chr15.8739_chr15_76690536_76691557_-_2.R 

fitVsDatCorrelation=0.956682946820227
cont.fitVsDatCorrelation=0.248892052035204

fstatistic=11172.7521977749,52,692
cont.fstatistic=997.408550624845,52,692

residuals=-0.66464149349335,-0.102905725878566,0.00369994732373722,0.0902275029129664,1.13346994705760
cont.residuals=-1.15746270857962,-0.466232861095813,-0.0459073022233664,0.393942481634501,2.0113321796169

predictedValues:
Include	Exclude	Both
chr15.8739_chr15_76690536_76691557_-_2.R.tl.Lung	172.031445963251	392.574040644959	74.1050218483565
chr15.8739_chr15_76690536_76691557_-_2.R.tl.cerebhem	113.501675217787	140.416432396248	60.9391225100054
chr15.8739_chr15_76690536_76691557_-_2.R.tl.cortex	150.329951813985	214.184144072909	73.1244300996803
chr15.8739_chr15_76690536_76691557_-_2.R.tl.heart	147.190158060619	411.814040817183	98.1986644817683
chr15.8739_chr15_76690536_76691557_-_2.R.tl.kidney	155.061959679588	207.255767513766	56.5387506989765
chr15.8739_chr15_76690536_76691557_-_2.R.tl.liver	158.303767087060	200.732324283472	60.2599393303032
chr15.8739_chr15_76690536_76691557_-_2.R.tl.stomach	142.666887734684	323.309253625940	78.8608428660108
chr15.8739_chr15_76690536_76691557_-_2.R.tl.testicle	150.087416221607	321.361064284185	75.3383591508255


diffExp=-220.542594681707,-26.914757178461,-63.8541922589241,-264.623882756563,-52.1938078341774,-42.4285571964113,-180.642365891255,-171.273648062579
diffExpScore=0.999022935424166
diffExp1.5=-1,0,0,-1,0,0,-1,-1
diffExp1.5Score=0.8
diffExp1.4=-1,0,-1,-1,0,0,-1,-1
diffExp1.4Score=0.833333333333333
diffExp1.3=-1,0,-1,-1,-1,0,-1,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	109.772534884727	94.7131909033793	109.195152218058
cerebhem	120.762483989876	118.270999534034	102.430958005753
cortex	105.601551221521	123.476414572288	124.805316620653
heart	100.822879310799	123.051817326251	122.801357068174
kidney	108.906140928024	127.444175342654	108.09861507119
liver	128.261546353114	102.213311151034	122.253515635489
stomach	118.643307749180	123.041625790313	105.627193156417
testicle	137.553737640759	120.688971412897	121.159469044282
cont.diffExp=15.0593439813481,2.49148445584264,-17.8748633507672,-22.2289380154522,-18.5380344146300,26.0482352020806,-4.39831804113372,16.8647662278614
cont.diffExpScore=34.5337797269769

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

tran.correlation=0.499445671839535
cont.tran.correlation=-0.171516947697151

tran.covariance=0.0267069132636887
cont.tran.covariance=-0.00181918899354310

tran.mean=212.551270588578
cont.tran.mean=116.451543006928

weightedLogRatios:
wLogRatio
Lung	-4.58743218893715
cerebhem	-1.02954762623493
cortex	-1.83721990753137
heart	-5.66498199237258
kidney	-1.50544643287780
liver	-1.23079517729736
stomach	-4.39282084461664
testicle	-4.10510266568073

cont.weightedLogRatios:
wLogRatio
Lung	0.682397159032087
cerebhem	0.0997199514593182
cortex	-0.740893068258457
heart	-0.939016340710544
kidney	-0.74966164913586
liver	1.07615412790064
stomach	-0.174518797408671
testicle	0.635496769747784

