chr5.18336_chr5_137859499_137862748_-_2.R 

fitVsDatCorrelation=0.837687350882426
cont.fitVsDatCorrelation=0.226855267391017

fstatistic=7448.2641125782,47,577
cont.fstatistic=2333.78906685253,47,577

residuals=-0.426295173670156,-0.113627314802466,-0.00461653016125228,0.104882154828304,0.991072990250993
cont.residuals=-0.725765516268345,-0.241011938019111,-0.0253371729018641,0.181986947495024,1.29234945871845

predictedValues:
Include	Exclude	Both
chr5.18336_chr5_137859499_137862748_-_2.R.tl.Lung	96.842720353768	98.7248225682288	104.458166283059
chr5.18336_chr5_137859499_137862748_-_2.R.tl.cerebhem	84.1236207003339	105.6003849541	76.5428522191848
chr5.18336_chr5_137859499_137862748_-_2.R.tl.cortex	86.5058478079244	99.990078649487	91.6149328172297
chr5.18336_chr5_137859499_137862748_-_2.R.tl.heart	92.6048278591297	97.429128984815	76.1727097816455
chr5.18336_chr5_137859499_137862748_-_2.R.tl.kidney	85.900289524895	76.8731185360584	70.2526698530045
chr5.18336_chr5_137859499_137862748_-_2.R.tl.liver	87.3303805353502	72.370383201998	68.6061849585949
chr5.18336_chr5_137859499_137862748_-_2.R.tl.stomach	122.506054881923	108.481245313204	83.5184328627526
chr5.18336_chr5_137859499_137862748_-_2.R.tl.testicle	95.5772924411742	81.2221224462099	74.2053860806893


diffExp=-1.88210221446077,-21.476764253766,-13.4842308415627,-4.82430112568538,9.02717098883653,14.9599973333521,14.0248095687193,14.3551699949643
diffExpScore=8.03731282622919
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,-1,0,0,0,1,0,0
diffExp1.2Score=2

cont.predictedValues:
Include	Exclude	Both
Lung	86.9278172927444	97.1205284720916	95.9738258308602
cerebhem	85.784247661089	101.664316710701	93.4746229687919
cortex	85.9856199041084	88.1712697410514	90.8708004086622
heart	88.092859313528	90.7440875109844	104.500598755696
kidney	92.1535486891942	91.6789390200372	81.2538513644645
liver	85.0319631632178	90.2773010653037	89.8633698568427
stomach	87.3318056038806	80.9646627038582	86.6213229534142
testicle	89.1298980838799	103.052404402683	87.9825942049646
cont.diffExp=-10.1927111793472,-15.8800690496123,-2.18564983694299,-2.65122819745643,0.474609669156976,-5.24533790208594,6.36714290002246,-13.9225063188036
cont.diffExpScore=1.28672522027344

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=0.436597276430704
cont.tran.correlation=0.0663611513210105

tran.covariance=0.00774412861338764
cont.tran.covariance=0.000142117395253570

tran.mean=93.2551449224125
cont.tran.mean=90.2569543336471

weightedLogRatios:
wLogRatio
Lung	-0.088208903831234
cerebhem	-1.03363929215291
cortex	-0.656593692656949
heart	-0.23125658233905
kidney	0.488277660553708
liver	0.822208519953448
stomach	0.577200074935576
testicle	0.728875178689766

cont.weightedLogRatios:
wLogRatio
Lung	-0.501210704928678
cerebhem	-0.770527053702235
cortex	-0.112119843930104
heart	-0.133232172514646
kidney	0.0233435941027404
liver	-0.267745965969038
stomach	0.335500619185874
testicle	-0.662238174859512

varWeightedLogRatios=0.470515917971324
cont.varWeightedLogRatios=0.136215823207281

