chr7.21125_chr7_34726540_34726915_+_0.R 

fitVsDatCorrelation=0.83461115123489
cont.fitVsDatCorrelation=0.311231595341287

fstatistic=8012.5612106518,37,347
cont.fstatistic=2685.73486406832,37,347

residuals=-0.437422931520702,-0.0801420923446677,-0.0158110549230878,0.0701196748873248,0.703372534205602
cont.residuals=-0.450480285001141,-0.184764092003228,-0.0379174879548819,0.143310671788561,0.988891010870775

predictedValues:
Include	Exclude	Both
chr7.21125_chr7_34726540_34726915_+_0.R.tl.Lung	66.1530804587236	54.0652076098535	77.8100165721381
chr7.21125_chr7_34726540_34726915_+_0.R.tl.cerebhem	68.1869971782036	64.5800944321556	70.4094419316936
chr7.21125_chr7_34726540_34726915_+_0.R.tl.cortex	66.6790527869694	48.4452798084017	71.8074462422434
chr7.21125_chr7_34726540_34726915_+_0.R.tl.heart	69.6320878642272	47.5049200683852	83.8372928813864
chr7.21125_chr7_34726540_34726915_+_0.R.tl.kidney	62.7887115684755	50.1699655922027	74.9456180446331
chr7.21125_chr7_34726540_34726915_+_0.R.tl.liver	63.1604823152438	47.5691892775723	70.255214101115
chr7.21125_chr7_34726540_34726915_+_0.R.tl.stomach	76.901421788026	51.7221373410205	79.2121829433523
chr7.21125_chr7_34726540_34726915_+_0.R.tl.testicle	69.9984010222009	53.7400017656291	72.5464009119472


diffExp=12.0878728488701,3.60690274604792,18.2337729785677,22.1271677958419,12.6187459762728,15.5912930376715,25.1792844470055,16.2583992565718
diffExpScore=0.992107554402572
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,1,0,0,1,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,1,1,0,1,1,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	65.505619488339	62.4417137485877	61.126190163949
cerebhem	68.9715734993962	69.743667572791	61.1002743962629
cortex	65.2454303987069	63.4376288439244	57.434329713037
heart	67.7575024714136	59.8301917893508	66.6125442229048
kidney	58.8291574204351	65.1741793367641	53.3783564049501
liver	64.033970822715	62.2749755578746	62.4284378408943
stomach	62.3866432099113	66.8932306325562	68.2019186886172
testicle	62.8481031432257	66.8069289986149	63.9603596307211
cont.diffExp=3.06390573975127,-0.77209407339474,1.80780155478249,7.92731068206277,-6.34502191632904,1.75899526484043,-4.50658742264483,-3.95882585538918
cont.diffExpScore=14.8877766919778

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

tran.correlation=0.158209374565823
cont.tran.correlation=-0.0875259263278078

tran.covariance=0.00121213933993498
cont.tran.covariance=-0.000283222742135216

tran.mean=60.0810644298307
cont.tran.mean=64.5112823084129

weightedLogRatios:
wLogRatio
Lung	0.825500774102058
cerebhem	0.227992792584545
cortex	1.29065404226976
heart	1.54946446350555
kidney	0.903626398396802
liver	1.13508894265200
stomach	1.64375124866758
testicle	1.08800282917069

cont.weightedLogRatios:
wLogRatio
Lung	0.199186991460240
cerebhem	-0.0471922437838748
cortex	0.117006445650215
heart	0.516826038544995
kidney	-0.422593378225699
liver	0.115468668434255
stomach	-0.290719379049193
testicle	-0.254806022731082

varWeightedLogRatios=0.201058230043638
cont.varWeightedLogRatios=0.094786587001527

