chr15.8656_chr15_59323079_59326956_-_2.R 

fitVsDatCorrelation=0.764694967184474
cont.fitVsDatCorrelation=0.239589023372639

fstatistic=13955.6901619293,60,876
cont.fstatistic=6139.72165037622,60,876

residuals=-0.339913470555757,-0.0747951522923226,-0.00144962428880657,0.0630838304025973,0.912050696431419
cont.residuals=-0.476574243542312,-0.124961920562002,-0.0269750397090236,0.0827686349392919,1.21328716520171

predictedValues:
Include	Exclude	Both
chr15.8656_chr15_59323079_59326956_-_2.R.tl.Lung	49.5950454983131	44.3780909162723	54.8716140882978
chr15.8656_chr15_59323079_59326956_-_2.R.tl.cerebhem	51.2225251250271	42.408655288667	51.8376944276586
chr15.8656_chr15_59323079_59326956_-_2.R.tl.cortex	49.4427754279698	46.3634590090331	56.0824337635167
chr15.8656_chr15_59323079_59326956_-_2.R.tl.heart	49.9680993909145	46.8328969940808	57.6472333504639
chr15.8656_chr15_59323079_59326956_-_2.R.tl.kidney	50.3267459284596	47.3375202813272	55.4913748682716
chr15.8656_chr15_59323079_59326956_-_2.R.tl.liver	52.1925366848267	45.0601277634803	54.3831530183244
chr15.8656_chr15_59323079_59326956_-_2.R.tl.stomach	51.1009197762082	45.6816613713824	54.4131100482698
chr15.8656_chr15_59323079_59326956_-_2.R.tl.testicle	50.8250222826469	47.5274858964756	55.2048796767984


diffExp=5.21695458204077,8.81386983636011,3.07931641893672,3.13520239683363,2.98922564713238,7.13240892134642,5.41925840482578,3.29753638617126
diffExpScore=0.975052248446333
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=0,1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	51.1216298762601	48.7121364057504	51.0370266751675
cerebhem	48.5675929942858	51.223277837299	51.1278180176452
cortex	49.8840681254994	48.4357950700896	50.679735121373
heart	49.6833688475742	47.2574459663832	51.0675907676824
kidney	51.8991946962976	49.5901045000864	53.4039152113451
liver	49.3993565488722	55.5077316296509	49.1571575390551
stomach	50.380045209247	49.5243193016977	52.3515977388566
testicle	49.0486252742898	48.5786666156488	50.5993007877912
cont.diffExp=2.40949347050971,-2.65568484301321,1.4482730554098,2.42592288119099,2.3090901962112,-6.1083750807787,0.855725907549306,0.469958658641012
cont.diffExpScore=8.671782062451

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=-0.29193209639916
cont.tran.correlation=-0.257862044208246

tran.covariance=-0.000203759507630295
cont.tran.covariance=-0.0002779380839942

tran.mean=48.1414729771928
cont.tran.mean=49.9258349311833

weightedLogRatios:
wLogRatio
Lung	0.427721503820243
cerebhem	0.725428764322
cortex	0.248771799061421
heart	0.251354007819736
kidney	0.238070721561983
liver	0.570349751316567
stomach	0.434717311616837
testicle	0.2612687888012

cont.weightedLogRatios:
wLogRatio
Lung	0.188776072197171
cerebhem	-0.208136312631881
cortex	0.114755829228424
heart	0.194264920720073
kidney	0.178704886093265
liver	-0.461469915081313
stomach	0.0670010993551249
testicle	0.0374324136731491

varWeightedLogRatios=0.0324436981523690
cont.varWeightedLogRatios=0.0541565533543861

