chr8.22908_chr8_122496568_122497064_-_0.R 

fitVsDatCorrelation=0.932916066339536
cont.fitVsDatCorrelation=0.240574509077921

fstatistic=7905.68926105428,43,485
cont.fstatistic=1078.35933223336,43,485

residuals=-0.573125905590352,-0.095555938810987,0.00150292618163091,0.0904336934359548,0.91623224104073
cont.residuals=-0.718932671894533,-0.301881869044527,-0.119867980888874,0.178008227434814,1.85622091346879

predictedValues:
Include	Exclude	Both
chr8.22908_chr8_122496568_122497064_-_0.R.tl.Lung	107.113644570957	53.7555580370717	90.2223509025598
chr8.22908_chr8_122496568_122497064_-_0.R.tl.cerebhem	75.0357749533318	64.5020605484564	63.7909609465429
chr8.22908_chr8_122496568_122497064_-_0.R.tl.cortex	67.5424499409589	52.6057929614579	61.6714288056957
chr8.22908_chr8_122496568_122497064_-_0.R.tl.heart	90.9191990031621	51.372496597002	82.9169270947875
chr8.22908_chr8_122496568_122497064_-_0.R.tl.kidney	235.033700476571	57.537286192378	194.764594235144
chr8.22908_chr8_122496568_122497064_-_0.R.tl.liver	62.6470554861659	57.7711742078641	64.4104336520471
chr8.22908_chr8_122496568_122497064_-_0.R.tl.stomach	64.1875400035913	52.7844709570953	65.0909491280389
chr8.22908_chr8_122496568_122497064_-_0.R.tl.testicle	64.0389561784756	57.0749504909687	61.6413901799524


diffExp=53.3580865338855,10.5337144048753,14.9366569795010,39.5467024061602,177.496414284193,4.87588127830175,11.403069046496,6.96400568750693
diffExpScore=0.996876118062931
diffExp1.5=1,0,0,1,1,0,0,0
diffExp1.5Score=0.75
diffExp1.4=1,0,0,1,1,0,0,0
diffExp1.4Score=0.75
diffExp1.3=1,0,0,1,1,0,0,0
diffExp1.3Score=0.75
diffExp1.2=1,0,1,1,1,0,1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	65.6557516637786	65.3181978192701	77.6891006954912
cerebhem	81.251841867807	66.0880082560167	73.9701899338093
cortex	68.1375867572581	63.1065992782052	79.1768943591426
heart	66.0573365019034	72.5670255011068	75.0421551552872
kidney	65.612271756025	70.3485746309881	64.1527810254507
liver	86.1056717733224	75.7763269663583	68.617854129899
stomach	72.3016455175637	65.4926954365706	76.5585363817701
testicle	73.9039123340072	86.169437681622	72.5161579963854
cont.diffExp=0.337553844508491,15.1638336117903,5.0309874790529,-6.50968899920346,-4.7363028749631,10.3293448069641,6.80895008099306,-12.2655253476148
cont.diffExpScore=4.03598991667418

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

tran.correlation=0.0799940663955861
cont.tran.correlation=0.271844615994842

tran.covariance=0.00158558206989013
cont.tran.covariance=0.00298612275035942

tran.mean=75.8701319128443
cont.tran.mean=71.4933052338627

weightedLogRatios:
wLogRatio
Lung	2.98471637877768
cerebhem	0.641728214775027
cortex	1.02166184389288
heart	2.41165406287348
kidney	6.69321249360184
liver	0.331966845019171
stomach	0.79488874639345
testicle	0.472239696343246

cont.weightedLogRatios:
wLogRatio
Lung	0.0215554616965573
cerebhem	0.887051023677041
cortex	0.320864945722533
heart	-0.398273720666881
kidney	-0.294036348692233
liver	0.56121000517672
stomach	0.418519745781364
testicle	-0.672477576073984

varWeightedLogRatios=4.64051700865853
cont.varWeightedLogRatios=0.284081437299019

