chr13.6324_chr13_113913913_113915635_-_0.R 

fitVsDatCorrelation=0.817443444978679
cont.fitVsDatCorrelation=0.270662147837298

fstatistic=5970.15438717784,37,347
cont.fstatistic=2131.37550526604,37,347

residuals=-0.387247024219178,-0.0812762898895044,-0.00601152349449041,0.0752429325476598,1.85347777365030
cont.residuals=-0.476365273680567,-0.190040685239853,-0.0593906889114625,0.133025706747668,2.05409286958048

predictedValues:
Include	Exclude	Both
chr13.6324_chr13_113913913_113915635_-_0.R.tl.Lung	61.9834068265496	46.4217591605862	74.5704632274463
chr13.6324_chr13_113913913_113915635_-_0.R.tl.cerebhem	57.3188903814241	52.5906505369402	59.8568677278316
chr13.6324_chr13_113913913_113915635_-_0.R.tl.cortex	57.4058120808278	48.0076212144035	65.087911866292
chr13.6324_chr13_113913913_113915635_-_0.R.tl.heart	57.5173458903347	50.1173378964937	68.2496770203456
chr13.6324_chr13_113913913_113915635_-_0.R.tl.kidney	66.9894505405673	46.9397517848356	75.2787881750805
chr13.6324_chr13_113913913_113915635_-_0.R.tl.liver	65.7251855094246	51.8413761404034	75.0043150835292
chr13.6324_chr13_113913913_113915635_-_0.R.tl.stomach	59.57368396771	48.8163497198586	79.7146955232003
chr13.6324_chr13_113913913_113915635_-_0.R.tl.testicle	55.2164464599396	47.5659484203755	71.4732267158388


diffExp=15.5616476659634,4.72823984448389,9.39819086642432,7.40000799384106,20.0496987557316,13.8838093690212,10.7573342478513,7.65049803956406
diffExpScore=0.988941652782993
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,1,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,0,0,1,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,0,0,1,1,1,0
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	54.886124438734	58.6976446310947	60.2147916762212
cerebhem	57.8022347033342	51.7922301494906	61.8496261072136
cortex	52.0081092482912	63.5973237084804	56.369706182295
heart	58.8408142203151	56.6003904052198	61.8243028606231
kidney	58.5988311154389	56.475390329811	65.1226745914449
liver	55.2032420549095	56.6447354934265	53.2067275811298
stomach	66.9946791009936	53.4958399979894	59.2524171627867
testicle	58.2769350527737	64.2507339793446	54.5574565262721
cont.diffExp=-3.81152019236064,6.01000455384366,-11.5892144601892,2.24042381509535,2.12344078562781,-1.44149343851699,13.4988391030043,-5.97379892657093
cont.diffExpScore=22.701007024658

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

tran.correlation=-0.090995254693901
cont.tran.correlation=-0.532567401092763

tran.covariance=-0.000305527014948323
cont.tran.covariance=-0.00304020444847026

tran.mean=54.6269385331671
cont.tran.mean=57.760328664353

weightedLogRatios:
wLogRatio
Lung	1.15128170185278
cerebhem	0.344848310846211
cortex	0.7081262558018
heart	0.5485682250283
kidney	1.43217740499067
liver	0.965033993318994
stomach	0.794133007196194
testicle	0.58713242570194

cont.weightedLogRatios:
wLogRatio
Lung	-0.271163119804362
cerebhem	0.439383449825486
cortex	-0.815144961192358
heart	0.15743102441448
kidney	0.149568002443881
liver	-0.103725819694065
stomach	0.920762504209548
testicle	-0.401471898211212

varWeightedLogRatios=0.124613875417642
cont.varWeightedLogRatios=0.285318690747762

