chr17.10527_chr17_23707119_23707624_-_1.R 

fitVsDatCorrelation=0.942748002005013
cont.fitVsDatCorrelation=0.255031030375491

fstatistic=9473.47911524038,42,462
cont.fstatistic=1117.30874923832,42,462

residuals=-0.617207840252221,-0.0927236658451838,-0.00486846754702938,0.082557020902099,0.703405401686529
cont.residuals=-0.932618984333668,-0.380529443483631,-0.0758712084194585,0.378120505416365,1.04589036743839

predictedValues:
Include	Exclude	Both
chr17.10527_chr17_23707119_23707624_-_1.R.tl.Lung	68.9005757868173	119.793646355943	100.520483423900
chr17.10527_chr17_23707119_23707624_-_1.R.tl.cerebhem	79.5394778709245	114.923065868338	80.3792807262671
chr17.10527_chr17_23707119_23707624_-_1.R.tl.cortex	67.3712780615395	115.436975917062	89.525340484978
chr17.10527_chr17_23707119_23707624_-_1.R.tl.heart	66.0844147060551	108.600748714572	90.82544494044
chr17.10527_chr17_23707119_23707624_-_1.R.tl.kidney	65.3882569306236	100.803672704179	84.3898712993412
chr17.10527_chr17_23707119_23707624_-_1.R.tl.liver	61.6450829375273	92.303842381996	75.3284336612702
chr17.10527_chr17_23707119_23707624_-_1.R.tl.stomach	71.2639489317398	131.533241232445	85.2260349624028
chr17.10527_chr17_23707119_23707624_-_1.R.tl.testicle	69.8206509044464	130.292984603411	98.084552763342


diffExp=-50.8930705691257,-35.3835879974131,-48.0656978555221,-42.5163340085167,-35.4154157735551,-30.6587594444686,-60.2692923007051,-60.4723336989643
diffExpScore=0.997257828493898
diffExp1.5=-1,0,-1,-1,-1,0,-1,-1
diffExp1.5Score=0.857142857142857
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	72.8381983106208	80.3929851005287	86.6915862512372
cerebhem	78.2395645913737	77.9772695610221	90.9917901098437
cortex	82.2392146962847	80.7887483551464	96.9320179229924
heart	98.4886634964114	74.8480295939927	97.74214482211
kidney	108.793477842318	84.6080211351336	91.0987929355604
liver	81.6141786118602	77.8431868450166	101.016633786002
stomach	89.157407460522	90.3086661840075	81.8167516603965
testicle	72.724287045346	85.5294462793486	92.6933366963005
cont.diffExp=-7.55478678990787,0.262295030351638,1.45046634113832,23.6406339024186,24.1854567071848,3.77099176684357,-1.15125872348551,-12.8051592340026
cont.diffExpScore=2.28122418415285

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=0,0,0,1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,1,1,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.566785241026312
cont.tran.correlation=0.05143190692036

tran.covariance=0.00560850163817929
cont.tran.covariance=0.000281360648513889

tran.mean=91.4813664942261
cont.tran.mean=83.5244590693083

weightedLogRatios:
wLogRatio
Lung	-2.49407581947640
cerebhem	-1.67821804879994
cortex	-2.41222211236686
heart	-2.20520449532044
kidney	-1.90305834321121
liver	-1.74525855052457
stomach	-2.80254287812598
testicle	-2.84344606497750

cont.weightedLogRatios:
wLogRatio
Lung	-0.428060533930724
cerebhem	0.0146348901734439
cortex	0.0783090983111959
heart	1.22218467650767
kidney	1.14742611889013
liver	0.207124952939588
stomach	-0.0576941630664155
testicle	-0.708387845863881

varWeightedLogRatios=0.206651715990717
cont.varWeightedLogRatios=0.467970835543391

