chr7.21583_chr7_104180146_104188504_-_0.R 

fitVsDatCorrelation=0.954810555548394
cont.fitVsDatCorrelation=0.237657342467870

fstatistic=11966.1204049556,61,899
cont.fstatistic=1106.96807601967,61,899

residuals=-0.83113363249841,-0.0816125936282785,-0.00738547155035004,0.0740572917342286,1.27114076026088
cont.residuals=-0.711381627870011,-0.270390901088484,-0.128094798486414,0.0677232986103525,3.03765656250844

predictedValues:
Include	Exclude	Both
chr7.21583_chr7_104180146_104188504_-_0.R.tl.Lung	46.6725386509169	51.7030126155401	72.2866238917885
chr7.21583_chr7_104180146_104188504_-_0.R.tl.cerebhem	53.4137869180712	52.5646567309013	68.7715761949698
chr7.21583_chr7_104180146_104188504_-_0.R.tl.cortex	47.1083221019027	53.0602948554621	66.2454112299366
chr7.21583_chr7_104180146_104188504_-_0.R.tl.heart	48.335823715307	54.5511804117734	68.4434953301607
chr7.21583_chr7_104180146_104188504_-_0.R.tl.kidney	46.8688346034776	52.191607362688	73.0212800610806
chr7.21583_chr7_104180146_104188504_-_0.R.tl.liver	53.2203081308025	52.2034601887501	77.5309490739283
chr7.21583_chr7_104180146_104188504_-_0.R.tl.stomach	47.6644285984387	53.8856390667206	75.1790402250465
chr7.21583_chr7_104180146_104188504_-_0.R.tl.testicle	50.0366991238129	51.0544697210476	76.0475429442956


diffExp=-5.03047396462323,0.849130187169905,-5.95197275355939,-6.21535669646637,-5.32277275921045,1.01684794205239,-6.22121046828191,-1.01777059723467
diffExpScore=1.09455236570137
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	63.5112162495855	58.3305669041007	69.3253027999778
cerebhem	54.4558976479285	70.7053816211365	62.9397129812505
cortex	56.086643183684	61.9476485491567	58.1855001104533
heart	60.413836422062	59.4636820877543	68.4209761838184
kidney	53.1177925711162	56.7847637887188	60.837504065052
liver	62.7838903860472	65.4849069675175	65.827957012002
stomach	57.662334975456	56.986700988076	59.4010491254252
testicle	54.8952295754141	67.7531830460146	61.6605903273156
cont.diffExp=5.18064934548477,-16.2494839732079,-5.86100536547271,0.950154334307669,-3.66697121760266,-2.70101658147029,0.675633987380067,-12.8579534706005
cont.diffExpScore=1.35499233999920

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,-1,0,0,0,0,0,-1
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.199065259664302
cont.tran.correlation=-0.213554695517201

tran.covariance=-0.000234835094118906
cont.tran.covariance=-0.00111866094876198

tran.mean=50.9084414247258
cont.tran.mean=60.0239796852355

weightedLogRatios:
wLogRatio
Lung	-0.398624609615622
cerebhem	0.0636198605191911
cortex	-0.465439525531382
heart	-0.47644495288045
kidney	-0.419640490197742
liver	0.0764859412357938
stomach	-0.481577943937512
testicle	-0.078991484606632

cont.weightedLogRatios:
wLogRatio
Lung	0.349607935573139
cerebhem	-1.07793545887851
cortex	-0.405180616288053
heart	0.0648885726533052
kidney	-0.267417584445827
liver	-0.175256041760908
stomach	0.047719170732714
testicle	-0.86506547280482

varWeightedLogRatios=0.0617576826154308
cont.varWeightedLogRatios=0.232510631371739

