chr5.17801_chr5_29860591_29861418_-_0.R 

fitVsDatCorrelation=0.936271259160648
cont.fitVsDatCorrelation=0.267757151922209

fstatistic=9969.76303143071,45,531
cont.fstatistic=1315.01044761415,45,531

residuals=-0.794574448366484,-0.0804472231560902,0.00261156700069701,0.0817700183205642,0.535633822879768
cont.residuals=-0.715863087312497,-0.279015783018700,-0.0975824104715104,0.173718177092377,1.75249578926844

predictedValues:
Include	Exclude	Both
chr5.17801_chr5_29860591_29861418_-_0.R.tl.Lung	100.005304122264	45.845183376414	71.7313308029273
chr5.17801_chr5_29860591_29861418_-_0.R.tl.cerebhem	75.9490158268762	48.7062834439647	70.1613718010934
chr5.17801_chr5_29860591_29861418_-_0.R.tl.cortex	71.6431355749116	59.1144986701339	72.8408958149548
chr5.17801_chr5_29860591_29861418_-_0.R.tl.heart	78.5418411480815	65.027165665254	71.86234994866
chr5.17801_chr5_29860591_29861418_-_0.R.tl.kidney	106.860156114195	50.1802142831498	76.6171073330606
chr5.17801_chr5_29860591_29861418_-_0.R.tl.liver	94.0091665391842	54.6851786833353	67.8014736568129
chr5.17801_chr5_29860591_29861418_-_0.R.tl.stomach	71.9436087745768	49.3586135508452	66.0064947102572
chr5.17801_chr5_29860591_29861418_-_0.R.tl.testicle	93.4586325890732	61.5155800105123	71.7329679805712


diffExp=54.16012074585,27.2427323829116,12.5286369047777,13.5146754828275,56.6799418310457,39.3239878558489,22.5849952237316,31.9430525785610
diffExpScore=0.996138670281613
diffExp1.5=1,1,0,0,1,1,0,1
diffExp1.5Score=0.833333333333333
diffExp1.4=1,1,0,0,1,1,1,1
diffExp1.4Score=0.857142857142857
diffExp1.3=1,1,0,0,1,1,1,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	72.6828542177321	87.0047930359232	67.3211108745899
cerebhem	81.3613428948514	68.5727651903125	71.4563445896377
cortex	80.2863736159138	65.7571431093566	69.6218104357223
heart	68.5056805307542	66.8067071042382	70.2990623830096
kidney	62.73240406653	67.5847888238452	81.0791165733667
liver	78.4352269424287	74.2382174802767	68.1654456377206
stomach	68.397642995177	70.408490713172	65.4499369729413
testicle	68.7074718116631	74.0579662821947	76.3165831964841
cont.diffExp=-14.3219388181910,12.7885777045390,14.5292305065572,1.69897342651602,-4.85238475731526,4.19700946215201,-2.01084771799511,-5.35049447053161
cont.diffExpScore=7.78177670345453

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

tran.correlation=-0.258239978526398
cont.tran.correlation=-0.000430090808571945

tran.covariance=-0.00474921158043881
cont.tran.covariance=0.000157299697659371

tran.mean=70.4277236482983
cont.tran.mean=72.2212418008981

weightedLogRatios:
wLogRatio
Lung	3.28769465233971
cerebhem	1.82496755215649
cortex	0.802635993041556
heart	0.806141016961301
kidney	3.24551118633334
liver	2.31483463458189
stomach	1.54004767971399
testicle	1.81026189822394

cont.weightedLogRatios:
wLogRatio
Lung	-0.787063456707363
cerebhem	0.7376117809271
cortex	0.855577971144231
heart	0.105836017003995
kidney	-0.311141832448218
liver	0.238386988536162
stomach	-0.122851071862027
testicle	-0.320009492732895

varWeightedLogRatios=0.91840429456135
cont.varWeightedLogRatios=0.308322092917644

