chr17.10612_chr17_31271308_31272654_+_0.R 

fitVsDatCorrelation=0.872392674542026
cont.fitVsDatCorrelation=0.264472726004931

fstatistic=10990.6405176500,61,899
cont.fstatistic=2812.54518261095,61,899

residuals=-0.515996362542629,-0.0873612694461988,-0.00386261674624806,0.081791771832429,0.892750753636648
cont.residuals=-0.798445031449192,-0.206169081975999,-0.0622333403707776,0.135209272763167,1.53289607585861

predictedValues:
Include	Exclude	Both
chr17.10612_chr17_31271308_31272654_+_0.R.tl.Lung	62.3610744811343	52.7669586146349	64.8401185471546
chr17.10612_chr17_31271308_31272654_+_0.R.tl.cerebhem	57.5273965142923	57.2099128990577	67.3152572797957
chr17.10612_chr17_31271308_31272654_+_0.R.tl.cortex	63.2851614173699	57.145477149638	63.2281966383238
chr17.10612_chr17_31271308_31272654_+_0.R.tl.heart	65.9184090782928	60.8340754325235	65.3181708526974
chr17.10612_chr17_31271308_31272654_+_0.R.tl.kidney	64.0903131126997	53.0552145991094	60.6940288684366
chr17.10612_chr17_31271308_31272654_+_0.R.tl.liver	65.3816723840458	58.2916986879975	62.2377330266685
chr17.10612_chr17_31271308_31272654_+_0.R.tl.stomach	79.191748634883	58.0185098444162	64.6436483623946
chr17.10612_chr17_31271308_31272654_+_0.R.tl.testicle	62.7762358442048	55.4105792841725	62.3659372005377


diffExp=9.5941158664994,0.317483615234664,6.1396842677319,5.08433364576929,11.0350985135902,7.08997369604828,21.1732387904668,7.36565656003231
diffExpScore=0.985465028595032
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=0,0,0,0,0,0,1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,0,1,0,1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	71.386552860167	66.4098398066177	67.4157251782543
cerebhem	70.4257182137084	68.3980753010894	73.9243546960945
cortex	67.0231950358636	70.7746959325456	69.0119759196589
heart	69.0354954389933	76.85283201214	67.809829705112
kidney	63.8540346584589	72.8083592501896	66.3833138667458
liver	73.7237939790798	87.053718575656	69.3572734384779
stomach	76.6655716549069	67.2302174380045	73.7285551666505
testicle	69.9927580584502	71.5404639033658	69.2561250867918
cont.diffExp=4.97671305354928,2.02764291261904,-3.75150089668205,-7.81733657314679,-8.95432459173068,-13.3299245965762,9.43535421690237,-1.54770584491555
cont.diffExpScore=2.59707874829166

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=0.294234941671137
cont.tran.correlation=0.0414845155364219

tran.covariance=0.00129130775596508
cont.tran.covariance=6.0669826142782e-05

tran.mean=60.8290273736545
cont.tran.mean=71.4484576324523

weightedLogRatios:
wLogRatio
Lung	0.676479035081454
cerebhem	0.0224103451472835
cortex	0.41806325168572
heart	0.332973076280602
kidney	0.768276365428239
liver	0.47323024706944
stomach	1.31173847525185
testicle	0.508855945710949

cont.weightedLogRatios:
wLogRatio
Lung	0.305820802080250
cerebhem	0.123865210916963
cortex	-0.230501200690705
heart	-0.460007805721464
kidney	-0.554085546497706
liver	-0.728524381842672
stomach	0.561276234989078
testicle	-0.0931576171177727

varWeightedLogRatios=0.141823362441969
cont.varWeightedLogRatios=0.19864817902219

