chr9.25191_chr9_84478766_84507522_+_1.R 

fitVsDatCorrelation=0.86239814251553
cont.fitVsDatCorrelation=0.276233706755002

fstatistic=9746.7574913751,53,715
cont.fstatistic=2694.38758138257,53,715

residuals=-0.514303755792801,-0.101725604693569,-0.003420473794084,0.0954831612119043,0.81937632732126
cont.residuals=-0.665222434096429,-0.220714024683127,-0.0414657125816994,0.182353836375610,1.47402645715700

predictedValues:
Include	Exclude	Both
chr9.25191_chr9_84478766_84507522_+_1.R.tl.Lung	101.118886545082	81.5512752598999	65.2982070412355
chr9.25191_chr9_84478766_84507522_+_1.R.tl.cerebhem	77.0230387858513	68.8348632909698	67.3523957923416
chr9.25191_chr9_84478766_84507522_+_1.R.tl.cortex	99.6051858298891	73.8796238853464	72.0702266086947
chr9.25191_chr9_84478766_84507522_+_1.R.tl.heart	84.2366205687424	75.1246768939746	61.1665886677867
chr9.25191_chr9_84478766_84507522_+_1.R.tl.kidney	114.355438200328	83.3635855204615	67.2116427366156
chr9.25191_chr9_84478766_84507522_+_1.R.tl.liver	119.037966915396	79.9529914031902	65.0413485623403
chr9.25191_chr9_84478766_84507522_+_1.R.tl.stomach	128.329818884321	84.4507518213328	94.7716054638908
chr9.25191_chr9_84478766_84507522_+_1.R.tl.testicle	128.480191330978	76.6235029877005	78.7299915333969


diffExp=19.5676112851824,8.18817549488156,25.7255619445427,9.11194367476774,30.9918526798661,39.084975512206,43.8790670629886,51.8566883432773
diffExpScore=0.995640913748827
diffExp1.5=0,0,0,0,0,0,1,1
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,0,0,0,1,1,1
diffExp1.4Score=0.75
diffExp1.3=0,0,1,0,1,1,1,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,0,1,0,1,1,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	70.8961706020526	78.1279777975927	73.5705779295285
cerebhem	70.9386609385603	75.7094855281016	75.1202706145595
cortex	72.6845320867947	71.3642102124258	69.0289032829657
heart	84.3007028556343	74.4300333531777	74.6449694032623
kidney	70.0508743279978	78.570589844761	70.8540797766217
liver	67.8631540042552	62.21159333297	85.0502717002717
stomach	78.6862181756805	76.7889492872324	78.1196897527111
testicle	70.6912875697826	80.8352678474671	71.1751004961287
cont.diffExp=-7.23180719554016,-4.77082458954122,1.32032187436894,9.8706695024566,-8.51971551676323,5.65156067128528,1.89726888844805,-10.1439802776846
cont.diffExpScore=3.8220804646363

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=0.723054299005217
cont.tran.correlation=0.175604151796179

tran.covariance=0.00990058298420203
cont.tran.covariance=0.00125704870996511

tran.mean=92.2480261327165
cont.tran.mean=74.0093567352804

weightedLogRatios:
wLogRatio
Lung	0.969677210310012
cerebhem	0.481936065010653
cortex	1.33010384755862
heart	0.501011942265142
kidney	1.44813625423645
liver	1.82303204897558
stomach	1.94379266422627
testicle	2.37623081881657

cont.weightedLogRatios:
wLogRatio
Lung	-0.41861692451725
cerebhem	-0.27951077224902
cortex	0.078405757046335
heart	0.544463630623346
kidney	-0.494294132913971
liver	0.362938593306567
stomach	0.106251348309199
testicle	-0.579992982724913

varWeightedLogRatios=0.465400489634663
cont.varWeightedLogRatios=0.174546626078699

