chr5.18351_chr5_32370781_32371277_+_1.R 

fitVsDatCorrelation=0.878352191657
cont.fitVsDatCorrelation=0.252071493784370

fstatistic=12274.9770525527,56,784
cont.fstatistic=2984.52610459113,56,784

residuals=-0.4620117985504,-0.0832767747145385,-0.00673229566991756,0.0738902502476237,0.851354581714862
cont.residuals=-0.566295947026055,-0.211929646003149,-0.0246012339202065,0.148912770448577,1.26422608440783

predictedValues:
Include	Exclude	Both
chr5.18351_chr5_32370781_32371277_+_1.R.tl.Lung	97.0511785154494	49.6782435810261	61.6523969371797
chr5.18351_chr5_32370781_32371277_+_1.R.tl.cerebhem	88.8491920415006	52.1628926851795	65.6749907311205
chr5.18351_chr5_32370781_32371277_+_1.R.tl.cortex	84.0930181109905	48.4599688171277	59.8548795918669
chr5.18351_chr5_32370781_32371277_+_1.R.tl.heart	88.5888829629453	46.7906888131028	57.1988570866577
chr5.18351_chr5_32370781_32371277_+_1.R.tl.kidney	97.525458864331	51.8566217688406	59.425635184974
chr5.18351_chr5_32370781_32371277_+_1.R.tl.liver	101.045938653634	50.0928262322839	59.3176663439359
chr5.18351_chr5_32370781_32371277_+_1.R.tl.stomach	99.1183840361289	48.6298878915264	65.1969098100974
chr5.18351_chr5_32370781_32371277_+_1.R.tl.testicle	93.1812899517772	47.9795031398764	65.9444932646959


diffExp=47.3729349344232,36.6862993563211,35.6330492938628,41.7981941498425,45.6688370954903,50.9531124213505,50.4884961446025,45.2017868119008
diffExpScore=0.997181532239665
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
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	65.7259300427066	63.7290041900874	64.9043482645124
cerebhem	64.317956261024	70.1552924068306	62.3576521952989
cortex	62.4744614869802	61.6624919188873	67.0032742753713
heart	62.3393018580819	58.881358414389	62.0287512526982
kidney	63.4625434123251	61.4573360657648	61.461481399504
liver	65.9304858408909	59.720219963635	64.7604703644482
stomach	68.12561764552	65.4651887537206	69.1685188992686
testicle	63.1580426989511	62.1488593786879	60.285783234496
cont.diffExp=1.99692585261921,-5.8373361458066,0.811969568092813,3.45794344369286,2.00520734656028,6.21026587725586,2.66042889179941,1.00918332026322
cont.diffExpScore=1.80172756136087

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=0.267012203080854
cont.tran.correlation=0.358498091362530

tran.covariance=0.000672412997181723
cont.tran.covariance=0.000640605842292738

tran.mean=71.5689985041075
cont.tran.mean=63.6721306461551

weightedLogRatios:
wLogRatio
Lung	2.83967633319276
cerebhem	2.24779086873042
cortex	2.29090916136763
heart	2.65851300316746
kidney	2.69346208955516
liver	2.99254770018736
stomach	3.01940182693787
testicle	2.78961272124943

cont.weightedLogRatios:
wLogRatio
Lung	0.128662140925115
cerebhem	-0.365496072669824
cortex	0.0540054216293699
heart	0.234208471910284
kidney	0.132741634049797
liver	0.409485889326123
stomach	0.167363306645689
testicle	0.0666471411509016

varWeightedLogRatios=0.0840249230970696
cont.varWeightedLogRatios=0.0485777059936619