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81435706188186	0.0683869717910887	55.7760778402955	3.82760824680462e-294	***
df.mm.trans1	-0.0955814320116189	0.058286075453604	-1.63986734855227	0.101382587206350	   
df.mm.trans2	0.106071304647259	0.0514867457609196	2.06016719603535	0.0396698865015822	*  
df.mm.exp2	0.201289438854633	0.065407651868276	3.0774600999285	0.00215101594165588	** 
df.mm.exp3	0.122479481642784	0.065407651868276	1.87255585767616	0.0614541357823369	.  
df.mm.exp4	0.143270907988395	0.065407651868276	2.19043038384755	0.0287492230982822	*  
df.mm.exp5	0.0034908427627584	0.065407651868276	0.0533705562430001	0.95744852157521	   
df.mm.exp6	0.0708787340550065	0.065407651868276	1.08364590427048	0.278812368606907	   
df.mm.exp7	0.0231440205856584	0.065407651868276	0.353842706848244	0.723539733303297	   
df.mm.exp8	0.0062582039069899	0.065407651868276	0.0956799965788903	0.923796051525224	   
df.mm.trans1:exp2	-0.066376498484786	0.0590827330276434	-1.12345003494896	0.261546322550908	   
df.mm.trans2:exp2	-0.184761521132270	0.0422463519141379	-4.37343137953738	1.36618761608239e-05	***
df.mm.trans1:exp3	-0.113185760426054	0.0590827330276434	-1.91571639675329	0.055718147777734	.  
df.mm.trans2:exp3	-0.0965666268392993	0.0422463519141379	-2.28579800299829	0.0224980268476971	*  
df.mm.trans1:exp4	-0.108253884678443	0.0590827330276433	-1.83224233428391	0.0672459847856594	.  
df.mm.trans2:exp4	-0.0896476077744907	0.0422463519141379	-2.12202009671017	0.0341089781013633	*  
df.mm.trans1:exp5	0.000706150158625214	0.0590827330276434	0.0119518871663371	0.990466652507893	   
df.mm.trans2:exp5	0.00591480979671207	0.0422463519141379	0.140007587133995	0.888685356404306	   
df.mm.trans1:exp6	0.0604053669741333	0.0590827330276434	1.02238613345579	0.306873214580386	   
df.mm.trans2:exp6	-0.0612460051328115	0.0422463519141379	-1.44973476662053	0.147481251624743	   
df.mm.trans1:exp7	-0.00211458683589085	0.0590827330276434	-0.0357902677741988	0.971457538446785	   
df.mm.trans2:exp7	0.0182039343084372	0.0422463519141379	0.430899556615802	0.666644699287	   
df.mm.trans1:exp8	0.0633425605045906	0.0590827330276434	1.07209936403846	0.283963116356000	   
df.mm.trans2:exp8	-0.0188811582892068	0.0422463519141379	-0.446929910719419	0.655033314250814	   
df.mm.trans1:probe2	0.341619351821340	0.0432441326037569	7.8997850402406	8.13450048457662e-15	***
df.mm.trans1:probe3	0.288452259888576	0.0432441326037569	6.67032132501409	4.45472095662447e-11	***
df.mm.trans1:probe4	0.272391793247819	0.0432441326037569	6.29893067213828	4.68467977605187e-10	***
df.mm.trans1:probe5	0.143541957337593	0.0432441326037569	3.31933949636262	0.000938585260965872	***
df.mm.trans1:probe6	0.265368668118673	0.0432441326037569	6.13652424365238	1.26328209076833e-09	***
df.mm.trans1:probe7	0.0183514896504153	0.0432441326037569	0.424369470387319	0.671397914228053	   
df.mm.trans1:probe8	0.113239563168288	0.0432441326037569	2.61861104270247	0.00897744285499451	** 
df.mm.trans1:probe9	0.0494330200164297	0.0432441326037569	1.14311507804726	0.253295131424581	   
df.mm.trans1:probe10	0.195812810473634	0.0432441326037569	4.52807811565683	6.75312473497568e-06	***
df.mm.trans1:probe11	0.278490204446805	0.0432441326037569	6.43995353077359	1.94379064951157e-10	***
df.mm.trans1:probe12	0.264472250885525	0.0432441326037569	6.11579502146261	1.43147166362077e-09	***
df.mm.trans1:probe13	0.112010890958652	0.0432441326037569	2.59019858219843	0.0097473082242682	** 
df.mm.trans1:probe14	0.709391444013314	0.0432441326037569	16.4043397635795	4.21154296661086e-53	***
df.mm.trans1:probe15	0.63325447325659	0.0432441326037569	14.6437085247855	1.04516656273256e-43	***
df.mm.trans1:probe16	0.161426554654417	0.0432441326037569	3.73291230358481	0.000201176353260062	***
df.mm.trans1:probe17	0.263289382964451	0.0432441326037569	6.08844176334752	1.68719686015028e-09	***
df.mm.trans1:probe18	0.0305093681417048	0.0432441326037569	0.705514628337216	0.480672794855711	   
