chr19.12425_chr19_7256549_7305596_+_2.R 

fitVsDatCorrelation=0.882765924305744
cont.fitVsDatCorrelation=0.261646373249623

fstatistic=10083.8385875955,53,715
cont.fstatistic=2379.02433219513,53,715

residuals=-0.905918578020774,-0.0828991436217227,-0.00668613651893384,0.0790799679469394,0.859841495504441
cont.residuals=-0.691515023044077,-0.250606890950033,-0.0558350824234705,0.224642239551623,1.01421153030898

predictedValues:
Include	Exclude	Both
chr19.12425_chr19_7256549_7305596_+_2.R.tl.Lung	58.7770774555807	63.1104186735901	94.9578735011207
chr19.12425_chr19_7256549_7305596_+_2.R.tl.cerebhem	56.7456770927755	57.8037386290784	90.8646170263653
chr19.12425_chr19_7256549_7305596_+_2.R.tl.cortex	62.9128601105944	67.4125809410688	92.099121774259
chr19.12425_chr19_7256549_7305596_+_2.R.tl.heart	65.5961292112639	70.1290535944852	94.2459255863726
chr19.12425_chr19_7256549_7305596_+_2.R.tl.kidney	56.6554228218163	66.6822492678991	85.3321732796616
chr19.12425_chr19_7256549_7305596_+_2.R.tl.liver	58.1285157863932	68.2305019394044	80.8433758697282
chr19.12425_chr19_7256549_7305596_+_2.R.tl.stomach	58.761436990888	68.5153693972651	86.3152211228885
chr19.12425_chr19_7256549_7305596_+_2.R.tl.testicle	58.6019847274015	73.6399988997415	89.9229269052002


diffExp=-4.33334121800935,-1.05806153630283,-4.49972083047437,-4.5329243832213,-10.0268264460828,-10.1019861530112,-9.75393240637709,-15.0380141723401
diffExpScore=0.983428565815389
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,-1
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	68.0851259747428	63.0474110830841	67.8598658591944
cerebhem	65.4418570153734	64.7950944278737	74.7127086013079
cortex	73.2564520462687	65.3587009428948	74.4739775181701
heart	66.6268557709243	66.2023829028175	65.7426097137115
kidney	70.097586297845	67.0786382223852	72.2028028124036
liver	67.0060656845001	60.7619599941127	68.336891900929
stomach	63.6862692327272	67.7031462369495	72.0449036186862
testicle	70.579300045458	68.4378856180204	69.7883015128918
cont.diffExp=5.03771489165869,0.646762587499623,7.89775110337388,0.424472868106747,3.01894807545990,6.24410569038739,-4.01687700422227,2.14141442743758
cont.diffExpScore=1.31408690248045

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=0.393088977664858
cont.tran.correlation=0.109835740292942

tran.covariance=0.00151362343933745
cont.tran.covariance=0.000184454528618282

tran.mean=63.2314384712029
cont.tran.mean=66.7602957184986

weightedLogRatios:
wLogRatio
Lung	-0.292312002887054
cerebhem	-0.0747792677793881
cortex	-0.288502640043812
heart	-0.281777077893526
kidney	-0.671108041254714
liver	-0.66381888220607
stomach	-0.637366744853842
testicle	-0.955931842304896

cont.weightedLogRatios:
wLogRatio
Lung	0.321502582293237
cerebhem	0.0414786970080184
cortex	0.483330797585992
heart	0.0268172417858059
kidney	0.186122667404125
liver	0.406524278008174
stomach	-0.255942470156408
testicle	0.130676899824821

varWeightedLogRatios=0.0852713385710094
cont.varWeightedLogRatios=0.0567539625970815

