chr10.2867_chr10_28680428_28740560_+_2.R 

fitVsDatCorrelation=0.805308106716383
cont.fitVsDatCorrelation=0.261426402666157

fstatistic=10016.5332369777,55,761
cont.fstatistic=3770.24479074337,55,761

residuals=-0.527261679735362,-0.0875908673721581,-0.00600349435170597,0.0677950134138619,0.954827561393944
cont.residuals=-0.492047714087974,-0.142413256911284,-0.0429183101138509,0.0727775658319302,1.35887165355914

predictedValues:
Include	Exclude	Both
chr10.2867_chr10_28680428_28740560_+_2.R.tl.Lung	49.581953352297	44.7242356762257	67.402970186185
chr10.2867_chr10_28680428_28740560_+_2.R.tl.cerebhem	53.3759638415466	44.4195248925122	63.6686842383931
chr10.2867_chr10_28680428_28740560_+_2.R.tl.cortex	48.9884234662762	46.3440781442131	61.020009330299
chr10.2867_chr10_28680428_28740560_+_2.R.tl.heart	55.8132545599538	46.3551366963388	75.0188938158224
chr10.2867_chr10_28680428_28740560_+_2.R.tl.kidney	48.2104877231475	44.8541031770343	70.0814207941475
chr10.2867_chr10_28680428_28740560_+_2.R.tl.liver	50.2699373167597	48.4261499798039	62.5072000125187
chr10.2867_chr10_28680428_28740560_+_2.R.tl.stomach	50.0658519524607	44.0587426678167	68.3771354927336
chr10.2867_chr10_28680428_28740560_+_2.R.tl.testicle	51.0037838707352	48.121348159555	65.3965874761583


diffExp=4.8577176760713,8.95643894903446,2.64434532206310,9.45811786361499,3.35638454611322,1.84378733695586,6.00710928464406,2.88243571118017
diffExpScore=0.975613525110334
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=0,1,0,1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	49.4679350517134	50.384099854302	48.2453895165785
cerebhem	47.9250193673664	48.5413015782999	50.5569078724661
cortex	53.1450094557034	58.7962876434162	47.4226414321002
heart	53.5433288006847	50.2149060258157	51.8132745729454
kidney	50.9161083406896	48.2882382682199	53.3313434488605
liver	53.0138842205347	53.1496393109424	53.5611368151673
stomach	54.7244405492021	51.8204061924469	52.6666131339679
testicle	51.2827739750909	52.6725366063992	54.6894342120539
cont.diffExp=-0.916164802588547,-0.616282210933555,-5.65127818771277,3.32842277486904,2.62787007246975,-0.135755090407756,2.90403435675526,-1.38976263130827
cont.diffExpScore=15.2634958315967

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=0.0765384335308716
cont.tran.correlation=0.502058224095832

tran.covariance=0.000145593465046587
cont.tran.covariance=0.00145170768193230

tran.mean=48.4133109672923
cont.tran.mean=51.7428697025517

weightedLogRatios:
wLogRatio
Lung	0.397192395900831
cerebhem	0.713697747389032
cortex	0.214406323616484
heart	0.729565733828237
kidney	0.277063832011642
liver	0.145684673222186
stomach	0.492016524917687
testicle	0.227041908523034

cont.weightedLogRatios:
wLogRatio
Lung	-0.0717614449205684
cerebhem	-0.049525234631933
cortex	-0.406598032188491
heart	0.253405628050111
kidney	0.206861378212448
liver	-0.0101578539826585
stomach	0.216745041522661
testicle	-0.105639557077913

varWeightedLogRatios=0.0513774981792914
cont.varWeightedLogRatios=0.0482904151856853

