chr6.19893_chr6_88950042_88958417_+_2.R 

fitVsDatCorrelation=0.875505913046982
cont.fitVsDatCorrelation=0.313261041307550

fstatistic=6664.48622425849,38,370
cont.fstatistic=1718.18913310141,38,370

residuals=-0.764402613031831,-0.0950654062546024,0.00507430173730891,0.0904655675869225,1.11284380503227
cont.residuals=-0.631372117888083,-0.241224865886858,-0.0692266446391806,0.183325003245047,1.1831338965378

predictedValues:
Include	Exclude	Both
chr6.19893_chr6_88950042_88958417_+_2.R.tl.Lung	84.5746540430241	96.2861920562877	171.812971153735
chr6.19893_chr6_88950042_88958417_+_2.R.tl.cerebhem	72.216314020921	100.568327455949	128.028112175374
chr6.19893_chr6_88950042_88958417_+_2.R.tl.cortex	90.5062877141859	82.8960990893855	167.114397644202
chr6.19893_chr6_88950042_88958417_+_2.R.tl.heart	78.98752499567	90.277801903644	138.095307678624
chr6.19893_chr6_88950042_88958417_+_2.R.tl.kidney	85.3050805475806	101.584778127896	147.432100315544
chr6.19893_chr6_88950042_88958417_+_2.R.tl.liver	72.7787431979835	104.236526632714	124.642436500773
chr6.19893_chr6_88950042_88958417_+_2.R.tl.stomach	97.2337599580515	123.105001723593	201.614605227157
chr6.19893_chr6_88950042_88958417_+_2.R.tl.testicle	83.90425866531	93.8952125769128	162.996098462714


diffExp=-11.7115380132636,-28.3520134350278,7.61018862480043,-11.2902769079740,-16.279697580315,-31.4577834347303,-25.8712417655419,-9.99095391160287
diffExpScore=1.11079951528337
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,-1,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,-1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,-1,0,0,0,-1,-1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	98.3959121283109	103.473338378095	115.662710590814
cerebhem	96.2954542495897	118.897540292198	117.322466739656
cortex	107.902176852862	103.496015821129	114.379356111998
heart	90.958541095957	105.525646257165	121.693756444313
kidney	116.09782690383	97.905166879266	133.608291885593
liver	113.070630323786	109.274128354531	104.240520795186
stomach	122.535546025066	101.397550544116	102.756005320046
testicle	114.949727624274	100.908242824166	100.650129030198
cont.diffExp=-5.07742624978444,-22.6020860426081,4.40616103173291,-14.5671051612076,18.1926600245641,3.79650196925557,21.1379954809499,14.0414848001079
cont.diffExpScore=5.10726444115209

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

tran.correlation=0.260498830700855
cont.tran.correlation=-0.538101282522939

tran.covariance=0.00203566074756841
cont.tran.covariance=-0.00341362166547848

tran.mean=91.1472851693192
cont.tran.mean=106.317715284646

weightedLogRatios:
wLogRatio
Lung	-0.583927951416592
cerebhem	-1.47214019947082
cortex	0.391859757829295
heart	-0.592669159306832
kidney	-0.791830852082261
liver	-1.60473476204538
stomach	-1.10766129398837
testicle	-0.504680477876638

cont.weightedLogRatios:
wLogRatio
Lung	-0.232159996158607
cerebhem	-0.985226611319454
cortex	0.194300195218706
heart	-0.681054994729483
kidney	0.79579248214451
liver	0.160892633403795
stomach	0.892554325834368
testicle	0.6096414178431

varWeightedLogRatios=0.398465355942914
cont.varWeightedLogRatios=0.468920321873962

