chr5.18830_chr5_133105076_133112772_-_2.R 

fitVsDatCorrelation=0.896892880070694
cont.fitVsDatCorrelation=0.260577035393734

fstatistic=7085.12108558825,54,738
cont.fstatistic=1475.87730462425,54,738

residuals=-0.975333299122902,-0.0959021330700557,-0.0081278231154571,0.0945648515398566,0.760448284827544
cont.residuals=-0.852571303203275,-0.297932541807652,-0.0742763167249002,0.203370540518128,1.62405689822959

predictedValues:
Include	Exclude	Both
chr5.18830_chr5_133105076_133112772_-_2.R.tl.Lung	71.8435890618248	87.1311409290302	70.4077223126049
chr5.18830_chr5_133105076_133112772_-_2.R.tl.cerebhem	83.2884111485091	69.427290910418	69.8385034088001
chr5.18830_chr5_133105076_133112772_-_2.R.tl.cortex	133.215741068163	77.809954153622	104.362708157351
chr5.18830_chr5_133105076_133112772_-_2.R.tl.heart	65.5939288991785	77.0476484120722	69.2195517610365
chr5.18830_chr5_133105076_133112772_-_2.R.tl.kidney	70.8156198974199	83.242059123634	76.8672044320119
chr5.18830_chr5_133105076_133112772_-_2.R.tl.liver	68.5416465658765	75.5703667837197	69.1294141076501
chr5.18830_chr5_133105076_133112772_-_2.R.tl.stomach	71.7214533628334	90.5051339481888	75.9548616009361
chr5.18830_chr5_133105076_133112772_-_2.R.tl.testicle	67.664678071397	84.0798772889352	69.2639765089833


diffExp=-15.2875518672054,13.8611202380911,55.4057869145414,-11.4537195128936,-12.4264392262141,-7.02872021784326,-18.7836805853554,-16.4151992175382
diffExpScore=11.4760502351461
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=-1,0,1,0,0,0,-1,-1
diffExp1.2Score=1.33333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	75.2655779949768	79.9909308995333	78.58714472081
cerebhem	89.5880502672722	99.9383184299183	85.1769724835897
cortex	79.0875157864786	92.7257728062327	97.0393143974751
heart	81.4359862353967	87.2018142442863	82.1678675670173
kidney	81.6909710046663	89.3300323956176	85.3986142400457
liver	87.3526253450591	89.2855639027899	83.2412691473245
stomach	78.5509712340406	90.090066712553	80.4720619967352
testicle	73.6824713411108	97.1598615423817	91.75931863249
cont.diffExp=-4.72535290455649,-10.3502681626461,-13.6382570197541,-5.76582800888967,-7.6390613909513,-1.93293855773076,-11.5390954785124,-23.4773902012709
cont.diffExpScore=0.98751064588241

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,-1
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,0,0,0,-1
cont.diffExp1.2Score=0.5

tran.correlation=-0.25761674246099
cont.tran.correlation=0.338879426831941

tran.covariance=-0.00544389462432597
cont.tran.covariance=0.00151112292695635

tran.mean=79.8436587265514
cont.tran.mean=85.7735331338946

weightedLogRatios:
wLogRatio
Lung	-0.843257203756257
cerebhem	0.788422921979687
cortex	2.48585401848232
heart	-0.686244514344084
kidney	-0.701809556037388
liver	-0.417459322670286
stomach	-1.02097739564044
testicle	-0.939004427772354

cont.weightedLogRatios:
wLogRatio
Lung	-0.264962483010291
cerebhem	-0.497444832809477
cortex	-0.707972863048988
heart	-0.303321944123524
kidney	-0.397593535053973
liver	-0.0980721475248602
stomach	-0.607497806392091
testicle	-1.22753556423236

varWeightedLogRatios=1.47877502090239
cont.varWeightedLogRatios=0.121245505751623