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.200165035879	0.0852827168836468	49.2498971580535	7.52631371670803e-191	***
df.mm.trans1	0.438620649752894	0.068273274771421	6.42448529415641	3.16358294450824e-10	***
df.mm.trans2	-0.178208282615764	0.068273274771421	-2.61022022471320	0.00932826196797644	** 
df.mm.exp2	0.172990419059211	0.0914225661603051	1.89220699357619	0.059058724616464	.  
df.mm.exp3	-0.102298470598969	0.091422566160305	-1.11896301860082	0.26370979459584	   
df.mm.exp4	-0.124825343125912	0.091422566160305	-1.36536687131531	0.172770705668457	   
df.mm.exp5	0.0843102355164875	0.091422566160305	0.922203773723148	0.356880815411829	   
df.mm.exp6	-0.127329264798485	0.091422566160305	-1.39275531355376	0.164331902221392	   
df.mm.exp7	-0.20381960909307	0.0914225661603051	-2.22942340882974	0.0262429144079795	*  
df.mm.exp8	-0.0735368975064713	0.0914225661603051	-0.804362649124591	0.421581660001071	   
df.mm.trans1:exp2	-0.528915789501208	0.0717177613590959	-7.37496234514195	7.16680242679399e-13	***
df.mm.trans2:exp2	0.00925968371725689	0.0717177613590959	0.129112838183738	0.897321888358963	   
df.mm.trans1:exp3	-0.358835610803563	0.0717177613590959	-5.00344132336825	7.8893590606836e-07	***
df.mm.trans2:exp3	0.0806776496229424	0.0717177613590959	1.12493262608942	0.261173990924796	   
df.mm.trans1:exp4	-0.0390938374646047	0.0717177613590959	-0.545106773046904	0.585930660406386	   
df.mm.trans2:exp4	0.0794812197134944	0.0717177613590959	1.10825014901854	0.268303076883957	   
df.mm.trans1:exp5	0.701528305001232	0.0717177613590959	9.78179312497821	9.61697235990374e-21	***
df.mm.trans2:exp5	-0.016324109352675	0.0717177613590959	-0.227615991399104	0.820040707379345	   
df.mm.trans1:exp6	-0.409044424014702	0.0717177613590959	-5.70353028682236	2.04274373944747e-08	***
df.mm.trans2:exp6	0.199372132946973	0.0717177613590959	2.77995477227325	0.00564793624151835	** 
df.mm.trans1:exp7	-0.308261649616127	0.0717177613590959	-4.29826090182374	2.08150527056701e-05	***
df.mm.trans2:exp7	0.185589578882512	0.0717177613590959	2.58777707733029	0.0099493932765244	** 
df.mm.trans1:exp8	-0.440861883649993	0.0717177613590959	-6.14717854120637	1.64818291884027e-09	***
df.mm.trans2:exp8	0.133455155481147	0.0717177613590959	1.86083827704727	0.0633719334168411	.  
df.mm.trans1:probe2	0.0915139645023287	0.0491017945876865	1.86376007782979	0.0629594572941168	.  
df.mm.trans1:probe3	-0.0082039599993958	0.0491017945876865	-0.167080654959466	0.867376225071404	   
df.mm.trans1:probe4	0.204800302866047	0.0491017945876865	4.17093315195053	3.5943853585716e-05	***
df.mm.trans1:probe5	0.283582358894987	0.0491017945876865	5.77539703540901	1.37298853866602e-08	***
df.mm.trans1:probe6	-0.0100177223258959	0.0491017945876865	-0.204019474441126	0.838423859476262	   
df.mm.trans2:probe2	-0.175709522727283	0.0491017945876865	-3.57847455887787	0.000380346895631241	***
df.mm.trans2:probe3	-0.0722987958318006	0.0491017945876865	-1.4724267501606	0.141554290412543	   
df.mm.trans2:probe4	-0.015960547848085	0.0491017945876865	-0.325050193829118	0.745283249846462	   
df.mm.trans2:probe5	-0.137228711259248	0.0491017945876865	-2.79477995481782	0.00539930556613161	** 
df.mm.trans2:probe6	-0.198957402502536	0.0491017945876865	-4.05193749379641	5.91451168846715e-05	***
df.mm.trans3:probe2	-0.102288843335450	0.0491017945876865	-2.08319969146508	0.0377561970870229	*  
df.mm.trans3:probe3	-0.159028560953682	0.0491017945876865	-3.23875251992445	0.00128286432632041	** 
df.mm.trans3:probe4	-0.0122866095080197	0.0491017945876865	-0.250227300472249	0.802517532117526	   
df.mm.trans3:probe5	0.115618276569419	0.0491017945876865	2.35466498811866	0.0189370440352028	*  
df.mm.trans3:probe6	0.121888314322958	0.0491017945876865	2.48235966417252	0.0133888653788186	*  
df.mm.trans3:probe7	0.274049403228127	0.0491017945876865	5.5812502481702	3.97846378228943e-08	***
df.mm.trans3:probe8	-0.189481902027598	0.0491017945876865	-3.85896083063154	0.000129265799775588	***
df.mm.trans3:probe9	-0.0735099887689849	0.0491017945876865	-1.4970937291856	0.135019353454961	   