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.4836770092251	0.0793619961735239	43.8960355988033	4.21194867885701e-205	***
df.mm.trans1	0.396039866498588	0.0698210271300465	5.67221484383116	2.04725142860016e-08	***
df.mm.trans2	0.586151056006915	0.063129879613578	9.28484355735798	1.90220907271979e-19	***
df.mm.exp2	-0.0789421920233874	0.0841194738188152	-0.938453231334053	0.348328438846746	   
df.mm.exp3	0.16451239688756	0.0841194738188152	1.95569930979244	0.0508896686336316	.  
df.mm.exp4	0.222741807296409	0.0841194738188151	2.64792202309982	0.00827735235759521	** 
df.mm.exp5	0.125170483261149	0.0841194738188152	1.48800839542523	0.137189561908135	   
df.mm.exp6	0.227829951662089	0.0841194738188152	2.70840913903969	0.00692234335006102	** 
df.mm.exp7	0.177333487161881	0.0841194738188152	2.10811455554085	0.0353684784370878	*  
df.mm.exp8	0.205799517896449	0.0841194738188152	2.44651456498314	0.0146643203254013	*  
df.mm.trans1:exp2	0.0437697312001008	0.0791374921984999	0.553084637687451	0.580378338259989	   
df.mm.trans2:exp2	-0.00889022182955348	0.0650337194173212	-0.136701728106691	0.89130504520298	   
df.mm.trans1:exp3	-0.0965137405170213	0.0791374921984999	-1.21957036842836	0.223029927916397	   
df.mm.trans2:exp3	-0.0985666051450078	0.0650337194173212	-1.51562306489817	0.130056639796325	   
df.mm.trans1:exp4	-0.112977058719866	0.0791374921984998	-1.42760473678502	0.153842254651522	   
df.mm.trans2:exp4	-0.117290509458716	0.0650337194173212	-1.80353377462641	0.0717252521482038	.  
df.mm.trans1:exp5	-0.16193471506521	0.0791374921984998	-2.04624521913116	0.0410975032735475	*  
df.mm.trans2:exp5	-0.0701175632789016	0.0650337194173212	-1.07817242973537	0.281320415585053	   
df.mm.trans1:exp6	-0.238925542642914	0.0791374921984999	-3.01911945912588	0.00262530322181559	** 
df.mm.trans2:exp6	-0.149824113885993	0.0650337194173212	-2.30379125211295	0.0215202933481308	*  
df.mm.trans1:exp7	-0.177599620605182	0.0791374921984999	-2.24419065693554	0.0251252869525687	*  
df.mm.trans2:exp7	-0.0951612659015216	0.0650337194173212	-1.46326039405607	0.143835590376914	   
df.mm.trans1:exp8	-0.208782892525468	0.0791374921984998	-2.63822982919119	0.00851531764965927	** 
df.mm.trans2:exp8	-0.0514970460760742	0.0650337194173212	-0.791851466246576	0.428709909520597	   
df.mm.trans1:probe2	0.325812283144646	0.0462063405900922	7.0512461922706	4.18868367328853e-12	***
df.mm.trans1:probe3	0.164937608960104	0.0462063405900922	3.56958821784452	0.000381449770565625	***
df.mm.trans1:probe4	0.0993234337655775	0.0462063405900922	2.14956286295641	0.0319246915348880	*  
df.mm.trans1:probe5	0.141765740772334	0.0462063405900922	3.06810145451624	0.00223559240690460	** 
df.mm.trans1:probe6	0.762025572272394	0.0462063405900922	16.4917966352824	5.03753961440287e-52	***
df.mm.trans1:probe7	0.0831273459915457	0.0462063405900922	1.79904629819074	0.0724329306040728	.  
df.mm.trans1:probe8	0.118734894916329	0.0462063405900922	2.56966670374648	0.0103811580328846	*  
df.mm.trans1:probe9	0.198156416554812	0.0462063405900922	4.28851136065299	2.04586939180540e-05	***
df.mm.trans1:probe10	0.376694581809583	0.0462063405900922	8.1524435174673	1.59696107257468e-15	***
df.mm.trans1:probe11	0.652341356287434	0.0462063405900922	14.1180051905541	4.22073572827647e-40	***
df.mm.trans1:probe12	0.599299007330614	0.0462063405900922	12.9700599458231	1.06301460818309e-34	***
df.mm.trans1:probe13	0.540235026159766	0.0462063405900922	11.6917942269509	5.08244779246595e-29	***