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.41983378760334	0.0981820954480278	45.0166984869757	7.78176743358292e-214	***
df.mm.trans1	-0.519077159111543	0.0858577518050688	-6.04578093647332	2.36018716532413e-09	***
df.mm.trans2	-0.00253387733483361	0.0771618240715916	-0.0328384841250338	0.973812267917126	   
df.mm.exp2	-0.0711988172975484	0.101827199768905	-0.699212169824298	0.484639750653891	   
df.mm.exp3	0.110764102175725	0.101827199768905	1.08776537533293	0.277053813445959	   
df.mm.exp4	-0.196978960677804	0.101827199768905	-1.93444346034109	0.0534405363751838	.  
df.mm.exp5	-0.147849763903005	0.101827199768905	-1.45196729595380	0.146935678788230	   
df.mm.exp6	-0.17107732600068	0.101827199768905	-1.68007493468284	0.0933659421310382	.  
df.mm.exp7	-0.0395455302762361	0.101827199768905	-0.388359204279249	0.697862233409564	   
df.mm.exp8	-0.0791961660778192	0.101827199768905	-0.777750603547515	0.43696530545268	   
df.mm.trans1:exp2	0.219016853078210	0.0952506235474573	2.29937448093543	0.0217620705620135	*  
df.mm.trans2:exp2	-0.155935502422126	0.0763703998266788	-2.04183168840308	0.0415232821221631	*  
df.mm.trans1:exp3	0.506714443288568	0.0952506235474573	5.31980184923518	1.37876533752943e-07	***
df.mm.trans2:exp3	-0.223909084474052	0.0763703998266788	-2.93188309845450	0.00347333991267267	** 
df.mm.trans1:exp4	0.105970723229311	0.0952506235474573	1.11254624151109	0.266265622098948	   
df.mm.trans2:exp4	0.073988650975921	0.0763703998266787	0.968813193905452	0.33295579266025	   
df.mm.trans1:exp5	0.133437978442563	0.0952506235474574	1.40091448720101	0.161659981753223	   
df.mm.trans2:exp5	0.102188151594078	0.0763703998266788	1.33805966481768	0.181289117724163	   
df.mm.trans1:exp6	0.124027483741203	0.0952506235474573	1.30211728933625	0.193282535848121	   
df.mm.trans2:exp6	0.0287272078663900	0.0763703998266787	0.3761563109737	0.706908801057751	   
df.mm.trans1:exp7	0.037844061361606	0.0952506235474573	0.397310379209756	0.691253474952216	   
df.mm.trans2:exp7	0.0775377573647096	0.0763703998266787	1.01528547108147	0.310302500377486	   
df.mm.trans1:exp8	0.0192690860650709	0.0952506235474573	0.202298791833844	0.839738898135363	   
df.mm.trans2:exp8	0.043549082471737	0.0763703998266788	0.570235098553508	0.568691810366173	   
df.mm.trans1:probe2	0.340564931785169	0.0583288563432996	5.83870408465998	7.88185180474132e-09	***
df.mm.trans1:probe3	0.341855063947814	0.0583288563432996	5.86082233355985	6.94130811936307e-09	***
df.mm.trans1:probe4	0.094826078012285	0.0583288563432996	1.62571467978350	0.104437285595801	   
df.mm.trans1:probe5	0.292344975058075	0.0583288563432996	5.01201280781939	6.74892159872092e-07	***
df.mm.trans1:probe6	-0.0113523557562155	0.0583288563432996	-0.194626750255486	0.845738723708922	   
df.mm.trans1:probe7	0.0515937352227242	0.0583288563432996	0.884531918799586	0.376697219132414	   
df.mm.trans1:probe8	1.59790380976207	0.0583288563432996	27.3947392411993	1.53961219051507e-114	***
df.mm.trans1:probe9	0.103282361848848	0.0583288563432996	1.77069067222869	0.0770248934453935	.  
df.mm.trans1:probe10	0.415608713038428	0.0583288563432996	7.12526764784014	2.47462442582469e-12	***
df.mm.trans1:probe11	0.421761968141118	0.0583288563432996	7.23076011740742	1.20329499812972e-12	***
df.mm.trans1:probe12	0.318107541294383	0.0583288563432996	5.45369069851349	6.7355084528804e-08	***
df.mm.trans1:probe13	0.512115162754298	0.0583288563432996	8.77979091069776	1.13087415513217e-17	***