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.45978693128444	0.0762590209518732	58.4820900611745	1.4215455215033e-233	***
df.mm.trans1	0.0968913401760422	0.0607111288083677	1.59594035027542	0.111097054437450	   
df.mm.trans2	-0.640862779203934	0.0607111288083676	-10.5559358190620	8.93395703398214e-24	***
df.mm.exp2	-0.192493328603751	0.0809481717444902	-2.37798241091039	0.0177605689570772	*  
df.mm.exp3	-0.0946697340614394	0.0809481717444902	-1.16951046603326	0.242722716657637	   
df.mm.exp4	0.106118379723935	0.0809481717444902	1.31094226635401	0.190443917473119	   
df.mm.exp5	0.0907557092232287	0.0809481717444902	1.12115823331620	0.262727286745039	   
df.mm.exp6	0.17083533580621	0.0809481717444902	2.11042858812729	0.0352884774141116	*  
df.mm.exp7	-0.172323994823779	0.0809481717444902	-2.12881886162559	0.0337288553065455	*  
df.mm.exp8	0.22629319171858	0.0809481717444902	2.79553184268159	0.00536900929905222	** 
df.mm.trans1:exp2	-0.0826676264139977	0.0627021842144697	-1.31841701289443	0.187932475581505	   
df.mm.trans2:exp2	0.253031232443871	0.0627021842144696	4.0354452658043	6.25103285416199e-05	***
df.mm.trans1:exp3	-0.238856147013936	0.0627021842144696	-3.80937522362763	0.000155604255068134	***
df.mm.trans2:exp3	0.348875811338494	0.0627021842144696	5.56401368962175	4.18620867800983e-08	***
df.mm.trans1:exp4	-0.347710114482298	0.0627021842144696	-5.54542268085866	4.62912342354273e-08	***
df.mm.trans2:exp4	0.243416594757856	0.0627021842144696	3.88210710372164	0.000116599796021380	***
df.mm.trans1:exp5	-0.0244579075761052	0.0627021842144697	-0.390064682474986	0.696645132000496	   
df.mm.trans2:exp5	-0.000405039419763292	0.0627021842144696	-0.00645973381689344	0.99484834005564	   
df.mm.trans1:exp6	-0.232666267720414	0.0627021842144696	-3.7106565047972	0.000228521079819795	***
df.mm.trans2:exp6	0.0054872390617844	0.0627021842144696	0.0875127259206087	0.930296962072235	   
df.mm.trans1:exp7	-0.157016630298811	0.0627021842144696	-2.50416524186372	0.0125728418240071	*  
df.mm.trans2:exp7	0.246166144094675	0.0627021842144696	3.92595803764469	9.77630382489828e-05	***
df.mm.trans1:exp8	-0.293997511332463	0.0627021842144696	-4.68879218508337	3.49858849306975e-06	***
df.mm.trans2:exp8	0.0677271430989993	0.0627021842144696	1.08014009316393	0.280570148788052	   
df.mm.trans1:probe2	-0.00361877503947503	0.0443371396532596	-0.0816194970576769	0.934980051454664	   
df.mm.trans1:probe3	0.128769112228115	0.0443371396532596	2.90431708574704	0.00383381411074983	** 
df.mm.trans1:probe4	0.130674240350147	0.0443371396532596	2.94728621133638	0.00334668859962124	** 
df.mm.trans1:probe5	0.349753859954325	0.0443371396532596	7.8885075286676	1.75489703600642e-14	***
df.mm.trans1:probe6	0.268230740691969	0.0443371396532596	6.0497980426721	2.73960664849889e-09	***
df.mm.trans2:probe2	-0.00212589743280426	0.0443371396532596	-0.047948456969257	0.961775349034004	   
df.mm.trans2:probe3	0.0469424337977297	0.0443371396532596	1.05876098830112	0.290190027416141	   
df.mm.trans2:probe4	0.0374615130467973	0.0443371396532596	0.844923992385766	0.398533806235301	   
df.mm.trans2:probe5	0.00990066981846878	0.0443371396532596	0.223304207170272	0.823384631013209	   
df.mm.trans2:probe6	0.0220490888023435	0.0443371396532596	0.497305170671344	0.619179717581781	   
df.mm.trans3:probe2	0.13611686088164	0.0443371396532596	3.07004154860119	0.00224977083332417	** 
df.mm.trans3:probe3	0.874759087438042	0.0443371396532596	19.7297140564126	2.08478683409584e-65	***
df.mm.trans3:probe4	0.217456440377888	0.0443371396532596	4.90461139528881	1.24641553542764e-06	***
df.mm.trans3:probe5	0.0168163377760629	0.0443371396532596	0.37928332561766	0.70462910438874	   
df.mm.trans3:probe6	0.374151926487365	0.0443371396532596	8.43879261074205	3.06055640128685e-16	***
df.mm.trans3:probe7	0.155399340142425	0.0443371396532596	3.50494735018389	0.000495294150597458	***
df.mm.trans3:probe8	0.175558916878355	0.0443371396532596	3.95963560688218	8.52929527686747e-05	***
df.mm.trans3:probe9	1.67565219533914	0.0443371396532596	37.7934212365445	1.16093765928462e-152	***
df.mm.trans3:probe10	0.343054401671708	0.0443371396532596	7.73740490150196	5.14486818560926e-14	***
df.mm.trans3:probe11	0.129759461437484	0.0443371396532596	2.92665387195189	0.00357306217308840	** 
