chr6.20390_chr6_49458255_49469090_-_2.R 

fitVsDatCorrelation=0.837669022311155
cont.fitVsDatCorrelation=0.261200770531295

fstatistic=13752.5846006474,55,761
cont.fstatistic=4393.52929412048,55,761

residuals=-0.575734181174542,-0.0734155728077531,-0.00592218740586736,0.0683711163472292,0.581744405174043
cont.residuals=-0.572161977496689,-0.128780584153876,-0.0333153803514921,0.060113577136626,1.20043429338414

predictedValues:
Include	Exclude	Both
chr6.20390_chr6_49458255_49469090_-_2.R.tl.Lung	46.0859125486069	46.9968260859956	56.5019769537088
chr6.20390_chr6_49458255_49469090_-_2.R.tl.cerebhem	48.3773105637729	48.6838678908047	52.7351001680258
chr6.20390_chr6_49458255_49469090_-_2.R.tl.cortex	45.9551098889289	48.1597319582246	50.6194818603748
chr6.20390_chr6_49458255_49469090_-_2.R.tl.heart	46.7080306496607	50.9492700802136	54.7017888223167
chr6.20390_chr6_49458255_49469090_-_2.R.tl.kidney	45.6990519717388	48.2860941047912	55.0297840727811
chr6.20390_chr6_49458255_49469090_-_2.R.tl.liver	48.3615044076198	49.6349725983985	55.4727345018576
chr6.20390_chr6_49458255_49469090_-_2.R.tl.stomach	46.5825678136404	47.5141651449363	57.9727613060801
chr6.20390_chr6_49458255_49469090_-_2.R.tl.testicle	46.7013792877805	48.5895464163048	55.1294836155227


diffExp=-0.91091353738868,-0.306557327031761,-2.20462206929567,-4.24123943055283,-2.58704213305238,-1.27346819077862,-0.931597331295897,-1.88816712852429
diffExpScore=0.93482627713552
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	49.7704214590511	51.7826361142581	45.8909242083632
cerebhem	48.4198100730907	45.4546412425939	50.3278545111824
cortex	51.6675160601193	48.2565281246271	50.1504246963323
heart	49.588949099092	49.6779362851789	49.7199636801561
kidney	47.6315181952607	54.8569394630104	48.2905362819819
liver	51.1475999748444	48.5723436833272	48.8901426942809
stomach	53.2283339088649	52.072845512889	49.5386069231059
testicle	48.425461539781	47.4803140584664	53.1058125839058
cont.diffExp=-2.01221465520707,2.96516883049686,3.41098793549224,-0.0889871860868894,-7.2254212677497,2.57525629151721,1.15548839597588,0.945147481314542
cont.diffExpScore=7.47724331782515

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=0.408563445233972
cont.tran.correlation=0.0372990623844081

tran.covariance=0.000228192517883865
cont.tran.covariance=0.000111752491196023

tran.mean=47.7053338382136
cont.tran.mean=49.8771121746534

weightedLogRatios:
wLogRatio
Lung	-0.0751651109778276
cerebhem	-0.0245230391050097
cortex	-0.180455290590898
heart	-0.337868408322098
kidney	-0.211982884972861
liver	-0.101151290136738
stomach	-0.0762580564849546
testicle	-0.153132327099404

cont.weightedLogRatios:
wLogRatio
Lung	-0.155652232025231
cerebhem	0.243190596974965
cortex	0.267092227991416
heart	-0.00700062652265793
kidney	-0.555630357310358
liver	0.201937925339283
stomach	0.0869904321561557
testicle	0.0762832116594382

varWeightedLogRatios=0.00986398288605756
cont.varWeightedLogRatios=0.073508230688316