varWeightedLogRatios=3.3380258861647
cont.varWeightedLogRatios=0.579882294932784

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	6.69938561800221	0.0805108898726762	83.2109249891157	0	***
df.mm.trans1	-1.51771834994707	0.0673893972266039	-22.5216193111741	1.10921313546706e-84	***
df.mm.trans2	-0.673134521777186	0.0618574671517041	-10.8820252876882	1.44235267817701e-25	***
df.mm.exp2	-1.24836439541625	0.0800705353039211	-15.5908086623609	3.44056553268869e-47	***
df.mm.exp3	-0.727412965820935	0.0800705353039211	-9.08465221395007	1.07682210106442e-18	***
df.mm.exp4	-0.389614524260799	0.0800705353039211	-4.86589133920427	1.41212519469577e-06	***
df.mm.exp5	-0.472066999075569	0.0800705353039211	-5.8956393545236	5.83358247031305e-09	***
df.mm.exp6	-0.547098604861481	0.0800705353039211	-6.83270822138102	1.82874975123554e-11	***
df.mm.exp7	-0.443482195584932	0.0800705353039212	-5.53864407052633	4.33279088762686e-08	***
df.mm.exp8	-0.35312536400271	0.0800705353039211	-4.41017863390526	1.19732296058894e-05	***
df.mm.trans1:exp2	0.832504706386192	0.0691161346769144	12.0450125036326	1.8125608402356e-30	***
df.mm.trans2:exp2	0.220251762000323	0.0560605879517712	3.92881648315578	9.39297969751223e-05	***
df.mm.trans1:exp3	0.592568237455031	0.0691161346769145	8.57351529024315	6.53880465796113e-17	***
df.mm.trans2:exp3	0.121523939140354	0.0560605879517712	2.16772502002477	0.0305198598633326	*  
df.mm.trans1:exp4	0.233662581848377	0.0691161346769145	3.3807240948968	0.00076356934772732	***
df.mm.trans2:exp4	0.437461256459813	0.0560605879517712	7.80336547372924	2.22879678546616e-14	***
df.mm.trans1:exp5	0.368214490517757	0.0691161346769145	5.32747515813764	1.34848218462090e-07	***
df.mm.trans2:exp5	-0.166704536538856	0.0560605879517712	-2.97364945016758	0.00304512564071562	** 
df.mm.trans1:exp6	0.463937083133353	0.0691161346769145	6.71242807923275	3.9890506545512e-11	***
df.mm.trans2:exp6	-0.123654252607598	0.0560605879517712	-2.20572521847216	0.0277312017938767	*  
df.mm.trans1:exp7	0.256317366568926	0.0691161346769145	3.70850262051097	0.000225188842623892	***
df.mm.trans2:exp7	0.249366344300815	0.0560605879517712	4.44815784870727	1.008918144277e-05	***
df.mm.trans1:exp8	0.216665977691711	0.0691161346769145	3.13481039853462	0.00179237505811877	** 
df.mm.trans2:exp8	0.152965507842802	0.0560605879517712	2.72857480507336	0.0065223478154098	** 
df.mm.trans1:probe2	0.0593392328316182	0.0495113706815783	1.19849707278851	0.231133842053600	   
df.mm.trans1:probe3	0.496305969567699	0.0495113706815783	10.0240805846314	3.56705676615726e-22	***
df.mm.trans1:probe4	0.382851521749354	0.0495113706815783	7.7325979159733	3.72546799287542e-14	***
df.mm.trans1:probe5	0.0266990910711837	0.0495113706815783	0.539251705287926	0.589886638425266	   
df.mm.trans1:probe6	0.0282238184331633	0.0495113706815783	0.570047204200398	0.568830702374605	   
df.mm.trans1:probe7	-0.456401168940247	0.0495113706815783	-9.21810813672465	3.57679486285433e-19	***
df.mm.trans1:probe8	-0.124915798664568	0.0495113706815783	-2.52297193442567	0.0118597479551784	*  
df.mm.trans1:probe9	-0.259396967254102	0.0495113706815783	-5.23913928625321	2.14392016025415e-07	***
df.mm.trans1:probe10	-0.311620235363243	0.0495113706815783	-6.29391251087272	5.49417566769275e-10	***
df.mm.trans1:probe11	-0.258715629197339	0.0495113706815783	-5.22537804217163	2.30311930969708e-07	***