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.08105827080229	0.092215125617616	44.2558446184308	1.2587457577743e-187	***
df.mm.trans1	0.36487940625896	0.0781665861686036	4.66797162501025	3.78625621686203e-06	***
df.mm.trans2	0.577295977672433	0.0723059978861359	7.98406763684434	7.69498522534444e-15	***
df.mm.exp2	0.237460700699604	0.0952393722094538	2.49330392662996	0.0129351065846739	*  
df.mm.exp3	0.0310507392839966	0.0952393722094539	0.326028391028332	0.744521082644036	   
df.mm.exp4	0.25782528506363	0.0952393722094539	2.70712919544042	0.00698781942278361	** 
df.mm.exp5	0.0266071742250661	0.0952393722094539	0.279371583493334	0.780059786161575	   
df.mm.exp6	0.00647489311685206	0.0952393722094539	0.0679854661642691	0.945820755671766	   
df.mm.exp7	0.553032435582295	0.0952393722094539	5.80676271538252	1.05249133159585e-08	***
df.mm.exp8	0.133648218600365	0.0952393722094539	1.40328747974567	0.161069031150367	   
df.mm.trans1:exp2	-0.378261531600227	0.0839932312960437	-4.50347636069628	8.09294691620712e-06	***
df.mm.trans2:exp2	-0.170135093974086	0.0709872367982477	-2.39669976812347	0.0168606705803651	*  
df.mm.trans1:exp3	-0.143926945810916	0.0839932312960437	-1.71355409942057	0.0871477641442461	.  
df.mm.trans2:exp3	-0.0183161816616920	0.0709872367982477	-0.258020772293874	0.796482877344944	   
df.mm.trans1:exp4	-0.30257223095845	0.0839932312960437	-3.60234064447408	0.000342569445507641	***
df.mm.trans2:exp4	-0.271036463512486	0.0709872367982477	-3.81810133394538	0.000149036405062511	***
df.mm.trans1:exp5	-0.146508197648365	0.0839932312960437	-1.74428576431332	0.0816417590641252	.  
df.mm.trans2:exp5	-0.276787332664253	0.0709872367982477	-3.89911405413495	0.000107894637277417	***
df.mm.trans1:exp6	-0.109864712154934	0.0839932312960437	-1.30801863983186	0.191387841978966	   
df.mm.trans2:exp6	-0.317014159152153	0.0709872367982477	-4.46579094285831	9.59984217947871e-06	***
df.mm.trans1:exp7	-0.317960202107026	0.0839932312960437	-3.78554553981069	0.000169421145814165	***
df.mm.trans2:exp7	-0.458791541737656	0.0709872367982477	-6.46301451402572	2.18578242358823e-10	***
df.mm.trans1:exp8	-0.146801176418467	0.0839932312960437	-1.74777388788686	0.0810351021088916	.  
df.mm.trans2:exp8	-0.328796974558669	0.0709872367982477	-4.63177592745496	4.48399680513624e-06	***
df.mm.trans1:probe2	0.6401615639659	0.0549864771825792	11.6421636148881	2.76793895968677e-28	***
df.mm.trans1:probe3	0.0982395615068143	0.0549864771825791	1.78661311908773	0.0745249404561127	.  
df.mm.trans1:probe4	-0.12837884676139	0.0549864771825791	-2.33473489009154	0.0198993312639554	*  
df.mm.trans1:probe5	0.297152040992539	0.0549864771825791	5.40409308284771	9.54271571595568e-08	***
df.mm.trans1:probe6	-0.114199204654137	0.0549864771825791	-2.07685981182148	0.0382564288361554	*  
df.mm.trans1:probe7	0.082274775398495	0.0549864771825791	1.49627289497574	0.135129314258381	   
df.mm.trans1:probe8	0.416421168542426	0.0549864771825792	7.57315598087373	1.45411248041798e-13	***
df.mm.trans1:probe9	0.570744175026569	0.0549864771825791	10.3797188739960	2.94532441390628e-23	***
df.mm.trans1:probe10	0.33815715938884	0.0549864771825791	6.1498240424825	1.44963963493167e-09	***
df.mm.trans1:probe11	0.356366486230269	0.0549864771825791	6.48098413446231	1.95634090462535e-10	***
df.mm.trans1:probe12	-0.0139279629503938	0.0549864771825792	-0.253297968228567	0.800128187911028	   
df.mm.trans2:probe2	-0.184622300915681	0.0549864771825791	-3.35759463736246	0.000838023226782083	***
df.mm.trans2:probe3	-0.167410406549197	0.0549864771825792	-3.04457414126242	0.00243632354038946	** 
df.mm.trans2:probe4	-0.297539714226214	0.0549864771825791	-5.41114342055869	9.191943177579e-08	***
df.mm.trans2:probe5	-0.232724312094116	0.0549864771825792	-4.23239174463512	2.68962873764827e-05	***
df.mm.trans2:probe6	-0.0419530057195143	0.0549864771825791	-0.762969513035214	0.445793389794854	   
df.mm.trans3:probe2	-0.518983717993415	0.0549864771825791	-9.43838821079885	9.21640133268318e-20	***
df.mm.trans3:probe3	-0.0392242183144827	0.0549864771825791	-0.713342994937486	0.475921947581265	   
df.mm.trans3:probe4	-0.627016772464077	0.0549864771825791	-11.4031086294564	2.64143809159135e-27	***
df.mm.trans3:probe5	-0.640496233030618	0.0549864771825791	-11.6482500034307	2.61247486814863e-28	***
df.mm.trans3:probe6	-0.581921068557052	0.0549864771825792	-10.5829850969507	4.84102406576345e-24	***
df.mm.trans3:probe7	-0.434309225859491	0.0549864771825791	-7.89847337223286	1.43352680491702e-14	***