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.89771960037009	0.0735001951272553	53.0300578606865	5.05799663964276e-279	***
df.mm.trans1	0.0259065842518051	0.0617913108683037	0.419259340638039	0.67512676700037	   
df.mm.trans2	0.0625969761493725	0.0553363566302565	1.13120884643038	0.258268868332589	   
df.mm.exp2	-0.0373003953806259	0.0702981145271346	-0.530603069961829	0.595824909593392	   
df.mm.exp3	0.119598820824320	0.0702981145271346	1.70130908387530	0.0892307511167395	.  
df.mm.exp4	0.190395609329669	0.0702981145271346	2.70840278733476	0.00688924255808285	** 
df.mm.exp5	0.098879104967738	0.0702981145271346	1.40656837858107	0.159900990199679	   
df.mm.exp6	0.187838257765718	0.0702981145271346	2.67202412225741	0.0076759548287238	** 
df.mm.exp7	0.336842390321422	0.0702981145271346	4.79162766437218	1.93469171215454e-06	***
df.mm.exp8	0.0944259207516544	0.0702981145271346	1.34322124265803	0.179539324895454	   
df.mm.trans1:exp2	-0.0433795835046412	0.0615608887697976	-0.704661423373141	0.48120346894721	   
df.mm.trans2:exp2	0.118142370168258	0.0454050680674889	2.60196438848311	0.0094216231628127	** 
df.mm.trans1:exp3	-0.104889210276448	0.0615608887697976	-1.70382871937853	0.0887586693443596	.  
df.mm.trans2:exp3	-0.0398837847181384	0.0454050680674889	-0.878399403759426	0.379961710061320	   
df.mm.trans1:exp4	-0.134919132482668	0.0615608887697976	-2.19163717709125	0.0286615985333824	*  
df.mm.trans2:exp4	-0.0481307371729071	0.0454050680674889	-1.06003006319398	0.289415629968877	   
df.mm.trans1:exp5	-0.0715271482830037	0.0615608887697976	-1.16189271650177	0.245587418848476	   
df.mm.trans2:exp5	-0.093431159822108	0.0454050680674890	-2.05772535531131	0.0399043825217658	*  
df.mm.trans1:exp6	-0.140537551687989	0.0615608887697976	-2.28290322794915	0.0226687622765972	*  
df.mm.trans2:exp6	-0.0882637751329212	0.0454050680674889	-1.94391901366006	0.0522169106406378	.  
df.mm.trans1:exp7	-0.0979115551666976	0.0615608887697976	-1.59048313179543	0.112077511736959	   
df.mm.trans2:exp7	-0.241965506453337	0.0454050680674889	-5.32904181739548	1.24886411172122e-07	***
df.mm.trans1:exp8	-0.0877906033519496	0.0615608887697976	-1.42607758117717	0.154193095034501	   
df.mm.trans2:exp8	-0.0455405944352005	0.045405068067489	-1.00298482908362	0.316138063481793	   
df.mm.trans1:probe2	0.267127801974263	0.0464774517900090	5.74747090656303	1.24009581247919e-08	***
df.mm.trans1:probe3	0.209498952699222	0.0464774517900090	4.50753956231861	7.42473193546119e-06	***
df.mm.trans1:probe4	0.46161628824747	0.0464774517900091	9.93204813235266	3.96500663453486e-22	***
df.mm.trans1:probe5	0.239137274656206	0.0464774517900091	5.14523205223595	3.28130274757124e-07	***
df.mm.trans1:probe6	0.212862909377287	0.046477451790009	4.57991781345993	5.30697427264187e-06	***
df.mm.trans1:probe7	0.209598110726568	0.0464774517900091	4.50967302754803	7.35209659282552e-06	***
df.mm.trans1:probe8	0.293270740505338	0.046477451790009	6.30995739246575	4.37596488640146e-10	***
df.mm.trans1:probe9	0.500775512740078	0.046477451790009	10.7745905477487	1.52396680783899e-25	***
df.mm.trans1:probe10	0.400767591026511	0.046477451790009	8.62283915299895	2.91848723497451e-17	***
df.mm.trans1:probe11	0.641610520245863	0.046477451790009	13.8047697439339	1.86119491999276e-39	***
df.mm.trans1:probe12	1.00001155559188	0.0464774517900091	21.5160581546092	3.70840897780568e-83	***
df.mm.trans1:probe13	0.749507631686195	0.0464774517900090	16.1262634421647	1.40536600964223e-51	***
df.mm.trans1:probe14	0.898861481770405	0.046477451790009	19.3397324326552	6.03251456368007e-70	***
df.mm.trans1:probe15	1.03206667900301	0.046477451790009	22.2057500842777	1.92845537537361e-87	***
df.mm.trans2:probe2	0.0380540618991925	0.0464774517900091	0.8187639475401	0.413138083818657	   
df.mm.trans2:probe3	0.171342934365761	0.0464774517900091	3.68658193955878	0.000240903073586072	***
df.mm.trans2:probe4	-0.0836919124343443	0.0464774517900090	-1.80069924686222	0.0720853589439737	.  