df.mm.trans1:probe12	-0.432119398845279	0.0495113706815783	-8.72767998333881	1.93218763125046e-17	***
df.mm.trans2:probe2	-0.147383125377445	0.0495113706815783	-2.97675308416138	0.00301486096790628	** 
df.mm.trans2:probe3	-0.331573410497878	0.0495113706815783	-6.69691438417896	4.40747830054492e-11	***
df.mm.trans2:probe4	-0.202774167140250	0.0495113706815783	-4.09550703906681	4.71055107440451e-05	***
df.mm.trans2:probe5	-0.215259476893145	0.0495113706815783	-4.34767759263908	1.58262373584646e-05	***
df.mm.trans2:probe6	-0.120002646018562	0.0495113706815783	-2.42373912025851	0.0156169847118989	*  
df.mm.trans3:probe2	-0.165818598749517	0.0495113706815783	-3.34910135726082	0.000854698730375452	***
df.mm.trans3:probe3	-0.269537519487511	0.0495113706815783	-5.44395187968	7.24287032679535e-08	***
df.mm.trans3:probe4	-0.292421590637793	0.0495113706815783	-5.90615017545039	5.49024822800328e-09	***
df.mm.trans3:probe5	-0.347077241954589	0.0495113706815783	-7.01005116959377	5.67033796469771e-12	***
df.mm.trans3:probe6	-0.140028821223693	0.0495113706815783	-2.8282154037758	0.00481621565981127	** 
df.mm.trans3:probe7	0.0855237739542599	0.0495113706815783	1.72735621690394	0.0845499088392845	.  
df.mm.trans3:probe8	-0.263247348315629	0.0495113706815783	-5.31690689818803	1.42589781519362e-07	***
df.mm.trans3:probe9	0.122047815515984	0.0495113706815783	2.46504618708515	0.0139412897774251	*  
df.mm.trans3:probe10	-0.298234279991117	0.0495113706815783	-6.02355127490099	2.77096367335958e-09	***
df.mm.trans3:probe11	-0.436064821660245	0.0495113706815783	-8.8073671897452	1.02212740964893e-17	***
df.mm.trans3:probe12	-0.325324090145936	0.0495113706815783	-6.57069448224706	9.85188955171541e-11	***
df.mm.trans3:probe13	-0.309460062816437	0.0495113706815783	-6.25028268368214	7.16269481802719e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.45438268644225	0.267841935319082	16.6306395640985	1.68440617593043e-52	***
df.mm.trans1	0.234719465022726	0.224189629523468	1.04696843257933	0.295479684160924	   
df.mm.trans2	0.068691565502338	0.205786120884398	0.333800769493712	0.738631027999614	   
df.mm.exp2	0.381488307215477	0.266376973000215	1.43213695582902	0.152556159560838	   
df.mm.exp3	0.0928411939335376	0.266376973000215	0.348533106626535	0.727545864165983	   
df.mm.exp4	0.0592758128299619	0.266376973000215	0.222526039553404	0.823970066195999	   
df.mm.exp5	0.298993960750212	0.266376973000215	1.1224467242143	0.262061842372256	   
df.mm.exp6	0.118909566350433	0.266376973000215	0.446395816466977	0.65545100626267	   
df.mm.exp7	0.372601474724408	0.266376973000215	1.39877509128429	0.162328455229028	   
df.mm.exp8	0.363996834740508	0.266376973000215	1.36647259949235	0.172234504125707	   
df.mm.trans1:exp2	-0.286072993071074	0.229934103360616	-1.24415208048722	0.213864845114876	   
df.mm.trans2:exp2	-0.159362991672690	0.186501185067934	-0.854487823306006	0.393130468964065	   
df.mm.trans1:exp3	-0.131578493295453	0.229934103360616	-0.572244357719709	0.567342291618048	   
df.mm.trans2:exp3	0.172355686799020	0.186501185067934	0.924153306244348	0.355728636843086	   
df.mm.trans1:exp4	-0.144320865782392	0.229934103360616	-0.627661854736038	0.530432537093291	   
df.mm.trans2:exp4	0.202476450930322	0.186501185067934	1.08565771770602	0.278008503801996	   
df.mm.trans1:exp5	-0.306917902001472	0.229934103360616	-1.33480809290877	0.182378149062562	   