df.mm.trans3:probe12	0.186620599456739	0.0443371396532596	4.20912582354687	3.01040782385832e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.68756938645516	0.209163577247263	22.4110213075661	8.62180837919159e-79	***
df.mm.trans1	-0.409674557499710	0.1665187504609	-2.46023079302354	0.0142021552082062	*  
df.mm.trans2	-0.210688667740277	0.1665187504609	-1.26525491668128	0.206334893868229	   
df.mm.exp2	-0.184885846528494	0.222025000614533	-0.832725350824259	0.40537401313015	   
df.mm.exp3	-0.214104520563499	0.222025000614533	-0.964326179353174	0.335321374491319	   
df.mm.exp4	-0.366633163508204	0.222025000614533	-1.65131477308148	0.0992655491470575	.  
df.mm.exp5	-0.585759212737021	0.222025000614533	-2.63825790391048	0.00857783251500239	** 
df.mm.exp6	-0.094980399296038	0.222025000614533	-0.427791460570413	0.668976393350691	   
df.mm.exp7	-0.244228154551770	0.222025000614533	-1.10000294505476	0.271829272703166	   
df.mm.exp8	-0.342779041379028	0.222025000614533	-1.54387587177239	0.12321413290505	   
df.mm.trans1:exp2	0.297680589946321	0.171979825964356	1.73090412365005	0.0840497717550074	.  
df.mm.trans2:exp2	-0.0531819159601205	0.171979825964356	-0.309233456086547	0.757265245451375	   
df.mm.trans1:exp3	0.313598919889152	0.171979825964356	1.82346340991267	0.068795258262696	.  
df.mm.trans2:exp3	-0.0658903833370724	0.171979825964356	-0.383128561548427	0.701777771938006	   
df.mm.trans1:exp4	0.307444319017494	0.171979825964356	1.78767664924381	0.074398343340783	.  
df.mm.trans2:exp4	0.102473435258564	0.171979825964356	0.595845673665249	0.55153221257751	   
df.mm.trans1:exp5	0.438531824366504	0.171979825964356	2.54990271043415	0.0110550808217286	*  
df.mm.trans2:exp5	0.333178943562999	0.171979825964356	1.93731411050534	0.0532369248219897	.  
df.mm.trans1:exp6	0.171148035205339	0.171979825964356	0.99516343993051	0.320110046592027	   
df.mm.trans2:exp6	-0.0637037322515972	0.171979825964356	-0.370413982537698	0.711221764972252	   
df.mm.trans1:exp7	0.183461005674841	0.171979825964356	1.06675887503727	0.286565501253817	   
df.mm.trans2:exp7	0.0325788076605668	0.171979825964356	0.189433891317689	0.849825150351214	   
df.mm.trans1:exp8	0.286531480911625	0.171979825964356	1.66607611855015	0.0962880076978245	.  
df.mm.trans2:exp8	0.181663946575614	0.171979825964356	1.05630963141726	0.291307115276133	   
df.mm.trans1:probe2	0.0989715140969837	0.121608101166678	0.81385625749827	0.416092101562087	   
df.mm.trans1:probe3	0.0974178612532177	0.121608101166678	0.80108035828711	0.423443421609812	   
df.mm.trans1:probe4	0.131391461470391	0.121608101166678	1.08044990596723	0.280432358890358	   
df.mm.trans1:probe5	-0.127124303811780	0.121608101166678	-1.04536048661381	0.296332023364911	   
df.mm.trans1:probe6	-0.0528642062233419	0.121608101166678	-0.434709577044421	0.663949850473067	   
df.mm.trans2:probe2	0.0737245882137787	0.121608101166678	0.606247342952345	0.54460959350295	   
df.mm.trans2:probe3	-0.0691905592045636	0.121608101166678	-0.56896340408876	0.569621542254366	   
df.mm.trans2:probe4	-0.0864078680478703	0.121608101166678	-0.710543682689676	0.477679004182908	   
df.mm.trans2:probe5	-0.0300605380291370	0.121608101166678	-0.247191903670426	0.804855254526513	   
df.mm.trans2:probe6	-0.0845807887283012	0.121608101166678	-0.695519360279898	0.4870341503043	   
df.mm.trans3:probe2	0.176621623094788	0.121608101166678	1.45238369319415	0.146985570711279	   
df.mm.trans3:probe3	0.138269684454879	0.121608101166678	1.13701047157512	0.256046734250651	   
df.mm.trans3:probe4	0.163000198228243	0.121608101166678	1.34037285891695	0.180697353891385	   
df.mm.trans3:probe5	0.229146579466601	0.121608101166678	1.88430357244481	0.0600699206797758	.  
df.mm.trans3:probe6	0.147834625697929	0.121608101166678	1.21566428781997	0.224653019805825	   
df.mm.trans3:probe7	-0.0151133100051669	0.121608101166678	-0.124278809225483	0.90114155572668	   
df.mm.trans3:probe8	0.123076607739689	0.121608101166678	1.01207572981505	0.311962784643978	   
df.mm.trans3:probe9	0.131543089417866	0.121608101166678	1.08169676325733	0.279878282576538	   
df.mm.trans3:probe10	0.308376834165899	0.121608101166678	2.53582476173386	0.0115038658425574	*  
df.mm.trans3:probe11	0.176402454473472	0.121608101166678	1.45058143973231	0.147486885689401	   
df.mm.trans3:probe12	0.128054125782162	0.121608101166678	1.05300653947922	0.292816920186731