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.47752362045340	0.0923317021613598	37.6633760566446	1.23883951462347e-124	***
df.mm.trans1	0.625839752247344	0.076924978748727	8.1357156339507	7.3797565686828e-15	***
df.mm.trans2	0.346675379998275	0.076924978748727	4.50666851830637	9.01465439061212e-06	***
df.mm.exp2	0.266321906918431	0.105986460905021	2.51279177212167	0.0124307870596175	*  
df.mm.exp3	0.092875960597476	0.105986460905021	0.876300234996114	0.381473057187287	   
df.mm.exp4	0.0903903616247628	0.105986460905021	0.852848192617412	0.394331773144119	   
df.mm.exp5	0.0793111366654876	0.105986460905021	0.748313850545137	0.454777751755639	   
df.mm.exp6	0.163234623788015	0.105986460905021	1.54014599972630	0.124436154123000	   
df.mm.exp7	-0.0560652505133573	0.105986460905021	-0.528985023507861	0.597154045663369	   
df.mm.exp8	-0.0488354607231829	0.105986460905021	-0.460770746623445	0.645251733335513	   
df.mm.trans1:exp2	-0.344558379855597	0.0895749083774958	-3.84659483438737	0.000142505671803667	***
df.mm.trans2:exp2	-0.141551846156698	0.0895749083774957	-1.58026224888956	0.114957890218894	   
df.mm.trans1:exp3	-0.169597124029882	0.0895749083774957	-1.89335526099729	0.059142367726717	.  
df.mm.trans2:exp3	-0.0592844837301321	0.0895749083774957	-0.661842527153803	0.508511414998283	   
df.mm.trans1:exp4	-0.165170509051461	0.0895749083774957	-1.84393723692557	0.0660446694635773	.  
df.mm.trans2:exp4	-0.0137916443397786	0.0895749083774957	-0.153967719192705	0.877724693576718	   
df.mm.trans1:exp5	-0.00164270168565730	0.0895749083774957	-0.0183388597924595	0.985379066998257	   
df.mm.trans2:exp5	-0.0682145309179984	0.0895749083774957	-0.761536150620683	0.446854139223899	   
df.mm.trans1:exp6	-0.104619148062199	0.0895749083774957	-1.16795149397532	0.243628183129188	   
df.mm.trans2:exp6	-0.0528143229152128	0.0895749083774957	-0.589610683079209	0.555835385496514	   
df.mm.trans1:exp7	0.0164124655139341	0.0895749083774957	0.183226149054677	0.85472758855353	   
df.mm.trans2:exp7	0.106362245766534	0.0895749083774957	1.18741115891843	0.235877668088406	   
df.mm.trans1:exp8	-0.066770404650614	0.0895749083774957	-0.74541415514737	0.456525995362124	   
df.mm.trans2:exp8	0.073184299904831	0.0895749083774957	0.817017859470314	0.414478798279295	   
df.mm.trans1:probe2	-0.255278805217261	0.0490621979048129	-5.20316692115047	3.35903232153024e-07	***
df.mm.trans1:probe3	-0.17087680881876	0.0490621979048129	-3.48286086062193	0.000559536535803642	***
df.mm.trans1:probe4	-0.037794171263349	0.0490621979048129	-0.770331800802618	0.441627231697913	   
df.mm.trans1:probe5	0.383878885249356	0.0490621979048129	7.82433118862981	6.21799809678315e-14	***
df.mm.trans1:probe6	0.315104346997187	0.0490621979048129	6.42254852928788	4.41467772166799e-10	***
df.mm.trans2:probe2	0.00485591094703554	0.0490621979048129	0.0989745905076785	0.921215595713225	   
df.mm.trans2:probe3	-0.0153449702354137	0.0490621979048129	-0.312765650352334	0.75464659835383	   
df.mm.trans2:probe4	0.0901518810855152	0.0490621979048129	1.83750188404567	0.0669907134513545	.  
df.mm.trans2:probe5	0.0144935045702218	0.0490621979048129	0.295410829297560	0.76785674504753	   
df.mm.trans2:probe6	0.0415366364874830	0.0490621979048129	0.846611816455298	0.397795005076315	   
df.mm.trans3:probe2	-0.127136400624604	0.0490621979048129	-2.59133112770989	0.00996418010422177	** 
df.mm.trans3:probe3	-0.0197168204039855	0.0490621979048129	-0.401873973160328	0.688024184904978	   
