chr17.10536_chr17_27880080_27889171_-_2.R 

fitVsDatCorrelation=0.947837709103202
cont.fitVsDatCorrelation=0.239062668806491

fstatistic=6286.51024242672,49,623
cont.fstatistic=666.105279419974,49,623

residuals=-0.931690239394777,-0.104695669954369,-0.0023655476554883,0.106917938765793,0.902708097826607
cont.residuals=-1.13812006075754,-0.467424520157616,-0.164669093369028,0.287851884705818,2.24949784673134

predictedValues:
Include	Exclude	Both
chr17.10536_chr17_27880080_27889171_-_2.R.tl.Lung	96.7527706139119	135.241383644301	54.3159274725867
chr17.10536_chr17_27880080_27889171_-_2.R.tl.cerebhem	87.9797428014617	92.1289172788843	58.637162219701
chr17.10536_chr17_27880080_27889171_-_2.R.tl.cortex	71.5797683887597	88.9148841929156	54.0243774843368
chr17.10536_chr17_27880080_27889171_-_2.R.tl.heart	74.7101463973267	97.4699439090095	52.284096155799
chr17.10536_chr17_27880080_27889171_-_2.R.tl.kidney	99.2203209115658	142.001681705160	53.1513267681914
chr17.10536_chr17_27880080_27889171_-_2.R.tl.liver	87.7546833044741	115.773685969987	52.360567121993
chr17.10536_chr17_27880080_27889171_-_2.R.tl.stomach	80.6250593266174	102.908768305229	56.4600505786099
chr17.10536_chr17_27880080_27889171_-_2.R.tl.testicle	83.889345633378	118.418743124809	55.4762074632152


diffExp=-38.488613030389,-4.14917447742262,-17.3351158041559,-22.7597975116827,-42.7813607935947,-28.0190026655129,-22.283708978612,-34.5293974914310
diffExpScore=0.995268426220177
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,-1,0,0,-1
diffExp1.4Score=0.666666666666667
diffExp1.3=-1,0,0,-1,-1,-1,0,-1
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	81.883396399609	123.630414477475	86.7305663429166
cerebhem	82.0954777852306	86.3718111595921	97.8816208860628
cortex	92.9007269145335	97.2731771858264	93.47883222076
heart	108.30511056635	83.8256241239044	112.525731715332
kidney	97.4088669999823	78.4361612920743	85.370050210013
liver	86.3149050762676	70.7460267343595	81.6559030899406
stomach	85.578433042845	117.840014430973	113.960796643922
testicle	91.2421508125456	98.9969880089846	97.573059952393
cont.diffExp=-41.7470180778661,-4.27633337436157,-4.37245027129293,24.4794864424457,18.9727057079080,15.5688783419081,-32.2615813881282,-7.75483719643903
cont.diffExpScore=4.61339877250475

cont.diffExp1.5=-1,0,0,0,0,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=-1,0,0,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=-1,0,0,0,0,0,-1,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=-1,0,0,1,1,1,-1,0
cont.diffExp1.2Score=2.5

tran.correlation=0.852853992344787
cont.tran.correlation=-0.421147427224947

tran.covariance=0.0166458685230511
cont.tran.covariance=-0.0072929290784295

tran.mean=98.460615344237
cont.tran.mean=92.6780803131596

weightedLogRatios:
wLogRatio
Lung	-1.58730603766446
cerebhem	-0.207377060655940
cortex	-0.949714317655392
heart	-1.18247079583862
kidney	-1.71238900416154
liver	-1.27825101076477
stomach	-1.10103601321039
testicle	-1.58639279051428

cont.weightedLogRatios:
wLogRatio
Lung	-1.89985571981482
cerebhem	-0.225114598086742
cortex	-0.209470955100516
heart	1.16752581859428
kidney	0.968476080232814
liver	0.866941264669862
stomach	-1.47451593329991
testicle	-0.371505693804997

varWeightedLogRatios=0.232287483862708
cont.varWeightedLogRatios=1.27079295109884

