chr2.13995_chr2_30229278_30230605_+_2.R 

fitVsDatCorrelation=0.909629548461582
cont.fitVsDatCorrelation=0.255302476160821

fstatistic=7929.19422407374,64,968
cont.fstatistic=1451.44896148358,64,968

residuals=-1.32167135265938,-0.0974459820936404,-0.00177675157534672,0.097085298813272,1.37169907435985
cont.residuals=-0.917792812982279,-0.291791519450867,-0.110120359523594,0.164749051688995,2.27812510536375

predictedValues:
Include	Exclude	Both
chr2.13995_chr2_30229278_30230605_+_2.R.tl.Lung	66.1763452177824	45.1898222460625	63.9861807969175
chr2.13995_chr2_30229278_30230605_+_2.R.tl.cerebhem	73.8096452230725	48.4970625749887	74.2804902105838
chr2.13995_chr2_30229278_30230605_+_2.R.tl.cortex	76.6578310833045	46.5303260998238	76.2399684706623
chr2.13995_chr2_30229278_30230605_+_2.R.tl.heart	71.5679321024378	46.3167500230313	63.9381756512982
chr2.13995_chr2_30229278_30230605_+_2.R.tl.kidney	60.9388070144722	44.7974962654223	63.4683433185793
chr2.13995_chr2_30229278_30230605_+_2.R.tl.liver	64.5108262518821	51.6277280073608	58.4852586436284
chr2.13995_chr2_30229278_30230605_+_2.R.tl.stomach	71.7740799722813	46.7916531380724	60.3370908057485
chr2.13995_chr2_30229278_30230605_+_2.R.tl.testicle	66.7208929457054	45.5020167244574	62.0271515740203


diffExp=20.9865229717199,25.3125826480837,30.1275049834807,25.2511820794065,16.1413107490499,12.8830982445213,24.9824268342089,21.218876221248
diffExpScore=0.994378975268036
diffExp1.5=0,1,1,1,0,0,1,0
diffExp1.5Score=0.8
diffExp1.4=1,1,1,1,0,0,1,1
diffExp1.4Score=0.857142857142857
diffExp1.3=1,1,1,1,1,0,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	68.0538314437349	53.1316592872406	65.4857079225523
cerebhem	67.9591805490854	62.5089146894832	65.0791648005472
cortex	67.457435149691	67.3456277867923	59.2062347416167
heart	72.5078344911139	66.7040215769337	64.8010093714782
kidney	70.2292739345262	68.1065508372172	70.762682408434
liver	64.2425019522135	63.4769211916448	69.2635682486993
stomach	66.8480281325997	68.2851227720692	70.2978200843362
testicle	63.908041423883	79.4999370154134	58.6277171822938
cont.diffExp=14.9221721564942,5.45026585960215,0.111807362898674,5.80381291418017,2.12272309730906,0.765580760568731,-1.43709463946956,-15.5918955915303
cont.diffExpScore=3.51441738037225

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,-1
cont.diffExp1.2Score=2

tran.correlation=0.0827067102008086
cont.tran.correlation=-0.282680734130070

tran.covariance=0.00038083760317326
cont.tran.covariance=-0.00124098612742306

tran.mean=57.9630759306348
cont.tran.mean=66.8915551396026

weightedLogRatios:
wLogRatio
Lung	1.52641410642163
cerebhem	1.71837193815642
cortex	2.04178645993439
heart	1.76366912707980
kidney	1.21733564836051
liver	0.903448594733548
stomach	1.73677831810660
testicle	1.53454341435905

cont.weightedLogRatios:
wLogRatio
Lung	1.01399941672222
cerebhem	0.349198131453162
cortex	0.00698476303527179
heart	0.353905727462643
kidney	0.130023456473051
liver	0.0498327530161811
stomach	-0.0896122033446826
testicle	-0.931446005107225

varWeightedLogRatios=0.125715570522213
cont.varWeightedLogRatios=0.296143949000612