df.mm.trans2:probe5	-0.0420806463816569	0.0464774517900091	-0.905399172307951	0.365496533183915	   
df.mm.trans2:probe6	0.0555914249160493	0.046477451790009	1.19609450981130	0.231975047336968	   
df.mm.trans3:probe2	-0.0961882494207424	0.0464774517900091	-2.06956805324296	0.0387780050915053	*  
df.mm.trans3:probe3	0.069017470126486	0.0464774517900090	1.48496674125586	0.137903180738143	   
df.mm.trans3:probe4	0.194655379305285	0.0464774517900091	4.18816806447914	3.08912198986657e-05	***
df.mm.trans3:probe5	0.123537231903835	0.0464774517900091	2.65800355109810	0.00800008035452663	** 
df.mm.trans3:probe6	0.060022300507232	0.0464774517900090	1.29142838506767	0.196887112738179	   
df.mm.trans3:probe7	0.323923294786947	0.0464774517900091	6.96947191189554	6.1519578333114e-12	***
df.mm.trans3:probe8	-0.00109197301711592	0.0464774517900091	-0.0234946834445569	0.981260893051174	   
df.mm.trans3:probe9	0.305959838585764	0.0464774517900091	6.58297360982974	7.82917017672468e-11	***
df.mm.trans3:probe10	0.100249266039006	0.0464774517900091	2.15694411328627	0.0312744891344089	*  
df.mm.trans3:probe11	0.230756736424450	0.0464774517900091	4.96491798791032	8.22245705485342e-07	***
df.mm.trans3:probe12	0.217219185244123	0.0464774517900090	4.67364661525649	3.41171511486021e-06	***
df.mm.trans3:probe13	0.330007755314094	0.0464774517900090	7.10038400567033	2.52670610347811e-12	***
df.mm.trans3:probe14	0.0736577382793218	0.0464774517900091	1.58480586698506	0.113362125671548	   
df.mm.trans3:probe15	0.692335029692263	0.0464774517900091	14.8961486275177	5.13119579672408e-45	***
df.mm.trans3:probe16	0.22335415262889	0.0464774517900091	4.80564540495964	1.80710238160706e-06	***
df.mm.trans3:probe17	0.213343762625397	0.0464774517900091	4.59026376035655	5.05632428003245e-06	***
df.mm.trans3:probe18	0.124433292373575	0.0464774517900091	2.67728301748944	0.00755745382745848	** 
df.mm.trans3:probe19	0.343133712150811	0.0464774517900091	7.38279959282476	3.53116987297095e-13	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14255098839412	0.145012802598499	28.5667948909566	3.10395104637787e-128	***
df.mm.trans1	0.122445687443272	0.121911664992643	1.00438040486660	0.315465547717982	   
df.mm.trans2	0.0685710735932929	0.109176310983261	0.62807648450227	0.530113351453898	   
df.mm.exp2	-0.0762155889054791	0.138695231860546	-0.549518450512498	0.582786181262045	   
df.mm.exp3	-0.0228162145345757	0.138695231860546	-0.164506120567409	0.869369708377303	   
df.mm.exp4	0.106729504387353	0.138695231860546	0.769525404410918	0.441783606372061	   
df.mm.exp5	-0.00409163384008769	0.138695231860546	-0.0295008976530764	0.976471650657887	   
df.mm.exp6	0.274503508061237	0.138695231860546	1.97918489611266	0.0480999290030344	*  
df.mm.exp7	-0.00589107715019081	0.138695231860546	-0.0424749796454005	0.966129488667614	   
df.mm.exp8	0.0277669589448086	0.138695231860546	0.200201251134051	0.841368466853302	   
df.mm.trans1:exp2	0.0626645845771924	0.121457051855533	0.515940273700441	0.606022989272917	   
df.mm.trans2:exp2	0.105715038903061	0.0895822951387052	1.18008852909357	0.238277165221462	   
df.mm.trans1:exp3	-0.0402545477372829	0.121457051855533	-0.331430304970382	0.740396719690716	   
df.mm.trans2:exp3	0.0864725139061591	0.0895822951387052	0.965285760677029	0.334661288658957	   
df.mm.trans1:exp4	-0.140218221726488	0.121457051855533	-1.15446752234090	0.248615332959761	   
df.mm.trans2:exp4	0.0393175807226377	0.0895822951387052	0.438899010811903	0.660840174302685	   
df.mm.trans1:exp5	-0.107418112355049	0.121457051855533	-0.884412314591803	0.376710143518591	   
df.mm.trans2:exp5	0.0960771719540438	0.0895822951387052	1.07250179073088	0.283782521257873	   
df.mm.trans1:exp6	-0.242287428181386	0.121457051855533	-1.9948403528646	0.0463618131686394	*  
df.mm.trans2:exp6	-0.00382336042295143	0.0895822951387052	-0.0426798667865286	0.965966206069566	   
df.mm.trans1:exp7	0.077234298224752	0.121457051855533	0.635898015346349	0.525004739962547	   