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.74119291972073	0.0673245654429936	70.4229264388686	0	***
df.mm.trans1	0.256697136120821	0.0571455760846609	4.49198614675833	8.1175708220685e-06	***
df.mm.trans2	-0.832783741850145	0.0512147022519123	-16.2606381611649	1.8884230437239e-51	***
df.mm.exp2	-0.102699786051679	0.0657000008645317	-1.56316262861910	0.118417883571476	   
df.mm.exp3	-0.138554769150024	0.0657000008645317	-2.10890056814022	0.0352697448240665	*  
df.mm.exp4	-0.0761367424224499	0.0657000008645317	-1.15885451172882	0.246868342939795	   
df.mm.exp5	0.0845769683976967	0.0657000008645317	1.28732065882446	0.198362448541614	   
df.mm.exp6	0.0872524574145725	0.0657000008645317	1.32804347437499	0.184550238159783	   
df.mm.exp7	-0.0561522457618965	0.0657000008645317	-0.854676484368365	0.392991360643853	   
df.mm.exp8	-0.142785951961229	0.0657000008645317	-2.17330213215130	0.0300562764757005	*  
df.mm.trans1:exp2	0.014401794173843	0.0586098332290474	0.245723172723607	0.805960844200816	   
df.mm.trans2:exp2	0.151504077702729	0.0443412894155348	3.41677203571916	0.000666331923430927	***
df.mm.trans1:exp3	-0.00476013913723998	0.0586098332290474	-0.0812174148088314	0.935289778685025	   
df.mm.trans2:exp3	0.113725758710674	0.0443412894155348	2.56478240055037	0.01050911573211	*  
df.mm.trans1:exp4	-0.0150953352235703	0.0586098332290474	-0.257556358581975	0.796816899718513	   
df.mm.trans2:exp4	0.0162538862155018	0.0443412894155348	0.366563228759136	0.71404374445765	   
df.mm.trans1:exp5	-0.0797019608472693	0.0586098332290474	-1.35987353070591	0.174260873680513	   
df.mm.trans2:exp5	-0.0416614141243995	0.0443412894155348	-0.93956253130076	0.347731379790654	   
df.mm.trans1:exp6	-0.0469156587516563	0.0586098332290474	-0.80047418951543	0.423678605561415	   
df.mm.trans2:exp6	-0.0789417309036435	0.0443412894155348	-1.78032104939164	0.0754106275495749	.  
df.mm.trans1:exp7	0.0772287269182219	0.0586098332290474	1.31767525453300	0.187997293341357	   
df.mm.trans2:exp7	0.0348234824882638	0.0443412894155348	0.78535114669136	0.432484979486221	   
df.mm.trans1:exp8	0.102094448983482	0.0586098332290474	1.74193379094762	0.0819122023884346	.  
df.mm.trans2:exp8	0.107992771400763	0.0443412894155348	2.43549010018027	0.0150935035344660	*  
df.mm.trans1:probe2	-0.794821637913909	0.0419851774430624	-18.9310058053653	4.11979755338758e-66	***
df.mm.trans1:probe3	-1.00189679928813	0.0419851774430624	-23.8631074180129	4.88306295028787e-95	***
df.mm.trans1:probe4	-0.461404154536139	0.0419851774430624	-10.9896916634892	3.13842830187983e-26	***
df.mm.trans1:probe5	-0.318257527125830	0.0419851774430624	-7.58023537133863	9.75035368932274e-14	***
df.mm.trans1:probe6	-1.11848973126078	0.0419851774430624	-26.6401096619778	7.530103152433e-112	***
df.mm.trans1:probe7	-0.612458572708538	0.0419851774430624	-14.5874951591931	8.06034732028562e-43	***
df.mm.trans1:probe8	-1.10246715613156	0.0419851774430624	-26.2584850957617	1.57266739504159e-109	***
df.mm.trans1:probe9	-0.619519188136098	0.0419851774430624	-14.7556644002815	1.15288273487808e-43	***
df.mm.trans1:probe10	-0.825145005109294	0.0419851774430624	-19.6532456300394	3.16877039067463e-70	***
df.mm.trans1:probe11	-0.665916109717933	0.0419851774430624	-15.8607430115308	2.41476034790229e-49	***
df.mm.trans1:probe12	-0.677471845107588	0.0419851774430624	-16.1359767033576	8.62274302133593e-51	***
df.mm.trans1:probe13	-0.693631358177369	0.0419851774430624	-16.5208628477997	7.78760866385862e-53	***
df.mm.trans1:probe14	-0.517830859023804	0.0419851774430624	-12.3336589377538	4.53429381428237e-32	***
df.mm.trans1:probe15	-0.857842365989714	0.0419851774430624	-20.4320290691415	1.01834317040769e-74	***