df.mm.trans1:probe14	0.282335955979884	0.0583288563432996	4.84041645387612	1.57856996580332e-06	***
df.mm.trans1:probe15	0.318480686454371	0.0583288563432996	5.46008796366459	6.50635307958846e-08	***
df.mm.trans1:probe16	0.682737921138754	0.0583288563432996	11.7049769863554	3.78383265549625e-29	***
df.mm.trans1:probe17	0.743372590059565	0.0583288563432996	12.7445082359301	9.10202678320689e-34	***
df.mm.trans1:probe18	0.76236151936987	0.0583288563432996	13.0700577237950	2.89440567372261e-35	***
df.mm.trans1:probe19	0.985819242469054	0.0583288563432996	16.9010555713098	2.04642702278037e-54	***
df.mm.trans1:probe20	0.92595704612487	0.0583288563432996	15.8747677251731	4.84451097789057e-49	***
df.mm.trans1:probe21	0.911161393889309	0.0583288563432996	15.621108504624	9.74784236207139e-48	***
df.mm.trans2:probe2	0.229678410417918	0.0583288563432996	3.93764638665509	9.00780412926501e-05	***
df.mm.trans2:probe3	-0.105340753335647	0.0583288563432996	-1.80598009183748	0.0713287589666104	.  
df.mm.trans2:probe4	0.224604854236089	0.0583288563432996	3.85066446210016	0.000128020908857277	***
df.mm.trans2:probe5	0.133332936934128	0.0583288563432996	2.28588292815798	0.0225437258533181	*  
df.mm.trans2:probe6	0.119097837137393	0.0583288563432996	2.04183391555686	0.0415230605256416	*  
df.mm.trans3:probe2	0.255503482945038	0.0583288563432996	4.38039589600814	1.35612868302672e-05	***
df.mm.trans3:probe3	0.191236004930609	0.0583288563432996	3.27858313910824	0.00109219693094882	** 
df.mm.trans3:probe4	1.05264485572658	0.0583288563432996	18.0467254411976	1.38786131311279e-60	***
df.mm.trans3:probe5	0.350784124431204	0.0583288563432996	6.01390369059585	2.84827069573753e-09	***
df.mm.trans3:probe6	0.286312719556909	0.0583288563432996	4.90859477634518	1.12973541695010e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22566845437917	0.214337286564177	19.7150412889729	7.86568275161279e-70	***
df.mm.trans1	0.0245207315103823	0.187432519833927	0.130824317637668	0.895949938228485	   
df.mm.trans2	0.216747905976158	0.168448798351447	1.28672871577238	0.198592324696037	   
df.mm.exp2	0.316315971870239	0.222294763595147	1.42295736865096	0.1551710801546	   
df.mm.exp3	-0.0136427379981582	0.222294763595147	-0.0613722868569453	0.951079339028887	   
df.mm.exp4	0.120550052431246	0.222294763595147	0.54229821018545	0.587776796586402	   
df.mm.exp5	0.109223317787348	0.222294763595147	0.491344537410112	0.623328872474767	   
df.mm.exp6	0.201321629381029	0.222294763595147	0.905651694736657	0.365415809631028	   
df.mm.exp7	0.137919541733590	0.222294763595147	0.620435405238672	0.535162709005946	   
df.mm.exp8	0.0182255344100782	0.222294763595147	0.0819881409499657	0.93467837442808	   
df.mm.trans1:exp2	-0.142116927084923	0.207937711061737	-0.683459129944582	0.494531282440226	   
df.mm.trans2:exp2	-0.0936760563839222	0.16672107269636	-0.56187292265405	0.57437319935742	   
df.mm.trans1:exp3	0.0631748733303405	0.207937711061737	0.303816335227350	0.76135345681934	   
df.mm.trans2:exp3	0.161375931236328	0.166721072696360	0.967939616908732	0.333391622741004	   
df.mm.trans1:exp4	-0.0417556845209574	0.207937711061737	-0.200808618637530	0.840903551202486	   
df.mm.trans2:exp4	-0.0342381806708934	0.16672107269636	-0.205362046423787	0.837345901319025	   
df.mm.trans1:exp5	-0.0273027347958458	0.207937711061737	-0.131302468688518	0.89557182404258	   
df.mm.trans2:exp5	0.00120115808548523	0.166721072696360	0.0072045966719086	0.994253560301417	   
