chr17.10673_chr17_25191967_25197030_-_2.R 

fitVsDatCorrelation=0.932056155061386
cont.fitVsDatCorrelation=0.227447463006043

fstatistic=7273.71823442255,64,968
cont.fstatistic=993.889805952583,64,968

residuals=-0.96976713321854,-0.108168856392441,-0.00750103016750015,0.110412800547484,1.31718976796942
cont.residuals=-1.02957380926837,-0.428900191250056,-0.160266767680914,0.335038263513245,1.70524783194786

predictedValues:
Include	Exclude	Both
chr17.10673_chr17_25191967_25197030_-_2.R.tl.Lung	72.73388423006	268.825021023276	104.344057530672
chr17.10673_chr17_25191967_25197030_-_2.R.tl.cerebhem	74.7450781310638	174.911849801745	153.058869165065
chr17.10673_chr17_25191967_25197030_-_2.R.tl.cortex	93.5199005698862	276.11968900164	144.780008091477
chr17.10673_chr17_25191967_25197030_-_2.R.tl.heart	84.2906475538644	301.784601117824	121.929812730076
chr17.10673_chr17_25191967_25197030_-_2.R.tl.kidney	80.833123844705	247.285810979177	183.348335196234
chr17.10673_chr17_25191967_25197030_-_2.R.tl.liver	73.9079892540984	230.601063629206	101.424327080509
chr17.10673_chr17_25191967_25197030_-_2.R.tl.stomach	77.8772362759952	417.585792683572	120.652716692676
chr17.10673_chr17_25191967_25197030_-_2.R.tl.testicle	72.2122556811866	264.502311490579	103.654524160929


diffExp=-196.091136793216,-100.166771670681,-182.599788431754,-217.493953563960,-166.452687134472,-156.693074375107,-339.708556407577,-192.290055809392
diffExpScore=0.999355875967203
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	97.1341491290557	125.877791725880	82.755634970511
cerebhem	98.3001579352062	101.616750996506	95.6783204613609
cortex	86.9201900026366	84.4732842550945	127.251325679490
heart	93.2108246009975	91.068408641063	106.532412057441
kidney	100.978069593023	104.167325513715	109.369272822458
liver	95.0702501007086	110.519737695865	99.8242945537832
stomach	109.386152351635	121.715004335038	94.3788465519586
testicle	97.3666257784379	109.667633731301	99.188436706896
cont.diffExp=-28.7436425968238,-3.31659306129934,2.44690574754210,2.14241595993444,-3.18925592069176,-15.4494875951567,-12.3288519834027,-12.3010079528632
cont.diffExpScore=1.11400471749812

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

tran.correlation=0.198292500939444
cont.tran.correlation=0.710323733822228

tran.covariance=0.00551570101953285
cont.tran.covariance=0.00655627281514889

tran.mean=175.733515954242
cont.tran.mean=101.717022274135

weightedLogRatios:
wLogRatio
Lung	-6.45839882703991
cerebhem	-4.02924732425418
cortex	-5.49937705110525
heart	-6.46903496901821
kidney	-5.5365209480511
liver	-5.54341561321868
stomach	-8.72394012678003
testicle	-6.39867533245782

cont.weightedLogRatios:
wLogRatio
Lung	-1.21980520331792
cerebhem	-0.152793900106717
cortex	0.127090192239075
heart	0.105178348013012
kidney	-0.143984542702981
liver	-0.69716200729634
stomach	-0.507107055030887
testicle	-0.551782774166855

varWeightedLogRatios=1.78749511120572
cont.varWeightedLogRatios=0.207342060979696