df.mm.trans1:probe16	-0.721789365587601	0.0419851774430624	-17.1915282855823	1.88557697172497e-56	***
df.mm.trans2:probe2	-0.00875140364787535	0.0419851774430624	-0.208440315864889	0.834939319049302	   
df.mm.trans2:probe3	0.0258571713633779	0.0419851774430624	0.61586428682941	0.538162844100594	   
df.mm.trans2:probe4	0.0886265315574238	0.0419851774430624	2.11090048809758	0.0350969029541705	*  
df.mm.trans2:probe5	-0.127055814521497	0.0419851774430624	-3.02620644378132	0.00255741988856652	** 
df.mm.trans2:probe6	-0.0326762996236078	0.0419851774430624	-0.77828180357035	0.43663771565525	   
df.mm.trans3:probe2	-0.323668093346869	0.0419851774430624	-7.70910385661242	3.83947467682817e-14	***
df.mm.trans3:probe3	-0.307645518217656	0.0419851774430624	-7.32747929039635	5.83902569293521e-13	***
df.mm.trans3:probe4	0.0752144085599125	0.0419851774430624	1.79145148694235	0.0736064684210896	.  
df.mm.trans3:probe5	-0.0469734340464154	0.0419851774430624	-1.11880994453621	0.263563901157058	   
df.mm.trans3:probe6	0.117527560419033	0.0419851774430624	2.79926315849007	0.00524778515032217	** 
df.mm.trans3:probe7	0.0215706833755168	0.0419851774430624	0.513769017762747	0.60755825539358	   
df.mm.trans3:probe8	-0.113466647721847	0.0419851774430624	-2.70254062581307	0.00703001661917406	** 
df.mm.trans3:probe9	0.269200903614772	0.0419851774430624	6.41180816681898	2.48279352488253e-10	***
df.mm.trans3:probe10	0.0873611195274594	0.0419851774430624	2.08076099347044	0.0377799055767812	*  
df.mm.trans3:probe11	-0.149611382998400	0.0419851774430624	-3.56343338553931	0.000388168549951171	***
df.mm.trans3:probe12	-0.172617945165268	0.0419851774430624	-4.11140206324865	4.34743709736413e-05	***
df.mm.trans3:probe13	-0.0236163239044717	0.0419851774430624	-0.562491939839927	0.573941688291907	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.18811209146408	0.13629424193935	30.7284594849411	1.03927395286661e-136	***
df.mm.trans1	0.0159934274873528	0.115687534281097	0.138246766055986	0.890080895301945	   
df.mm.trans2	-0.0323994215012673	0.103680862604073	-0.312491820452839	0.754749875803392	   
df.mm.exp2	0.114445019526328	0.133005415695229	0.860453831358039	0.389801974264111	   
df.mm.exp3	-0.115526479312897	0.133005415695229	-0.868584776860643	0.385340090413541	   
df.mm.exp4	-0.0867000709182667	0.133005415695229	-0.651853689303396	0.514686606804991	   
df.mm.exp5	-0.0168362678499947	0.133005415695229	-0.126583325663773	0.899302650933826	   
df.mm.exp6	-0.0596424777924866	0.133005415695229	-0.448421423148307	0.653972985006163	   
df.mm.exp7	-0.000892662527587515	0.133005415695229	-0.00671147503973056	0.994646765234126	   
df.mm.exp8	0.00885767186421768	0.133005415695229	0.0665963248031518	0.946920050298292	   
df.mm.trans1:exp2	-0.136099690741668	0.118651828460871	-1.14705093471485	0.251710499110115	   
df.mm.trans2:exp2	-0.0183735554321585	0.0897660814850842	-0.204682605369284	0.83787323671895	   
df.mm.trans1:exp3	0.0647908153081113	0.118651828460871	0.546058296349627	0.585181211089435	   
df.mm.trans2:exp3	0.082562531064448	0.0897660814850842	0.919751978682135	0.357985280240968	   
df.mm.trans1:exp4	0.0337986252222987	0.118651828460871	0.284855494101761	0.775830082186117	   
df.mm.trans2:exp4	0.00758483221493718	0.0897660814850842	0.0844955253638592	0.932684031216436	   
df.mm.trans1:exp5	-0.0182073888465632	0.118651828460871	-0.153452239908529	0.878081138843005	   
df.mm.trans2:exp5	-0.0194603043365976	0.0897660814850842	-0.216789059014804	0.828429137649184	   
df.mm.trans1:exp6	0.0627498988993404	0.118651828460871	0.528857411751005	0.597054054902805	   