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.37217039323721	0.0997823403097083	33.7952625962724	3.73232057847312e-115	***
df.mm.trans1	1.03356225285516	0.0821747297694712	12.5776166925606	1.89152808399792e-30	***
df.mm.trans2	1.14885134766836	0.0821747297694713	13.9805917329000	5.88085952761115e-36	***
df.mm.exp2	0.179700442039611	0.112330575438965	1.59974647452288	0.110508335545862	   
df.mm.exp3	-0.0542243023195654	0.112330575438965	-0.482720773998244	0.629579484146926	   
df.mm.exp4	0.08568441072749	0.112330575438965	0.762787962161261	0.446075890247158	   
df.mm.exp5	0.215206943252219	0.112330575438965	1.91583584799805	0.0561563788818709	.  
df.mm.exp6	0.250084364883272	0.112330575438965	2.22632496901216	0.0265940273409657	*  
df.mm.exp7	0.225244619648590	0.112330575438965	2.00519421153483	0.0456701822002571	*  
df.mm.exp8	0.0195764671427319	0.112330575438965	0.174275499490953	0.861744268566374	   
df.mm.trans1:exp2	-0.337669089820263	0.0931395928039398	-3.62540869736312	0.000328910314927459	***
df.mm.trans2:exp2	-0.136187994220950	0.0931395928039398	-1.46219228709350	0.144537350958127	   
df.mm.trans1:exp3	0.122009003993149	0.0931395928039398	1.30995852912928	0.191022573249044	   
df.mm.trans2:exp3	-0.0955126161165223	0.0931395928039398	-1.02547813707515	0.305807593157283	   
df.mm.trans1:exp4	-0.154029106300672	0.0931395928039398	-1.65374468218801	0.0990275074094127	.  
df.mm.trans2:exp4	-0.150117730473095	0.0931395928039398	-1.61174991165245	0.107869016674523	   
df.mm.trans1:exp5	-0.206607553718981	0.0931395928039398	-2.21825700004823	0.0271438614126923	*  
df.mm.trans2:exp5	-0.161638164761648	0.0931395928039398	-1.73543989076588	0.0834955098402861	.  
df.mm.trans1:exp6	-0.400295065480082	0.0931395928039398	-4.29779703163089	2.20840610187031e-05	***
df.mm.trans2:exp6	-0.170746677343498	0.0931395928039399	-1.83323409737170	0.0675710054624622	.  
df.mm.trans1:exp7	-0.0857612679377985	0.0931395928039398	-0.920782079414145	0.357764165386279	   
df.mm.trans2:exp7	0.0204681202485906	0.0931395928039398	0.219757459018274	0.826181242138098	   
df.mm.trans1:exp8	-0.0275347202577017	0.0931395928039398	-0.295628523045647	0.767679623922338	   
df.mm.trans2:exp8	-0.0447219903532918	0.0931395928039398	-0.480160896209115	0.631396731299542	   
df.mm.trans1:probe2	0.0858274752945316	0.054381805993126	1.57823878275356	0.115365290646640	   
df.mm.trans1:probe3	-0.0402739727428091	0.054381805993126	-0.74057806664052	0.459419010043421	   
df.mm.trans1:probe4	0.0641936851171273	0.054381805993126	1.18042576822921	0.238589222403558	   
df.mm.trans1:probe5	-0.0544446605037582	0.054381805993126	-1.00115580035426	0.317405778841317	   
df.mm.trans1:probe6	0.295619231179135	0.054381805993126	5.43599510498975	9.91158653873556e-08	***
df.mm.trans2:probe2	0.237536651142162	0.054381805993126	4.36794341056248	1.63066703559783e-05	***
df.mm.trans2:probe3	0.149550036821967	0.054381805993126	2.75000129346331	0.00625210680492755	** 
df.mm.trans2:probe4	0.00318243778343278	0.054381805993126	0.0585202665728875	0.953365811842448	   
df.mm.trans2:probe5	0.219005235794508	0.054381805993126	4.02717842475094	6.85151062294024e-05	***
df.mm.trans2:probe6	-0.099939349113635	0.054381805993126	-1.8377350161241	0.0669030906088648	.  
df.mm.trans3:probe2	-0.289874000878241	0.054381805993126	-5.33034892064603	1.70840422518604e-07	***
df.mm.trans3:probe3	-0.452926742186537	0.054381805993126	-8.32864473540632	1.60965405296255e-15	***
df.mm.trans3:probe4	-0.421796395298854	0.054381805993126	-7.7562042597881	8.5863907556763e-14	***