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.14426380947023	0.103396216558484	49.7529211483332	3.5386631967525e-219	***
df.mm.trans1	-0.905324288662848	0.0889234495542536	-10.1809398218464	1.26833108559515e-22	***
df.mm.trans2	-0.47215772131369	0.0811106655174423	-5.82115457075316	9.35897121003528e-09	***
df.mm.exp2	-0.555475869266657	0.106877383250226	-5.19731913688563	2.74427918843328e-07	***
df.mm.exp3	-0.715346024813447	0.106877383250225	-6.6931468853298	4.87601822195309e-11	***
df.mm.exp4	-0.547934931223287	0.106877383250226	-5.12676222564731	3.9394176609862e-07	***
df.mm.exp5	0.095636004944369	0.106877383250226	0.89481985838353	0.37122896504288	   
df.mm.exp6	-0.216373907437083	0.106877383250225	-2.02450603539291	0.0433442295811122	*  
df.mm.exp7	-0.494283626192201	0.106877383250225	-4.62477290480592	4.56238628286223e-06	***
df.mm.exp8	-0.296631274208451	0.106877383250226	-2.77543541194273	0.00567826054380781	** 
df.mm.trans1:exp2	0.460423493471728	0.0969457879485997	4.74928826939694	2.53565820749786e-06	***
df.mm.trans2:exp2	0.171603531400995	0.0797624719201565	2.15143196129595	0.0318258694811582	*  
df.mm.trans1:exp3	0.413999526112244	0.0969457879485996	4.27042303613794	2.25558874236158e-05	***
df.mm.trans2:exp3	0.29596437074522	0.0797624719201565	3.71057169644247	0.000225283538113203	***
df.mm.trans1:exp4	0.289391874414162	0.0969457879485997	2.98508971392957	0.00294610836602444	** 
df.mm.trans2:exp4	0.220417785287065	0.0797624719201565	2.76342721057725	0.00588878726846658	** 
df.mm.trans1:exp5	-0.0704521320693233	0.0969457879485996	-0.726716792550872	0.467672424044759	   
df.mm.trans2:exp5	-0.0468583132350457	0.0797624719201565	-0.587473182651004	0.557098810342541	   
df.mm.trans1:exp6	0.118760171013873	0.0969457879485996	1.22501630578152	0.22103202975589	   
df.mm.trans2:exp6	0.0609500010638378	0.0797624719201565	0.764143833516717	0.44507084228549	   
df.mm.trans1:exp7	0.311934168805717	0.0969457879485996	3.21761445655693	0.00135965259527439	** 
df.mm.trans2:exp7	0.221065268492970	0.0797624719201565	2.77154485275056	0.00574571217503108	** 
df.mm.trans1:exp8	0.153970922406564	0.0969457879485996	1.58821673086199	0.112744687031189	   
df.mm.trans2:exp8	0.16379707873279	0.0797624719201565	2.05356071332335	0.0404351150190806	*  
df.mm.trans1:probe2	0.0257874470324515	0.0616079730449021	0.418573209893738	0.67567232698492	   
df.mm.trans1:probe3	0.283788079382579	0.0616079730449021	4.60635312860794	4.97091367130466e-06	***
df.mm.trans1:probe4	0.0153758121986178	0.0616079730449021	0.249575037753820	0.80299827038652	   
df.mm.trans1:probe5	0.0370481989894371	0.0616079730449021	0.601353967000912	0.547822995290796	   
df.mm.trans1:probe6	-0.0592354465344922	0.0616079730449021	-0.961489943701268	0.336678899421946	   
df.mm.trans1:probe7	0.283314769150619	0.0616079730449021	4.59867051532646	5.15148649118361e-06	***
df.mm.trans1:probe8	0.0516743834323334	0.0616079730449021	0.838761297903296	0.401924977677023	   
df.mm.trans1:probe9	-0.00568864897621253	0.0616079730449021	-0.092336246350232	0.926460574240075	   
df.mm.trans1:probe10	0.51370933368029	0.0616079730449021	8.33835798015754	4.8349940391453e-16	***