df.mm.trans1:probe14	0.789803595172683	0.0462063405900922	17.0929700358491	3.52470351561514e-55	***
df.mm.trans1:probe15	0.397863836324968	0.0462063405900922	8.61058961267927	4.61225885963668e-17	***
df.mm.trans1:probe16	-0.0519600203017872	0.0462063405900922	-1.12452143230163	0.261169254597623	   
df.mm.trans1:probe17	-0.0281345116711185	0.0462063405900922	-0.608888548883512	0.542791715417502	   
df.mm.trans1:probe18	-0.0196555166279529	0.0462063405900922	-0.425385701982371	0.670683526663386	   
df.mm.trans1:probe19	-0.0400678467036718	0.0462063405900922	-0.867150399533335	0.386150526444597	   
df.mm.trans1:probe20	-0.077708097794478	0.0462063405900922	-1.68176264993252	0.0930515399201024	.  
df.mm.trans1:probe21	0.0123169571952333	0.0462063405900922	0.266564221228858	0.789881562496119	   
df.mm.trans2:probe2	-0.0804561506920941	0.0462063405900922	-1.74123615210822	0.082072301910005	.  
df.mm.trans2:probe3	0.204721586885124	0.0462063405900922	4.43059511466748	1.08703754265699e-05	***
df.mm.trans2:probe4	0.229379594487889	0.0462063405900922	4.96424498366517	8.63082897157013e-07	***
df.mm.trans2:probe5	0.142469743828441	0.0462063405900922	3.08333752487187	0.00212566935564647	** 
df.mm.trans2:probe6	0.329521073576524	0.0462063405900922	7.13151202558511	2.43617521504775e-12	***
df.mm.trans3:probe2	0.487803522896983	0.0462063405900922	10.5570689361533	2.60051122825131e-24	***
df.mm.trans3:probe3	-0.0829074740031819	0.0462063405900922	-1.7942878173079	0.0731895942025301	.  
df.mm.trans3:probe4	-0.244189242827109	0.0462063405900922	-5.28475615486134	1.6733299278768e-07	***
df.mm.trans3:probe5	0.277120317395535	0.0462063405900922	5.99745216471344	3.18146999569150e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14148431431113	0.163037878082805	25.4019762953964	6.03163987129979e-102	***
df.mm.trans1	0.158208924717540	0.143437320855122	1.10298298779115	0.270405651448513	   
df.mm.trans2	-0.0390041007737296	0.129691314635233	-0.300745665840705	0.763695872712596	   
df.mm.exp2	-0.108459232158143	0.172811435928032	-0.62761605778978	0.530455868897185	   
df.mm.exp3	0.0162061397476998	0.172811435928032	0.0937793246185914	0.925310728128817	   
df.mm.exp4	0.0588759364843759	0.172811435928032	0.340694677804164	0.73343352489346	   
df.mm.exp5	0.0290741394221154	0.172811435928032	0.168241987377638	0.86644048857342	   
df.mm.exp6	-0.0599036750839968	0.172811435928032	-0.346641845560174	0.728962395000418	   
df.mm.exp7	-0.0553889406795125	0.172811435928032	-0.32051663931882	0.748670367989005	   
df.mm.exp8	0.0899960675511867	0.172811435928032	0.520776111070947	0.602683979407377	   
df.mm.trans1:exp2	0.0688625265925395	0.162576666754034	0.423569556243414	0.672007146322761	   
df.mm.trans2:exp2	0.135802129140234	0.133602481399905	1.01646412340011	0.30975224748628	   
df.mm.trans1:exp3	0.0570014119816024	0.162576666754034	0.350612502517604	0.725982346111766	   
df.mm.trans2:exp3	0.0197974349548766	0.133602481399905	0.148181641144958	0.882241198914832	   
df.mm.trans1:exp4	-0.080526975245437	0.162576666754034	-0.495316928641846	0.620528609685574	   
df.mm.trans2:exp4	-0.0100464789153439	0.133602481399905	-0.0751967988174736	0.940079165130255	   
df.mm.trans1:exp5	5.54469234742585e-05	0.162576666754034	0.000341050930501272	0.999727975895286	   
df.mm.trans2:exp5	0.0329044962906155	0.133602481399905	0.246286565532599	0.805531095723577	   