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.34319651594588	0.081296914645806	53.4238788134617	7.8927080458191e-252	***
df.mm.trans1	0.309992912538787	0.0639496396855458	4.84745362230481	1.53484848467643e-06	***
df.mm.trans2	0.0649122841117124	0.0639496396855458	1.01505316419139	0.310423840213152	   
df.mm.exp2	-0.47268784908378	0.0844541088277676	-5.59697871003246	3.109472115831e-08	***
df.mm.exp3	-0.212554026161006	0.0844541088277676	-2.51679911269303	0.0120603257877013	*  
df.mm.exp4	-0.199386568565984	0.0844541088277676	-2.36088653747569	0.0184987139395254	*  
df.mm.exp5	0.116112271693997	0.0844541088277676	1.37485639604334	0.169606649967029	   
df.mm.exp6	0.147293839886129	0.0844541088277676	1.74406955363787	0.081576701607267	.  
df.mm.exp7	-0.0992619454451067	0.0844541088277676	-1.1753358933375	0.240251594616616	   
df.mm.exp8	-0.00990988024619324	0.0844541088277676	-0.117340415803843	0.906623251970795	   
df.mm.trans1:exp2	0.200495512081179	0.0634933938612998	3.15773815019493	0.00165682866660251	** 
df.mm.trans2:exp2	0.303166232845429	0.0634933938612998	4.77476812009278	2.18302454385456e-06	***
df.mm.trans1:exp3	0.197471336846845	0.0634933938612998	3.11010838825559	0.00194448026537354	** 
df.mm.trans2:exp3	0.113759123881375	0.0634933938612998	1.79166865973300	0.073608833493363	.  
df.mm.trans1:exp4	0.0167193997672207	0.0634933938612998	0.263325028801325	0.792375935629169	   
df.mm.trans2:exp4	0.117303693788188	0.0634933938612998	1.84749446602957	0.0650884048592262	.  
df.mm.trans1:exp5	0.00690228603129444	0.0634933938612998	0.108708727184632	0.913464019449195	   
df.mm.trans2:exp5	-0.094132648844172	0.0634933938612998	-1.48255815478699	0.138632447340060	   
df.mm.trans1:exp6	0.0158517329346281	0.0634933938612999	0.249659562524819	0.802922340402103	   
df.mm.trans2:exp6	-0.167086952093060	0.0634933938612998	-2.63156435546757	0.00868250889337535	** 
df.mm.trans1:exp7	0.337568686258371	0.0634933938612998	5.31659540826852	1.41483621371423e-07	***
df.mm.trans2:exp7	0.134198524509189	0.0634933938612998	2.11358247445936	0.0348967126456827	*  
df.mm.trans1:exp8	0.249387700517893	0.0634933938612998	3.92777398326944	9.40433899327568e-05	***
df.mm.trans2:exp8	-0.0524182292652912	0.0634933938612998	-0.82556981250361	0.409323531358603	   
df.mm.trans1:probe2	-0.505548127708069	0.0482266946348151	-10.4827447026218	5.14892287246789e-24	***
df.mm.trans1:probe3	0.106217950380158	0.0482266946348151	2.20247212014979	0.0279503944964249	*  
df.mm.trans1:probe4	-0.283445409510551	0.0482266946348151	-5.87735509673371	6.39276716608956e-09	***
df.mm.trans1:probe5	0.139211820760941	0.0482266946348151	2.88661335418254	0.00401156721471978	** 
df.mm.trans1:probe6	-0.415641477548212	0.0482266946348151	-8.6184939833749	4.33282557085874e-17	***
df.mm.trans2:probe2	-0.0221610862088182	0.0482266946348151	-0.459519077071891	0.646001136114569	   
df.mm.trans2:probe3	-0.107592810988321	0.0482266946348151	-2.23098041039391	0.0259917995762302	*  
df.mm.trans2:probe4	-0.0463004417324436	0.0482266946348151	-0.96005836773684	0.337350377594608	   
df.mm.trans2:probe5	0.165104706154299	0.0482266946348151	3.42351279523746	0.000653412187821121	***
df.mm.trans2:probe6	-0.167848034216290	0.0482266946348151	-3.48039681108728	0.000531060395175662	***
df.mm.trans3:probe2	-0.350639852611904	0.0482266946348151	-7.27065902540158	9.40678184031039e-13	***
df.mm.trans3:probe3	-0.588187991741416	0.0482266946348151	-12.1963156752775	3.23142227757712e-31	***