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81775369201764	0.0809583268574894	47.1570231279361	1.06420277876423e-178	***
df.mm.trans1	0.462488867373734	0.0650420702187194	7.11061111398369	4.41869543408667e-12	***
df.mm.trans2	0.897773636307794	0.0650420702187194	13.8029683447777	1.56780813619797e-36	***
df.mm.exp2	0.325686285087404	0.0873313485321177	3.72931702717984	0.000215853055089453	***
df.mm.exp3	0.0563481226490154	0.0873313485321178	0.645222175039382	0.519103503105828	   
df.mm.exp4	-0.0384019015541585	0.0873313485321177	-0.439726423553799	0.660340882735494	   
df.mm.exp5	-0.0500035726492963	0.0873313485321177	-0.572573004880447	0.567212370145938	   
df.mm.exp6	-0.083452091739221	0.0873313485321177	-0.955580019568001	0.339784193232946	   
df.mm.exp7	0.292269555559271	0.0873313485321177	3.34667402338101	0.000884639692177343	***
df.mm.exp8	0.121811914807075	0.0873313485321177	1.39482461744280	0.163738967135398	   
df.mm.trans1:exp2	-0.182097344520461	0.0690414931238736	-2.63750588640578	0.0086334444055127	** 
df.mm.trans2:exp2	-0.367194021897429	0.0690414931238736	-5.31845424082317	1.63365599683440e-07	***
df.mm.trans1:exp3	-0.0787938719075918	0.0690414931238736	-1.14125387998519	0.254355490181527	   
df.mm.trans2:exp3	-0.0933940535199102	0.0690414931238736	-1.35272354774170	0.176805609131310	   
df.mm.trans1:exp4	-0.00332969779158253	0.0690414931238736	-0.0482274881513414	0.961555784296984	   
df.mm.trans2:exp4	-0.059690445577515	0.0690414931238736	-0.86455901917444	0.387729536776657	   
df.mm.trans1:exp5	-0.00231827747822916	0.0690414931238736	-0.0335780321852212	0.97322814189982	   
df.mm.trans2:exp5	-0.122592285668603	0.0690414931238736	-1.77563201665771	0.0764517841601054	.  
df.mm.trans1:exp6	-0.0278189743681333	0.0690414931238736	-0.402931239019132	0.687185197536969	   
df.mm.trans2:exp6	-0.177232787193120	0.0690414931238736	-2.56704742574340	0.0105706292639626	*  
df.mm.trans1:exp7	-0.258543515923828	0.0690414931238736	-3.74475556981295	0.000203371403930038	***
df.mm.trans2:exp7	-0.198780599697339	0.0690414931238736	-2.8791468825955	0.00417262530617679	** 
df.mm.trans1:exp8	-0.108546625398824	0.0690414931238736	-1.57219406023086	0.116590109688870	   
df.mm.trans2:exp8	-0.0377969213166598	0.0690414931238736	-0.547452258149239	0.584332421894229	   
df.mm.trans1:probe2	-0.271383825668249	0.0463144415679197	-5.8595940376452	8.82950448185606e-09	***
df.mm.trans1:probe3	-0.105542615672357	0.0463144415679197	-2.27882733979593	0.0231328095977173	*  
df.mm.trans1:probe4	0.00206992216890487	0.0463144415679197	0.0446928020468378	0.964371481469985	   
df.mm.trans1:probe5	-0.229555077823798	0.0463144415679197	-4.95644706170444	1.00961164200017e-06	***
df.mm.trans1:probe6	-0.109258771386898	0.0463144415679197	-2.35906485510941	0.0187363480721613	*  
df.mm.trans2:probe2	0.354323439270164	0.0463144415679197	7.65038781155446	1.17611708697257e-13	***
df.mm.trans2:probe3	0.328511502281944	0.0463144415679197	7.09306840718752	4.95454661585503e-12	***
df.mm.trans2:probe4	0.0386264475793153	0.0463144415679197	0.834004389811545	0.404709508926597	   
df.mm.trans2:probe5	0.314878099217560	0.0463144415679197	6.79870227423111	3.27312151508599e-11	***
df.mm.trans2:probe6	0.0173103195004841	0.0463144415679197	0.373756411919566	0.708757005770914	   
df.mm.trans3:probe2	-0.998490970082213	0.0463144415679197	-21.5589551828653	7.55240642178871e-72	***
df.mm.trans3:probe3	-1.00920105825081	0.0463144415679197	-21.7902024527453	6.25930198805031e-73	***
df.mm.trans3:probe4	-1.19183303697289	0.0463144415679197	-25.7335076625091	2.95046704635277e-91	***
df.mm.trans3:probe5	0.0310332161531256	0.0463144415679197	0.670054849039163	0.503157593036418	   
df.mm.trans3:probe6	-1.01004313328044	0.0463144415679197	-21.8083841472906	5.14631047927024e-73	***
df.mm.trans3:probe7	-0.407445768832985	0.0463144415679197	-8.7973805802121	2.80263911698327e-17	***
df.mm.trans3:probe8	0.134435519247104	0.0463144415679197	2.90266954962538	0.00387695103471272	** 