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93171454104524	0.0863122747378642	45.552206253237	5.45896135279154e-243	***
df.mm.trans1	0.133963735144386	0.0739360300918295	1.81188704584221	0.0703134261114883	.  
df.mm.trans2	-0.0971935568085215	0.0647298040129284	-1.50152712943653	0.133545387875302	   
df.mm.exp2	0.0306164807990841	0.0819219801913567	0.373727304046714	0.708689045779252	   
df.mm.exp3	0.00104236425864659	0.0819219801913567	0.0127238655146249	0.98985071989901	   
df.mm.exp4	0.103706323139905	0.0819219801913566	1.26591572735013	0.205847834150511	   
df.mm.exp5	-0.08304663431093	0.0819219801913567	-1.01372835613771	0.310965700962456	   
df.mm.exp6	0.197589386706986	0.0819219801913567	2.41192151661189	0.0160538959629380	*  
df.mm.exp7	0.174753392731614	0.0819219801913567	2.13316856261797	0.0331618711376654	*  
df.mm.exp8	0.0461747411999454	0.0819219801913567	0.563642884267306	0.573127741123389	   
df.mm.trans1:exp2	0.0785498602535227	0.0749465921842104	1.04807781066891	0.294864369521961	   
df.mm.trans2:exp2	0.0400148599100645	0.051952612293979	0.770218438365263	0.441358254548061	   
df.mm.trans1:exp3	0.145986326521851	0.0749465921842104	1.94787144107944	0.0517191099771418	.  
df.mm.trans2:exp3	0.0281900200152767	0.051952612293979	0.542610251352919	0.587523079635103	   
df.mm.trans1:exp4	-0.0253823012575094	0.0749465921842104	-0.338671853086029	0.734930393710794	   
df.mm.trans2:exp4	-0.0790745458514915	0.051952612293979	-1.52205139183455	0.12832278937537	   
df.mm.trans1:exp5	0.00059375518793755	0.0749465921842104	0.00792237739746946	0.993680555880839	   
df.mm.trans2:exp5	0.0743269953016213	0.051952612293979	1.43066906589864	0.152847803359919	   
df.mm.trans1:exp6	-0.223079404217581	0.0749465921842104	-2.97651164270787	0.00298801445379251	** 
df.mm.trans2:exp6	-0.0644023840003021	0.051952612293979	-1.23963706840909	0.215410103591986	   
df.mm.trans1:exp7	-0.0935530611200036	0.0749465921842104	-1.24826304163451	0.212236578083289	   
df.mm.trans2:exp7	-0.139920447401145	0.051952612293979	-2.69323218261656	0.00719861874736246	** 
df.mm.trans1:exp8	-0.0379796758538793	0.0749465921842104	-0.506756541518652	0.612441060909517	   
df.mm.trans2:exp8	-0.0392899825468316	0.051952612293979	-0.756265773980822	0.449673869241193	   
df.mm.trans1:probe2	1.14316751161054	0.0548552885848611	20.8396955170887	5.70360804188905e-80	***
df.mm.trans1:probe3	0.356041820134314	0.0548552885848612	6.49056507256392	1.36352515640698e-10	***
df.mm.trans1:probe4	-0.0206047671650794	0.0548552885848611	-0.375620431441242	0.707281394223775	   
df.mm.trans1:probe5	0.0880200040112887	0.0548552885848612	1.60458556106439	0.108911223583369	   
df.mm.trans1:probe6	0.0517967789265719	0.0548552885848611	0.944244033033247	0.345280572982997	   
df.mm.trans1:probe7	-0.160137260516086	0.0548552885848612	-2.91926748809923	0.00358983421208221	** 
df.mm.trans1:probe8	0.194469243926477	0.0548552885848612	3.54513209105869	0.000411193066934948	***
df.mm.trans1:probe9	-0.00276650108405685	0.0548552885848611	-0.0504327140632405	0.959787971765967	   