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.54445769773012	0.081039021340201	43.7376666094045	3.00147328626798e-143	***
df.mm.trans1	0.646746852936079	0.0675166259094642	9.57907543844575	1.93939545435066e-19	***
df.mm.trans2	0.458143915025729	0.0675166259094642	6.78564588876336	5.00728160335478e-11	***
df.mm.exp2	0.307940591480152	0.0930237271272662	3.3103445861598	0.00102968581744449	** 
df.mm.exp3	-0.021554569785006	0.0930237271272661	-0.231710451200446	0.816899493391368	   
df.mm.exp4	-0.152711304294486	0.093023727127266	-1.64163820361187	0.101570968009141	   
df.mm.exp5	-0.0894630529387138	0.0930237271272661	-0.961722946408276	0.336858468454622	   
df.mm.exp6	-0.0721625872454883	0.093023727127266	-0.775743882490995	0.438428595901174	   
df.mm.exp7	0.088387978339252	0.093023727127266	0.950165953018934	0.34268923893639	   
df.mm.exp8	0.120511512530577	0.093023727127266	1.29549219593949	0.196011637306169	   
df.mm.trans1:exp2	-0.277658159277101	0.0786193987723135	-3.53167492518247	0.000468710935754006	***
df.mm.trans2:exp2	-0.130225230295124	0.0786193987723135	-1.65640073987674	0.0985446082289756	.  
df.mm.trans1:exp3	0.0294739649544378	0.0786193987723135	0.374894306172401	0.707968095812988	   
df.mm.trans2:exp3	-0.0882013869942558	0.0786193987723135	-1.12187816711359	0.262689957922636	   
df.mm.trans1:exp4	0.203965340666139	0.0786193987723135	2.59433859646822	0.00987910784375281	** 
df.mm.trans2:exp4	0.0233537239632462	0.0786193987723135	0.297047857499903	0.766607706842814	   
df.mm.trans1:exp5	0.0372669009033073	0.0786193987723135	0.474016610216449	0.635786181756637	   
df.mm.trans2:exp5	0.0146887392156811	0.0786193987723135	0.186833522579085	0.851900348160985	   
df.mm.trans1:exp6	0.0258699557493769	0.0786193987723135	0.329053085540604	0.742314073379126	   
df.mm.trans2:exp6	-0.0558430115753539	0.0786193987723135	-0.710295581591493	0.477997940382216	   
df.mm.trans1:exp7	0.0621649295345285	0.0786193987723135	0.790707261887895	0.429654910320269	   
df.mm.trans2:exp7	-0.132692966451860	0.0786193987723135	-1.68778912741558	0.0923502547431457	.  
df.mm.trans1:exp8	-0.0640105705399769	0.0786193987723135	-0.814182905740037	0.41609854939565	   
df.mm.trans2:exp8	-0.126544742883023	0.0786193987723135	-1.60958675414835	0.108397421084809	   
df.mm.trans1:probe2	-0.0198695927387621	0.0430616181650901	-0.461422342806206	0.644784730899916	   
df.mm.trans1:probe3	0.0283214028571123	0.0430616181650901	0.657694811851552	0.511170302109009	   
df.mm.trans1:probe4	-0.0203916208607896	0.0430616181650901	-0.473545159929012	0.636122070715866	   
df.mm.trans1:probe5	0.0528417002506136	0.0430616181650901	1.22711831329767	0.220610015423756	   
df.mm.trans1:probe6	-0.0332328235914298	0.0430616181650901	-0.771750459168104	0.440787484176533	   
df.mm.trans2:probe2	-0.0480874666384104	0.0430616181650901	-1.11671294966325	0.264889722910058	   
df.mm.trans2:probe3	0.0159732221475169	0.0430616181650901	0.370938734496196	0.710909517352109	   
df.mm.trans2:probe4	-0.0466499240665819	0.0430616181650901	-1.08332956480490	0.279414341853226	   
df.mm.trans2:probe5	-0.0673739513099894	0.0430616181650901	-1.56459404409027	0.118589605775824	   
df.mm.trans2:probe6	0.0220306562570963	0.0430616181650901	0.511607719260223	0.609250987931545	   
df.mm.trans3:probe2	-0.121863248563640	0.0430616181650901	-2.82997373894402	0.00492615795056696	** 
df.mm.trans3:probe3	-0.613133324030872	0.0430616181650901	-14.2385110025414	1.50139270484304e-36	***