df.mm.trans1:exp6	0.0439280481498614	0.162576666754034	0.270198971518595	0.787085153746711	   
df.mm.trans2:exp6	0.0229806100219676	0.133602481399905	0.172007359303313	0.863480415615985	   
df.mm.trans1:exp7	-0.0114008484555022	0.162576666754034	-0.0701259823019425	0.944112994989374	   
df.mm.trans2:exp7	0.126634592500292	0.133602481399905	0.947846111639525	0.343528036622658	   
df.mm.trans1:exp8	-0.0540179413646741	0.162576666754034	-0.332261341330113	0.739789309983709	   
df.mm.trans2:exp8	-0.00795651319197684	0.133602481399905	-0.0595536333502748	0.952527788778921	   
df.mm.trans1:probe2	-0.113352117020423	0.0949243225599864	-1.19413142979021	0.232822568019858	   
df.mm.trans1:probe3	0.0237555683871220	0.0949243225599864	0.250257971260315	0.802459745842213	   
df.mm.trans1:probe4	-0.158876531012398	0.0949243225599865	-1.67371782834686	0.0946234396109398	.  
df.mm.trans1:probe5	-0.073436540620848	0.0949243225599864	-0.773632496291354	0.439403851680368	   
df.mm.trans1:probe6	-0.0395672790850739	0.0949243225599864	-0.41682972306776	0.676928069659779	   
df.mm.trans1:probe7	0.097334390399827	0.0949243225599864	1.02538936043834	0.305526283217847	   
df.mm.trans1:probe8	-0.147695347846823	0.0949243225599864	-1.55592733099031	0.120167870610222	   
df.mm.trans1:probe9	-0.132287805855451	0.0949243225599865	-1.39361337840312	0.16386739897361	   
df.mm.trans1:probe10	-0.211037016034584	0.0949243225599865	-2.2232132960572	0.0265132678166834	*  
df.mm.trans1:probe11	-0.135818039598582	0.0949243225599864	-1.43080335930503	0.152923469255002	   
df.mm.trans1:probe12	-0.123594124016952	0.0949243225599864	-1.30202798064582	0.193326109768825	   
df.mm.trans1:probe13	-0.241456653655104	0.0949243225599864	-2.54367528936030	0.011178712166342	*  
df.mm.trans1:probe14	-0.0972749748629111	0.0949243225599864	-1.02476343511895	0.30582139395502	   
df.mm.trans1:probe15	-0.0593568323098223	0.0949243225599864	-0.625306883515681	0.531969181983522	   
df.mm.trans1:probe16	-0.0705454497764073	0.0949243225599864	-0.743175699060974	0.457619426779135	   
df.mm.trans1:probe17	-0.112903243466058	0.0949243225599864	-1.18940267806189	0.234676073072549	   
df.mm.trans1:probe18	-0.0569543671425389	0.0949243225599864	-0.599997615011129	0.548697926434719	   
df.mm.trans1:probe19	-0.116601147419478	0.0949243225599864	-1.22835901563367	0.219716399127544	   
df.mm.trans1:probe20	-0.137528143811296	0.0949243225599864	-1.44881880747041	0.147826595720853	   
df.mm.trans1:probe21	-0.145100414372077	0.0949243225599864	-1.52859046510848	0.126808256372127	   
df.mm.trans2:probe2	0.206768052225492	0.0949243225599864	2.17824100977731	0.0297141159012197	*  
df.mm.trans2:probe3	-0.00702775023583584	0.0949243225599864	-0.0740352951309684	0.941003008004387	   
df.mm.trans2:probe4	0.118058248257793	0.0949243225599864	1.24370914718077	0.214014234575146	   
df.mm.trans2:probe5	0.0690547403807579	0.0949243225599864	0.727471511183232	0.467175295712531	   
df.mm.trans2:probe6	0.0686213630667751	0.0949243225599864	0.72290600781913	0.469974029290274	   
df.mm.trans3:probe2	0.00271838776428235	0.0949243225599864	0.0286374207470851	0.977161758032534	   
df.mm.trans3:probe3	-0.0317880747076771	0.0949243225599864	-0.334878078140499	0.737815276424952	   
df.mm.trans3:probe4	-0.166682246369573	0.0949243225599864	-1.75594875869922	0.0795252913704593	.  
df.mm.trans3:probe5	-0.0204698448343093	0.0949243225599864	-0.215643833764245	0.829326885112079