df.mm.trans1:probe10	-0.0832998000185547	0.0548552885848611	-1.51853726718965	0.1292055326561	   
df.mm.trans1:probe11	-0.227874778815775	0.0548552885848611	-4.15410773864133	3.55412290255722e-05	***
df.mm.trans1:probe12	-0.165635669920169	0.0548552885848611	-3.01950229764868	0.00259831111843653	** 
df.mm.trans1:probe13	-0.180381809941572	0.0548552885848611	-3.2883212283629	0.00104416835537583	** 
df.mm.trans1:probe14	-0.133013038262792	0.0548552885848611	-2.4247988059897	0.0154990126884630	*  
df.mm.trans1:probe15	-0.218501611193295	0.0548552885848612	-3.983236928109	7.30780619304646e-05	***
df.mm.trans1:probe16	-0.224794743528491	0.0548552885848612	-4.09795936413193	4.51717048040692e-05	***
df.mm.trans1:probe17	1.12322164921991	0.0548552885848612	20.4760867766156	1.06029816591385e-77	***
df.mm.trans1:probe18	1.14521090649334	0.0548552885848612	20.8769461621134	3.33282433536448e-80	***
df.mm.trans1:probe19	0.674906159017908	0.0548552885848612	12.3033927343911	1.99665541042431e-32	***
df.mm.trans1:probe20	0.155853674661981	0.0548552885848612	2.84117864808741	0.00458902258008025	** 
df.mm.trans1:probe21	0.975347567383722	0.0548552885848612	17.7803743731082	2.02721124799823e-61	***
df.mm.trans1:probe22	0.194832261509117	0.0548552885848612	3.55174982276708	0.000401119698266468	***
df.mm.trans2:probe2	-0.01803947062828	0.0548552885848612	-0.328855632586326	0.74233594233545	   
df.mm.trans2:probe3	-0.134720473265065	0.0548552885848611	-2.45592497534038	0.0142270385648242	*  
df.mm.trans2:probe4	-0.137129296035134	0.0548552885848612	-2.49983729140345	0.0125895877889268	*  
df.mm.trans2:probe5	-0.1091659323861	0.0548552885848612	-1.99007124385501	0.0468640217976136	*  
df.mm.trans2:probe6	-0.097575808001084	0.0548552885848612	-1.77878579291647	0.0755886515335227	.  
df.mm.trans3:probe2	0.458857968005198	0.0548552885848612	8.3648811234553	2.08215995053518e-16	***
df.mm.trans3:probe3	0.207799349327426	0.0548552885848612	3.78813701811012	0.000161143699442523	***
df.mm.trans3:probe4	0.184193323287229	0.0548552885848612	3.35780428904831	0.000816251697540438	***
df.mm.trans3:probe5	0.0205253657977648	0.0548552885848612	0.374172961755767	0.708357583160896	   
df.mm.trans3:probe6	0.281826019673443	0.0548552885848612	5.13762714487334	3.36442846257563e-07	***
df.mm.trans3:probe7	0.100083705454929	0.0548552885848611	1.82450421895237	0.0683840085378792	.  
df.mm.trans3:probe8	0.0734337819804071	0.0548552885848611	1.33868190059387	0.180988555244033	   
df.mm.trans3:probe9	0.443060166835854	0.0548552885848611	8.07689063836462	1.96519726265529e-15	***
df.mm.trans3:probe10	-0.0369342973803705	0.0548552885848612	-0.673304221583542	0.500914483517049	   
df.mm.trans3:probe11	0.466391419216098	0.0548552885848612	8.50221430326796	6.97493423364441e-17	***
df.mm.trans3:probe12	0.536455000650037	0.0548552885848612	9.779458179682	1.32752921160289e-21	***
df.mm.trans3:probe13	-0.0468434178287281	0.0548552885848612	-0.85394533575849	0.393346548176399	   
df.mm.trans3:probe14	-0.0652032292576356	0.0548552885848611	-1.18864071158365	0.234872599929099	   