df.mm.trans3:probe4	-0.515624051148284	0.0482266946348151	-10.6916730464885	7.48179336551407e-25	***
df.mm.trans3:probe5	-0.618917615909382	0.0482266946348151	-12.8335068491835	4.47562471337039e-34	***
df.mm.trans3:probe6	-0.92636774292304	0.0482266946348151	-19.2086094628242	1.26801245260488e-66	***
df.mm.trans3:probe7	-0.618663431365701	0.0482266946348151	-12.8282362299631	4.73009214104937e-34	***
df.mm.trans3:probe8	-0.687102298032208	0.0482266946348151	-14.2473437840831	1.00136996822236e-40	***
df.mm.trans3:probe9	-0.869917599693601	0.0482266946348151	-18.0380929334021	3.12654596202505e-60	***
df.mm.trans3:probe10	-0.759681321538864	0.0482266946348151	-15.7522991631785	3.26337073189366e-48	***
df.mm.trans3:probe11	-0.605558578842019	0.0482266946348151	-12.5565018176648	8.03086460796744e-33	***
df.mm.trans3:probe12	-0.563655567759149	0.0482266946348151	-11.6876259512972	5.29622903912467e-29	***
df.mm.trans3:probe13	-0.484207401454101	0.0482266946348151	-10.040236120693	2.79588744035428e-22	***
df.mm.trans3:probe14	-0.588407983443226	0.0482266946348151	-12.2008772921056	3.08502037916298e-31	***
df.mm.trans3:probe15	-0.456965348139901	0.0482266946348151	-9.4753611376471	3.80649575963401e-20	***
df.mm.trans3:probe16	-0.652239658345645	0.0482266946348151	-13.5244528633897	2.82705313555321e-37	***
df.mm.trans3:probe17	-0.684074844026071	0.0482266946348151	-14.1845683019759	2.01481493897693e-40	***
df.mm.trans3:probe18	-0.562877729331263	0.0482266946348151	-11.6714971571972	6.21105604984823e-29	***
df.mm.trans3:probe19	-0.0923155067699443	0.0482266946348151	-1.91419933439314	0.0559935747206646	.  
df.mm.trans3:probe20	-0.157338155847323	0.0482266946348151	-3.26247023642670	0.00115693173584025	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21049955231394	0.154344064456888	27.2799577173895	7.31215607937174e-113	***
df.mm.trans1	0.035050753786546	0.121409863493877	0.288697744794959	0.772896365487593	   
df.mm.trans2	0.137440342257751	0.121409863493877	1.13203604964668	0.257998784660770	   
df.mm.exp2	-0.0516908618781853	0.160338070311189	-0.322386703157036	0.747253997897909	   
df.mm.exp3	-0.00191967513849407	0.160338070311189	-0.0119726720844794	0.990450757808295	   
df.mm.exp4	0.110187149857130	0.160338070311189	0.687217637353841	0.49216842984919	   
df.mm.exp5	0.031277206279113	0.160338070311189	0.195070367370701	0.845393277394812	   
df.mm.exp6	-0.416527361228281	0.160338070311189	-2.59780699879867	0.0095752315016831	** 
df.mm.exp7	0.0269671727937300	0.160338070311189	0.168189455825378	0.8664817985985	   
df.mm.exp8	0.0642731882196814	0.160338070311189	0.400860432553154	0.688642640301608	   
df.mm.trans1:exp2	0.0522900142488318	0.120543670290698	0.433784819416329	0.664575557398355	   
df.mm.trans2:exp2	0.0202460955344439	0.120543670290698	0.167956521363746	0.866664979168648	   
df.mm.trans1:exp3	0.0268318526227905	0.120543670290698	0.222590307380586	0.823917949628593	   
df.mm.trans2:exp3	-0.0886320618559335	0.120543670290698	-0.735269314781875	0.462416595050943	   
df.mm.trans1:exp4	0.0629866318430586	0.120543670290698	0.522521271263459	0.601469297537209	   
df.mm.trans2:exp4	-0.158675838510066	0.120543670290698	-1.31633488616540	0.188483480592936	   
df.mm.trans1:exp5	-0.0432718729142424	0.120543670290698	-0.358972584872270	0.719721574898905	   
df.mm.trans2:exp5	-0.0256279750072904	0.120543670290698	-0.212603241177966	0.831697039947563	   