df.mm.trans3:probe10	0.442037771155329	0.0491017945876865	9.0024768924878	5.03287859089299e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05553325114537	0.229878990304650	17.6420352541601	3.55187638264876e-54	***
df.mm.trans1	0.104876141689718	0.184030153385694	0.569885639718599	0.56901913848151	   
df.mm.trans2	0.110693737127953	0.184030153385694	0.601497825717501	0.54778949983485	   
df.mm.exp2	0.273897716686252	0.246428912773309	1.11146745568046	0.266917859488228	   
df.mm.exp3	-0.0163110864738854	0.246428912773309	-0.0661898244419478	0.947253972892475	   
df.mm.exp4	0.146002787570551	0.246428912773309	0.592474259320615	0.553809171035894	   
df.mm.exp5	0.264976915830832	0.246428912773309	1.07526715452657	0.282789756043103	   
df.mm.exp6	0.543827540801057	0.246428912773309	2.20683334062073	0.0277934285920281	*  
df.mm.exp7	0.113748962880178	0.246428912773309	0.461589354918008	0.644582785552766	   
df.mm.exp8	0.464291012789453	0.246428912773309	1.88407686242789	0.0601524557382006	.  
df.mm.trans1:exp2	-0.0607694345270974	0.193314743837624	-0.314354887375486	0.753386693088828	   
df.mm.trans2:exp2	-0.262181082193566	0.193314743837624	-1.35623945172949	0.175654030911432	   
df.mm.trans1:exp3	0.0534148741948924	0.193314743837624	0.276310399995969	0.782427372515851	   
df.mm.trans2:exp3	-0.0181342426989730	0.193314743837624	-0.0938068268305752	0.9253013480735	   
df.mm.trans1:exp4	-0.139904895734534	0.193314743837624	-0.723715599530514	0.469589266095489	   
df.mm.trans2:exp4	-0.0407628408089671	0.193314743837624	-0.210862555021701	0.833083088461018	   
df.mm.trans1:exp5	-0.265639375860486	0.193314743837624	-1.37412889770896	0.170036344290212	   
df.mm.trans2:exp5	-0.190785071276710	0.193314743837624	-0.986914228523413	0.324176774846335	   
df.mm.trans1:exp6	-0.272677465218360	0.193314743837624	-1.41053630884666	0.159022424175993	   
df.mm.trans2:exp6	-0.395312283774444	0.193314743837624	-2.04491533303062	0.0414024262789438	*  
df.mm.trans1:exp7	-0.0173272823314174	0.193314743837624	-0.089632492521996	0.928616264712908	   
df.mm.trans2:exp7	-0.111081024202259	0.193314743837624	-0.574612272179106	0.565819976518819	   
df.mm.trans1:exp8	-0.34595045325235	0.193314743837624	-1.78957096796989	0.0741467026041316	.  
df.mm.trans2:exp8	-0.187246126937356	0.193314743837624	-0.968607583778682	0.333223785700096	   
df.mm.trans1:probe2	0.0699706614644325	0.132353557372750	0.528664758646214	0.59727986498932	   
df.mm.trans1:probe3	0.0172928556049540	0.132353557372750	0.130656522939174	0.896101202252557	   
df.mm.trans1:probe4	0.0303982606567925	0.132353557372750	0.229674677887058	0.818441402091399	   
df.mm.trans1:probe5	0.0626982493205032	0.132353557372750	0.473717900486233	0.635914253323721	   
df.mm.trans1:probe6	0.203893014567701	0.132353557372750	1.54051782675907	0.124086344077032	   
df.mm.trans2:probe2	0.0571425997843963	0.132353557372750	0.431742077196041	0.66612074306528	   
df.mm.trans2:probe3	-0.0251061472904999	0.132353557372750	-0.189690007498575	0.849631412395305	   
df.mm.trans2:probe4	0.0801584583089695	0.132353557372750	0.605638865325077	0.545037892631595	   
df.mm.trans2:probe5	-0.043770750397562	0.132353557372750	-0.330710796645153	0.741005767024891	   
df.mm.trans2:probe6	0.140274870295173	0.132353557372750	1.05984964121602	0.289740531008608	   
df.mm.trans3:probe2	0.131649966413535	0.132353557372750	0.994684004168975	0.320385971142762	   
df.mm.trans3:probe3	0.107863864584552	0.132353557372750	0.81496762705647	0.415490843041015	   
df.mm.trans3:probe4	0.203322344764718	0.132353557372750	1.5362061194328	0.12513992329802	   
df.mm.trans3:probe5	0.106159689055314	0.132353557372750	0.802091694115443	0.422892746656277	   
df.mm.trans3:probe6	-0.0464657514322785	0.132353557372750	-0.351072931885134	0.725686083574082	   
df.mm.trans3:probe7	0.0546823260379879	0.132353557372750	0.413153428766445	0.679676805492063	   
df.mm.trans3:probe8	0.143742103394400	0.132353557372750	1.08604639155694	0.277997958326465	   
df.mm.trans3:probe9	0.0895664307692672	0.132353557372750	0.67672099297656	0.498905607700878	   
df.mm.trans3:probe10	0.0255974602316081	0.132353557372750	0.193402132437721	0.846725012911011