df.mm.trans1:probe19	0.0878755461879886	0.0432441326037569	2.03208021289700	0.0424391072540032	*  
df.mm.trans2:probe2	0.368846507260901	0.0432441326037569	8.52940006082713	6.18174403143607e-17	***
df.mm.trans2:probe3	-0.0758664001172401	0.0432441326037569	-1.75437442143652	0.079707048257283	.  
df.mm.trans2:probe4	0.215435348923564	0.0432441326037569	4.98183998503529	7.5525330836456e-07	***
df.mm.trans2:probe5	0.000672332067681079	0.0432441326037569	0.0155473593109524	0.987598951386598	   
df.mm.trans2:probe6	0.0177535838701661	0.0432441326037569	0.410543183576856	0.681505358818638	   
df.mm.trans3:probe2	-0.00861654914089843	0.0432441326037569	-0.199253600941688	0.842109428375853	   
df.mm.trans3:probe3	0.100761369764234	0.0432441326037569	2.33005875473336	0.0200235782725341	*  
df.mm.trans3:probe4	0.0424275060287626	0.0432441326037569	0.981115898832404	0.326799544399189	   
df.mm.trans3:probe5	-0.00107086344468036	0.0432441326037569	-0.0247632078666627	0.980249333329076	   
df.mm.trans3:probe6	0.00861555373112922	0.0432441326037569	0.199230582564182	0.842127428040751	   
df.mm.trans3:probe7	0.112805931736111	0.0432441326037569	2.60858352206400	0.00924270635172319	** 
df.mm.trans3:probe8	0.054316167295457	0.0432441326037569	1.25603553650046	0.209429400392366	   
df.mm.trans3:probe9	0.127385283784617	0.0432441326037569	2.94572410439677	0.00330511333969258	** 
df.mm.trans3:probe10	2.09990083647035	0.0432441326037569	48.5592081522736	1.57939187328259e-253	***
df.mm.trans3:probe11	1.41027670992768	0.0432441326037569	32.6119782040712	1.41420218638674e-154	***
df.mm.trans3:probe12	-0.0362407115229222	0.0432441326037569	-0.838049218260277	0.402225812272089	   
df.mm.trans3:probe13	0.229007200400416	0.0432441326037569	5.29568259580538	1.49147276355583e-07	***
df.mm.trans3:probe14	1.19781807713541	0.0432441326037569	27.6989733638765	1.37257509347450e-122	***
df.mm.trans3:probe15	1.49949602461523	0.0432441326037569	34.6751324244381	6.25734132974861e-168	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.79576734944000	0.223500169453231	16.9832862262518	2.56495430024812e-56	***
df.mm.trans1	0.332416046366039	0.190488734907572	1.74506931618466	0.0813144814470619	.  
df.mm.trans2	0.283413318775998	0.168267377554221	1.68430341576265	0.0924701593731901	.  
df.mm.exp2	0.135202689836098	0.213763248952669	0.632488000152142	0.527228874706632	   
df.mm.exp3	0.111018335722069	0.213763248952669	0.519351835575116	0.603643245900011	   
df.mm.exp4	-0.0176283717741778	0.213763248952669	-0.0824668031597941	0.934293872794862	   
df.mm.exp5	-0.0749592710570584	0.213763248952669	-0.350664912815091	0.725921902140479	   
df.mm.exp6	0.155940755007946	0.213763248952669	0.729502174821799	0.465884558391141	   
df.mm.exp7	0.0345773988749071	0.213763248952669	0.161755582609821	0.871534677991057	   
df.mm.exp8	0.121120071865456	0.213763248952669	0.5666084907433	0.571121630388636	   
df.mm.trans1:exp2	-0.289028057381762	0.193092346357688	-1.49683849636568	0.134786272782335	   
df.mm.trans2:exp2	0.0571927396506615	0.138068210425147	0.414235394770095	0.67880053545742	   
df.mm.trans1:exp3	-0.235337165111066	0.193092346357688	-1.21878038954026	0.223247269770673	   
df.mm.trans2:exp3	-0.0508549452341889	0.138068210425147	-0.368332037313973	0.712712379739483	   
df.mm.trans1:exp4	-0.0323699938368326	0.193092346357688	-0.167639963196003	0.866904232517366	   
df.mm.trans2:exp4	0.0368678532554945	0.138068210425147	0.267026371544680	0.789510097170299	   
df.mm.trans1:exp5	-0.103745304304308	0.193092346357688	-0.537283358254542	0.59120485390523	   
df.mm.trans2:exp5	0.0481010580144784	0.138068210425147	0.348386191624871	0.727631737229978	   
df.mm.trans1:exp6	-0.167458761108957	0.193092346357688	-0.867247015574369	0.386038099014725	   
df.mm.trans2:exp6	-0.0402473265258737	0.138068210425147	-0.291503209912998	0.770733735304317	   
df.mm.trans1:exp7	-0.131189736051606	0.193092346357688	-0.67941447978776	0.497050165479241	   