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.653025211529	0.0608043795296149	60.0783239593751	0	***
df.mm.trans1	0.259258165836132	0.0523978384718749	4.9478790232023	8.9957135276755e-07	***
df.mm.trans2	0.121276339240527	0.046184241630553	2.62592466518486	0.00879215904795826	** 
df.mm.exp2	0.0437736062514489	0.059163178777508	0.739879214672797	0.459571520688595	   
df.mm.exp3	0.018864264450574	0.0591631787775079	0.318851434969644	0.74991518279147	   
df.mm.exp4	0.0119877455406706	0.059163178777508	0.202621728385358	0.839477755178452	   
df.mm.exp5	0.0679716125732911	0.059163178777508	1.14888371412409	0.250917533128563	   
df.mm.exp6	0.0752421887649538	0.0591631787775079	1.27177393641936	0.203790979876561	   
df.mm.exp7	0.0672536404953592	0.059163178777508	1.13674825939080	0.255954397222032	   
df.mm.exp8	0.08700497056699	0.059163178777508	1.47059323661741	0.141760241931592	   
df.mm.trans1:exp2	-0.0114851666994368	0.0545457556974002	-0.210560226961605	0.833279417448005	   
df.mm.trans2:exp2	-0.0891670305004352	0.0396878668534442	-2.24670756001332	0.0249070528400781	*  
df.mm.trans1:exp3	-0.0219392551743698	0.0545457556974002	-0.402217457506331	0.687622115943867	   
df.mm.trans2:exp3	0.0249014633457162	0.0396878668534442	0.627432646800345	0.530539178604518	   
df.mm.trans1:exp4	-0.00449389550533364	0.0545457556974002	-0.0823876293925438	0.934357293787008	   
df.mm.trans2:exp4	0.0418522378482229	0.0396878668534442	1.05453482805642	0.291928883750823	   
df.mm.trans1:exp5	-0.0533258881796677	0.0545457556974002	-0.977635885649841	0.328524344097458	   
df.mm.trans2:exp5	-0.00341429072546945	0.0396878668534442	-0.086028577400681	0.931463358507021	   
df.mm.trans1:exp6	-0.0241936190794931	0.0545457556974002	-0.443547234246977	0.657479488822025	   
df.mm.trans2:exp6	-0.0599903181779971	0.0396878668534442	-1.51155310008280	0.131008231648914	   
df.mm.trans1:exp7	-0.0373420835079983	0.0545457556974002	-0.684601084549244	0.493776764591616	   
df.mm.trans2:exp7	-0.0383026058536742	0.0396878668534442	-0.96509610846848	0.334763022396874	   
df.mm.trans1:exp8	-0.0625071122283678	0.0545457556974002	-1.14595739721958	0.252125724241050	   
df.mm.trans2:exp8	-0.0184426762489221	0.0396878668534442	-0.464693058889398	0.642266755040403	   
df.mm.trans1:probe2	-0.104075145619832	0.0379982395745117	-2.73894650871255	0.0062888054893821	** 
df.mm.trans1:probe3	-0.000874100938550595	0.0379982395745117	-0.0230037219707652	0.981652542772937	   
df.mm.trans1:probe4	-0.0457580488515071	0.0379982395745117	-1.20421496795342	0.228831772029179	   
df.mm.trans1:probe5	-0.0580545317168967	0.0379982395745117	-1.52782161402652	0.126917812421023	   
df.mm.trans1:probe6	0.204340908029441	0.0379982395745117	5.37764144648711	9.68604437553939e-08	***
df.mm.trans1:probe7	0.414118085429238	0.0379982395745117	10.8983492410795	5.03486420807946e-26	***
df.mm.trans1:probe8	-0.057649083973091	0.0379982395745117	-1.51715144224104	0.129589227834906	   
df.mm.trans1:probe9	0.0338562068809322	0.0379982395745117	0.890994089727306	0.373176988738456	   
df.mm.trans1:probe10	0.226914196831346	0.0379982395745117	5.9717028834029	3.40966919667168e-09	***
df.mm.trans1:probe11	0.0339539464520429	0.0379982395745117	0.893566302866788	0.371799400292268	   
df.mm.trans1:probe12	-0.0332294122646647	0.0379982395745117	-0.874498730382083	0.382086397393436	   
df.mm.trans1:probe13	-0.0744503735167137	0.0379982395745117	-1.95931112468308	0.0503931783544786	.  
df.mm.trans1:probe14	-0.0305405263859117	0.0379982395745117	-0.80373529742145	0.421767904447574	   
df.mm.trans1:probe15	0.0304548891761356	0.0379982395745117	0.801481582230036	0.423070246083380	   
df.mm.trans1:probe16	-0.061856127337271	0.0379982395745117	-1.62786823889501	0.103912409250203	   
df.mm.trans1:probe17	-0.0931299376180399	0.0379982395745117	-2.45090137492867	0.0144443966864631	*  
