chr7.21414_chr7_45472338_45473522_+_2.R 

fitVsDatCorrelation=0.826172280232635
cont.fitVsDatCorrelation=0.245086183616523

fstatistic=10785.7128539272,64,968
cont.fstatistic=3632.59452706954,64,968

residuals=-0.595694854063383,-0.0914185393766334,-0.00696681673153462,0.0739108261675726,0.989462643730623
cont.residuals=-0.553258790150128,-0.188284781770594,-0.0298602672267743,0.140287478850014,1.12694745122465

predictedValues:
Include	Exclude	Both
chr7.21414_chr7_45472338_45473522_+_2.R.tl.Lung	59.2298552002515	42.4112129755348	55.9666754445889
chr7.21414_chr7_45472338_45473522_+_2.R.tl.cerebhem	64.1718206346724	45.1904977139735	57.9803889117903
chr7.21414_chr7_45472338_45473522_+_2.R.tl.cortex	60.113050712068	42.7318346812087	62.2700298297401
chr7.21414_chr7_45472338_45473522_+_2.R.tl.heart	59.813084224951	42.3566264561986	52.6817133548887
chr7.21414_chr7_45472338_45473522_+_2.R.tl.kidney	58.1494806481043	40.2730185372574	56.5461789641649
chr7.21414_chr7_45472338_45473522_+_2.R.tl.liver	61.2917364737934	45.7393957634272	55.0410921287287
chr7.21414_chr7_45472338_45473522_+_2.R.tl.stomach	62.5389147944204	42.0248791615962	57.3935914727783
chr7.21414_chr7_45472338_45473522_+_2.R.tl.testicle	59.9518339660819	45.4359409444214	55.2572521324512


diffExp=16.8186422247167,18.9813229206989,17.3812160308592,17.4564577687524,17.8764621108469,15.5523407103662,20.5140356328241,14.5158930216605
diffExpScore=0.992862056333102
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,1,1,1,1,0,1,0
diffExp1.4Score=0.833333333333333
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	55.292856623801	51.9857934468032	49.8731174920364
cerebhem	56.9012367994414	54.2130005709337	49.8339937492143
cortex	56.1212904064966	53.7639898481461	59.0425956983193
heart	55.0435501200357	57.2818547481206	63.0237584775291
kidney	54.5069237026729	49.6676045816322	51.9510144923005
liver	55.0328753789663	52.2485204117271	50.3811548194236
stomach	55.5062389249421	51.9291295186659	62.8710090309797
testicle	54.1752997297504	54.1401102080257	55.8434701547741
cont.diffExp=3.30706317699778,2.68823622850763,2.35730055835054,-2.23830462808486,4.83931912104071,2.78435496723920,3.57710940627612,0.0351895217247389
cont.diffExpScore=1.18945822423251

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=0.537841115151348
cont.tran.correlation=0.203811963938143

tran.covariance=0.000782418403625265
cont.tran.covariance=0.000141012859420397

tran.mean=51.9639489304975
cont.tran.mean=54.23814218876

weightedLogRatios:
wLogRatio
Lung	1.30746668896232
cerebhem	1.39787898347577
cortex	1.33973476883932
heart	1.35233289629199
kidney	1.42502163560267
liver	1.161755505432
stomach	1.56507466776168
testicle	1.09645491088927

cont.weightedLogRatios:
wLogRatio
Lung	0.245570972492515
cerebhem	0.194413831095962
cortex	0.171905381789545
heart	-0.160555063077490
kidney	0.367421692483063
liver	0.206740628902580
stomach	0.265342166593730
testicle	0.00259377800131464

varWeightedLogRatios=0.0218347814084779
cont.varWeightedLogRatios=0.0274947954833432