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.92923374902826	0.0622648461446005	47.0447440314164	1.87998939898955e-227	***
df.mm.trans1	0.797311189722493	0.0545219675506714	14.6236686887994	7.31577347111262e-43	***
df.mm.trans2	0.901124307010154	0.0488936457283821	18.4302948488675	5.37767573894759e-63	***
df.mm.exp2	0.152785602870882	0.064479030532853	2.36953939301298	0.0180588283531852	*  
df.mm.exp3	0.131540004026555	0.064479030532853	2.04004314177045	0.0416908090623070	*  
df.mm.exp4	0.126538374320053	0.064479030532853	1.96247327657291	0.050071389924987	.  
df.mm.exp5	0.0450348381552068	0.064479030532853	0.698441614010634	0.485114432218881	   
df.mm.exp6	0.121196421804517	0.064479030532853	1.87962537282202	0.0605410079001014	.  
df.mm.exp7	-0.00403075767231306	0.064479030532853	-0.0625126903894643	0.95017097109825	   
df.mm.exp8	0.0711856867297223	0.064479030532853	1.10401298129711	0.269936467685039	   
df.mm.trans1:exp2	-0.104262007742564	0.060486692197886	-1.72371812631884	0.0851648933740004	.  
df.mm.trans2:exp2	-0.117517951812870	0.0482516881770544	-2.43552000464006	0.0150989892849306	*  
df.mm.trans1:exp3	-0.134382274663525	0.060486692197886	-2.22168331215542	0.0265981411725403	*  
df.mm.trans2:exp3	-0.107096838037560	0.0482516881770544	-2.21954592851922	0.0267436706097334	*  
df.mm.trans1:exp4	-0.113129580582748	0.060486692197886	-1.87032182571065	0.0618228075264035	.  
df.mm.trans2:exp4	-0.0457880102965074	0.0482516881770544	-0.948941104992912	0.342951814699105	   
df.mm.trans1:exp5	-0.0534646037443096	0.060486692197886	-0.883906885987364	0.377025638586642	   
df.mm.trans2:exp5	-0.0179712950274807	0.0482516881770544	-0.372449041814599	0.709662245337732	   
df.mm.trans1:exp6	-0.0729996067033966	0.060486692197886	-1.20687053715177	0.227857015817036	   
df.mm.trans2:exp6	-0.0665808131696678	0.0482516881770544	-1.37986494742643	0.168033494519539	   
df.mm.trans1:exp7	0.0147498288925259	0.060486692197886	0.243852463352946	0.807410810272195	   
df.mm.trans2:exp7	0.0149785684890835	0.0482516881770544	0.310425791406949	0.756322180945862	   
df.mm.trans1:exp8	-0.057919306093964	0.060486692197886	-0.957554529589375	0.338591538046775	   
df.mm.trans2:exp8	-0.0378573426186892	0.0482516881770544	-0.78458068616741	0.432943615674012	   
df.mm.trans1:probe2	0.0122040677739316	0.0370403830284013	0.329480064085027	0.741883523477818	   
df.mm.trans1:probe3	0.104975196621506	0.0370403830284013	2.83407427350345	0.00471758008725622	** 
df.mm.trans1:probe4	0.0469384238441912	0.0370403830284013	1.26722296063193	0.205463227979641	   
df.mm.trans1:probe5	0.255697292388297	0.0370403830284013	6.90320324690587	1.07244126036347e-11	***
df.mm.trans1:probe6	0.281910697733341	0.0370403830284013	7.61090125653349	8.06149800799725e-14	***
df.mm.trans1:probe7	0.176288877660263	0.0370403830284013	4.75936972695697	2.32430645895376e-06	***
df.mm.trans1:probe8	0.0479956251666806	0.0370403830284013	1.29576481781733	0.195449396630792	   
df.mm.trans1:probe9	0.842136719910506	0.0370403830284013	22.7356374599254	1.00098068561362e-87	***
df.mm.trans1:probe10	0.249403416964348	0.0370403830284012	6.73328396126773	3.26838977987303e-11	***
df.mm.trans1:probe11	-0.0554293117175623	0.0370403830284013	-1.49645622387493	0.134949409367456	   
df.mm.trans1:probe12	0.086658880867494	0.0370403830284013	2.33957842177407	0.0195632206890798	*  
df.mm.trans1:probe13	0.0693852495630628	0.0370403830284013	1.87323250706832	0.061419389989951	.  
df.mm.trans1:probe14	0.0383261293724756	0.0370403830284013	1.03471201534521	0.301132113609942	   