df.mm.trans1:probe11	0.808378731765867	0.0616079730449021	13.1213330322796	6.6702258493861e-35	***
df.mm.trans1:probe12	1.75490269480813	0.0616079730449021	28.4849932252940	6.43324697737009e-115	***
df.mm.trans1:probe13	1.71149324584427	0.0616079730449021	27.7803855776407	4.02621480748546e-111	***
df.mm.trans1:probe14	1.38392934053860	0.0616079730449021	22.4634778931931	2.59449422202152e-82	***
df.mm.trans1:probe15	0.526349904320586	0.0616079730449021	8.5435354923455	1.00057215775222e-16	***
df.mm.trans2:probe2	0.226174775822195	0.0616079730449021	3.67119326677654	0.000262133114298679	***
df.mm.trans2:probe3	0.82326266625051	0.0616079730449021	13.3629240755979	5.41805897487854e-36	***
df.mm.trans2:probe4	0.321502533432122	0.0616079730449021	5.21852152476758	2.45974822261105e-07	***
df.mm.trans2:probe5	1.09091873025484	0.0616079730449021	17.7074277295204	3.9035490915828e-57	***
df.mm.trans2:probe6	0.592557862811925	0.0616079730449021	9.61820091662564	1.63850968824537e-20	***
df.mm.trans3:probe2	0.182783986364363	0.0616079730449021	2.96688849398021	0.00312361792130926	** 
df.mm.trans3:probe3	0.201394938685243	0.0616079730449021	3.26897524348772	0.00113879683566511	** 
df.mm.trans3:probe4	0.0528863705609149	0.0616079730449021	0.85843386735625	0.390983076791224	   
df.mm.trans3:probe5	0.724716773235064	0.0616079730449021	11.7633601207244	5.45108599633665e-29	***
df.mm.trans3:probe6	0.101233188657881	0.0616079730449021	1.64318323837887	0.100849583598208	   
df.mm.trans3:probe7	0.333234698885825	0.0616079730449021	5.40895410798455	9.04767609266714e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.66472562898652	0.314971324089365	14.8100010134987	9.75153316383526e-43	***
df.mm.trans1	-0.290780962082468	0.270883573702668	-1.07345365430552	0.283483224090404	   
df.mm.trans2	0.166856512291132	0.247083835039051	0.675303231653181	0.499733688464048	   
df.mm.exp2	-0.477000954457093	0.32557584830475	-1.46509932152769	0.143398283483667	   
df.mm.exp3	-0.188466718984048	0.32557584830475	-0.578871927894469	0.562884813987445	   
df.mm.exp4	-0.369277294185566	0.32557584830475	-1.13422815638312	0.257135089224927	   
df.mm.exp5	-0.265579485805439	0.32557584830475	-0.815722318434527	0.414970726733083	   
df.mm.exp6	-0.445201895136306	0.32557584830475	-1.36742911814387	0.171983955557393	   
df.mm.exp7	-0.276879756668063	0.32557584830475	-0.850430884568853	0.395412289107892	   
df.mm.exp8	-0.231781486539914	0.32557584830475	-0.711912409187547	0.476785513832355	   
df.mm.trans1:exp2	0.479587647467744	0.295321668542733	1.62395008071799	0.104892276823075	   
df.mm.trans2:exp2	0.118365730728759	0.242976892477707	0.487148096766523	0.626324738019435	   
df.mm.trans1:exp3	0.314701949329206	0.295321668542733	1.06562431020421	0.287006308604395	   
df.mm.trans2:exp3	-0.0513065876713341	0.242976892477707	-0.211158300479299	0.832832799927202	   
df.mm.trans1:exp4	0.648933395914465	0.295321668542733	2.19737819820886	0.0283608078136123	*  
df.mm.trans2:exp4	-0.0192805545089168	0.242976892477707	-0.079351391452526	0.936778611732058	   