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0913400185686	0.076237120789126	53.6659828731644	2.39608582589898e-292	***
df.mm.trans1	0.317094223792923	0.0677327675511538	4.68154831490451	3.25289466672933e-06	***
df.mm.trans2	-0.328943226602228	0.0602946051564112	-5.45559964691554	6.19938199851593e-08	***
df.mm.exp2	0.10826411185284	0.079975179079552	1.35372140580203	0.176141259486602	   
df.mm.exp3	-0.0843911722344302	0.079975179079552	-1.05521704615985	0.291589362958400	   
df.mm.exp4	0.0689988367509483	0.079975179079552	0.862753138474558	0.388486902461427	   
df.mm.exp5	-0.0804410314907948	0.079975179079552	-1.00582496240214	0.314751133536356	   
df.mm.exp6	0.126442893994825	0.079975179079552	1.58102670666171	0.114198602440327	   
df.mm.exp7	0.0200360757444579	0.079975179079552	0.250528676210001	0.802231694417394	   
df.mm.exp8	0.0937632455442278	0.0799751790795521	1.17240432123272	0.241323210928718	   
df.mm.trans1:exp2	-0.0281256540224634	0.0767705888038355	-0.366359753919957	0.714176704381708	   
df.mm.trans2:exp2	-0.0447900589280163	0.0609677302352592	-0.734651901180882	0.462729446962433	   
df.mm.trans1:exp3	0.099192414570111	0.0767705888038355	1.29206270416354	0.196643803601092	   
df.mm.trans2:exp3	0.0919225733299267	0.0609677302352591	1.50772503708471	0.131951194411488	   
df.mm.trans1:exp4	-0.0592001255622495	0.0767705888038355	-0.77113027898637	0.440817897306615	   
df.mm.trans2:exp4	-0.0702867432788809	0.0609677302352592	-1.15285156602783	0.249255915948404	   
df.mm.trans1:exp5	0.0620322535875453	0.0767705888038355	0.808021073617794	0.419276970366668	   
df.mm.trans2:exp5	0.028709976768018	0.0609677302352592	0.470904471221635	0.637815051152617	   
df.mm.trans1:exp6	-0.0922235903916326	0.0767705888038355	-1.20128804309789	0.229933324622396	   
df.mm.trans2:exp6	-0.0508956999772058	0.0609677302352592	-0.834797355597987	0.404037789711173	   
df.mm.trans1:exp7	0.0343271983677530	0.0767705888038355	0.447139964700102	0.654874033656865	   
df.mm.trans2:exp7	-0.0291870560927119	0.0609677302352592	-0.478729583340012	0.632239129886122	   
df.mm.trans1:exp8	-0.0816474985273141	0.0767705888038355	-1.06352575640575	0.287808822678221	   
df.mm.trans2:exp8	-0.0248725871744172	0.0609677302352592	-0.407963148348153	0.683390945663177	   
df.mm.trans1:probe2	-0.352143106867425	0.0448243667448333	-7.85606428914057	1.05038941645955e-14	***
df.mm.trans1:probe3	-0.420191811102804	0.0448243667448333	-9.37418287456872	4.77663899864202e-20	***
df.mm.trans1:probe4	-0.0440685753890134	0.0448243667448333	-0.98313882803694	0.32578477414807	   
df.mm.trans1:probe5	-0.130154062557846	0.0448243667448333	-2.90364531636909	0.00377224706791437	** 
df.mm.trans1:probe6	-0.267953575830717	0.0448243667448333	-5.97785524458308	3.17591568677854e-09	***
df.mm.trans1:probe7	-0.220308177712024	0.0448243667448333	-4.91492002477465	1.04268903002097e-06	***
df.mm.trans1:probe8	-0.664340084269096	0.0448243667448333	-14.8209586105457	6.10867451718737e-45	***
df.mm.trans1:probe9	-0.171343384343953	0.0448243667448333	-3.82255002774139	0.000140515372910256	***