df.mm.trans3:probe15	0.229091078466830	0.0548552885848612	4.17628061718107	3.230498557228e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94292681502797	0.200885652943371	19.62771734694	1.81015613126650e-72	***
df.mm.trans1	0.288382625385641	0.172080827740277	1.67585563814755	0.0940894036202711	.  
df.mm.trans2	0.0279140057902766	0.150653994272835	0.18528553408099	0.8530438981502	   
df.mm.exp2	0.167371889275728	0.190667556047334	0.877820499436086	0.380258956364901	   
df.mm.exp3	0.329067856254974	0.190667556047334	1.72587231449740	0.0846894972046047	.  
df.mm.exp4	0.301398600247482	0.190667556047334	1.58075451584778	0.114260852984833	   
df.mm.exp5	0.202266775140408	0.190667556047334	1.06083478140452	0.289029598539489	   
df.mm.exp6	0.064182270227875	0.190667556047334	0.336618728211640	0.736477297675045	   
df.mm.exp7	0.162132855329833	0.190667556047334	0.85034317684118	0.395344594490712	   
df.mm.exp8	0.450753770153148	0.190667556047334	2.36408217264424	0.0182714301219623	*  
df.mm.trans1:exp2	-0.168763681260063	0.174432838811522	-0.967499482379094	0.333536058197326	   
df.mm.trans2:exp2	-0.00483567835104965	0.120915993402864	-0.0399920491488524	0.96810771156942	   
df.mm.trans1:exp3	-0.337870078530948	0.174432838811522	-1.93696370954568	0.0530404743523703	.  
df.mm.trans2:exp3	-0.0920028439746122	0.120915993402864	-0.760882339758646	0.446912650538184	   
df.mm.trans1:exp4	-0.238003013152503	0.174432838811522	-1.36443925796375	0.172746467808656	   
df.mm.trans2:exp4	-0.0739063263142315	0.120915993402864	-0.611220436886234	0.541197157489717	   
df.mm.trans1:exp5	-0.170800574291596	0.174432838811522	-0.979176716123674	0.327737320713291	   
df.mm.trans2:exp5	0.0460336570897831	0.120915993402864	0.380707760770815	0.703503639522417	   
df.mm.trans1:exp6	-0.121816285269294	0.174432838811522	-0.698356376581812	0.485122080028842	   
df.mm.trans2:exp6	0.11372115312947	0.120915993402864	0.940497199163535	0.347197216271727	   
df.mm.trans1:exp7	-0.180010079985245	0.174432838811522	-1.03197357339205	0.302342258537367	   
df.mm.trans2:exp7	0.0887860950570771	0.120915993402864	0.734279168192932	0.462956433214035	   
df.mm.trans1:exp8	-0.513607603681879	0.174432838811522	-2.9444433008216	0.00331271466799039	** 
df.mm.trans2:exp8	-0.0477705114301267	0.120915993402864	-0.395071901456133	0.6928768638528	   
df.mm.trans1:probe2	0.0199401183075026	0.127671765090588	0.156182679023466	0.875921594256574	   
df.mm.trans1:probe3	-0.122793712209943	0.127671765090588	-0.961792234350457	0.336394165134391	   
df.mm.trans1:probe4	0.079318635023374	0.127671765090588	0.621269980618616	0.534568269611885	   
df.mm.trans1:probe5	0.116881070340291	0.127671765090588	0.915480962116874	0.360167188257911	   
df.mm.trans1:probe6	0.237536509775204	0.127671765090588	1.86052499240269	0.0631143565470541	.  
df.mm.trans1:probe7	-0.0770606203570994	0.127671765090588	-0.603583887967884	0.546261734189116	   
df.mm.trans1:probe8	-0.133214158718767	0.127671765090588	-1.04341127127245	0.297018358581085	   
df.mm.trans1:probe9	-0.0952403082240662	0.127671765090588	-0.74597784526978	0.455861910252024	   