df.mm.trans2:exp6	-0.0053266500485996	0.0897660814850842	-0.0593392288097671	0.952697037695618	   
df.mm.trans1:exp7	0.03675246079368	0.118651828460871	0.309750479789701	0.756833022644145	   
df.mm.trans2:exp7	0.0277714107256494	0.0897660814850842	0.309375326027392	0.757118240404536	   
df.mm.trans1:exp8	-0.0487109935563919	0.118651828460871	-0.410537234767148	0.681524066766676	   
df.mm.trans2:exp8	-0.0339649905464882	0.0897660814850842	-0.378372209018971	0.705256540123394	   
df.mm.trans1:probe2	-0.0630024960346427	0.0849962847088353	-0.741238234711848	0.458770965559163	   
df.mm.trans1:probe3	-0.151548327862535	0.0849962847088353	-1.78299943793640	0.0749732098775075	.  
df.mm.trans1:probe4	-0.0181081887683379	0.0849962847088353	-0.213046827050967	0.83134582292004	   
df.mm.trans1:probe5	0.132952663746548	0.0849962847088353	1.56421735611142	0.118170061784388	   
df.mm.trans1:probe6	-0.00380568024005155	0.0849962847088353	-0.0447746657761378	0.96429832010052	   
df.mm.trans1:probe7	0.00629459264573897	0.0849962847088353	0.0740572681182693	0.940983707581656	   
df.mm.trans1:probe8	-0.0952067572698616	0.0849962847088353	-1.12012845733203	0.263002045192779	   
df.mm.trans1:probe9	-0.00270472677156097	0.0849962847088353	-0.0318217058642778	0.97462233574184	   
df.mm.trans1:probe10	-0.0866910816190949	0.0849962847088353	-1.01993965872821	0.308071704886642	   
df.mm.trans1:probe11	-0.095768843483433	0.0849962847088353	-1.12674152536785	0.260196527756264	   
df.mm.trans1:probe12	0.00263866439749851	0.0849962847088353	0.0310444674909917	0.975241977923765	   
df.mm.trans1:probe13	-0.0408917416597051	0.0849962847088353	-0.481100342206539	0.630579422549796	   
df.mm.trans1:probe14	-0.0030465823822786	0.0849962847088353	-0.0358437123777236	0.97141610202661	   
df.mm.trans1:probe15	-0.0613281916545283	0.0849962847088353	-0.721539675111862	0.470792662016603	   
df.mm.trans1:probe16	-0.00369524893665067	0.0849962847088353	-0.0434754171821648	0.965333630249275	   
df.mm.trans2:probe2	0.042190032047946	0.0849962847088353	0.496375014419429	0.61976898578493	   
df.mm.trans2:probe3	0.0675904216882619	0.0849962847088353	0.795216189975842	0.426728432527747	   
df.mm.trans2:probe4	-0.0645511363935763	0.0849962847088353	-0.759458329439972	0.447806719096004	   
df.mm.trans2:probe5	-0.0143000681549453	0.0849962847088353	-0.168243449745266	0.866435151888199	   
df.mm.trans2:probe6	-0.0513140871480109	0.0849962847088353	-0.60372153116802	0.546203430015915	   
df.mm.trans3:probe2	-0.0047756234924906	0.0849962847088353	-0.056186261656613	0.955207741032366	   
df.mm.trans3:probe3	0.0124109872214047	0.0849962847088353	0.146017996715033	0.883944716036403	   
df.mm.trans3:probe4	0.0527384488006874	0.0849962847088353	0.62047945955931	0.535122492444929	   
df.mm.trans3:probe5	-0.148868827075719	0.0849962847088353	-1.75147452133569	0.0802552194445635	.  
df.mm.trans3:probe6	-0.0627393995873845	0.0849962847088353	-0.738142847093913	0.460648535348027	   
df.mm.trans3:probe7	0.120628233585352	0.0849962847088353	1.41921772226372	0.156232997279865	   
df.mm.trans3:probe8	-0.103053591965305	0.0849962847088353	-1.21244819486319	0.225706092582783	   
df.mm.trans3:probe9	0.0097638406267393	0.0849962847088353	0.11487373430717	0.908574617467836	   
df.mm.trans3:probe10	0.00709432219627724	0.0849962847088353	0.0834662623264025	0.93350210879875	   
df.mm.trans3:probe11	0.0342058855366712	0.0849962847088353	0.40243977314829	0.687470104118892	   
df.mm.trans3:probe12	0.0662116115483476	0.0849962847088353	0.778994185159542	0.436218202221212	   
df.mm.trans3:probe13	0.03209491769869	0.0849962847088353	0.377603771842909	0.705827161839749