df.mm.trans3:probe4	-0.406323815696597	0.0490621979048129	-8.2818102948612	2.66470801711151e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.90329276795787	0.15431249623915	25.2947289629003	9.36499633435578e-81	***
df.mm.trans1	0.0904681572345366	0.128563485953228	0.70368469370416	0.482101061131924	   
df.mm.trans2	0.139029259996547	0.128563485953228	1.08140549368058	0.280267741908276	   
df.mm.exp2	-0.100180618707219	0.177133476010489	-0.565565701998005	0.572054558355601	   
df.mm.exp3	0.0922970095769598	0.177133476010489	0.521059099926965	0.60265795565171	   
df.mm.exp4	0.0068129354191251	0.177133476010489	0.0384621561805838	0.969341322235016	   
df.mm.exp5	-0.0514952127179161	0.177133476010489	-0.290714177115041	0.771443601065398	   
df.mm.exp6	0.093893746998637	0.177133476010489	0.530073417590909	0.596400040763068	   
df.mm.exp7	0.12266837492629	0.177133476010489	0.692519436128643	0.489074360401868	   
df.mm.exp8	0.249002999333877	0.177133476010489	1.40573653801686	0.160697115784899	   
df.mm.trans1:exp2	0.151947482218746	0.149705110905116	1.01497858890770	0.31082320593951	   
df.mm.trans2:exp2	-0.0249788408861033	0.149705110905116	-0.166853628009634	0.867582407613437	   
df.mm.trans1:exp3	-0.146157930178356	0.149705110905116	-0.97630554691611	0.329593199135357	   
df.mm.trans2:exp3	-0.0121252206655987	0.149705110905116	-0.0809940328175148	0.935493392036571	   
df.mm.trans1:exp4	0.0627622239822558	0.149705110905116	0.419239019982657	0.675300866029517	   
df.mm.trans2:exp4	-0.0431966531090392	0.149705110905116	-0.288544945779556	0.773101916781963	   
df.mm.trans1:exp5	0.116949388159563	0.149705110905116	0.781198366926068	0.435218473766846	   
df.mm.trans2:exp5	0.0129005861045516	0.149705110905116	0.086173317841687	0.931378342187667	   
df.mm.trans1:exp6	-0.0881326366683652	0.149705110905116	-0.588708268779308	0.55644000194525	   
df.mm.trans2:exp6	-0.129494294726550	0.149705110905116	-0.864995817067492	0.387638678174721	   
df.mm.trans1:exp7	0.0766842508010804	0.149705110905116	0.512235356144142	0.608812167575613	   
df.mm.trans2:exp7	-0.215464081588479	0.149705110905116	-1.4392566845967	0.150979251487984	   
df.mm.trans1:exp8	-0.189057183531411	0.149705110905116	-1.26286392220261	0.207486035690796	   
df.mm.trans2:exp8	-0.158609452314756	0.149705110905116	-1.05947920786274	0.2901185107376	   
df.mm.trans1:probe2	0.083302434905687	0.0819968662165447	1.01592217787561	0.310374265588406	   
df.mm.trans1:probe3	-0.0279856247717308	0.0819968662165447	-0.341301150434504	0.733083455620481	   
df.mm.trans1:probe4	0.0371072550380357	0.0819968662165447	0.452544795310098	0.651159351136363	   
df.mm.trans1:probe5	0.00869239427149694	0.0819968662165447	0.106008859515939	0.915636578771648	   
df.mm.trans1:probe6	0.0138800297011444	0.0819968662165447	0.169275124057653	0.865678876657257	   
df.mm.trans2:probe2	0.0593264456744513	0.0819968662165447	0.72352089063717	0.469847365991734	   
df.mm.trans2:probe3	0.0824555455575041	0.0819968662165447	1.00559386427950	0.315311681026004	   
df.mm.trans2:probe4	0.0134852745845573	0.0819968662165447	0.164460853283639	0.869464118598862	   
df.mm.trans2:probe5	0.075675114109211	0.0819968662165447	0.922902515681042	0.356699275298406	   
df.mm.trans2:probe6	0.0698333454700178	0.0819968662165447	0.851658711999988	0.394990906775344	   
df.mm.trans3:probe2	-0.0169016928330685	0.0819968662165447	-0.206126082775321	0.83681330848889	   
df.mm.trans3:probe3	-0.0723799753333296	0.0819968662165447	-0.882716360673859	0.378000636579227	   
df.mm.trans3:probe4	-0.0502069484269358	0.0819968662165447	-0.612303259179988	0.540738152430409