df.mm.trans1:probe10	-0.239410398275517	0.0448243667448333	-5.34107708957457	1.15184883665862e-07	***
df.mm.trans1:probe11	-0.500463979420485	0.0448243667448333	-11.1649983204318	2.59606730723340e-27	***
df.mm.trans1:probe12	-0.53032753708339	0.0448243667448333	-11.8312332241597	2.92193312731294e-30	***
df.mm.trans1:probe13	-0.476577724881983	0.0448243667448333	-10.6321128326239	4.77907271773688e-25	***
df.mm.trans1:probe14	-0.207992521082068	0.0448243667448333	-4.64016641364025	3.95981359191174e-06	***
df.mm.trans1:probe15	-0.336948886777974	0.0448243667448333	-7.51709195795416	1.27424732580528e-13	***
df.mm.trans1:probe16	-0.375856143971456	0.0448243667448333	-8.3850854181845	1.77436461069393e-16	***
df.mm.trans1:probe17	-0.654874157017968	0.0448243667448333	-14.6097804514651	7.83175283543767e-44	***
df.mm.trans1:probe18	-0.507429064646214	0.0448243667448333	-11.3203844581854	5.47056909247019e-28	***
df.mm.trans1:probe19	-0.679528994482046	0.0448243667448333	-15.1598124821333	9.72073553950292e-47	***
df.mm.trans1:probe20	-0.66529102630858	0.0448243667448333	-14.8421734565891	4.72170846988127e-45	***
df.mm.trans1:probe21	-0.596209677648249	0.0448243667448333	-13.3010173025360	3.3808038425679e-37	***
df.mm.trans1:probe22	-0.632986358084786	0.0448243667448333	-14.1214790983689	2.61160595613867e-41	***
df.mm.trans1:probe23	-0.343012352315028	0.0448243667448333	-7.65236359651563	4.75994987741988e-14	***
df.mm.trans1:probe24	-0.563712057214943	0.0448243667448333	-12.5760183166429	1.05287308067085e-33	***
df.mm.trans1:probe25	-0.394176456840919	0.0448243667448333	-8.7937986739401	6.51247932249777e-18	***
df.mm.trans1:probe26	-0.429191322919581	0.0448243667448333	-9.5749556343493	8.22015200712764e-21	***
df.mm.trans1:probe27	-0.297250123171571	0.0448243667448333	-6.6314405480326	5.5182978074831e-11	***
df.mm.trans1:probe28	-0.186248161451354	0.0448243667448333	-4.15506509019053	3.53953485920628e-05	***
df.mm.trans1:probe29	-0.652046896908577	0.0448243667448333	-14.5467062729611	1.67043435446409e-43	***
df.mm.trans1:probe30	-0.552831215894359	0.0448243667448333	-12.3332744228468	1.44946817706619e-32	***
df.mm.trans1:probe31	-0.250654156102963	0.0448243667448333	-5.59191739461339	2.92107156896128e-08	***
df.mm.trans1:probe32	0.244206869769745	0.0448243667448333	5.4480829848621	6.45894570440744e-08	***
df.mm.trans2:probe2	-0.00453399484622481	0.0448243667448333	-0.101150226439003	0.919452154162723	   
df.mm.trans2:probe3	-0.0425804158531393	0.0448243667448333	-0.949939038637893	0.342380333826337	   
df.mm.trans2:probe4	-0.0147437374718543	0.0448243667448333	-0.328922381788109	0.742285503779355	   
df.mm.trans2:probe5	-0.0329959550007752	0.0448243667448333	-0.73611647853516	0.461838153603822	   
df.mm.trans2:probe6	-0.0699699817576603	0.0448243667448333	-1.56098093155383	0.118855102736274	   
df.mm.trans3:probe2	-0.096117153851992	0.0448243667448333	-2.14430589503132	0.0322568503232935	*  
df.mm.trans3:probe3	-0.0241226827781014	0.0448243667448333	-0.538160035041251	0.590590281368583	   
df.mm.trans3:probe4	-0.191627400585426	0.0448243667448333	-4.2750721204001	2.09985270389026e-05	***