df.mm.trans3:probe9	-0.657346832220032	0.0463144415679197	-14.1931287513429	3.32246687865465e-38	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09859479250018	0.234722265645370	17.4614657081256	8.42461936940098e-53	***
df.mm.trans1	0.185870168351360	0.188576304335898	0.985649649917212	0.324820965635192	   
df.mm.trans2	0.239100709192957	0.188576304335898	1.26792552242971	0.205463157183435	   
df.mm.exp2	-0.00738706222891196	0.25319955074427	-0.0291748630959177	0.976737727391686	   
df.mm.exp3	0.0146494615307075	0.25319955074427	0.057857375685091	0.95388725064454	   
df.mm.exp4	0.110257792813827	0.25319955074427	0.435458090228552	0.663433319842454	   
df.mm.exp5	0.402725299255557	0.25319955074427	1.59054507826638	0.112395998382364	   
df.mm.exp6	-0.0713964192118333	0.25319955074427	-0.281976879508539	0.778087505161437	   
df.mm.exp7	0.376344239529951	0.25319955074427	1.48635429416720	0.137867516034173	   
df.mm.exp8	-0.00657107857369157	0.25319955074427	-0.0259521731155373	0.97930669164074	   
df.mm.trans1:exp2	0.0789220032377006	0.200171820720819	0.394271296296864	0.69356267891225	   
df.mm.trans2:exp2	-0.0231224918206812	0.200171820720819	-0.115513221278685	0.908088530730915	   
df.mm.trans1:exp3	0.106742271540447	0.200171820720819	0.533253237923642	0.594114717820373	   
df.mm.trans2:exp3	-0.00973868111562147	0.200171820720819	-0.0486516088056375	0.96121796546206	   
df.mm.trans1:exp4	0.191443137670091	0.200171820720819	0.956394046777932	0.33937336755808	   
df.mm.trans2:exp4	-0.18172492940924	0.200171820720819	-0.907844714380119	0.364433563158870	   
df.mm.trans1:exp5	-0.00151443259639615	0.200171820720819	-0.00756566329337807	0.993966797379177	   
df.mm.trans2:exp5	-0.351623147050764	0.200171820720819	-1.75660662816858	0.0796473421185596	.  
df.mm.trans1:exp6	0.185158903922486	0.200171820720819	0.924999848908444	0.355448879206987	   
df.mm.trans2:exp6	0.0391658748353986	0.200171820720819	0.19566128086542	0.844961316961961	   
df.mm.trans1:exp7	-0.174181328481757	0.200171820720819	-0.87015908560221	0.384665350018484	   
df.mm.trans2:exp7	-0.260037735066946	0.200171820720819	-1.29907263735000	0.194566937983278	   
df.mm.trans1:exp8	0.0050059598104429	0.200171820720819	0.0250083143192505	0.98005912974247	   
df.mm.trans2:exp8	0.0685048737670805	0.200171820720819	0.342230357501842	0.73233317172214	   
df.mm.trans1:probe2	0.0513847321942111	0.134279339493496	0.382670427096494	0.70214013180647	   
df.mm.trans1:probe3	0.048955766290579	0.134279339493496	0.364581524419476	0.715590624310052	   
df.mm.trans1:probe4	-0.0178105217502407	0.134279339493496	-0.132637841513239	0.894537552027656	   
df.mm.trans1:probe5	-0.00922225318898277	0.134279339493496	-0.0686796138837836	0.945274360059076	   
df.mm.trans1:probe6	-0.0166743413644887	0.134279339493496	-0.124176522072455	0.901229518726193	   
df.mm.trans2:probe2	0.170223447178573	0.134279339493496	1.26768159435889	0.20555021184657	   
df.mm.trans2:probe3	0.237172058622768	0.134279339493496	1.76625875222045	0.0780127875088475	.  
df.mm.trans2:probe4	0.155016056758128	0.134279339493496	1.15442969367329	0.248920835375485	   
df.mm.trans2:probe5	0.101154396318824	0.134279339493496	0.753313180571045	0.451645447689242	   
df.mm.trans2:probe6	0.0749053514995812	0.134279339493496	0.557832290374124	0.57722910619041	   
df.mm.trans3:probe2	-0.218574366191435	0.134279339493496	-1.62775872309100	0.104257792525181	   
df.mm.trans3:probe3	-0.0514221507364356	0.134279339493496	-0.382949089043788	0.701933642567516	   
df.mm.trans3:probe4	0.00935404426884635	0.134279339493496	0.0696610834111189	0.94449355851362	   
df.mm.trans3:probe5	-0.0733884782093689	0.134279339493496	-0.54653588918587	0.584961471448269	   
df.mm.trans3:probe6	-0.032342324973521	0.134279339493496	-0.240858534868558	0.809771609567879	   
df.mm.trans3:probe7	0.0763096592343493	0.134279339493496	0.568290397630722	0.570113899827877	   
df.mm.trans3:probe8	-0.144931288541370	0.134279339493496	-1.07932679061466	0.281005228380786	   
df.mm.trans3:probe9	-0.115884613080620	0.134279339493496	-0.863011491698863	0.388578922025774