df.mm.trans1:probe10	-0.204146409368322	0.127671765090588	-1.59899418029877	0.110148220892460	   
df.mm.trans1:probe11	-0.0615308810917154	0.127671765090588	-0.481945879326231	0.629953337250836	   
df.mm.trans1:probe12	0.242253224734727	0.127671765090588	1.89746906501088	0.0580634118529489	.  
df.mm.trans1:probe13	-0.133644087598376	0.127671765090588	-1.04677872592699	0.295462947561984	   
df.mm.trans1:probe14	0.112299787389961	0.127671765090588	0.879597672283144	0.379295603429981	   
df.mm.trans1:probe15	-0.0306884498699683	0.127671765090588	-0.240369903621163	0.810094402669148	   
df.mm.trans1:probe16	0.0466411749473105	0.127671765090588	0.365320984747230	0.71495162303344	   
df.mm.trans1:probe17	0.00637445554488878	0.127671765090588	0.0499284672720383	0.960189691971884	   
df.mm.trans1:probe18	-0.0899186298901503	0.127671765090588	-0.704295345383137	0.48141826033525	   
df.mm.trans1:probe19	-0.0753793794635796	0.127671765090588	-0.590415425134094	0.555049978639427	   
df.mm.trans1:probe20	-0.116917184893895	0.127671765090588	-0.915763832441248	0.360018848495336	   
df.mm.trans1:probe21	-0.0706848755270748	0.127671765090588	-0.553645322259947	0.579949457723012	   
df.mm.trans1:probe22	-0.0574114318240235	0.127671765090588	-0.449679941240633	0.653041909817549	   
df.mm.trans2:probe2	-0.0581354388805675	0.127671765090588	-0.455350788322839	0.648959008623838	   
df.mm.trans2:probe3	0.0380523980707906	0.127671765090588	0.298048656598356	0.76572998685039	   
df.mm.trans2:probe4	0.127480055157424	0.127671765090588	0.99849841558133	0.318287262104045	   
df.mm.trans2:probe5	-0.0233280113407066	0.127671765090588	-0.182718640446104	0.855057048850095	   
df.mm.trans2:probe6	-0.0434938559520394	0.127671765090588	-0.340669340015617	0.733426442357612	   
df.mm.trans3:probe2	-0.116824425778955	0.127671765090588	-0.915037288754203	0.360399931991294	   
df.mm.trans3:probe3	-0.0883983396805744	0.127671765090588	-0.69238754252244	0.488860043024211	   
df.mm.trans3:probe4	-0.0614107571854687	0.127671765090588	-0.481004998575022	0.630621646899217	   
df.mm.trans3:probe5	-0.113118250261993	0.127671765090588	-0.886008352604282	0.375833074534842	   
df.mm.trans3:probe6	-0.0633724927722038	0.127671765090588	-0.496370460040547	0.619745754175312	   
df.mm.trans3:probe7	-0.165244955585666	0.127671765090588	-1.29429522234943	0.195872147911553	   
df.mm.trans3:probe8	-0.0197136795214161	0.127671765090588	-0.154409077899319	0.877319383052361	   
df.mm.trans3:probe9	-0.032854750807588	0.127671765090588	-0.257337640662180	0.796972828682446	   
df.mm.trans3:probe10	-0.113235855362308	0.127671765090588	-0.88692950459299	0.375337154098276	   
df.mm.trans3:probe11	-0.201865639556724	0.127671765090588	-1.58112985602958	0.114175018976001	   
df.mm.trans3:probe12	-0.136859497976531	0.127671765090588	-1.07196370222832	0.284003513739461	   
df.mm.trans3:probe13	-0.0672535654091304	0.127671765090588	-0.526769292814363	0.59847449490519	   
df.mm.trans3:probe14	0.0445104649252972	0.127671765090588	0.348632016591259	0.727441399515009	   
df.mm.trans3:probe15	-0.0252311604373883	0.127671765090588	-0.197625218226488	0.84337979300538