df.mm.trans3:probe5	0.0381943197057713	0.0448243667448333	0.852088327832848	0.394375827635674	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04447995191417	0.131185093838150	30.8303316602727	5.88945056715085e-146	***
df.mm.trans1	-0.0344570615960862	0.116551220391617	-0.295638788511258	0.767569352152008	   
df.mm.trans2	-0.115492534464617	0.103751995792924	-1.1131596417203	0.265916215741325	   
df.mm.exp2	0.0714083897743703	0.137617360987357	0.518890852593304	0.60395545850441	   
df.mm.exp3	-0.120272059888014	0.137617360987357	-0.87395993517899	0.38235683690293	   
df.mm.exp4	-0.141535252750844	0.137617360987357	-1.02846945861610	0.303985963723326	   
df.mm.exp5	-0.100752683829303	0.137617360987357	-0.732121900219833	0.464271382876248	   
df.mm.exp6	-0.00980695259252168	0.137617360987357	-0.071262466611481	0.94320358650929	   
df.mm.exp7	-0.228841902666840	0.137617360987357	-1.66288541667256	0.0966590916267134	.  
df.mm.exp8	-0.0928842748440892	0.137617360987357	-0.67494590927828	0.499871305899838	   
df.mm.trans1:exp2	-0.0427350377165305	0.132103059401972	-0.323497713906031	0.746388212538713	   
df.mm.trans2:exp2	-0.0294581254675389	0.104910276374869	-0.280793516950408	0.778928779015182	   
df.mm.trans1:exp3	0.135143583350852	0.132103059401972	1.02301630229189	0.306555725834502	   
df.mm.trans2:exp3	0.153905490965767	0.104910276374869	1.46702016507731	0.142695371189757	   
df.mm.trans1:exp4	0.137016219815233	0.132103059401972	1.03719187455236	0.299905460832834	   
df.mm.trans2:exp4	0.238548676949589	0.104910276374869	2.27383517794957	0.0231941459440489	*  
df.mm.trans1:exp5	0.086436692637558	0.132103059401972	0.65431257253886	0.51306586824001	   
df.mm.trans2:exp5	0.055135106813672	0.104910276374869	0.525545339492397	0.599324504568107	   
df.mm.trans1:exp6	0.00509396820809631	0.132103059401972	0.0385605619669719	0.969248696745024	   
df.mm.trans2:exp6	0.0148480472489749	0.104910276374869	0.141530913481909	0.887479988875213	   
df.mm.trans1:exp7	0.232693604955364	0.132103059401972	1.76145507915383	0.0784770679529495	.  
df.mm.trans2:exp7	0.227751319483281	0.104910276374869	2.17091525590373	0.0301799859114787	*  
df.mm.trans1:exp8	0.0724656296643395	0.132103059401972	0.548553757894708	0.583438217717186	   
df.mm.trans2:exp8	0.133489116331413	0.104910276374869	1.27241220730774	0.203532251391927	   
df.mm.trans1:probe2	-0.0193890982440277	0.0771315691987065	-0.251376945204854	0.801576043840088	   
df.mm.trans1:probe3	-0.0521930265600371	0.0771315691987065	-0.676675284870418	0.498773658952798	   
df.mm.trans1:probe4	0.0581096306429053	0.0771315691987065	0.753383228768536	0.451402852413878	   
df.mm.trans1:probe5	-0.00565592685446581	0.0771315691987065	-0.0733283001139909	0.941560020988017	   
df.mm.trans1:probe6	-0.0294402887419994	0.0771315691987065	-0.381689223334161	0.702775663235633	   
df.mm.trans1:probe7	0.136368127467795	0.0771315691987065	1.76799368772705	0.0773769186852403	.  
df.mm.trans1:probe8	-0.111475356777418	0.0771315691987066	-1.44526239950123	0.148707836805219	   
df.mm.trans1:probe9	0.130209570215766	0.0771315691987065	1.68814885485241	0.0917047648466633	.  