df.mm.trans3:probe5	-0.876290289555078	0.054381805993126	-16.1136665756530	1.23647846985803e-44	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43966560514119	0.196106269341067	22.6390804335773	7.80303461331257e-72	***
df.mm.trans1	0.163479315000911	0.161501320165302	1.01224754592460	0.312081191663281	   
df.mm.trans2	0.188061233184219	0.161501320165302	1.16445632142035	0.244989393794059	   
df.mm.exp2	0.103122009510083	0.220767823383347	0.467106156729275	0.640698878785603	   
df.mm.exp3	0.103602604389014	0.220767823383347	0.469283081208423	0.63914371770906	   
df.mm.exp4	-0.109784820411057	0.220767823383347	-0.49728632881624	0.619282525485975	   
df.mm.exp5	-0.0341147786399599	0.220767823383347	-0.154527856990836	0.877277803903964	   
df.mm.exp6	0.297536454954928	0.220767823383347	1.34773469428231	0.178568346590992	   
df.mm.exp7	0.317457845632345	0.220767823383347	1.43797153392731	0.151287359497633	   
df.mm.exp8	0.269421113880919	0.220767823383347	1.22038216326973	0.223097243102028	   
df.mm.trans1:exp2	-0.124700155658035	0.183051008986503	-0.68123173069879	0.496151006671124	   
df.mm.trans2:exp2	0.0358261274658554	0.183051008986503	0.195716634746859	0.844939402550478	   
df.mm.trans1:exp3	-0.0113768173868920	0.183051008986503	-0.0621510771772413	0.950476081337316	   
df.mm.trans2:exp3	-0.103383466215515	0.183051008986503	-0.564779548541782	0.572565963283797	   
df.mm.trans1:exp4	0.0311893709782757	0.183051008986503	0.170386228139149	0.864799531599974	   
df.mm.trans2:exp4	0.129424856960808	0.183051008986503	0.707042576150733	0.479985071368482	   
df.mm.trans1:exp5	0.199548689932914	0.183051008986503	1.09012614045535	0.276367023425295	   
df.mm.trans2:exp5	-0.0211998754133766	0.183051008986503	-0.115814032005361	0.907862759091521	   
df.mm.trans1:exp6	-0.158523044173804	0.183051008986503	-0.866004754912288	0.387048746328637	   
df.mm.trans2:exp6	-0.242990770169432	0.183051008986503	-1.32744840640211	0.185178852370572	   
df.mm.trans1:exp7	-0.0980559458897997	0.183051008986503	-0.53567552800012	0.592504661830673	   
df.mm.trans2:exp7	-0.337722890445847	0.183051008986503	-1.84496601420399	0.0658414783109907	.  
df.mm.trans1:exp8	-0.113925491973118	0.183051008986503	-0.622370193990672	0.53408182462029	   
df.mm.trans2:exp8	-0.294523476162582	0.183051008986503	-1.60896942220241	0.108475888264193	   
df.mm.trans1:probe2	-0.0474439492901092	0.106878762917771	-0.443904364112178	0.657371217578715	   
df.mm.trans1:probe3	-0.171900531845501	0.106878762917771	-1.60836940054926	0.108607204588206	   
df.mm.trans1:probe4	-0.105631042553876	0.106878762917771	-0.988325834526597	0.323638959937963	   
df.mm.trans1:probe5	-0.0256559494951516	0.106878762917771	-0.240047216067522	0.81042658841701	   
df.mm.trans1:probe6	0.195029209243086	0.106878762917771	1.82477045877800	0.0688418861875468	.  
df.mm.trans2:probe2	0.0096329362679583	0.106878762917771	0.0901295636755226	0.928233025788014	   
df.mm.trans2:probe3	0.00770815972895808	0.106878762917771	0.072120592702673	0.942544906772937	   
df.mm.trans2:probe4	0.0446573682807835	0.106878762917771	0.417832009481074	0.676312318450198	   
df.mm.trans2:probe5	-0.0239494273368565	0.106878762917771	-0.224080319448331	0.822818486913272	   
df.mm.trans2:probe6	0.0894095138802716	0.106878762917771	0.836550793061299	0.403385064155832	   
df.mm.trans3:probe2	-0.055206168545225	0.106878762917771	-0.516530759134057	0.605792338437001	   
df.mm.trans3:probe3	-0.0895729079652548	0.106878762917771	-0.838079572778827	0.402527072065416	   
df.mm.trans3:probe4	0.0248962670414526	0.106878762917771	0.232939326408624	0.815937325429244	   
df.mm.trans3:probe5	-0.0827565156179056	0.106878762917771	-0.77430270858932	0.439246289607371