chr7.21339_chr7_11071252_11071818_-_0.R 

fitVsDatCorrelation=0.962856669547021
cont.fitVsDatCorrelation=0.349746727913817

fstatistic=8295.05161974617,38,370
cont.fstatistic=680.126670175771,38,370

residuals=-0.756641293588024,-0.0938518149729851,0.000109233583943244,0.0959612013057553,0.54216411644187
cont.residuals=-1.35176907648027,-0.407421057671051,-0.0964046383914004,0.2959650368615,1.45509159640933

predictedValues:
Include	Exclude	Both
chr7.21339_chr7_11071252_11071818_-_0.R.tl.Lung	108.659893767549	55.3651853915236	227.040767109138
chr7.21339_chr7_11071252_11071818_-_0.R.tl.cerebhem	88.8361968896875	53.6828242567047	129.976063956311
chr7.21339_chr7_11071252_11071818_-_0.R.tl.cortex	88.9002721825162	51.2589410478226	192.752466313294
chr7.21339_chr7_11071252_11071818_-_0.R.tl.heart	92.3290933251991	50.4179218532062	208.894521158529
chr7.21339_chr7_11071252_11071818_-_0.R.tl.kidney	106.759757984901	58.3859110341424	186.921605653083
chr7.21339_chr7_11071252_11071818_-_0.R.tl.liver	100.493063673392	58.4689486194077	180.033050788331
chr7.21339_chr7_11071252_11071818_-_0.R.tl.stomach	100.39827663556	50.4412976428612	243.543805646659
chr7.21339_chr7_11071252_11071818_-_0.R.tl.testicle	101.901980147324	53.1711449425919	246.322741021869


diffExp=53.2947083760249,35.1533726329828,37.6413311346936,41.9111714719929,48.3738469507586,42.0241150539838,49.9569789926989,48.7308352047323
diffExpScore=0.99720737757085
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	96.7785684852397	91.8617714643907	91.1148451805196
cerebhem	103.818506756188	65.495415746951	85.2057906679659
cortex	80.1303394581351	84.9711389144531	87.0084007028398
heart	71.7203815794619	96.016678712977	82.3095841762314
kidney	142.621282953301	107.584555235352	90.0444474795124
liver	109.787512332078	78.7488234588834	85.7258968689372
stomach	98.4027759889388	107.915252622404	110.730894161277
testicle	93.7537380240081	87.7333639606686	90.9723683396038
cont.diffExp=4.91679702084896,38.3230910092365,-4.84079945631798,-24.2962971335151,35.0367277179490,31.0386888731948,-9.51247663346531,6.02037406333953
cont.diffExpScore=1.98214662703918

cont.diffExp1.5=0,1,0,0,0,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,1,0,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,1,0,-1,1,1,0,0
cont.diffExp1.3Score=1.33333333333333
cont.diffExp1.2=0,1,0,-1,1,1,0,0
cont.diffExp1.2Score=1.33333333333333

tran.correlation=0.573508886977999
cont.tran.correlation=0.213932638192189

tran.covariance=0.00269017723374758
cont.tran.covariance=0.00374796789521666

tran.mean=76.2169193371493
cont.tran.mean=94.8337566058394

weightedLogRatios:
wLogRatio
Lung	2.93381513038677
cerebhem	2.13314571179713
cortex	2.31934467269144
heart	2.55487923749198
kidney	2.63661576093083
liver	2.35012982227810
stomach	2.93573255149232
testicle	2.79632643715

cont.weightedLogRatios:
wLogRatio
Lung	0.237049721952801
cerebhem	2.03259380926974
cortex	-0.258852811226700
heart	-1.28912717859237
kidney	1.35861773335194
liver	1.50604321057073
stomach	-0.427724009228126
testicle	0.299158643957680

varWeightedLogRatios=0.0889678036709713
cont.varWeightedLogRatios=1.25834622541334