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.71689214474454	0.106933886677898	53.4619316883595	3.77919689923111e-291	***
df.mm.trans1	-1.48287757802855	0.095005267968207	-15.6083721433719	3.71151961021355e-49	***
df.mm.trans2	-0.116078651553925	0.084572140295255	-1.37254007228239	0.170213292422688	   
df.mm.exp2	-0.785631478831876	0.112177068706367	-7.00349445650369	4.66699405697547e-12	***
df.mm.exp3	-0.0493810862256305	0.112177068706367	-0.440206601893741	0.659885753165399	   
df.mm.exp4	0.107364501348652	0.112177068706367	0.9570984746418	0.338756522434021	   
df.mm.exp5	-0.541630550277787	0.112177068706367	-4.82835357104535	1.59923206989992e-06	***
df.mm.exp6	-0.108977186013694	0.112177068706367	-0.971474716449859	0.331554617678370	   
df.mm.exp7	0.363533035644485	0.112177068706367	3.24070721259496	0.00123299797796571	** 
df.mm.exp8	-0.016778081203327	0.112177068706367	-0.149567834110954	0.881136742949799	   
df.mm.trans1:exp2	0.812907485633364	0.10768215481343	7.54913836040713	1.01048918246683e-13	***
df.mm.trans2:exp2	0.355852921948655	0.0855162982088346	4.16122925573376	3.44696082867838e-05	***
df.mm.trans1:exp3	0.300747981291637	0.10768215481343	2.79292313394650	0.00532638972762354	** 
df.mm.trans2:exp3	0.0761548252634163	0.0855162982088346	0.890529955792085	0.373402666671887	   
df.mm.trans1:exp4	0.0400990561820685	0.10768215481343	0.372383485931759	0.709688858974146	   
df.mm.trans2:exp4	0.00828833173599453	0.0855162982088346	0.0969210771466517	0.922809133063037	   
df.mm.trans1:exp5	0.647210021470974	0.10768215481343	6.01037398065009	2.61872215493551e-09	***
df.mm.trans2:exp5	0.458114658936902	0.0855162982088346	5.35704501401786	1.05726643322571e-07	***
df.mm.trans1:exp6	0.124990758209956	0.10768215481343	1.16073789966884	0.246034674258622	   
df.mm.trans2:exp6	-0.044394282200542	0.0855162982088346	-0.519132412538826	0.603787072140411	   
df.mm.trans1:exp7	-0.295206701625499	0.10768215481343	-2.74146354274739	0.00622934468095895	** 
df.mm.trans2:exp7	0.0768962905126991	0.0855162982088346	0.89920041118846	0.368769543016809	   
df.mm.trans1:exp8	0.00958050007267521	0.10768215481343	0.0889701742064354	0.92912401702772	   
df.mm.trans2:exp8	0.000567383077405099	0.0855162982088347	0.00663479464487019	0.994707605680033	   
df.mm.trans1:probe2	0.334888607233454	0.0628728328704697	5.32644374277503	1.24569114369115e-07	***
df.mm.trans1:probe3	-0.310468381646224	0.0628728328704697	-4.93803710556274	9.29089105766716e-07	***
df.mm.trans1:probe4	-0.110967686889700	0.0628728328704697	-1.76495446162439	0.0778867063071378	.  
df.mm.trans1:probe5	-0.224420069154765	0.0628728328704697	-3.56942830327232	0.000375330641039188	***
df.mm.trans1:probe6	-0.142424800912765	0.0628728328704697	-2.26528365925848	0.0237155637247791	*  
df.mm.trans1:probe7	-0.287263106909410	0.0628728328704697	-4.56895440835675	5.53452195026618e-06	***
df.mm.trans1:probe8	-0.0908714735489556	0.0628728328704697	-1.44532176140637	0.148691173384109	   
df.mm.trans1:probe9	-0.0578846445303366	0.0628728328704697	-0.920662262023253	0.357456163684077	   
df.mm.trans1:probe10	-0.212089255316026	0.0628728328704697	-3.37330521996633	0.00077215918268049	***
df.mm.trans1:probe11	0.232431204238325	0.0628728328704697	3.69684637428663	0.000230553477090835	***
df.mm.trans1:probe12	-0.0074523179167541	0.0628728328704697	-0.118530016487524	0.905672304492359	   
df.mm.trans1:probe13	0.0867032804517925	0.0628728328704697	1.37902614680045	0.168205236198560	   
df.mm.trans1:probe14	0.0114983783311472	0.0628728328704697	0.182883096023939	0.854928041959108	   
df.mm.trans1:probe15	0.0405536121810592	0.0628728328704697	0.645010099427325	0.519073476448922	   
df.mm.trans1:probe16	0.0879700406606992	0.0628728328704697	1.39917412091061	0.162081117189980	   
df.mm.trans1:probe17	0.652606002458689	0.0628728328704697	10.3797772847803	5.2722621201868e-24	***
df.mm.trans1:probe18	0.387217844981619	0.0628728328704697	6.15874658899756	1.07341259038311e-09	***
df.mm.trans1:probe19	0.605994878926321	0.0628728328704697	9.6384217357406	4.6835816944954e-21	***
df.mm.trans1:probe20	0.450909785521494	0.0628728328704697	7.17177459540365	1.46953457553372e-12	***