df.mm.trans1:exp6	0.372804176785154	0.120543670290698	3.09268977695897	0.00206067906285686	** 
df.mm.trans2:exp6	0.188720508455839	0.120543670290698	1.56557791878021	0.117889914683281	   
df.mm.trans1:exp7	0.0772844279904398	0.120543670290698	0.64113219552768	0.521642243954895	   
df.mm.trans2:exp7	-0.0442546556217141	0.120543670290698	-0.367125503271897	0.713634034365133	   
df.mm.trans1:exp8	-0.0671672747146191	0.120543670290698	-0.557202834065376	0.577563234129476	   
df.mm.trans2:exp8	-0.0302080578023716	0.120543670290698	-0.250598457219057	0.802196567145687	   
df.mm.trans1:probe2	0.0406702982524434	0.0915594902671076	0.444195332824543	0.657035772565133	   
df.mm.trans1:probe3	0.101188578988850	0.0915594902671076	1.10516756584874	0.269458732818420	   
df.mm.trans1:probe4	0.0382964936795978	0.0915594902671076	0.418268969910984	0.675876068866032	   
df.mm.trans1:probe5	0.122544698986049	0.0915594902671076	1.33841613390974	0.181186260643468	   
df.mm.trans1:probe6	0.104618912927098	0.0915594902671076	1.14263319533444	0.253573384789081	   
df.mm.trans2:probe2	0.0317680575830448	0.0915594902671076	0.346966300165799	0.728718731286225	   
df.mm.trans2:probe3	-0.0487765569587987	0.0915594902671076	-0.532730761349832	0.59438549108618	   
df.mm.trans2:probe4	0.0867466578852832	0.0915594902671076	0.947434915072333	0.34373729573058	   
df.mm.trans2:probe5	0.105888407149711	0.0915594902671076	1.15649843441463	0.247863594310433	   
df.mm.trans2:probe6	0.0949899771002141	0.0915594902671076	1.03746729938206	0.299868853367048	   
df.mm.trans3:probe2	-0.136476291981295	0.0915594902671076	-1.49057505216719	0.136514103372630	   
df.mm.trans3:probe3	-0.131859904390614	0.0915594902671076	-1.44015550988694	0.150261113169812	   
df.mm.trans3:probe4	-0.0540479054010614	0.0915594902671076	-0.590303694826028	0.555173579378254	   
df.mm.trans3:probe5	-0.0929826483292005	0.0915594902671076	-1.01554353413219	0.310190323287666	   
df.mm.trans3:probe6	0.00499089163628254	0.0915594902671076	0.0545098233041988	0.956544209416	   
df.mm.trans3:probe7	-0.100575376423825	0.0915594902671076	-1.09847025284234	0.272368956225911	   
df.mm.trans3:probe8	-0.102744503232395	0.0915594902671076	-1.12216115372264	0.262170620867937	   
df.mm.trans3:probe9	-0.111654889626354	0.0915594902671076	-1.21947915285048	0.223064505228104	   
df.mm.trans3:probe10	-0.120534893637481	0.0915594902671076	-1.31646531982478	0.188439747413829	   
df.mm.trans3:probe11	0.000667080256802174	0.0915594902671076	0.00728575765173106	0.99418889016737	   
df.mm.trans3:probe12	-0.198872578853402	0.0915594902671076	-2.17205860663083	0.0301791839572323	*  
df.mm.trans3:probe13	-0.11539491486145	0.0915594902671076	-1.26032718754557	0.207962514089144	   
df.mm.trans3:probe14	-0.140365086571332	0.0915594902671076	-1.53304792503588	0.125706379074840	   
df.mm.trans3:probe15	-0.0850793509422834	0.0915594902671076	-0.92922482086871	0.353086184422854	   
df.mm.trans3:probe16	-0.0850131766898918	0.0915594902671076	-0.928502074901049	0.353460530320738	   
df.mm.trans3:probe17	0.0384682565347385	0.0915594902671076	0.420144939891152	0.674505802142868	   
df.mm.trans3:probe18	-0.0780987186715369	0.0915594902671076	-0.852983327492307	0.393954194675927	   
df.mm.trans3:probe19	-0.0357397974009223	0.0915594902671076	-0.390345089260088	0.696397649064419	   
df.mm.trans3:probe20	-0.149595370206241	0.0915594902671076	-1.63385979727306	0.10272850087439