df.mm.trans1:probe18	-0.00830275951405995	0.0379982395745117	-0.218503794044954	0.82708749555498	   
df.mm.trans1:probe19	-0.117844781599083	0.0379982395745117	-3.10132213804269	0.00198827731860129	** 
df.mm.trans1:probe20	-0.135815384457382	0.0379982395745117	-3.57425464911494	0.000370417015119543	***
df.mm.trans1:probe21	-0.201928030714897	0.0379982395745117	-5.31414173330139	1.36019540850847e-07	***
df.mm.trans1:probe22	-0.197080434715046	0.0379982395745117	-5.18656750738638	2.66193488883182e-07	***
df.mm.trans2:probe2	0.0308042245254397	0.0379982395745117	0.810675043643402	0.417772491215273	   
df.mm.trans2:probe3	0.0156401272351084	0.0379982395745117	0.41160136391159	0.680732316161553	   
df.mm.trans2:probe4	0.048174320950139	0.0379982395745117	1.26780402170139	0.205204817627643	   
df.mm.trans2:probe5	0.169180968901178	0.0379982395745117	4.45233702391466	9.59112626828882e-06	***
df.mm.trans2:probe6	0.049754292263635	0.0379982395745117	1.30938414044341	0.190747585644459	   
df.mm.trans3:probe2	-0.00413993808019764	0.0379982395745117	-0.108950786314180	0.91326647588243	   
df.mm.trans3:probe3	0.211095103621144	0.0379982395745117	5.55539167037468	3.67482314441382e-08	***
df.mm.trans3:probe4	-0.181686972604114	0.0379982395745117	-4.78145763168422	2.04080489608672e-06	***
df.mm.trans3:probe5	-0.0402690444130548	0.0379982395745117	-1.05976079060427	0.289545549838948	   
df.mm.trans3:probe6	0.122413139596273	0.0379982395745117	3.22154765502307	0.0013220446258418	** 
df.mm.trans3:probe7	-0.101245448022427	0.0379982395745117	-2.66447733253253	0.00785256580965308	** 
df.mm.trans3:probe8	-0.228920527794561	0.0379982395745117	-6.02450351273944	2.49523227804666e-09	***
df.mm.trans3:probe9	-0.264315117483377	0.0379982395745117	-6.95598323614636	6.84990785413159e-12	***
df.mm.trans3:probe10	-0.137485366937453	0.0379982395745117	-3.61820359250733	0.000313638832811731	***
df.mm.trans3:probe11	0.310355128966866	0.0379982395745117	8.16761861712785	1.09288437253934e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.83699286749474	0.0916109694759977	41.8835526950737	2.38601606784951e-211	***
df.mm.trans1	0.0580965248117602	0.0789452473323444	0.735909086042696	0.461983026706591	   
df.mm.trans2	0.0300451081714706	0.0695835264337823	0.431784787453427	0.666004087616164	   
df.mm.exp2	-0.00276267824445025	0.0891382529847123	-0.0309931836438847	0.975282035463326	   
df.mm.exp3	-0.0231698621784538	0.0891382529847123	-0.259931750989416	0.794977498190605	   
df.mm.exp4	-0.0594541069738991	0.0891382529847123	-0.666987572485808	0.504955714074863	   
df.mm.exp5	-0.0123739973686966	0.0891382529847123	-0.138818037760049	0.889625845298975	   
df.mm.exp6	0.133852739145235	0.0891382529847123	1.50163072152862	0.133552862181923	   
df.mm.exp7	-0.0235079578412334	0.0891382529847123	-0.263724686698372	0.792054095570546	   
df.mm.exp8	-0.0355256400822050	0.0891382529847123	-0.398545393169169	0.690325326012075	   
df.mm.trans1:exp2	-0.0484885169829172	0.0821814086237976	-0.590018080669357	0.555330685153329	   
df.mm.trans2:exp2	0.0530285456780304	0.0597957579208171	0.886827887494161	0.375414969236131	   
df.mm.trans1:exp3	-0.00133615498505433	0.0821814086237975	-0.0162586040739562	0.987031784490916	   
df.mm.trans2:exp3	0.0174807635677613	0.0597957579208171	0.292341199034717	0.770094985653422	   
df.mm.trans1:exp4	0.0309166603421371	0.0821814086237976	0.37620017543949	0.706859176804666	   
df.mm.trans2:exp4	0.0291361285365244	0.0597957579208171	0.487260794906341	0.626195432800688	   
df.mm.trans1:exp5	0.0274695780717343	0.0821814086237976	0.334255381256385	0.738266862729222	   
df.mm.trans2:exp5	0.0302370985971696	0.059795757920817	0.505672971604612	0.613213458188302	   
df.mm.trans1:exp6	-0.168123033262100	0.0821814086237975	-2.04575506890759	0.0410786646918997	*  
df.mm.trans2:exp6	-0.00325862600482097	0.059795757920817	-0.0544959394801237	0.956552469310771	   