df.mm.trans3:probe8	-0.153624652816658	0.0549864771825791	-2.7938624310584	0.00538127823612178	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.53779129862803	0.164443697330682	27.5948021863247	1.64215108142934e-107	***
df.mm.trans1	-0.105131037333591	0.139391475652092	-0.754214250489667	0.45102826561358	   
df.mm.trans2	0.053225918343331	0.128940513304569	0.412794372995916	0.679910616728261	   
df.mm.exp2	0.0588664319216807	0.169836720306802	0.346606033226157	0.729013675809215	   
df.mm.exp3	-0.052932830062971	0.169836720306802	-0.311668936890388	0.75540471834577	   
df.mm.exp4	-0.139713353899438	0.169836720306802	-0.822633371905986	0.411056344834981	   
df.mm.exp5	0.167215302038517	0.169836720306802	0.984565067768918	0.325250416693513	   
df.mm.exp6	-0.0293324747783678	0.169836720306802	-0.172709851705686	0.86294006338724	   
df.mm.exp7	-0.0747738403367053	0.169836720306802	-0.440268984243396	0.659907180713943	   
df.mm.exp8	0.171238122334112	0.169836720306802	1.00825146661322	0.313756377627127	   
df.mm.trans1:exp2	-0.0721091239608733	0.149781908472877	-0.481427461407538	0.630395248021426	   
df.mm.trans2:exp2	-0.0131428273010952	0.126588817227209	-0.103822972589322	0.9173459114948	   
df.mm.trans1:exp3	0.0420348139337921	0.149781908472877	0.280640127785553	0.779087029759483	   
df.mm.trans2:exp3	-0.0437387686170296	0.126588817227209	-0.345518423942019	0.7298305911695	   
df.mm.trans1:exp4	0.153026743512713	0.149781908472877	1.02166373144073	0.307368237911857	   
df.mm.trans2:exp4	0.071803904797237	0.126588817227209	0.567221547448059	0.570784274506172	   
df.mm.trans1:exp5	-0.108837196727575	0.149781908472877	-0.726637801836287	0.467742540963255	   
df.mm.trans2:exp5	-0.224875390513229	0.126588817227209	-1.77642382193689	0.0761899391130824	.  
df.mm.trans1:exp6	0.00728160981461538	0.149781908472877	0.0486147485290853	0.961243138709861	   
df.mm.trans2:exp6	-0.0437342376059119	0.126588817227209	-0.345482630803123	0.729857481023307	   
df.mm.trans1:exp7	0.0794104742072291	0.149781908472877	0.530174004436652	0.596195252347195	   
df.mm.trans2:exp7	-0.107166131827144	0.126588817227209	-0.846568711000719	0.397586525932825	   
df.mm.trans1:exp8	-0.146221375630991	0.149781908472877	-0.97622855204485	0.329360319144127	   
df.mm.trans2:exp8	-0.111953251611473	0.126588817227209	-0.884385003854906	0.376856778379522	   
df.mm.trans1:probe2	0.0645656059193371	0.098055276187415	0.658461313146796	0.510504392861176	   
df.mm.trans1:probe3	0.0756488527816594	0.098055276187415	0.771491914795796	0.440731241049677	   
df.mm.trans1:probe4	0.0985550706039286	0.098055276187415	1.00509706806147	0.315271420496697	   
df.mm.trans1:probe5	-0.0551932197912712	0.098055276187415	-0.562878632719155	0.573736169424941	   
df.mm.trans1:probe6	0.090400792654304	0.098055276187415	0.921937056008278	0.356946731082762	   
df.mm.trans1:probe7	0.0575127728280401	0.098055276187415	0.586534198507735	0.557745976558783	   
df.mm.trans1:probe8	0.181452131054791	0.098055276187415	1.85050859178631	0.0647512338666668	.  
df.mm.trans1:probe9	0.091273027380535	0.098055276187415	0.930832393007419	0.352329554140206	   
df.mm.trans1:probe10	0.039697466191625	0.098055276187415	0.404847834151733	0.685739407660648	   
df.mm.trans1:probe11	-0.0250949965040208	0.098055276187415	-0.255927039112676	0.798098387447944	   
df.mm.trans1:probe12	0.0295390306746361	0.098055276187415	0.301248763179021	0.763333337619341	   
df.mm.trans2:probe2	-0.074030724310213	0.098055276187415	-0.75498970773094	0.450563207052848	   
df.mm.trans2:probe3	-0.0739776428574777	0.098055276187415	-0.754448365594144	0.450887832999461	   
df.mm.trans2:probe4	0.00164752017650827	0.098055276187415	0.0168019533529163	0.986600419626853	   
df.mm.trans2:probe5	-0.0818503141391968	0.098055276187415	-0.834736460103938	0.404211579237283	   
df.mm.trans2:probe6	0.0173088857597448	0.098055276187415	0.1765217174715	0.859946027712356	   
df.mm.trans3:probe2	0.0990774749483013	0.098055276187415	1.01042471961358	0.312715373313192	   
df.mm.trans3:probe3	0.0374107267428952	0.098055276187415	0.381526912140774	0.702952759497669	   
df.mm.trans3:probe4	0.0946889916580592	0.098055276187415	0.965669521720363	0.334614131346229	   
df.mm.trans3:probe5	0.129924594881102	0.098055276187415	1.32501380785236	0.185691039083873	   
df.mm.trans3:probe6	0.139050574519849	0.098055276187415	1.41808355375063	0.156706205556202	   
df.mm.trans3:probe7	0.0578886817909028	0.098055276187415	0.590367841912545	0.555175207087605	   
df.mm.trans3:probe8	0.0674735719368322	0.098055276187415	0.68811770830025	0.49165519948249