chr8.22783_chr8_3659392_3660925_-_2.R 

fitVsDatCorrelation=0.825534208654447
cont.fitVsDatCorrelation=0.306181172563748

fstatistic=14305.6478671725,52,692
cont.fstatistic=5018.94106525159,52,692

residuals=-0.376537746869810,-0.0750026225014013,-0.0039440820804487,0.0714576327098345,0.660439187654802
cont.residuals=-0.66964778315666,-0.148929993503781,-0.0281741573381535,0.132306511519322,0.948420988624981

predictedValues:
Include	Exclude	Both
chr8.22783_chr8_3659392_3660925_-_2.R.tl.Lung	54.5218930025494	54.1316255214308	60.0959075758343
chr8.22783_chr8_3659392_3660925_-_2.R.tl.cerebhem	53.2768987238546	54.2147788906935	53.8316440570653
chr8.22783_chr8_3659392_3660925_-_2.R.tl.cortex	51.0327494874943	46.4170244891071	56.2136209972796
chr8.22783_chr8_3659392_3660925_-_2.R.tl.heart	52.9891528628117	44.9572457440679	57.0820838744959
chr8.22783_chr8_3659392_3660925_-_2.R.tl.kidney	52.7859671535801	51.4487612914927	59.7535982375609
chr8.22783_chr8_3659392_3660925_-_2.R.tl.liver	56.889004466583	50.7482053234636	53.4015892442316
chr8.22783_chr8_3659392_3660925_-_2.R.tl.stomach	52.8136666481597	47.9349605792451	51.2085411138255
chr8.22783_chr8_3659392_3660925_-_2.R.tl.testicle	51.7521679265425	52.5302436000738	55.439665102559


diffExp=0.39026748111862,-0.937880166838916,4.6157249983872,8.03190711874382,1.33720586208744,6.14079914311932,4.87870606891464,-0.778075673531298
diffExpScore=1.09854312146653
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	54.7833416610049	51.3549674929734	62.8647733173952
cerebhem	52.6851663242073	53.2657295814286	57.8530484175192
cortex	51.3608686688315	53.078862978492	55.7996760307018
heart	52.0118358450382	48.5871340125127	52.4853977896737
kidney	52.7604740299055	55.4248685692393	52.7202629777775
liver	56.2931077269151	49.0535686207953	66.2484699416122
stomach	54.6093547224402	46.6545321511631	53.8559803806956
testicle	53.5460020579273	48.6357584894839	59.9718876596554
cont.diffExp=3.42837416803152,-0.5805632572213,-1.71799430966050,3.42470183252547,-2.66439453933375,7.23953910611976,7.95482257127713,4.91024356844344
cont.diffExpScore=1.38817174831747

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=0.313626458746718
cont.tran.correlation=-0.463515282780526

tran.covariance=0.00075431309084916
cont.tran.covariance=-0.000826740353725928

tran.mean=51.7777716069469
cont.tran.mean=52.1315983082724

weightedLogRatios:
wLogRatio
Lung	0.0286991051008187
cerebhem	-0.0695276788251447
cortex	0.368309296757973
heart	0.639074606766724
kidney	0.101440681396985
liver	0.45507444987695
stomach	0.379781532564808
testicle	-0.0590034695655264

cont.weightedLogRatios:
wLogRatio
Lung	0.256628641786982
cerebhem	-0.0435060209924533
cortex	-0.130139044741331
heart	0.266825094022758
kidney	-0.196591103416523
liver	0.545370183655162
stomach	0.617379897129857
testicle	0.3782313238991

varWeightedLogRatios=0.06997886343627
cont.varWeightedLogRatios=0.0939125195747004