df.mm.trans2:exp5	-0.00216881445585457	0.186501185067934	-0.0116289580415511	0.990724994746428	   
df.mm.trans1:exp6	0.0367515835678396	0.229934103360616	0.159835287722415	0.873057467582518	   
df.mm.trans2:exp6	-0.0427009329401261	0.186501185067934	-0.228957971095851	0.81896923059247	   
df.mm.trans1:exp7	-0.294890256851177	0.229934103360616	-1.28249899663073	0.200097087884757	   
df.mm.trans2:exp7	-0.110932037518255	0.186501185067934	-0.594806073097326	0.552167540592206	   
df.mm.trans1:exp8	-0.138392534902222	0.229934103360616	-0.601879116144746	0.547451745231724	   
df.mm.trans2:exp8	-0.121633364675752	0.186501185067934	-0.652185478775627	0.514498092109351	   
df.mm.trans1:probe2	-0.119941251641069	0.164713386780675	-0.728181564263369	0.466748776071104	   
df.mm.trans1:probe3	0.234886425390092	0.164713386780675	1.42603118047021	0.154310291081148	   
df.mm.trans1:probe4	0.0952933070664833	0.164713386780675	0.578540147397805	0.563087738709945	   
df.mm.trans1:probe5	-0.066318605240651	0.164713386780675	-0.402630329791944	0.687344541298135	   
df.mm.trans1:probe6	-0.189923924743237	0.164713386780675	-1.15305700681227	0.249285112995901	   
df.mm.trans1:probe7	-0.209484535666102	0.164713386780675	-1.27181244803765	0.203867013766340	   
df.mm.trans1:probe8	0.237813772321060	0.164713386780675	1.44380354851013	0.149246804183398	   
df.mm.trans1:probe9	0.0711672493085025	0.164713386780675	0.432067184698629	0.665827179460111	   
df.mm.trans1:probe10	0.0260290773330719	0.164713386780675	0.158026483710951	0.874482031251072	   
df.mm.trans1:probe11	0.14041915175694	0.164713386780675	0.852506007565226	0.394228257587488	   
df.mm.trans1:probe12	0.0127645505885994	0.164713386780675	0.0774955262476394	0.938251751139642	   
df.mm.trans2:probe2	0.115117523893608	0.164713386780675	0.698895980123907	0.48485186076189	   
df.mm.trans2:probe3	0.0696142334338778	0.164713386780675	0.422638589336841	0.672690259595873	   
df.mm.trans2:probe4	0.0720579513406854	0.164713386780675	0.437474772081729	0.661903566630931	   
df.mm.trans2:probe5	0.205246697250125	0.164713386780675	1.24608388705784	0.213155283699728	   
df.mm.trans2:probe6	0.065765164644947	0.164713386780675	0.399270307837924	0.689817232471935	   
df.mm.trans3:probe2	-0.0051532750370441	0.164713386780675	-0.0312863158105417	0.975050224621962	   
df.mm.trans3:probe3	-0.158275631609797	0.164713386780675	-0.960915410115088	0.336930332745598	   
df.mm.trans3:probe4	0.0250238527384380	0.164713386780675	0.151923612449052	0.87929146541962	   
df.mm.trans3:probe5	0.0430577291604407	0.164713386780675	0.26141001652631	0.793854116008687	   
df.mm.trans3:probe6	-0.103466827530396	0.164713386780675	-0.628162832133175	0.53010452753839	   
df.mm.trans3:probe7	-0.0685202616666016	0.164713386780675	-0.415996920504344	0.677541233903643	   
df.mm.trans3:probe8	-0.107800052557453	0.164713386780675	-0.654470499723226	0.513026181645967	   
df.mm.trans3:probe9	0.0325659137350336	0.164713386780675	0.197712610805563	0.84332798455	   
df.mm.trans3:probe10	-0.123655881646312	0.164713386780675	-0.750733647478007	0.453068166031896	   
df.mm.trans3:probe11	-0.0725769862497712	0.164713386780675	-0.440625911884209	0.659621451208236	   
df.mm.trans3:probe12	-0.247630410616814	0.164713386780675	-1.50340185128090	0.133191666836755	   
df.mm.trans3:probe13	-0.05410979596277	0.164713386780675	-0.328508793488779	0.742626378928308