df.mm.trans3:probe4	-0.445316607729503	0.0430616181650901	-10.3413811813166	4.9966345578186e-22	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.23394109093349	0.139812297442642	30.2830378184042	1.95436508281793e-99	***
df.mm.trans1	-0.000343350137119976	0.116482830466943	-0.00294764589548172	0.997649816090724	   
df.mm.trans2	-0.089833951329773	0.116482830466943	-0.77122053928169	0.441101052453961	   
df.mm.exp2	0.162575657747417	0.160488870562022	1.01300269095350	0.311764691224399	   
df.mm.exp3	0.0741419529634978	0.160488870562022	0.46197566662322	0.644388271258434	   
df.mm.exp4	-0.0948763630052925	0.160488870562022	-0.591170980723096	0.554790748419133	   
df.mm.exp5	0.0708665658302075	0.160488870562022	0.44156685496033	0.659077705626601	   
df.mm.exp6	-0.0464765565675411	0.160488870562022	-0.289593642255585	0.772300087694987	   
df.mm.exp7	-0.0894527764051818	0.160488870562022	-0.557376820535429	0.577629356541703	   
df.mm.exp8	-0.0191650098047774	0.160488870562022	-0.119416441387261	0.90501454107979	   
df.mm.trans1:exp2	-0.111017148982374	0.135637851792067	-0.818482064671474	0.413643693141447	   
df.mm.trans2:exp2	-0.0519825702507065	0.135637851792067	-0.383245307736043	0.701772565718824	   
df.mm.trans1:exp3	-0.078121874093025	0.135637851792067	-0.575959240439652	0.565016081499458	   
df.mm.trans2:exp3	-0.0583182942886857	0.135637851792067	-0.429955897400142	0.667494603571286	   
df.mm.trans1:exp4	0.128675621543355	0.135637851792067	0.948670447395572	0.343448463478902	   
df.mm.trans2:exp4	0.0521532343772651	0.135637851792067	0.384503541512999	0.700840805087912	   
df.mm.trans1:exp5	-0.178364892009795	0.135637851792067	-1.31500823445087	0.189375484116519	   
df.mm.trans2:exp5	-0.0280367393739281	0.135637851792067	-0.206702915178194	0.836363115488343	   
df.mm.trans1:exp6	0.0237543605345382	0.135637851792067	0.175130763431388	0.861079029991803	   
df.mm.trans2:exp6	0.0438026836322844	0.135637851792067	0.322938494332938	0.746936330354376	   
df.mm.trans1:exp7	0.0406680450689013	0.135637851792067	0.299828141861502	0.764487770886654	   
df.mm.trans2:exp7	0.158317010463816	0.135637851792067	1.16720375892207	0.243929539372328	   
df.mm.trans1:exp8	-0.0222501689871349	0.135637851792067	-0.164041001041836	0.869794373073581	   
df.mm.trans2:exp8	0.0867382708957615	0.135637851792067	0.639484257158034	0.522929890982404	   
df.mm.trans1:probe2	0.0299579253288405	0.0742919110780577	0.403246125912201	0.687015520439809	   
df.mm.trans1:probe3	-0.150036569342606	0.0742919110780577	-2.01955458091480	0.0441979274020799	*  
df.mm.trans1:probe4	-0.155930348055502	0.0742919110780577	-2.09888729193771	0.0365490583286280	*  
df.mm.trans1:probe5	-0.140252968728719	0.0742919110780577	-1.88786325043324	0.0598784614516715	.  
df.mm.trans1:probe6	-0.0983561204033699	0.0742919110780577	-1.32391425898343	0.186402983001730	   
df.mm.trans2:probe2	0.000288010983730729	0.0742919110780577	0.00387674754292051	0.996909038478412	   
df.mm.trans2:probe3	-0.0419148080958246	0.0742919110780577	-0.564190737424767	0.572988805879732	   
df.mm.trans2:probe4	-0.0168542298855267	0.0742919110780577	-0.226864939142812	0.82066235602245	   
df.mm.trans2:probe5	0.0350765893028179	0.0742919110780577	0.472145470399373	0.637119734612457	   
df.mm.trans2:probe6	-0.0753315423777404	0.0742919110780577	-1.01399386938089	0.311292174031567	   
df.mm.trans3:probe2	0.00249380883142024	0.0742919110780577	0.0335677033371779	0.973241176846409	   
df.mm.trans3:probe3	-0.118416494044330	0.0742919110780577	-1.59393522559827	0.11186095516957	   
df.mm.trans3:probe4	-0.00737079829216703	0.0742919110780577	-0.0992140084325279	0.921025643044532	   