df.mm.trans1:probe10	-0.00418628800018303	0.0771315691987065	-0.0542746380460419	0.956727554770998	   
df.mm.trans1:probe11	0.0142173362931407	0.0771315691987065	0.184325775306269	0.853796501566674	   
df.mm.trans1:probe12	0.0396378411492801	0.0771315691987065	0.513899063134124	0.607439847975253	   
df.mm.trans1:probe13	0.0282441194561173	0.0771315691987065	0.366181055948113	0.71430999152957	   
df.mm.trans1:probe14	-0.0237822742395031	0.0771315691987065	-0.308333857155624	0.75789464399549	   
df.mm.trans1:probe15	0.12271398001257	0.0771315691987065	1.59096957688536	0.111942923573393	   
df.mm.trans1:probe16	-0.0159781298544640	0.0771315691987065	-0.207154217403526	0.83593298918919	   
df.mm.trans1:probe17	-0.0134931049404917	0.0771315691987065	-0.174936217176274	0.861166334328354	   
df.mm.trans1:probe18	-0.0174322352359967	0.0771315691987065	-0.2260064901712	0.82124408267177	   
df.mm.trans1:probe19	-0.0172964551657998	0.0771315691987065	-0.22424612056369	0.822613119722964	   
df.mm.trans1:probe20	0.0205652805388834	0.0771315691987065	0.266625984049451	0.789813932688073	   
df.mm.trans1:probe21	0.0463393548605644	0.0771315691987065	0.600783250515555	0.548125001160737	   
df.mm.trans1:probe22	0.0410612940114296	0.0771315691987065	0.532353930277852	0.594603054285878	   
df.mm.trans1:probe23	-0.128132593062517	0.0771315691987065	-1.66122113673613	0.0969928542747374	.  
df.mm.trans1:probe24	0.0287908584858695	0.0771315691987065	0.373269450952027	0.709029636392902	   
df.mm.trans1:probe25	0.00155915272002281	0.0771315691987065	0.0202141968096891	0.983876668538705	   
df.mm.trans1:probe26	-0.0687275310042702	0.0771315691987065	-0.891042820964969	0.373127613067671	   
df.mm.trans1:probe27	0.0305598511670598	0.0771315691987065	0.396204193490883	0.692041714734222	   
df.mm.trans1:probe28	-0.0380945289742876	0.0771315691987065	-0.493890236773848	0.621495745179843	   
df.mm.trans1:probe29	0.0263989311961541	0.0771315691987065	0.342258448394135	0.732230699209252	   
df.mm.trans1:probe30	-0.00553351235299804	0.0771315691987065	-0.0717412132345265	0.942822676568962	   
df.mm.trans1:probe31	-0.0486889957754599	0.0771315691987065	-0.63124601614194	0.528028700562271	   
df.mm.trans1:probe32	-0.0283050915065184	0.0771315691987065	-0.366971550048447	0.713720444368532	   
df.mm.trans2:probe2	0.018722532126892	0.0771315691987065	0.242735008782966	0.808262107086434	   
df.mm.trans2:probe3	0.0782332114525593	0.0771315691987065	1.01428263764497	0.310701354253592	   
df.mm.trans2:probe4	0.0463198878565013	0.0771315691987065	0.600530863532309	0.548293068844031	   
df.mm.trans2:probe5	0.0919312053410536	0.0771315691987065	1.19187521135763	0.233602298195458	   
df.mm.trans2:probe6	0.0066068326726012	0.0771315691987065	0.0856566609656374	0.931757067943006	   
df.mm.trans3:probe2	0.0144312678580021	0.0771315691987065	0.187099368104702	0.851621930345873	   
df.mm.trans3:probe3	-0.00174587343158439	0.0771315691987065	-0.0226350047032839	0.98194608613726	   
df.mm.trans3:probe4	-0.0192850485970481	0.0771315691987065	-0.250027956093645	0.802618780347387	   
df.mm.trans3:probe5	0.081358524788832	0.0771315691987065	1.05480188765817	0.291779136864627