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.17313759839714	0.0877362630392444	36.1667740165523	1.58639795605358e-123	***
df.mm.trans1	1.58769347706864	0.072254305560036	21.9736867548942	4.43336240394816e-69	***
df.mm.trans2	0.809737653595105	0.072254305560036	11.2067737322905	2.70156159915346e-25	***
df.mm.exp2	0.325492838598677	0.0987696308131575	3.29547489363823	0.00107750907952563	** 
df.mm.exp3	-0.114045686298547	0.0987696308131575	-1.15466348673802	0.248973450776797	   
df.mm.exp4	-0.173167586118204	0.0987696308131575	-1.75324727542806	0.0803876564164728	.  
df.mm.exp5	0.22992221609392	0.0987696308131575	2.32786347585792	0.0204578911602952	*  
df.mm.exp6	0.208399932908201	0.0987696308131575	2.10995962212749	0.0355327582146212	*  
df.mm.exp7	-0.242385691967850	0.0987696308131575	-2.45405080460785	0.0145857969668232	*  
df.mm.exp8	-0.186159543234438	0.0987696308131575	-1.88478524928979	0.0602423641716555	.  
df.mm.trans1:exp2	-0.526921412488145	0.0818954515222922	-6.43407420917285	3.84828182072597e-10	***
df.mm.trans2:exp2	-0.356350708166796	0.0818954515222922	-4.35128815511563	1.7530595414189e-05	***
df.mm.trans1:exp3	-0.0866618729793559	0.0818954515222922	-1.05820129651237	0.290654041304441	   
df.mm.trans2:exp3	0.0369847748931320	0.0818954515222922	0.451609634059647	0.651814827520538	   
df.mm.trans1:exp4	0.0103041184536603	0.0818954515222922	0.125820399816166	0.899942431706179	   
df.mm.trans2:exp4	0.0795633166357216	0.0818954515222922	0.971523023034609	0.331922569773680	   
df.mm.trans1:exp5	-0.247563921931848	0.0818954515222922	-3.02292639371383	0.00267787758709408	** 
df.mm.trans2:exp5	-0.176798578457789	0.0818954515222922	-2.15883269670555	0.0315055440321764	*  
df.mm.trans1:exp6	-0.286533989429817	0.0818954515222922	-3.49877782103468	0.00052433120064571	***
df.mm.trans2:exp6	-0.153855086098769	0.0818954515222923	-1.87867681585331	0.0610746387470146	.  
df.mm.trans1:exp7	0.163307970630024	0.0818954515222922	1.99410306182354	0.0468743031358555	*  
df.mm.trans2:exp7	0.149244955520628	0.0818954515222922	1.82238393886873	0.0692037839831642	.  
df.mm.trans1:exp8	0.121948152070936	0.0818954515222922	1.48907112427046	0.137320487141781	   
df.mm.trans2:exp8	0.145724430461792	0.0818954515222922	1.77939589748919	0.075995654137132	.  
df.mm.trans1:probe2	-0.145429775237103	0.0478166419063018	-3.04140503053471	0.00252264653498816	** 
df.mm.trans1:probe3	-0.0252321086620501	0.0478166419063018	-0.527684664922584	0.598034508475592	   
df.mm.trans1:probe4	-0.252259132038572	0.0478166419063018	-5.27555097936158	2.25827220927050e-07	***
df.mm.trans1:probe5	-0.230241341557447	0.0478166419063018	-4.81508806094355	2.14852689879967e-06	***
df.mm.trans1:probe6	-0.145529074528026	0.0478166419063018	-3.04348169855162	0.00250572792447517	** 
df.mm.trans2:probe2	2.83388532851893e-05	0.0478166419063018	0.000592656701838666	0.999527447788578	   
df.mm.trans2:probe3	0.137772123894531	0.0478166419063018	2.8812588756128	0.00419175360443574	** 
df.mm.trans2:probe4	0.197245116946793	0.0478166419063018	4.12503072326369	4.5815881459715e-05	***
df.mm.trans2:probe5	-0.0204127924321566	0.0478166419063018	-0.426897239504106	0.669702468706486	   
df.mm.trans2:probe6	0.02720015127375	0.0478166419063018	0.568842775012297	0.569807956747423	   
df.mm.trans3:probe2	-0.522648511695164	0.0478166419063018	-10.9302638340708	2.74232904548415e-24	***
df.mm.trans3:probe3	-0.039291016719105	0.0478166419063017	-0.821701716237141	0.411775843935795	   
df.mm.trans3:probe4	0.356942938571932	0.0478166419063018	7.4648265612498	6.03028803060181e-13	***
