chr7.21769_chr7_25950498_25954385_+_2.R 

fitVsDatCorrelation=0.868075784949885
cont.fitVsDatCorrelation=0.248863215639051

fstatistic=13371.2306714572,61,899
cont.fstatistic=3501.95888113403,61,899

residuals=-0.518788659390663,-0.0909474037858023,-0.0054173604701182,0.0850338342759664,0.663326374370367
cont.residuals=-0.643450756920842,-0.208944412802882,-0.00135754135133052,0.174560428174068,1.04600491031786

predictedValues:
Include	Exclude	Both
chr7.21769_chr7_25950498_25954385_+_2.R.tl.Lung	69.9622519119818	67.9251089277809	59.4825523317184
chr7.21769_chr7_25950498_25954385_+_2.R.tl.cerebhem	60.398117709483	52.1259407442224	56.4755425096497
chr7.21769_chr7_25950498_25954385_+_2.R.tl.cortex	61.8864817321182	56.496401292688	59.3641067483471
chr7.21769_chr7_25950498_25954385_+_2.R.tl.heart	64.0759265604808	61.91361198055	63.7773373640518
chr7.21769_chr7_25950498_25954385_+_2.R.tl.kidney	88.9098575870089	76.5768571039836	108.438400426752
chr7.21769_chr7_25950498_25954385_+_2.R.tl.liver	67.535844363762	66.988834974751	63.1355982112073
chr7.21769_chr7_25950498_25954385_+_2.R.tl.stomach	62.8970391771463	57.9483108765416	62.5074791931386
chr7.21769_chr7_25950498_25954385_+_2.R.tl.testicle	62.6865476525944	57.2542444108522	61.795507406436


diffExp=2.03714298420093,8.27217696526058,5.39008043943021,2.16231457993079,12.3330004830253,0.547009389011052,4.94872830060465,5.43230324174225
diffExpScore=0.976259863174607
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	68.6160801523781	64.4020925679282	69.4556316834305
cerebhem	71.4411459787875	69.8984171924052	68.6145773041226
cortex	70.3106716093258	71.6799448743695	71.9039232743688
heart	63.3392247048634	65.9606272836462	74.590081909349
kidney	71.3929448837529	61.6733998648235	69.8636041294371
liver	70.233325244972	61.1937159759984	67.0532513880277
stomach	69.9086074742676	66.7402654316903	75.9416313027093
testicle	74.5934790095085	69.7856846760034	68.9748483681381
cont.diffExp=4.21398758444994,1.54272878638227,-1.36927326504366,-2.62140257878281,9.71954501892938,9.03960926897354,3.16834204257727,4.8077943335051
cont.diffExpScore=1.23664531076431

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=0.913966997662706
cont.tran.correlation=0.231518268463699

tran.covariance=0.0141733508091544
cont.tran.covariance=0.000582030745500496

tran.mean=64.7238360628716
cont.tran.mean=68.198101682795

weightedLogRatios:
wLogRatio
Lung	0.125090828026800
cerebhem	0.593203533090509
cortex	0.371765534050036
heart	0.142220416213828
kidney	0.65897884717934
liver	0.0342264655161163
stomach	0.33602848754859
testicle	0.370994452924364

cont.weightedLogRatios:
wLogRatio
Lung	0.265998797041956
cerebhem	0.092955374637343
cortex	-0.0822138887306577
heart	-0.169057454748208
kidney	0.613926667182343
liver	0.576317967747414
stomach	0.195910454268125
testicle	0.285067663732606

varWeightedLogRatios=0.0493840483908646
cont.varWeightedLogRatios=0.0782664723584559