df.mm.trans1:probe15	0.0242650646427226	0.0370403830284013	0.65509756268225	0.5126029058198	   
df.mm.trans1:probe16	0.112087245438664	0.0370403830284013	3.0260822452273	0.00256092851557644	** 
df.mm.trans1:probe17	0.153656102855381	0.0370403830284013	4.14834000872948	3.7264238224545e-05	***
df.mm.trans1:probe18	0.0618214995458797	0.0370403830284013	1.66902970464633	0.0955227963020918	.  
df.mm.trans1:probe19	0.120771023673875	0.0370403830284013	3.26052307777898	0.00116155882909914	** 
df.mm.trans1:probe20	0.0984373074344955	0.0370403830284013	2.65756721141402	0.0080358041199135	** 
df.mm.trans1:probe21	0.144121001965564	0.0370403830284013	3.89091554088566	0.000108612200379816	***
df.mm.trans1:probe22	0.0392961261231511	0.0370403830284013	1.06089956178423	0.289072124775302	   
df.mm.trans2:probe2	0.0769836645355054	0.0370403830284013	2.07837117873422	0.0380097574033534	*  
df.mm.trans2:probe3	0.0543727426295337	0.0370403830284013	1.46793143547794	0.142536028572358	   
df.mm.trans2:probe4	0.0319658370777698	0.0370403830284012	0.862999636187874	0.38840955600196	   
df.mm.trans2:probe5	0.067325147545611	0.0370403830284013	1.81761477720110	0.069516141571364	.  
df.mm.trans2:probe6	0.00601676788583845	0.0370403830284013	0.162438057976479	0.871004024128465	   
df.mm.trans3:probe2	-0.793563923485748	0.0370403830284013	-21.4242904258649	4.77921344207703e-80	***
df.mm.trans3:probe3	-0.662985596821366	0.0370403830284013	-17.8989940874265	4.60200798439885e-60	***
df.mm.trans3:probe4	-0.755157856500214	0.0370403830284012	-20.3874202899356	4.69925472273609e-74	***
df.mm.trans3:probe5	-0.72724301952404	0.0370403830284013	-19.6337877760717	9.41398484325948e-70	***
df.mm.trans3:probe6	-0.621411311005796	0.0370403830284013	-16.7765897703952	5.49694320537194e-54	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07771221562804	0.110043374721559	37.0554994877777	1.43354661457110e-172	***
df.mm.trans1	-0.113368456428699	0.096359048118446	-1.17652113260132	0.239754575318484	   
df.mm.trans2	-0.151026783532698	0.0864118698036496	-1.74775506971288	0.0809097515233942	.  
df.mm.exp2	-0.250143105518302	0.113956599236291	-2.19507345072334	0.028459662724294	*  
df.mm.exp3	-0.121874974390524	0.113956599236291	-1.06948588504132	0.285189889930043	   
df.mm.exp4	-0.125286007099408	0.113956599236291	-1.09941862023826	0.271933191578755	   
df.mm.exp5	-0.037220596415039	0.113956599236291	-0.326620807083418	0.744044512909548	   
df.mm.exp6	-0.100014292405656	0.113956599236291	-0.87765248415561	0.380409329093723	   
df.mm.exp7	-0.00372621909957784	0.113956599236291	-0.0326985810786741	0.973923529447452	   
df.mm.exp8	-0.260153838621133	0.113956599236291	-2.28292034304831	0.0227100211643230	*  
df.mm.trans1:exp2	0.222631273562641	0.106900765798756	2.08259755577196	0.0376213436630277	*  
df.mm.trans2:exp2	0.119803155556468	0.0852773102608212	1.40486555204484	0.160469309541018	   
df.mm.trans1:exp3	0.159283381410758	0.106900765798756	1.49001160300959	0.136635515082555	   
df.mm.trans2:exp3	0.0513512080228143	0.0852773102608212	0.602167304125285	0.547242145788181	   
df.mm.trans1:exp4	0.121633154616009	0.106900765798756	1.13781368830404	0.255556385916782	   
df.mm.trans2:exp4	0.0837920207879124	0.0852773102608212	0.98258282926178	0.326125076292041	   
df.mm.trans1:exp5	-0.00670557547995118	0.106900765798756	-0.0627271042433372	0.950000285206226	   
df.mm.trans2:exp5	0.0948944093145417	0.0852773102608213	1.11277441823982	0.266156704863027	   
df.mm.trans1:exp6	0.127309001420490	0.106900765798756	1.19090822660852	0.234060937589774	   