df.mm.trans2:exp7	-0.0578857338911321	0.138068210425147	-0.41925461127429	0.675130221728876	   
df.mm.trans1:exp8	-0.266910144257556	0.193092346357688	-1.38229271792641	0.167225156577718	   
df.mm.trans2:exp8	0.0286251093900246	0.138068210425147	0.207325852213776	0.835802337722929	   
df.mm.trans1:probe2	0.0311235484722663	0.141329126172203	0.220220341802323	0.825749552866825	   
df.mm.trans1:probe3	0.0692819409924528	0.141329126172203	0.490217005290445	0.624099962837237	   
df.mm.trans1:probe4	0.0712632173768495	0.141329126172203	0.504235887583558	0.614219177533437	   
df.mm.trans1:probe5	0.228040145707357	0.141329126172203	1.61353962826814	0.106978251976513	   
df.mm.trans1:probe6	0.159911352592417	0.141329126172203	1.13148193103219	0.258154034655911	   
df.mm.trans1:probe7	-0.096837078069811	0.141329126172203	-0.685188401659114	0.493401669080062	   
df.mm.trans1:probe8	0.0091065680834453	0.141329126172203	0.0644351828252965	0.94863804091723	   
df.mm.trans1:probe9	0.164962290368838	0.141329126172203	1.16722076217919	0.24343072474646	   
df.mm.trans1:probe10	-0.108623674678525	0.141329126172203	-0.768586614949931	0.442340607133109	   
df.mm.trans1:probe11	0.052841205597927	0.141329126172203	0.373887584456882	0.70857611227921	   
df.mm.trans1:probe12	0.0721166725209551	0.141329126172203	0.510274665061498	0.609984308586471	   
df.mm.trans1:probe13	-0.0116068493392770	0.141329126172203	-0.082126378713575	0.93456449767272	   
df.mm.trans1:probe14	0.0362403831165033	0.141329126172203	0.256425438252169	0.7976810148251	   
df.mm.trans1:probe15	-0.0296350790465154	0.141329126172203	-0.209688404995913	0.833958384431379	   
df.mm.trans1:probe16	0.0236529657012086	0.141329126172203	0.167360871335103	0.86712374913935	   
df.mm.trans1:probe17	-0.0231382709485284	0.141329126172203	-0.163719054771028	0.869989114490036	   
df.mm.trans1:probe18	0.092867430454397	0.141329126172203	0.657100436192057	0.511284521091049	   
df.mm.trans1:probe19	0.0415595948763415	0.141329126172203	0.294062490881767	0.768778031503431	   
df.mm.trans2:probe2	-0.00974521841682614	0.141329126172203	-0.0689540697007638	0.945041513208431	   
df.mm.trans2:probe3	-0.0518858412473407	0.141329126172203	-0.367127729807939	0.713610144211431	   
df.mm.trans2:probe4	0.0197915249107031	0.141329126172203	0.140038542986447	0.88866090522108	   
df.mm.trans2:probe5	-0.0686710749509973	0.141329126172203	-0.485894711238253	0.627160224161869	   
df.mm.trans2:probe6	-0.163631970607974	0.141329126172203	-1.15780784216126	0.247249965497131	   
df.mm.trans3:probe2	-0.166530687201497	0.141329126172203	-1.17831823992591	0.238981555694519	   
df.mm.trans3:probe3	-0.340129314000501	0.141329126172203	-2.40664697513285	0.0163005201727944	*  
df.mm.trans3:probe4	-0.281742425671713	0.141329126172203	-1.99351990139968	0.046506339334799	*  
df.mm.trans3:probe5	-0.0758964279584501	0.141329126172203	-0.537019013801683	0.59138735945038	   
df.mm.trans3:probe6	-0.268786976114084	0.141329126172203	-1.90185125595823	0.057510019866917	.  
df.mm.trans3:probe7	-0.216508892619401	0.141329126172203	-1.53194814461383	0.125887034906926	   
df.mm.trans3:probe8	-0.280278987446115	0.141329126172203	-1.98316507741375	0.0476529187647511	*  
df.mm.trans3:probe9	-0.0967138437728567	0.141329126172203	-0.684316434922376	0.493951735639031	   
df.mm.trans3:probe10	-0.073478721503724	0.141329126172203	-0.51991209097404	0.603252840873834	   
df.mm.trans3:probe11	-0.309582692394474	0.141329126172203	-2.19050878456053	0.0287435234578987	*  
df.mm.trans3:probe12	0.0141670516308045	0.141329126172203	0.100241556814995	0.92017490406135	   
df.mm.trans3:probe13	-0.101896940429001	0.141329126172203	-0.720990380318666	0.471102877263594	   
df.mm.trans3:probe14	-0.186951411005307	0.141329126172203	-1.32280879439894	0.186235349945362	   
df.mm.trans3:probe15	-0.207471060374463	0.141329126172203	-1.46799931474613	0.142454183202015