df.mm.trans1:exp5	0.439200489128136	0.295321668542733	1.48719357876912	0.137469612611199	   
df.mm.trans2:exp5	-0.189432038783900	0.242976892477707	-0.779629852255521	0.435904844840762	   
df.mm.trans1:exp6	0.497907950525004	0.295321668542733	1.68598515978165	0.0922993564582836	.  
df.mm.trans2:exp6	-0.112998315726121	0.242976892477707	-0.465057868564552	0.642052519803469	   
df.mm.trans1:exp7	0.32101681749388	0.295321668542733	1.08700732688509	0.277453945611959	   
df.mm.trans2:exp7	0.228911064707747	0.242976892477707	0.942110430228459	0.346501276113482	   
df.mm.trans1:exp8	0.340002216667079	0.295321668542733	1.15129451335157	0.250052745493976	   
df.mm.trans2:exp8	0.00957432548289202	0.242976892477707	0.0394042634476876	0.968580704447256	   
df.mm.trans1:probe2	-0.0388266290139821	0.18767364503554	-0.206883758274261	0.836168239418862	   
df.mm.trans1:probe3	-0.027834636055558	0.18767364503554	-0.14831403764917	0.882142928312178	   
df.mm.trans1:probe4	0.112327252895313	0.18767364503554	0.598524384572173	0.549707633695393	   
df.mm.trans1:probe5	0.053303169750336	0.18767364503554	0.284020538633658	0.776489008864852	   
df.mm.trans1:probe6	0.273371168850409	0.18767364503554	1.45663057164282	0.145722177170241	   
df.mm.trans1:probe7	-0.0860664264026023	0.18767364503554	-0.458596231699469	0.646684079400753	   
df.mm.trans1:probe8	-0.0474179625342589	0.18767364503554	-0.252661808349698	0.800612902941503	   
df.mm.trans1:probe9	0.0595806906212546	0.18767364503554	0.317469672473041	0.750993617624922	   
df.mm.trans1:probe10	0.185664262462417	0.18767364503554	0.98929320857629	0.322903768397223	   
df.mm.trans1:probe11	0.16751269977746	0.18767364503554	0.892574446165512	0.372429736596512	   
df.mm.trans1:probe12	-0.059742716351346	0.18767364503554	-0.318333010157247	0.750339029902698	   
df.mm.trans1:probe13	-0.00447592674655142	0.18767364503554	-0.0238495221089984	0.980980275072674	   
df.mm.trans1:probe14	0.078608400831968	0.18767364503554	0.418856898191975	0.675465083584406	   
df.mm.trans1:probe15	0.0237312631717319	0.18767364503554	0.126449631045627	0.899416815473748	   
df.mm.trans2:probe2	-0.072096926316433	0.18767364503554	-0.384161166064526	0.700990081153575	   
df.mm.trans2:probe3	0.0266835692505265	0.18767364503554	0.142180694819848	0.886983247176425	   
df.mm.trans2:probe4	0.0760770538333487	0.18767364503554	0.405368872219336	0.685345554413386	   
df.mm.trans2:probe5	-0.00083964012733712	0.18767364503554	-0.00447393733509101	0.996431758568611	   
df.mm.trans2:probe6	-0.215536267747005	0.18767364503554	-1.14846316170920	0.251218177133887	   
df.mm.trans3:probe2	0.0305406662493775	0.18767364503554	0.162732845326226	0.87078155389061	   
df.mm.trans3:probe3	-0.0825070035920741	0.187673645035540	-0.43963020794129	0.660357415664475	   
df.mm.trans3:probe4	-0.140685683341378	0.18767364503554	-0.749629407553397	0.453760904131271	   
df.mm.trans3:probe5	-0.0573453160337407	0.18767364503554	-0.305558705501143	0.760042732492011	   
df.mm.trans3:probe6	-0.0583621169966997	0.18767364503554	-0.310976626396571	0.755922410623127	   
df.mm.trans3:probe7	-0.237604178247941	0.18767364503554	-1.26604978660134	0.205968464074198