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.94968358154552	0.073667416213157	40.0405461895201	1.66736785488759e-189	***
df.mm.trans1	1.03285574647441	0.0645065831687412	16.0116331657590	6.01834090217082e-50	***
df.mm.trans2	0.879011969830372	0.0578475459762093	15.1953199569067	9.7045905504904e-46	***
df.mm.exp2	0.123893444761070	0.0762870845011548	1.62404220283441	0.10478090062828	   
df.mm.exp3	0.123022323514763	0.0762870845011549	1.61262321557066	0.107240986930725	   
df.mm.exp4	0.0471501100290161	0.0762870845011548	0.618061501987303	0.536719719035876	   
df.mm.exp5	-0.0641194429883131	0.0762870845011549	-0.840501946136667	0.400891007727274	   
df.mm.exp6	0.1687120584536	0.0762870845011548	2.21154156770857	0.0272948068484700	*  
df.mm.exp7	-0.0196288889012107	0.0762870845011548	-0.257302910834318	0.797014500745728	   
df.mm.exp8	0.131702274338537	0.0762870845011549	1.72640329879881	0.0846807340812556	.  
df.mm.trans1:exp2	-0.0501598392740887	0.0715636286830397	-0.700912463456124	0.483571900294682	   
df.mm.trans2:exp2	-0.130729861993880	0.0570880266478388	-2.28996988808736	0.0222958804038707	*  
df.mm.trans1:exp3	-0.135065232927225	0.0715636286830397	-1.88734466673621	0.059494302492096	.  
df.mm.trans2:exp3	-0.087444343385019	0.0570880266478388	-1.53174577086787	0.126000763962239	   
df.mm.trans1:exp4	0.0712343442475447	0.0715636286830397	0.995398718014238	0.319858775965224	   
df.mm.trans2:exp4	-0.0113335399134301	0.0570880266478388	-0.198527442248858	0.842685440177487	   
df.mm.trans1:exp5	0.0360691041732711	0.0715636286830397	0.504014467083882	0.614397090751719	   
df.mm.trans2:exp5	0.0670189741351758	0.0570880266478388	1.17395850006514	0.240778906889442	   
df.mm.trans1:exp6	-0.154931751501281	0.0715636286830397	-2.16495102823090	0.0307015998224923	*  
df.mm.trans2:exp6	-0.0891876416362778	0.0570880266478388	-1.56228279156421	0.118637118625503	   
df.mm.trans1:exp7	0.0293411430233882	0.0715636286830397	0.41000077222666	0.681920773075926	   
df.mm.trans2:exp7	0.00463715320990489	0.0570880266478388	0.0812281222910415	0.935281892698238	   
df.mm.trans1:exp8	-0.103429374642602	0.0715636286830397	-1.44527851013114	0.148791322463279	   
df.mm.trans2:exp8	-0.0584919068493836	0.0570880266478388	-1.02459149989901	0.305881552309721	   
df.mm.trans1:probe2	-0.179044442260172	0.0438235936038624	-4.08557189258873	4.86335391624905e-05	***
df.mm.trans1:probe3	-0.0790225103242147	0.0438235936038624	-1.80319558086743	0.0717529428703877	.  
df.mm.trans1:probe4	-0.0354796209693451	0.0438235936038624	-0.80960090334121	0.418422653797227	   
df.mm.trans1:probe5	-0.172460203448884	0.0438235936038624	-3.9353277371047	9.06894144491278e-05	***
df.mm.trans1:probe6	0.228283976185133	0.0438235936038624	5.20915692694387	2.44448747784673e-07	***
df.mm.trans1:probe7	-0.124189891267982	0.0438235936038624	-2.83385913968125	0.00472072331371365	** 
df.mm.trans1:probe8	-0.129512666232931	0.0438235936038624	-2.95531825627180	0.00321991411021825	** 
df.mm.trans1:probe9	-0.172178170242027	0.0438235936038624	-3.92889208946232	9.31007896097618e-05	***
df.mm.trans1:probe10	-0.0388083360863017	0.0438235936038624	-0.885558049782602	0.37613545554804	   
df.mm.trans1:probe11	-0.133062742269114	0.0438235936038624	-3.03632658407517	0.00247650821592378	** 
df.mm.trans1:probe12	-0.0118522712395422	0.0438235936038624	-0.270454115348896	0.786884243790448	   
df.mm.trans1:probe13	-0.140303500162221	0.0438235936038624	-3.20155168995212	0.00142370159510758	** 
df.mm.trans1:probe14	-0.0993844512618865	0.0438235936038624	-2.26782979415744	0.0236191195984965	*  