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87206229525613	0.0669817264184084	57.8077410407263	4.53669255758535e-305	***
df.mm.trans1	0.208381267131557	0.0576163679646411	3.6167025880465	0.000314991168077056	***
df.mm.trans2	0.321345444402619	0.0506807070537833	6.34058723887971	3.61907957933349e-10	***
df.mm.exp2	-0.359865565716858	0.0646897649319488	-5.56294440233974	3.49943800801333e-08	***
df.mm.exp3	-0.304889636750879	0.0646897649319488	-4.71310472486044	2.82604137565759e-06	***
df.mm.exp4	-0.250267688269429	0.0646897649319488	-3.86873701786831	0.000117296581652457	***
df.mm.exp5	-0.240942914600163	0.0646897649319488	-3.72459097437788	0.000207824147090485	***
df.mm.exp6	-0.108778883806186	0.0646897649319488	-1.68154705648748	0.0930040183564578	.  
df.mm.exp7	-0.314914270294342	0.0646897649319488	-4.86806947939323	1.33081035109937e-06	***
df.mm.exp8	-0.318860580370641	0.0646897649319488	-4.92907310308006	9.83601772516256e-07	***
df.mm.trans1:exp2	0.212867668224750	0.059505928958937	3.57725140921071	0.000365763298106977	***
df.mm.trans2:exp2	0.0951225345647671	0.0427098189269566	2.22718187420668	0.0261820556398605	*  
df.mm.trans1:exp3	0.182235565575354	0.059505928958937	3.06247744995476	0.00226048230581283	** 
df.mm.trans2:exp3	0.120660819981019	0.0427098189269566	2.82513068452423	0.00483077023462054	** 
df.mm.trans1:exp4	0.162380582779559	0.059505928958937	2.72881350850958	0.00648034921818036	** 
df.mm.trans2:exp4	0.157601987581179	0.0427098189269566	3.69006452241611	0.000237677720061096	***
df.mm.trans1:exp5	0.480610096769203	0.059505928958937	8.0766758065546	2.13122819437208e-15	***
df.mm.trans2:exp5	0.360832060158446	0.0427098189269566	8.44845680042684	1.17789059848795e-16	***
df.mm.trans1:exp6	0.0734815301536972	0.059505928958937	1.23486065068246	0.217205015955225	   
df.mm.trans2:exp6	0.094899088183921	0.0427098189269566	2.22195014093175	0.0265349351031784	*  
df.mm.trans1:exp7	0.208457522790359	0.059505928958937	3.50313870293847	0.000482475749979813	***
df.mm.trans2:exp7	0.156059932750521	0.0427098189269566	3.65395912863547	0.000273178985165339	***
df.mm.trans1:exp8	0.209051615797858	0.059505928958937	3.51312246452144	0.000464941744569127	***
df.mm.trans2:exp8	0.14795659927873	0.0427098189269566	3.46422913971536	0.00055687704853456	***
df.mm.trans1:probe2	0.0227120023406056	0.0420770458876693	0.539771789142197	0.589488093785845	   
df.mm.trans1:probe3	0.456599095955733	0.0420770458876693	10.8515007725278	7.25581780170822e-26	***
df.mm.trans1:probe4	0.00425754105011613	0.0420770458876693	0.101184409701248	0.91942663424987	   
df.mm.trans1:probe5	-0.00202574153302958	0.0420770458876693	-0.0481436253494979	0.961612471522312	   
df.mm.trans1:probe6	0.0669379992211269	0.0420770458876693	1.59084360151679	0.111996336903456	   
df.mm.trans1:probe7	0.321493310068003	0.0420770458876693	7.64058653086711	5.53456279001586e-14	***
df.mm.trans1:probe8	0.154442859974375	0.0420770458876693	3.67047773236463	0.000256359904628685	***
df.mm.trans1:probe9	0.555383085072564	0.0420770458876693	13.1991938444357	1.72942445803084e-36	***
df.mm.trans1:probe10	-0.0226780792605981	0.0420770458876693	-0.538965575699883	0.590044045548816	   
df.mm.trans1:probe11	0.361833894027569	0.0420770458876693	8.5993179034844	3.52764316621057e-17	***
df.mm.trans1:probe12	0.243183530738618	0.0420770458876693	5.77948203369199	1.03272771457752e-08	***
df.mm.trans1:probe13	0.329968433638208	0.0420770458876693	7.84200569876284	1.25311649520605e-14	***
df.mm.trans1:probe14	0.311792810421864	0.0420770458876693	7.4100451646306	2.91048700794211e-13	***
df.mm.trans1:probe15	0.289012533819020	0.0420770458876693	6.8686507743576	1.20898865376685e-11	***
df.mm.trans1:probe16	0.359335428955541	0.0420770458876693	8.53993956502647	5.68187099101683e-17	***
df.mm.trans1:probe17	0.292750508356855	0.0420770458876693	6.95748720426797	6.66935486372037e-12	***
df.mm.trans1:probe18	0.246332055083408	0.0420770458876693	5.8543096333575	6.70988544209255e-09	***