df.mm.trans2:exp7	0.0181686525045848	0.0895822951387052	0.202815215623280	0.839325356338513	   
df.mm.trans1:exp8	-0.0474846948717175	0.121457051855533	-0.390958731059915	0.69592037346029	   
df.mm.trans2:exp8	0.0466510240342359	0.0895822951387052	0.520761652310912	0.602661054762888	   
df.mm.trans1:probe2	-0.0888988216817001	0.0916980632505369	-0.969473274902342	0.332569855811013	   
df.mm.trans1:probe3	0.103040522141354	0.0916980632505369	1.12369354911922	0.261443022274599	   
df.mm.trans1:probe4	-0.135146993398874	0.0916980632505369	-1.47382603959286	0.140878493621050	   
df.mm.trans1:probe5	-0.021742193158611	0.0916980632505369	-0.237106350863781	0.812628304594088	   
df.mm.trans1:probe6	0.0706153465205733	0.0916980632505369	0.770085474189771	0.441451498489535	   
df.mm.trans1:probe7	0.0285394798398199	0.0916980632505369	0.311233180158282	0.755695514641188	   
df.mm.trans1:probe8	0.0435510938978447	0.0916980632505369	0.474940171624505	0.634944973952197	   
df.mm.trans1:probe9	-0.0543169026538794	0.0916980632505369	-0.592345145889015	0.553768439164151	   
df.mm.trans1:probe10	-0.0314439761759394	0.0916980632505369	-0.342907745936011	0.731748028163184	   
df.mm.trans1:probe11	0.0170363071965366	0.0916980632505369	0.185786990396843	0.852653716915954	   
df.mm.trans1:probe12	-0.00554147481771589	0.0916980632505369	-0.0604317541862962	0.951825199889632	   
df.mm.trans1:probe13	0.0941677189216765	0.0916980632505369	1.02693247363788	0.304728487039366	   
df.mm.trans1:probe14	0.0411030317663498	0.0916980632505369	0.44824318321804	0.65408570637134	   
df.mm.trans1:probe15	0.0448734378902862	0.0916980632505369	0.48936080326673	0.624705655624408	   
df.mm.trans2:probe2	-0.165492973927515	0.0916980632505369	-1.80475975239908	0.0714468065320062	.  
df.mm.trans2:probe3	-0.0399701307048476	0.0916980632505369	-0.435888494129276	0.663022277348951	   
df.mm.trans2:probe4	-0.103375023221368	0.0916980632505369	-1.12734140239066	0.259898960362647	   
df.mm.trans2:probe5	-0.0720409284394916	0.0916980632505369	-0.785631952145618	0.432290144536858	   
df.mm.trans2:probe6	-0.00104160934064503	0.0916980632505369	-0.0113591203971141	0.99093944829719	   
df.mm.trans3:probe2	-0.0893939421201468	0.0916980632505369	-0.974872739415501	0.329885628367490	   
df.mm.trans3:probe3	-0.140713620693764	0.0916980632505369	-1.53453209049036	0.125250639801626	   
df.mm.trans3:probe4	-0.177802973812789	0.091698063250537	-1.93900468025149	0.0528133965291824	.  
df.mm.trans3:probe5	-0.156506688792731	0.0916980632505369	-1.70676111626397	0.0882117927657746	.  
df.mm.trans3:probe6	-0.0768814598191762	0.0916980632505369	-0.838419668789745	0.402017907829404	   
df.mm.trans3:probe7	-0.160140148079335	0.0916980632505369	-1.74638528233471	0.0810855643223504	.  
df.mm.trans3:probe8	-0.177759865671736	0.0916980632505369	-1.93853457063822	0.0528707546567016	.  
df.mm.trans3:probe9	-0.194680999999819	0.091698063250537	-2.12306555993351	0.0340210360503536	*  
df.mm.trans3:probe10	0.0381851241936942	0.0916980632505369	0.416422363135032	0.677200365291473	   
df.mm.trans3:probe11	-0.0894887785761051	0.0916980632505369	-0.975906964704417	0.32937309364398	   
df.mm.trans3:probe12	-0.147591228801729	0.0916980632505369	-1.60953485351683	0.107850503455806	   
df.mm.trans3:probe13	-0.180873415183987	0.0916980632505369	-1.97248893566929	0.0488599068967856	*  
df.mm.trans3:probe14	-0.113602343762022	0.0916980632505369	-1.23887397110709	0.215715532408903	   
df.mm.trans3:probe15	-0.185822635426397	0.0916980632505369	-2.02646194302592	0.0430122640719039	*  
df.mm.trans3:probe16	-0.141936203423684	0.0916980632505369	-1.54786479007617	0.122006789660621	   
df.mm.trans3:probe17	0.000758481666279709	0.0916980632505369	0.0082715123896062	0.993402198325689	   
df.mm.trans3:probe18	-0.117666183894762	0.0916980632505369	-1.28319159340667	0.199755648602375	   
df.mm.trans3:probe19	-0.219182619830476	0.0916980632505369	-2.39026444028187	0.0170412482959875	*