df.mm.trans2:exp6	0.0360137184016596	0.0852773102608212	0.422313019623994	0.672915843179402	   
df.mm.trans1:exp7	0.0708962049730975	0.106900765798756	0.663196418130076	0.507405599015506	   
df.mm.trans2:exp7	0.00931494969806231	0.0852773102608212	0.109231279335294	0.913047847311336	   
df.mm.trans1:exp8	0.232758717922564	0.106900765798757	2.17733442958433	0.0297621750699856	*  
df.mm.trans2:exp8	0.173414139962483	0.0852773102608213	2.03353200789395	0.0423452738352388	*  
df.mm.trans1:probe2	-0.109406481835867	0.0654630823299302	-1.67126994241525	0.0950795001453207	.  
df.mm.trans1:probe3	-0.0176351262872245	0.0654630823299302	-0.269390405394363	0.787702293402236	   
df.mm.trans1:probe4	-0.0842548345929623	0.0654630823299302	-1.28705877563667	0.198465136368314	   
df.mm.trans1:probe5	-0.0474569005264989	0.0654630823299302	-0.72494143015324	0.468710801765804	   
df.mm.trans1:probe6	-0.081755247935857	0.0654630823299302	-1.24887562617072	0.212094599146258	   
df.mm.trans1:probe7	-0.0423905471612863	0.0654630823299302	-0.647548903176303	0.517472050247371	   
df.mm.trans1:probe8	-0.097004504042708	0.0654630823299302	-1.48181999059884	0.138802141320573	   
df.mm.trans1:probe9	-0.126467351933605	0.0654630823299302	-1.93188813347066	0.053744241707234	.  
df.mm.trans1:probe10	-0.100398641593581	0.0654630823299302	-1.53366810758433	0.125526933319033	   
df.mm.trans1:probe11	-0.116228999375755	0.0654630823299302	-1.77548925652427	0.0762167271446586	.  
df.mm.trans1:probe12	-0.116957278710693	0.0654630823299302	-1.78661429538614	0.0743978241493524	.  
df.mm.trans1:probe13	-0.0804001447183652	0.0654630823299302	-1.22817536016946	0.219760857624826	   
df.mm.trans1:probe14	-0.06599845444564	0.0654630823299302	-1.00817822957085	0.31368935514497	   
df.mm.trans1:probe15	-0.03718515687247	0.0654630823299302	-0.568032477985973	0.570180470084278	   
df.mm.trans1:probe16	-0.089413595180652	0.0654630823299302	-1.36586289551739	0.172385522051893	   
df.mm.trans1:probe17	0.00316325218724915	0.0654630823299302	0.0483211617092293	0.961472968122476	   
df.mm.trans1:probe18	-0.0358002062218814	0.0654630823299302	-0.546876269000753	0.58462405854786	   
df.mm.trans1:probe19	0.00696830056787343	0.0654630823299302	0.106446264365518	0.915256341375808	   
df.mm.trans1:probe20	-0.117540790254957	0.0654630823299302	-1.79552789253885	0.0729662632093315	.  
df.mm.trans1:probe21	-0.101359265170415	0.0654630823299302	-1.54834238723392	0.121955628418272	   
df.mm.trans1:probe22	-0.136319176342911	0.0654630823299302	-2.08238248935286	0.0376410267273042	*  
df.mm.trans2:probe2	0.0391370661886017	0.0654630823299302	0.59784942590013	0.550118273456068	   
df.mm.trans2:probe3	0.0374347715540505	0.0654630823299302	0.571845538304802	0.567595489847461	   
df.mm.trans2:probe4	0.125057403667166	0.0654630823299302	1.91035006626917	0.056463735788895	.  
df.mm.trans2:probe5	0.0754835740521393	0.0654630823299302	1.15307088156507	0.249243336560813	   
df.mm.trans2:probe6	-0.0326794055600603	0.0654630823299302	-0.49920358768562	0.617780246750138	   
df.mm.trans3:probe2	0.0124915816955545	0.0654630823299302	0.190818721804110	0.84871851456178	   
df.mm.trans3:probe3	0.00988090557533773	0.0654630823299302	0.15093859353488	0.880064155924866	   
df.mm.trans3:probe4	0.0598906063901018	0.0654630823299302	0.91487605316622	0.360546491110643	   
df.mm.trans3:probe5	-0.00227122823173183	0.0654630823299302	-0.0346947951562221	0.972332209364951	   
df.mm.trans3:probe6	-0.00228951232838474	0.0654630823299302	-0.0349740990936804	0.972109565565197