df.mm.trans1:probe19	0.431834140151546	0.0420770458876693	10.2629386412818	1.91652411020897e-23	***
df.mm.trans1:probe20	0.551475325570262	0.0420770458876693	13.1063223174579	4.84536101289346e-36	***
df.mm.trans1:probe21	0.355926778519619	0.0420770458876693	8.45892982767414	1.08393070997934e-16	***
df.mm.trans1:probe22	0.364849865345087	0.0420770458876693	8.6709952575831	1.9771067046049e-17	***
df.mm.trans2:probe2	-0.0054553768914204	0.0420770458876693	-0.129652088836852	0.896870692095574	   
df.mm.trans2:probe3	0.159347708594563	0.0420770458876693	3.78704600650827	0.000162579003836414	***
df.mm.trans2:probe4	0.0292337930886533	0.0420770458876693	0.694768191823568	0.487380102100656	   
df.mm.trans2:probe5	0.192484641645098	0.0420770458876693	4.57457593764932	5.44101320642426e-06	***
df.mm.trans2:probe6	0.0743535809673635	0.0420770458876693	1.76708177579436	0.0775537749552691	.  
df.mm.trans3:probe2	0.106486755851168	0.0420770458876693	2.53075646364172	0.0115512636770364	*  
df.mm.trans3:probe3	-0.0253180919494278	0.0420770458876693	-0.601707924482582	0.547520278710496	   
df.mm.trans3:probe4	-0.151770888396224	0.0420770458876693	-3.60697585095203	0.000326856115554329	***
df.mm.trans3:probe5	-0.507333238220027	0.0420770458876693	-12.0572446928529	3.87959414899674e-31	***
df.mm.trans3:probe6	-0.268104992588068	0.0420770458876693	-6.37176367618138	2.98053315331739e-10	***
df.mm.trans3:probe7	-0.59414232892878	0.0420770458876693	-14.1203432036301	4.88964691796043e-41	***
df.mm.trans3:probe8	-0.670649368040517	0.0420770458876693	-15.9386039084329	1.47167054565804e-50	***
df.mm.trans3:probe9	-0.444633154372296	0.0420770458876693	-10.5671190786375	1.10589491136900e-24	***
df.mm.trans3:probe10	-0.568305875940121	0.0420770458876693	-13.5063159485411	5.53927403539205e-38	***
df.mm.trans3:probe11	-0.197395414786594	0.0420770458876693	-4.69128501353374	3.13677952675752e-06	***
df.mm.trans3:probe12	-0.472105106994787	0.0420770458876693	-11.2200154985961	1.96201000540231e-27	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0398902400183	0.130681401812114	30.9140412024859	1.57308833124198e-143	***
df.mm.trans1	0.210765026716215	0.112409579978706	1.87497388350834	0.0611203128632008	.  
df.mm.trans2	0.107753553400232	0.0988780999947069	1.08976156910377	0.276110204607234	   
df.mm.exp2	0.134427091348528	0.126209783118995	1.06510833016633	0.287112887589923	   
df.mm.exp3	0.096818876927682	0.126209783118995	0.767126561309417	0.443207682519375	   
df.mm.exp4	-0.127429621588080	0.126209783118995	-1.00966516571806	0.312927405066576	   
df.mm.exp5	-0.00947794005769369	0.126209783118995	-0.0750967145610063	0.940154468840843	   
df.mm.exp6	0.00739543852675405	0.126209783118995	0.0585963967609497	0.953286602602095	   
df.mm.exp7	-0.0349526861578752	0.126209783118995	-0.276941179155031	0.781888934679017	   
df.mm.exp8	0.170755185016068	0.126209783118995	1.35294729771521	0.176412606465521	   
df.mm.trans1:exp2	-0.0940800255895001	0.116096114989786	-0.810363254599665	0.417945887266301	   
df.mm.trans2:exp2	-0.0525302121238383	0.0833268908844118	-0.630411282195881	0.528585740335513	   
df.mm.trans1:exp3	-0.0724222004625264	0.116096114989786	-0.623812437383438	0.532909017607937	   
df.mm.trans2:exp3	0.0102459968137096	0.0833268908844118	0.122961467840226	0.902165093453206	   
df.mm.trans1:exp4	0.0474075105929471	0.116096114989786	0.408347088936765	0.683116113694718	   
df.mm.trans2:exp4	0.151341503422379	0.0833268908844118	1.81623845335013	0.0696667525438344	.  
df.mm.trans1:exp5	0.049150081552167	0.116096114989786	0.423356815656502	0.672136206821485	   
df.mm.trans2:exp5	-0.0338154683686427	0.0833268908844119	-0.405816993886767	0.684973638390396	   
df.mm.trans1:exp6	0.0159005666341318	0.116096114989786	0.136960368015163	0.891092787205722	   
df.mm.trans2:exp6	-0.058497060284407	0.0833268908844118	-0.702018996071174	0.482849023738992	   
df.mm.trans1:exp7	0.0536145558317045	0.116096114989786	0.461811799959209	0.644328015521968	   