df.mm.trans1:probe15	-0.107995346097727	0.0438235936038624	-2.46431972407229	0.0139473252042060	*  
df.mm.trans1:probe16	-0.138575223328040	0.0438235936038624	-3.16211455821429	0.00162842471266175	** 
df.mm.trans1:probe17	-0.107201951325370	0.0438235936038624	-2.44621544034951	0.0146618467065333	*  
df.mm.trans1:probe18	-0.0593842940806562	0.0438235936038624	-1.35507586660858	0.175795477293355	   
df.mm.trans1:probe19	-0.201644280592724	0.0438235936038624	-4.60127214612889	4.91824620067682e-06	***
df.mm.trans1:probe20	-0.0697386288533064	0.0438235936038624	-1.59134893143861	0.111946357110113	   
df.mm.trans1:probe21	-0.221736017952179	0.0438235936038624	-5.05974064921586	5.26756924296555e-07	***
df.mm.trans1:probe22	-0.21625674578821	0.0438235936038624	-4.93471046083154	9.86563668205907e-07	***
df.mm.trans2:probe2	-0.0838162771447837	0.0438235936038624	-1.91258338835537	0.056176515254696	.  
df.mm.trans2:probe3	-0.0435269494872003	0.0438235936038624	-0.993230949535001	0.320913120305168	   
df.mm.trans2:probe4	-0.101276897673285	0.0438235936038624	-2.31101307183442	0.0210985454163904	*  
df.mm.trans2:probe5	-0.0279536530322357	0.0438235936038624	-0.637867658342194	0.523751711725778	   
df.mm.trans2:probe6	-0.081586361084497	0.0438235936038624	-1.86169947225201	0.063030771572388	.  
df.mm.trans3:probe2	-0.836934392678809	0.0438235936038624	-19.09780380505	1.00314330951138e-66	***
df.mm.trans3:probe3	-0.787057100454788	0.0438235936038624	-17.9596659180734	2.13694562188102e-60	***
df.mm.trans3:probe4	-0.84460136363732	0.0438235936038624	-19.2727545639452	1.03806381466013e-67	***
df.mm.trans3:probe5	-0.696990617856373	0.0438235936038624	-15.9044606007606	2.17852835298750e-49	***
df.mm.trans3:probe6	-0.739589802525032	0.0438235936038624	-16.8765211089363	1.60700387787899e-54	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97665575959349	0.119937139040771	33.156166566902	7.28938539786561e-150	***
df.mm.trans1	-0.0453177846042191	0.105022483918373	-0.431505549225454	0.666223033166068	   
df.mm.trans2	-0.0580300782854068	0.094180976089085	-0.616154988991796	0.537976485520032	   
df.mm.exp2	-0.115746942849724	0.124202193196996	-0.931923502076478	0.351671617251984	   
df.mm.exp3	0.243303080498746	0.124202193196996	1.95892740889724	0.0504860944206693	.  
df.mm.exp4	0.00445668332610692	0.124202193196996	0.0358824849335647	0.97138547178515	   
df.mm.exp5	-0.113856914816086	0.124202193196996	-0.916706153775384	0.359587051065204	   
df.mm.exp6	0.0181413585051988	0.124202193196996	0.146063109178957	0.883910249727608	   
df.mm.exp7	0.0414126461176284	0.124202193196996	0.333429266035134	0.73890212116771	   
df.mm.exp8	-0.0449214934205543	0.124202193196996	-0.361680355751082	0.717691306716002	   
df.mm.trans1:exp2	0.0840599529560001	0.116511984875165	0.721470439681074	0.470841719554059	   
df.mm.trans2:exp2	0.0784863111175419	0.0929444107258131	0.844443581971564	0.398686961503968	   
df.mm.trans1:exp3	-0.171603558534072	0.116511984875165	-1.47284040107920	0.141207540680687	   
df.mm.trans2:exp3	-0.088900009125352	0.0929444107258132	-0.956485800825698	0.339130602654674	   
df.mm.trans1:exp4	0.0747098439780321	0.116511984875165	0.641220249213664	0.521572656635363	   
df.mm.trans2:exp4	-0.0078204141410841	0.0929444107258132	-0.0841407684444243	0.932966639754453	   
df.mm.trans1:exp5	0.142711575710337	0.116511984875165	1.22486605874274	0.221004688463580	   
df.mm.trans2:exp5	0.071369285482687	0.0929444107258132	0.767870654355183	0.442802224910596	   
df.mm.trans1:exp6	0.0510878042213077	0.116511984875165	0.438476816578525	0.661165118295499	   
