485337554941	0.553674626274025	   
df.mm.trans3:probe7	-0.00159747550067811	0.0855397589460975	-0.0186752396822248	0.985104324509739	   
df.mm.trans3:probe8	-0.169619507707082	0.0855397589460975	-1.98293179448830	0.0476790216950974	*  
df.mm.trans3:probe9	0.0323099494721844	0.0855397589460975	0.377718500382312	0.705728881127087	   
df.mm.trans3:probe10	-0.0359321397634166	0.0855397589460975	-0.420063607918969	0.674539361726084	   
df.mm.trans3:probe11	0.00110679725794687	0.0855397589460975	0.0129389803242760	0.98967934617986	   
df.mm.trans3:probe12	-0.0339472767123141	0.0855397589460975	-0.396859625635675	0.691565169337734	   
df.mm.trans3:probe13	-0.0577877521176911	0.0855397589460975	-0.675565992114916	0.499489968550681