df.mm.trans2:exp7	0.0706150107005155	0.0833268908844118	0.847445643909482	0.396972322375653	   
df.mm.trans1:exp8	-0.0872290061852445	0.116096114989786	-0.751351638191499	0.452637696484702	   
df.mm.trans2:exp8	-0.0904724127565877	0.0833268908844118	-1.08575289196963	0.277879383299535	   
df.mm.trans1:probe2	-0.0112267653165460	0.082092350178691	-0.136757752605554	0.891252897783298	   
df.mm.trans1:probe3	-0.0424010294011967	0.082092350178691	-0.516504026366672	0.605629452280842	   
df.mm.trans1:probe4	-0.0313775124328444	0.082092350178691	-0.382222123797707	0.702386949744895	   
df.mm.trans1:probe5	-0.0166798249225573	0.082092350178691	-0.203183669199995	0.83903745482731	   
df.mm.trans1:probe6	-0.107563115577674	0.082092350178691	-1.3102696578127	0.190439295844384	   
df.mm.trans1:probe7	0.0217957153441002	0.082092350178691	0.265502392082299	0.790683337061469	   
df.mm.trans1:probe8	-0.0746068135365381	0.082092350178691	-0.908815661558488	0.363691005731139	   
df.mm.trans1:probe9	-0.0165268099924624	0.082092350178691	-0.201319732672878	0.840494114508989	   
df.mm.trans1:probe10	0.0316934626528563	0.082092350178691	0.386070840752749	0.699535551184523	   
df.mm.trans1:probe11	0.0553191939250224	0.082092350178691	0.673865394334657	0.500570112339355	   
df.mm.trans1:probe12	-0.066240720967818	0.082092350178691	-0.806904916519399	0.41993468300161	   
df.mm.trans1:probe13	-0.0597030088158378	0.082092350178691	-0.727266410157364	0.467252114360135	   
df.mm.trans1:probe14	-0.0213250274906859	0.082092350178691	-0.259768753656919	0.795101631865939	   
df.mm.trans1:probe15	0.0094692897892712	0.082092350178691	0.115349234961106	0.90819407368928	   
df.mm.trans1:probe16	-0.0792205242201557	0.082092350178691	-0.965017130679239	0.334795743741305	   
df.mm.trans1:probe17	-0.0794949299064302	0.082092350178691	-0.968359776926754	0.333125159785669	   
df.mm.trans1:probe18	-0.0277396035226847	0.082092350178691	-0.337907289318721	0.735511942721229	   
df.mm.trans1:probe19	-0.0505278772944072	0.082092350178691	-0.615500435599941	0.538380062856835	   
df.mm.trans1:probe20	-0.113531267771591	0.082092350178691	-1.38297012479807	0.167017409665204	   
df.mm.trans1:probe21	-0.0385480050095984	0.082092350178691	-0.469568783518692	0.638776999984665	   
df.mm.trans1:probe22	-0.0339288927275779	0.082092350178691	-0.413301515350999	0.67948428291224	   
df.mm.trans2:probe2	0.0226768130049274	0.082092350178691	0.27623539776321	0.782430760522797	   
df.mm.trans2:probe3	0.108609645769265	0.082092350178691	1.32301786381866	0.18616584309644	   
df.mm.trans2:probe4	0.107354381979174	0.082092350178691	1.30772698973163	0.19130023602546	   
df.mm.trans2:probe5	-0.0235184830813384	0.082092350178691	-0.286488120149388	0.77457029926292	   
df.mm.trans2:probe6	0.099919626512473	0.082092350178691	1.21716123725265	0.223862286267599	   
df.mm.trans3:probe2	-0.144235444352910	0.082092350178691	-1.7569900732401	0.0792598891774623	.  
df.mm.trans3:probe3	-0.206556814418597	0.082092350178691	-2.51615179695774	0.0120375793157725	*  
df.mm.trans3:probe4	-0.124477872672985	0.082092350178691	-1.51631513048455	0.129791216181225	   
df.mm.trans3:probe5	-0.186576872494082	0.082092350178691	-2.27276807263965	0.0232754573674894	*  
df.mm.trans3:probe6	-0.0509916287130601	0.082092350178691	-0.621149578518173	0.534658667425859	   
df.mm.trans3:probe7	-0.147785658217573	0.082092350178691	-1.80023665902958	0.0721584013503034	.  
df.mm.trans3:probe8	-0.25088456764474	0.082092350178691	-3.05612602268832	0.00230841169918718	** 
df.mm.trans3:probe9	-0.0645070422915344	0.082092350178691	-0.78578627790679	0.432199758521623	   
df.mm.trans3:probe10	-0.0871573082804163	0.082092350178691	-1.06169829576934	0.288657798848133	   
df.mm.trans3:probe11	-0.071573782945616	0.082092350178691	-0.871869093646617	0.383512577246039	   
df.mm.trans3:probe12	-0.0779009815816953	0.082092350178691	-0.948943250036424	0.342904493973364