df.mm.trans2:exp6	0.0352943137901574	0.0929444107258132	0.379735731439258	0.704247539900066	   
df.mm.trans1:exp7	0.0595730913480317	0.116511984875165	0.51130440711194	0.609286232879547	   
df.mm.trans2:exp7	-0.0133042787098337	0.0929444107258132	-0.143142321371873	0.886215673695265	   
df.mm.trans1:exp8	0.0809517163043794	0.116511984875165	0.694793041171805	0.487397081367685	   
df.mm.trans2:exp8	0.0893400398986788	0.0929444107258132	0.961220144396124	0.336746798987018	   
df.mm.trans1:probe2	-0.10229081902221	0.0713487279657565	-1.43367403930879	0.152076034503762	   
df.mm.trans1:probe3	-0.0767506436556362	0.0713487279657565	-1.07571145055974	0.282397243272399	   
df.mm.trans1:probe4	-0.0451499849522283	0.0713487279657565	-0.632807146525412	0.527049686855357	   
df.mm.trans1:probe5	-0.0921050129102951	0.0713487279657565	-1.29091317443670	0.197125808006598	   
df.mm.trans1:probe6	-0.0196530012446135	0.0713487279657565	-0.275449917678222	0.78304537142661	   
df.mm.trans1:probe7	-0.0127007050298935	0.0713487279657565	-0.178008850220696	0.8587633983087	   
df.mm.trans1:probe8	-0.0592128114424445	0.0713487279657565	-0.829907037317657	0.406851604980966	   
df.mm.trans1:probe9	-0.0385901113202266	0.0713487279657565	-0.540866143244316	0.588758123730033	   
df.mm.trans1:probe10	-0.103098069846935	0.0713487279657565	-1.44498819791736	0.148872829698605	   
df.mm.trans1:probe11	-0.0178866160731726	0.0713487279657565	-0.250692851619684	0.80211925625847	   
df.mm.trans1:probe12	-0.0718609847109783	0.0713487279657565	-1.00717961987308	0.314168597984809	   
df.mm.trans1:probe13	0.0794711608299643	0.0713487279657565	1.11384131288376	0.265698942182684	   
df.mm.trans1:probe14	0.0258118103881481	0.0713487279657565	0.361769734711126	0.717624536133707	   
df.mm.trans1:probe15	-0.0120420654438631	0.0713487279657565	-0.168777577221036	0.866016444779914	   
df.mm.trans1:probe16	-0.00873087355494598	0.0713487279657565	-0.122369014891707	0.902639083702967	   
df.mm.trans1:probe17	-0.0131997366185073	0.0713487279657565	-0.185003110704966	0.853275888631534	   
df.mm.trans1:probe18	-0.143507792290943	0.0713487279657565	-2.011357404435	0.0446398445072824	*  
df.mm.trans1:probe19	-0.0189959210768787	0.0713487279657565	-0.266240500965843	0.790126111125167	   
df.mm.trans1:probe20	-0.093111125961009	0.0713487279657565	-1.30501451975005	0.192282362704568	   
df.mm.trans1:probe21	-0.0192742731577789	0.0713487279657565	-0.270141790993519	0.787124413461743	   
df.mm.trans1:probe22	0.00250539981514844	0.0713487279657565	0.035114849088142	0.971997369211418	   
df.mm.trans2:probe2	0.00584722489168924	0.0713487279657565	0.0819527559691826	0.934705831727416	   
df.mm.trans2:probe3	-0.00594285966555046	0.0713487279657565	-0.0832931410971014	0.93364035823545	   
df.mm.trans2:probe4	-0.0150995433389248	0.0713487279657565	-0.211630168742066	0.832452234402818	   
df.mm.trans2:probe5	0.0430657454511355	0.0713487279657565	0.60359514008161	0.546292710771274	   
df.mm.trans2:probe6	-0.0152709883711755	0.0713487279657565	-0.214033085193961	0.83057859011231	   
df.mm.trans3:probe2	-0.0282468731103848	0.0713487279657565	-0.395898762539141	0.692290682001601	   
df.mm.trans3:probe3	-0.0679429493215534	0.0713487279657565	-0.952265741221936	0.341264578857396	   
df.mm.trans3:probe4	0.0784427440863562	0.0713487279657565	1.09942736644169	0.271929380850543	   
df.mm.trans3:probe5	-0.00207118080856301	0.0713487279657565	-0.0290289801600536	0.976849088448046	   
df.mm.trans3:probe6	0.037772493884131	0.0713487279657565	0.529406689664596	0.596677754479298