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.78125900264003	0.0672179548290147	56.2537050146585	2.39418529160324e-260	***
df.mm.trans1	0.0466872721502356	0.0603715533243293	0.773332299393082	0.439589825749279	   
df.mm.trans2	0.216243772822353	0.0555198424905388	3.89489168416145	0.000107769518039268	***
df.mm.exp2	0.0885157160375023	0.0760484346833396	1.16393869783218	0.244849999493565	   
df.mm.exp3	-0.153104393697657	0.0760484346833397	-2.01324845587122	0.0444752361866873	*  
df.mm.exp4	-0.162770298841944	0.0760484346833397	-2.14035041641170	0.0326753933691306	*  
df.mm.exp5	-0.077476814888041	0.0760484346833397	-1.01878250631520	0.308662275431535	   
df.mm.exp6	0.0960586547559224	0.0760484346833397	1.26312468042115	0.206969793940418	   
df.mm.exp7	0.00662959049445583	0.0760484346833397	0.0871758968091977	0.930556917039271	   
df.mm.exp8	-0.00151909041517651	0.0760484346833397	-0.0199753015496229	0.984068833190996	   
df.mm.trans1:exp2	-0.111615226109882	0.0728108328682667	-1.53294807534783	0.12574567754128	   
df.mm.trans2:exp2	-0.08698076181285	0.0633736955694497	-1.37250575386644	0.170350718135112	   
df.mm.trans1:exp3	0.0869696396719995	0.0728108328682667	1.19446016816412	0.232707261755553	   
df.mm.trans2:exp3	-0.000647897583636726	0.0633736955694498	-0.0102234464601597	0.991845958566739	   
df.mm.trans1:exp4	0.134255201006375	0.0728108328682667	1.84389047230481	0.065626439680836	.  
df.mm.trans2:exp4	-0.0229363479023172	0.0633736955694498	-0.361922209147198	0.717520651201908	   
df.mm.trans1:exp5	0.0451198691942038	0.0728108328682667	0.619686211745951	0.535668375840809	   
df.mm.trans2:exp5	0.0266446105987882	0.0633736955694497	0.420436434381343	0.67429718024781	   
df.mm.trans1:exp6	-0.0535589028910991	0.0728108328682667	-0.735589757474698	0.462229637898533	   
df.mm.trans2:exp6	-0.160600990856155	0.0633736955694498	-2.53419008333128	0.0114901623843274	*  
df.mm.trans1:exp7	-0.0384619227172485	0.0728108328682667	-0.528244509808532	0.597498996217492	   
df.mm.trans2:exp7	-0.128203076657581	0.0633736955694497	-2.02296986952712	0.0434602823033753	*  
df.mm.trans1:exp8	-0.050616913492017	0.0728108328682667	-0.695183827708657	0.487173470854082	   
df.mm.trans2:exp8	-0.0285104275905957	0.0633736955694497	-0.449877939646929	0.652939342616025	   
df.mm.trans1:probe2	0.0350159814963114	0.0364054164341334	0.961834389661884	0.336468763834256	   
df.mm.trans1:probe3	-0.0268114690658137	0.0364054164341334	-0.736469231558509	0.461694771793992	   
df.mm.trans1:probe4	0.0941758549417324	0.0364054164341334	2.58686382868660	0.00988830219691513	** 
df.mm.trans1:probe5	0.0401241654515605	0.0364054164341334	1.10214823456711	0.27078040455085	   
df.mm.trans1:probe6	0.465884129811606	0.0364054164341334	12.7971103051248	8.31866892249896e-34	***
df.mm.trans1:probe7	0.345665380681514	0.0364054164341334	9.49488879784992	3.49945285278313e-20	***
df.mm.trans1:probe8	0.160530010044186	0.0364054164341334	4.40950896234427	1.20092933836983e-05	***
df.mm.trans1:probe9	0.176040847543251	0.0364054164341334	4.83556747281694	1.63714553871408e-06	***
df.mm.trans1:probe10	0.105406845084008	0.0364054164341334	2.89536160847701	0.00390658138687384	** 
df.mm.trans1:probe11	0.433564635056832	0.0364054164341334	11.9093441999561	7.02783280894894e-30	***
df.mm.trans1:probe12	0.40662115650786	0.0364054164341334	11.1692488738191	9.54600494190093e-27	***