df.mm.trans1:exp6	-0.0523914365339886	0.207937711061737	-0.251957359088335	0.801144171759044	   
df.mm.trans2:exp6	-0.0913950774820044	0.16672107269636	-0.548191515348855	0.583726182721439	   
df.mm.trans1:exp7	-0.0951947113242978	0.207937711061737	-0.457803978115515	0.647228031289183	   
df.mm.trans2:exp7	-0.0190228950523278	0.16672107269636	-0.114100123905592	0.909189457163669	   
df.mm.trans1:exp8	-0.0394835002280715	0.207937711061737	-0.189881383354984	0.849454294646069	   
df.mm.trans2:exp8	0.176218880183453	0.166721072696360	1.05696824842527	0.290871937296661	   
df.mm.trans1:probe2	0.291362559443567	0.127335322595884	2.28815189300023	0.0224105835881016	*  
df.mm.trans1:probe3	-0.0883918993418816	0.127335322595884	-0.694166375361573	0.48779632549719	   
df.mm.trans1:probe4	0.117278258050959	0.127335322595884	0.921019051588362	0.35734144799588	   
df.mm.trans1:probe5	-0.0852035583287031	0.127335322595884	-0.669127439203245	0.503623449091393	   
df.mm.trans1:probe6	0.149916596796581	0.127335322595884	1.17733707929858	0.239440540551712	   
df.mm.trans1:probe7	0.095650143057836	0.127335322595884	0.75116739886382	0.452791372456982	   
df.mm.trans1:probe8	0.304421448860244	0.127335322595884	2.39070701400244	0.0170656649048915	*  
df.mm.trans1:probe9	0.121895657788656	0.127335322595884	0.957280786695048	0.338739032340407	   
df.mm.trans1:probe10	0.0988086459952971	0.127335322595884	0.775972008245344	0.43801411063374	   
df.mm.trans1:probe11	0.112865514297407	0.127335322595884	0.886364537321671	0.375709855024844	   
df.mm.trans1:probe12	-0.0316982003447758	0.127335322595884	-0.248934857183142	0.803480432513921	   
df.mm.trans1:probe13	0.172970158580637	0.127335322595884	1.35838316544405	0.174757211490685	   
df.mm.trans1:probe14	0.129063607331556	0.127335322595884	1.01357270473297	0.311118866950809	   
df.mm.trans1:probe15	0.0117510066034972	0.127335322595884	0.0922839504698205	0.926497483950634	   
df.mm.trans1:probe16	0.161589684510557	0.127335322595884	1.26900911087635	0.204837893533576	   
df.mm.trans1:probe17	0.0573419764927917	0.127335322595884	0.450322623163835	0.652610084577319	   
df.mm.trans1:probe18	0.00901733492504053	0.127335322595884	0.0708156601107318	0.94356365318514	   
df.mm.trans1:probe19	0.227611167101318	0.127335322595884	1.78749432962661	0.07426787173008	.  
df.mm.trans1:probe20	-0.0182629095611109	0.127335322595884	-0.143423750682838	0.885994677848563	   
df.mm.trans1:probe21	0.0745230549336646	0.127335322595884	0.58525045065597	0.558558226183104	   
df.mm.trans2:probe2	-0.0374584476476087	0.127335322595884	-0.294171694734603	0.768709426362862	   
df.mm.trans2:probe3	-0.121572343827885	0.127335322595884	-0.954741711486536	0.340020947201468	   
df.mm.trans2:probe4	-0.211454108595525	0.127335322595884	-1.66060841787477	0.0972168809955165	.  
df.mm.trans2:probe5	-0.185231571649423	0.127335322595884	-1.45467548103114	0.14618429835987	   
df.mm.trans2:probe6	-0.170320678640813	0.127335322595884	-1.33757605642033	0.181446730197477	   
df.mm.trans3:probe2	-0.0882985924122335	0.127335322595884	-0.693433609874779	0.488255655458554	   
df.mm.trans3:probe3	-0.0636938951245712	0.127335322595884	-0.50020602159789	0.617079137296876	   
df.mm.trans3:probe4	-0.144491500326788	0.127335322595884	-1.13473227523326	0.256855928031729	   
df.mm.trans3:probe5	-0.172989754476707	0.127335322595884	-1.35853705751163	0.174708431633865	   
df.mm.trans3:probe6	-0.146900011522813	0.127335322595884	-1.15364698913136	0.249018400277211