df.mm.trans1:exp7	0.00889543323082297	0.0821814086237975	0.108241430510685	0.913828968061128	   
df.mm.trans2:exp7	0.0400435992404371	0.0597957579208171	0.66967291046739	0.50324279483528	   
df.mm.trans1:exp8	-0.00586989422635666	0.0821814086237976	-0.0714260600378283	0.94307497285118	   
df.mm.trans2:exp8	0.0327819095406256	0.0597957579208171	0.548231357549412	0.58367272993123	   
df.mm.trans1:probe2	0.0744121116870443	0.057250079562226	1.29977307029180	0.194020673933762	   
df.mm.trans1:probe3	0.0263892571379833	0.057250079562226	0.46094708234074	0.644950987380214	   
df.mm.trans1:probe4	0.101660601146659	0.057250079562226	1.77572855660686	0.0761247671138967	.  
df.mm.trans1:probe5	0.0892551712266624	0.057250079562226	1.55904012551894	0.119348063409219	   
df.mm.trans1:probe6	-0.00293358179798911	0.057250079562226	-0.0512415322462662	0.959144739009607	   
df.mm.trans1:probe7	0.116916555422985	0.057250079562226	2.04220773695007	0.0414299267474843	*  
df.mm.trans1:probe8	0.0206964288138312	0.057250079562226	0.361509171202739	0.717806025047338	   
df.mm.trans1:probe9	0.083419654150217	0.057250079562226	1.45710983789196	0.14544441903493	   
df.mm.trans1:probe10	0.0252295601907546	0.057250079562226	0.440690395256695	0.65954587975262	   
df.mm.trans1:probe11	0.0480028126726995	0.057250079562226	0.838475911994576	0.401992198991471	   
df.mm.trans1:probe12	0.0586375093507941	0.057250079562226	1.02423454778015	0.306007255175215	   
df.mm.trans1:probe13	0.0281939174672047	0.057250079562226	0.492469489698444	0.62251088701467	   
df.mm.trans1:probe14	0.0186482402561105	0.057250079562226	0.325733001573237	0.744704163501847	   
df.mm.trans1:probe15	0.0476848788255394	0.057250079562226	0.832922490067633	0.405115471257333	   
df.mm.trans1:probe16	0.0539304571808217	0.057250079562226	0.942015410165567	0.346444456634342	   
df.mm.trans1:probe17	0.097728503295526	0.057250079562226	1.70704572016015	0.088167926758862	.  
df.mm.trans1:probe18	0.095055964516979	0.057250079562226	1.66036388497349	0.097199141087818	.  
df.mm.trans1:probe19	0.0478436697900409	0.057250079562226	0.835696127514352	0.40355375118262	   
df.mm.trans1:probe20	0.0292991497475976	0.057250079562226	0.511774830212277	0.608937590985383	   
df.mm.trans1:probe21	0.115665530512661	0.057250079562226	2.02035580381932	0.0436503894694296	*  
df.mm.trans1:probe22	0.115167527972733	0.057250079562226	2.01165708158634	0.044561904876806	*  
df.mm.trans2:probe2	0.0598213447694658	0.057250079562226	1.0449128669672	0.296351490852623	   
df.mm.trans2:probe3	0.0575877232093266	0.057250079562226	1.00589769742999	0.314742550905980	   
df.mm.trans2:probe4	0.0136735587763158	0.057250079562226	0.238839122685476	0.811286195271528	   
df.mm.trans2:probe5	0.0356709850169969	0.057250079562226	0.623073108190627	0.533398656172529	   
df.mm.trans2:probe6	0.154380313282632	0.057250079562226	2.69659561110012	0.00713969657765217	** 
df.mm.trans3:probe2	-0.0272167967683716	0.057250079562226	-0.475401902957868	0.634619054624715	   
df.mm.trans3:probe3	0.0594998613142309	0.057250079562226	1.03929744323865	0.298953192407102	   
df.mm.trans3:probe4	0.0342391809949487	0.057250079562226	0.598063465706341	0.549952171374008	   
df.mm.trans3:probe5	0.0195523609832887	0.057250079562226	0.341525481410675	0.732789931432644	   
df.mm.trans3:probe6	0.0274648055833562	0.057250079562226	0.479733928640296	0.631536350920571	   
df.mm.trans3:probe7	-0.0126350038424632	0.057250079562226	-0.220698450361628	0.825378664848439	   
df.mm.trans3:probe8	0.0101735925639512	0.057250079562226	0.177704426644392	0.858996191035499	   
df.mm.trans3:probe9	0.0171776784807547	0.057250079562226	0.300046368705637	0.764212971561887	   
df.mm.trans3:probe10	-0.0751078890527111	0.057250079562226	-1.31192636983282	0.189888667982728	   
df.mm.trans3:probe11	0.0284379124559114	0.057250079562226	0.496731404975634	0.619503101774075