df.mm.trans1:probe13	0.369974350314934	0.0364054164341334	10.1626182736932	1.0420297130986e-22	***
df.mm.trans1:probe14	0.589238627293289	0.0364054164341334	16.1854659281091	3.30019828078038e-50	***
df.mm.trans1:probe15	0.440473041651584	0.0364054164341334	12.0991073525697	1.05302462580778e-30	***
df.mm.trans1:probe16	0.500111734316371	0.0364054164341334	13.7372892086319	3.78412389840494e-38	***
df.mm.trans1:probe17	0.00781334086791704	0.0364054164341334	0.214620285474645	0.830126606279246	   
df.mm.trans1:probe18	0.0567580452134027	0.0364054164341334	1.55905496414503	0.119440565127622	   
df.mm.trans1:probe19	0.0282823856556764	0.0364054164341334	0.77687301577353	0.437499164097535	   
df.mm.trans1:probe20	0.0211124932884801	0.0364054164341334	0.579927256887118	0.562152438900085	   
df.mm.trans1:probe21	0.0196293782443312	0.0364054164341334	0.539188400161436	0.589930290684888	   
df.mm.trans1:probe22	-0.0032096172668366	0.0364054164341334	-0.0881631795819067	0.929772488619455	   
df.mm.trans2:probe2	0.0308563511929482	0.0364054164341334	0.847575833908538	0.39696728582957	   
df.mm.trans2:probe3	-0.0149228982361780	0.0364054164341334	-0.409908736057924	0.681999760250743	   
df.mm.trans2:probe4	-0.0294964277294446	0.0364054164341334	-0.810220857734482	0.41809178457073	   
df.mm.trans2:probe5	-0.06076226273086	0.0364054164341334	-1.66904457310066	0.0955608557785598	.  
df.mm.trans2:probe6	0.019567571153518	0.0364054164341334	0.537490655790758	0.591101531924029	   
df.mm.trans3:probe2	0.0518574749746022	0.0364054164341334	1.42444394417038	0.154768794728490	   
df.mm.trans3:probe3	0.103397619115226	0.0364054164341334	2.84017130534130	0.00464136994230803	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.68660460237422	0.113386125269421	32.5137188841609	1.77790962973908e-141	***
df.mm.trans1	0.288911008146172	0.101837321967838	2.83698552321923	0.00468738763917654	** 
df.mm.trans2	0.200289989934904	0.0936532483260478	2.13863366743679	0.0328148233175235	*  
df.mm.exp2	0.0805590778091187	0.128281756912760	0.627985457541745	0.530220649832181	   
df.mm.exp3	0.0877254285267134	0.128281756912760	0.683849602920329	0.494299064457988	   
df.mm.exp4	0.0731332062125103	0.128281756912760	0.57009825849396	0.568796095617795	   
df.mm.exp5	0.214628949339350	0.128281756912760	1.67310578296267	0.0947584337480498	.  
df.mm.exp6	-0.0710891929958856	0.128281756912760	-0.554164479086693	0.579645318474756	   
df.mm.exp7	0.0554999521592288	0.128281756912760	0.432641035599257	0.665410369866037	   
df.mm.exp8	-0.0301376145872035	0.128281756912760	-0.234932973421146	0.814330256329235	   
df.mm.trans1:exp2	-0.119611299260132	0.122820431498886	-0.973871348605518	0.330460717676831	   
df.mm.trans2:exp2	-0.0440275952694104	0.106901464093967	-0.411852126091649	0.680575349675327	   
df.mm.trans1:exp3	-0.15223501940724	0.122820431498886	-1.23949262797225	0.215583306444486	   
df.mm.trans2:exp3	-0.0547083098256721	0.106901464093967	-0.511763896681369	0.608979528234405	   
df.mm.trans1:exp4	-0.125048064579426	0.122820431498886	-1.01813731684016	0.308968525050357	   
df.mm.trans2:exp4	-0.128536112237628	0.106901464093967	-1.20237934369771	0.229627851246613	   
df.mm.trans1:exp5	-0.252252800338079	0.122820431498886	-2.05383418100406	0.0403669465417406	*  
df.mm.trans2:exp5	-0.13836223479707	0.106901464093967	-1.29429691136362	0.195994633998997	   