df.mm.trans1:probe21	0.699812241534407	0.0628728328704697	11.1305982184095	3.65661934477108e-27	***
df.mm.trans1:probe22	0.723475719075281	0.0628728328704697	11.5069686865515	8.25489009385633e-29	***
df.mm.trans1:probe23	-0.0032931444306461	0.0628728328704697	-0.0523778598847394	0.958238421779895	   
df.mm.trans1:probe24	0.350186314178907	0.0628728328704697	5.56975561925703	3.30488718217325e-08	***
df.mm.trans1:probe25	-0.302793471012882	0.0628728328704697	-4.81596672503521	1.69923494388141e-06	***
df.mm.trans1:probe26	-0.0478275985178634	0.0628728328704697	-0.760703730598518	0.447019298669669	   
df.mm.trans1:probe27	-0.254688298644744	0.0628728328704697	-4.05084814882529	5.51170105044844e-05	***
df.mm.trans1:probe28	-0.0593809315249628	0.0628728328704697	-0.94446088738039	0.345169851226052	   
df.mm.trans1:probe29	-0.171317058071893	0.0628728328704697	-2.72481849871215	0.00654967614129747	** 
df.mm.trans1:probe30	-0.0589929496603455	0.0628728328704697	-0.938289988966817	0.348329452850682	   
df.mm.trans1:probe31	-0.136502996499593	0.0628728328704697	-2.17109664488661	0.0301662329951246	*  
df.mm.trans1:probe32	-0.232276413740666	0.0628728328704697	-3.69438441272720	0.000232767034489478	***
df.mm.trans2:probe2	-0.116333256697712	0.0628728328704697	-1.85029449742437	0.0645756760104193	.  
df.mm.trans2:probe3	0.152868056525876	0.0628728328704697	2.43138490102417	0.0152218056701378	*  
df.mm.trans2:probe4	0.300203811347853	0.0628728328704697	4.77477787530795	2.07690614578114e-06	***
df.mm.trans2:probe5	-0.0843686971891963	0.0628728328704697	-1.34189431805963	0.179944937021126	   
df.mm.trans2:probe6	-0.326650765793636	0.0628728328704697	-5.19541987978497	2.4905665146862e-07	***
df.mm.trans3:probe2	0.417449394835286	0.0628728328704697	6.63958304050516	5.23447719241598e-11	***
df.mm.trans3:probe3	0.460598399761674	0.0628728328704697	7.3258731749943	4.99577247566731e-13	***
df.mm.trans3:probe4	0.374791233369682	0.0628728328704697	5.9610998305393	3.50655500754786e-09	***
df.mm.trans3:probe5	1.39594981834127	0.0628728328704697	22.2027504505356	1.32539540658160e-88	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.06399718969432	0.287406037840484	17.6196618127589	1.75057588930147e-60	***
df.mm.trans1	-0.592474028154204	0.255345508229428	-2.32028372953350	0.0205326655856576	*  
df.mm.trans2	-0.140417924601544	0.227304407508950	-0.617752757812297	0.536883627869121	   
df.mm.exp2	-0.347270133826298	0.30149812987318	-1.15181521680540	0.249681406729415	   
df.mm.exp3	-0.940250835113105	0.30149812987318	-3.11859591138627	0.00187085843570579	** 
df.mm.exp4	-0.617486876141842	0.30149812987318	-2.04806204403847	0.0408232759906714	*  
df.mm.exp5	-0.429340539467599	0.30149812987318	-1.42402388913057	0.154761819724340	   
df.mm.exp6	-0.339113791207294	0.30149812987318	-1.12476250300437	0.260968505887785	   
df.mm.exp7	-0.0462628040188828	0.30149812987318	-0.153443087817302	0.878080848806368	   
df.mm.exp8	-0.316596085849262	0.301498129873180	-1.05007644983547	0.29394504863418	   
df.mm.trans1:exp2	0.35920276386523	0.289417156923094	1.24112463712951	0.214860392385219	   
df.mm.trans2:exp2	0.133166998043711	0.229842019237728	0.579384911798806	0.562464288225287	   
df.mm.trans1:exp3	0.829148172669092	0.289417156923095	2.86488949544003	0.00426177039047473	** 
df.mm.trans2:exp3	0.541374627474757	0.229842019237728	2.35542060268278	0.0187004537655301	*  
df.mm.trans1:exp4	0.576257731098475	0.289417156923094	1.99109733930391	0.0467509237574664	*  
df.mm.trans2:exp4	0.293786314199683	0.229842019237729	1.27820976849240	0.201481876669667	   
df.mm.trans1:exp5	0.468150896211518	0.289417156923094	1.61756442219463	0.106082289068494	   
df.mm.trans2:exp5	0.240027515537688	0.229842019237729	1.04431520543432	0.296600298322398	   
df.mm.trans1:exp6	0.317636880497127	0.289417156923094	1.09750535826572	0.272693468314581	   
df.mm.trans2:exp6	0.208996388546265	0.229842019237729	0.909304526819777	0.363415743052573	   
df.mm.trans1:exp7	0.165054104078187	0.289417156923095	0.570298270610286	0.568607756984708	   