df.mm.trans3:probe5	-0.522199094731808	0.0478166419063017	-10.9208650777918	2.96561167848940e-24	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.40790019358812	0.304412044721011	14.4800452873929	5.82049096346128e-38	***
df.mm.trans1	0.116666819972284	0.250695438049246	0.465372728279825	0.641938347364059	   
df.mm.trans2	0.00944597846686782	0.250695438049246	0.0376790999484094	0.969963860718283	   
df.mm.exp2	-0.201034785386468	0.34269370760325	-0.58663109630018	0.55780922764093	   
df.mm.exp3	-0.220628252180612	0.34269370760325	-0.643805962250238	0.520100124320941	   
df.mm.exp4	-0.153780460108492	0.342693707603250	-0.448740250248569	0.65388173851557	   
df.mm.exp5	0.557576648422614	0.34269370760325	1.62704081239841	0.104579700904685	   
df.mm.exp6	0.0330653813436659	0.34269370760325	0.0964866894549083	0.923186284551081	   
df.mm.exp7	-0.0172774319081796	0.34269370760325	-0.0504165426001413	0.959817659420845	   
df.mm.exp8	-0.0761718067556825	0.34269370760325	-0.222273724511655	0.824223444404573	   
df.mm.trans1:exp2	0.271253462850792	0.284146611533936	0.954625013426902	0.340390623543278	   
df.mm.trans2:exp2	-0.137270026123056	0.284146611533936	-0.483095770109728	0.629313464512954	   
df.mm.trans1:exp3	0.0318572344801758	0.284146611533936	0.112115482596107	0.910792639716231	   
df.mm.trans2:exp3	0.142654945725278	0.284146611533936	0.502046971298269	0.615933108346167	   
df.mm.trans1:exp4	-0.145870140587590	0.284146611533936	-0.513362238599735	0.608004348793589	   
df.mm.trans2:exp4	0.198017409974138	0.284146611533936	0.696884643125468	0.486312587737458	   
df.mm.trans1:exp5	-0.169809472081179	0.284146611533936	-0.597612166354827	0.550464314197754	   
df.mm.trans2:exp5	-0.399584512807751	0.284146611533936	-1.40626175568533	0.160485682377823	   
df.mm.trans1:exp6	0.0930558404022857	0.284146611533936	0.327492345940476	0.743480746198693	   
df.mm.trans2:exp6	-0.187087007059432	0.284146611533936	-0.658417167283685	0.510679586235539	   
df.mm.trans1:exp7	0.0339208770140859	0.284146611533936	0.119378080319056	0.905040616049086	   
df.mm.trans2:exp7	0.178338689949865	0.284146611533936	0.627629127748955	0.530634068162163	   
df.mm.trans1:exp8	0.0444177732433383	0.284146611533936	0.156319911765105	0.87586611727389	   
df.mm.trans2:exp8	0.0301891035291933	0.284146611533936	0.106244812726151	0.915445695354242	   
df.mm.trans1:probe2	0.0198551046227167	0.165905877799683	0.119676920950866	0.904804037374087	   
df.mm.trans1:probe3	0.0718187186560661	0.165905877799683	0.432888331676719	0.665348120346744	   
df.mm.trans1:probe4	0.247160934314124	0.165905877799683	1.48976599016311	0.137137687431353	   
df.mm.trans1:probe5	0.106910874556787	0.165905877799683	0.644406792421625	0.519710974406328	   
df.mm.trans1:probe6	0.0806984867507025	0.165905877799683	0.486411258123954	0.626963584401753	   
df.mm.trans2:probe2	0.232966117341628	0.165905877799683	1.40420653222977	0.161096192872075	   
df.mm.trans2:probe3	0.119220299692252	0.165905877799683	0.7186020246745	0.472839707719965	   
df.mm.trans2:probe4	0.405429045939036	0.165905877799683	2.44372924766751	0.0150027673869090	*  
df.mm.trans2:probe5	-0.0448685200302922	0.165905877799683	-0.270445632339001	0.78696822960505	   
df.mm.trans2:probe6	0.419579758610238	0.165905877799683	2.52902286630764	0.0118528195996321	*  
df.mm.trans3:probe2	-0.0230901646091074	0.165905877799683	-0.139176290287839	0.88938656540851	   
df.mm.trans3:probe3	0.112403919181003	0.165905877799683	0.677516195759631	0.498501913943794	   
df.mm.trans3:probe4	-0.113379468184474	0.165905877799683	-0.683396331029153	0.494784155084858	   
df.mm.trans3:probe5	-0.085395517669271	0.165905877799682	-0.514722677712353	0.60705415486724