df.mm.trans1:exp6	0.0982751367133	0.122820431498886	0.800152999903697	0.423896754257209	   
df.mm.trans2:exp6	0.0252404614860624	0.106901464093967	0.236109595878648	0.813417493107472	   
df.mm.trans1:exp7	-0.0586809156243307	0.122820431498886	-0.477778126230269	0.632959011020613	   
df.mm.trans2:exp7	-0.151491546993675	0.106901464093967	-1.41711386534906	0.156899690455608	   
df.mm.trans1:exp8	0.00729258712548596	0.122820431498886	0.0593760096466696	0.952669763166687	   
df.mm.trans2:exp8	-0.0242650232958328	0.106901464093967	-0.226984948255748	0.820502484107165	   
df.mm.trans1:probe2	-0.00349468937071619	0.0614102157494429	-0.0569072967431789	0.954635468995546	   
df.mm.trans1:probe3	-0.00396062588190532	0.0614102157494429	-0.0644945768968618	0.948595051057607	   
df.mm.trans1:probe4	0.0250495717880779	0.0614102157494429	0.407905614438508	0.683469140586112	   
df.mm.trans1:probe5	0.0560890134563674	0.0614102157494429	0.913349884410335	0.36137665646738	   
df.mm.trans1:probe6	0.0106934687754753	0.0614102157494429	0.174131757151045	0.861812844276896	   
df.mm.trans1:probe7	0.00317505611477746	0.0614102157494429	0.0517024093797662	0.958780737955781	   
df.mm.trans1:probe8	0.0955871587600481	0.0614102157494429	1.5565351398544	0.120038093467114	   
df.mm.trans1:probe9	0.00462943870330333	0.0614102157494429	0.0753854818258853	0.939929797786252	   
df.mm.trans1:probe10	0.0533094412746973	0.0614102157494429	0.86808750993813	0.385647370886058	   
df.mm.trans1:probe11	0.0141352442614691	0.0614102157494429	0.230177407601720	0.818021942056121	   
df.mm.trans1:probe12	-0.00981399939237957	0.0614102157494429	-0.159810534332288	0.873076959890561	   
df.mm.trans1:probe13	0.092567167526678	0.0614102157494429	1.50735779702122	0.132175334662142	   
df.mm.trans1:probe14	0.0828312461686344	0.0614102157494429	1.34881868037381	0.177836430303843	   
df.mm.trans1:probe15	0.101393523925509	0.0614102157494429	1.65108561642577	0.0991748045436433	.  
df.mm.trans1:probe16	-0.0524439875607257	0.0614102157494429	-0.853994517373136	0.393403552620042	   
df.mm.trans1:probe17	-0.0248648550457989	0.0614102157494429	-0.404897698898322	0.685677839831944	   
df.mm.trans1:probe18	0.103402036803183	0.0614102157494429	1.68379211082842	0.0926728395288907	.  
df.mm.trans1:probe19	-0.00215526723342885	0.0614102157494429	-0.0350962328844838	0.972013126275158	   
df.mm.trans1:probe20	0.0439429897493339	0.0614102157494429	0.715564816261576	0.474501471440331	   
df.mm.trans1:probe21	0.0568829445568814	0.0614102157494429	0.926278207342032	0.354624342098309	   
df.mm.trans1:probe22	0.0498089459393397	0.0614102157494429	0.811085669240489	0.417595345430531	   
df.mm.trans2:probe2	0.152912278807333	0.0614102157494429	2.49001370441725	0.0130074088970128	*  
df.mm.trans2:probe3	0.114567052489458	0.0614102157494429	1.86560250751923	0.0625199252632023	.  
df.mm.trans2:probe4	0.0846606615364262	0.0614102157494429	1.37860876245487	0.168460905796327	   
df.mm.trans2:probe5	0.0319537176001685	0.0614102157494429	0.52033228039031	0.602998427217898	   
df.mm.trans2:probe6	0.0827099854632046	0.0614102157494429	1.34684407885271	0.178471366169014	   
df.mm.trans3:probe2	-0.073956108531165	0.0614102157494429	-1.20429651041954	0.228886739548824	   
df.mm.trans3:probe3	-0.0305028003091116	0.0614102157494429	-0.496705636625096	0.619554397235343