df.mm.trans2:exp7	0.0126335565866441	0.229842019237729	0.0549662617329214	0.956176684614079	   
df.mm.trans1:exp8	0.318986582855761	0.289417156923095	1.10216887708742	0.270662214654399	   
df.mm.trans2:exp8	0.178738836776273	0.229842019237729	0.777659530529104	0.436959771827212	   
df.mm.trans1:probe2	-0.0448318411849082	0.168983213315144	-0.265303519239508	0.79083214018156	   
df.mm.trans1:probe3	0.0600580047806668	0.168983213315144	0.35540811186175	0.722361301159171	   
df.mm.trans1:probe4	0.356051512928478	0.168983213315144	2.10702297549794	0.0353723565265776	*  
df.mm.trans1:probe5	0.148049830528332	0.168983213315144	0.87612152487731	0.381181326885458	   
df.mm.trans1:probe6	0.0571050259866974	0.168983213315144	0.337933128778891	0.7354868536241	   
df.mm.trans1:probe7	0.207480414269019	0.168983213315144	1.22781671740423	0.219814253423576	   
df.mm.trans1:probe8	0.183472743988473	0.168983213315144	1.08574538493540	0.277861963076066	   
df.mm.trans1:probe9	0.069398397047737	0.168983213315144	0.410682195504904	0.68139647272294	   
df.mm.trans1:probe10	0.204094089660870	0.168983213315144	1.20777730318245	0.227427934462628	   
df.mm.trans1:probe11	0.341535856984599	0.168983213315144	2.02112298780621	0.0435418787124724	*  
df.mm.trans1:probe12	0.276423147998676	0.168983213315144	1.63580241241574	0.102206029694272	   
df.mm.trans1:probe13	0.0340799287024039	0.168983213315144	0.201676415271183	0.840212074142549	   
df.mm.trans1:probe14	0.161521823152103	0.168983213315144	0.95584537649236	0.339389002740644	   
df.mm.trans1:probe15	-0.107098969386081	0.168983213315144	-0.633784665855225	0.526371086190852	   
df.mm.trans1:probe16	0.189516625747868	0.168983213315144	1.12151155153163	0.262348306876311	   
df.mm.trans1:probe17	0.130574979820852	0.168983213315144	0.772709769563544	0.439882789608067	   
df.mm.trans1:probe18	0.141214974903182	0.168983213315144	0.835674574608925	0.403544220584997	   
df.mm.trans1:probe19	0.0605089854823687	0.168983213315144	0.358076901813453	0.720363831648982	   
df.mm.trans1:probe20	-0.0343402352355901	0.168983213315144	-0.203216843625452	0.839008256635131	   
df.mm.trans1:probe21	-0.0158782699792054	0.168983213315144	-0.093963593588396	0.925157521861767	   
df.mm.trans1:probe22	0.203017881522287	0.168983213315144	1.20140857508533	0.229886610843518	   
df.mm.trans1:probe23	0.144464985103843	0.168983213315144	0.854907314577004	0.392813995794728	   
df.mm.trans1:probe24	0.0813407077681376	0.168983213315144	0.481353775753109	0.630373874370792	   
df.mm.trans1:probe25	0.127596260775246	0.168983213315144	0.755082462169101	0.450383177625736	   
df.mm.trans1:probe26	0.238207686545777	0.168983213315144	1.40965295825884	0.158963373769571	   
df.mm.trans1:probe27	0.180081029066335	0.168983213315144	1.06567407219612	0.286836731315670	   
df.mm.trans1:probe28	0.0911398735116777	0.168983213315144	0.539342765021913	0.589774394057452	   
df.mm.trans1:probe29	0.190981524320724	0.168983213315144	1.13018045150174	0.258680151881794	   
df.mm.trans1:probe30	0.123514650785488	0.168983213315144	0.730928524569714	0.464999691312897	   
df.mm.trans1:probe31	-0.0158787291753377	0.168983213315144	-0.0939663109951917	0.925155363803205	   
df.mm.trans1:probe32	0.0856812663910088	0.168983213315144	0.507040105996908	0.612242161084821	   
df.mm.trans2:probe2	-0.189795965842567	0.168983213315144	-1.12316461569830	0.26164606762819	   
df.mm.trans2:probe3	-0.146257517602923	0.168983213315144	-0.865515069417937	0.386970599882657	   
df.mm.trans2:probe4	-0.175934912118476	0.168983213315144	-1.04113839870217	0.298071281695314	   
df.mm.trans2:probe5	-0.251874256549883	0.168983213315144	-1.49052826969358	0.136411154116485	   
df.mm.trans2:probe6	-0.207082441057602	0.168983213315144	-1.22546161239936	0.220699420821964	   
df.mm.trans3:probe2	0.237989503345193	0.168983213315144	1.40836180515372	0.159345053804546	   
df.mm.trans3:probe3	0.122748143689462	0.168983213315144	0.726392529064669	0.467773773865469	   
df.mm.trans3:probe4	0.143173760122568	0.168983213315144	0.847266171081487	0.397056204651238	   
df.mm.trans3:probe5	-0.079977050554619	0.168983213315144	-0.473283996591225	0.636117286493251