chr13.6557_chr13_3322482_3335486_+_2.R 

fitVsDatCorrelation=0.941179162364483
cont.fitVsDatCorrelation=0.269419557936666

fstatistic=9402.44797189397,59,853
cont.fstatistic=1144.93841988834,59,853

residuals=-0.812286661161454,-0.102892990840664,-0.00185782643592898,0.105993631355817,0.983493371809696
cont.residuals=-0.989989898581845,-0.398225910870246,-0.0574283233355932,0.289595998934264,1.68036154669205

predictedValues:
Include	Exclude	Both
chr13.6557_chr13_3322482_3335486_+_2.R.tl.Lung	78.9293255790026	63.5596349320087	138.213775717204
chr13.6557_chr13_3322482_3335486_+_2.R.tl.cerebhem	83.7691779390852	62.057940763368	123.116702942280
chr13.6557_chr13_3322482_3335486_+_2.R.tl.cortex	87.1034272084115	61.1525387854971	150.639250567836
chr13.6557_chr13_3322482_3335486_+_2.R.tl.heart	79.6768402324388	66.8344016436321	137.600682625579
chr13.6557_chr13_3322482_3335486_+_2.R.tl.kidney	84.5067429435014	70.1830143974936	145.790699610373
chr13.6557_chr13_3322482_3335486_+_2.R.tl.liver	76.0027564537134	78.0788212821123	114.240073165139
chr13.6557_chr13_3322482_3335486_+_2.R.tl.stomach	75.4994008519197	67.1879769897853	133.476370954166
chr13.6557_chr13_3322482_3335486_+_2.R.tl.testicle	87.207770551478	70.4643284179752	147.185807636189


diffExp=15.3696906469939,21.7112371757172,25.9508884229144,12.8424385888067,14.3237285460078,-2.07606482839886,8.31142386213439,16.7434421335028
diffExpScore=1.02760744812779
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,1,1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,1,0,1,0,0,1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	87.656908890798	89.3771975736936	68.3042643025456
cerebhem	79.98838677925	101.555155621323	80.7308603958932
cortex	91.1379247708099	89.6621916101235	84.9665345021085
heart	86.8425435847797	80.2920292330113	74.749328159691
kidney	103.543506526194	98.9923272360033	80.0706966054445
liver	98.101008220353	104.275262646734	75.9773851414942
stomach	81.6085120497456	100.239510020525	99.9268117419072
testicle	85.4531517232476	83.493591328895	89.609893030024
cont.diffExp=-1.72028868289554,-21.5667688420733,1.47573316068636,6.55051435176837,4.55117929019093,-6.17425442638098,-18.6309979707795,1.95956039435251
cont.diffExpScore=1.81243560123312

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,-1,0,0,0,0,-1,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.373441327097743
cont.tran.correlation=0.231455032387523

tran.covariance=-0.00174658826064429
cont.tran.covariance=0.00177533280439708

tran.mean=74.5133811857139
cont.tran.mean=91.388700488468

weightedLogRatios:
wLogRatio
Lung	0.922663815141444
cerebhem	1.28340574329423
cortex	1.51756231956143
heart	0.75403205423454
kidney	0.806783643655355
liver	-0.117074067048477
stomach	0.497523082511608
testicle	0.929857030810772

cont.weightedLogRatios:
wLogRatio
Lung	-0.0871306857165916
cerebhem	-1.07453912025507
cortex	0.0735305061847453
heart	0.347027158015907
kidney	0.207555236957006
liver	-0.281776041487808
stomach	-0.926306157521146
testicle	0.102916728264311

varWeightedLogRatios=0.244345876193917
cont.varWeightedLogRatios=0.277642306776548

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.82493809142646	0.0845817391364112	33.3989123452578	4.58649504199325e-157	***
df.mm.trans1	1.63094429810341	0.0730427209792332	22.3286355743389	2.50860309748289e-87	***
df.mm.trans2	1.40334054400766	0.0645329138379423	21.7461208637160	9.10096529122698e-84	***
df.mm.exp2	0.151271050769722	0.0830099609590445	1.82232408041194	0.0687558315213811	.  
df.mm.exp3	-0.0261501508523468	0.0830099609590445	-0.315024251911753	0.752820286115276	   
df.mm.exp4	0.064111161813039	0.0830099609590445	0.772330947663862	0.440132369807616	   
df.mm.exp5	0.114035770898511	0.0830099609590445	1.37376008350099	0.169877080382646	   
df.mm.exp6	0.358456464246199	0.0830099609590444	4.31823434326218	1.75756648410390e-05	***
df.mm.exp7	0.045964721415934	0.0830099609590445	0.553725370845701	0.579911923294342	   
df.mm.exp8	0.139974403347958	0.0830099609590445	1.68623622672246	0.0921159897747773	.  
df.mm.trans1:exp2	-0.0917587550994314	0.076727833105209	-1.19589921135414	0.232068221466476	   
df.mm.trans2:exp2	-0.175181171659941	0.0566672602592015	-3.09140005814019	0.00205690216742276	** 
df.mm.trans1:exp3	0.124693542771231	0.076727833105209	1.62514093940659	0.104501926916674	   
df.mm.trans2:exp3	-0.0124570685106297	0.0566672602592015	-0.219828318038491	0.826057460205493	   
df.mm.trans1:exp4	-0.0546850441784293	0.076727833105209	-0.712714564784402	0.476217313077676	   
df.mm.trans2:exp4	-0.0138718171464184	0.0566672602592015	-0.244794208913002	0.806674652746061	   
df.mm.trans1:exp5	-0.045757280709179	0.076727833105209	-0.596358307766058	0.551094109682618	   
df.mm.trans2:exp5	-0.0149080472703780	0.0566672602592015	-0.263080431314081	0.792552117568239	   
df.mm.trans1:exp6	-0.396239694627808	0.076727833105209	-5.16422370594626	3.00664015523694e-07	***
df.mm.trans2:exp6	-0.152716216516624	0.0566672602592015	-2.69496382599204	0.00717811252997494	** 
df.mm.trans1:exp7	-0.0903928400666454	0.076727833105209	-1.17809713122875	0.239086443788027	   
df.mm.trans2:exp7	0.0095509982868093	0.0566672602592015	0.168545263051754	0.866194293923815	   
df.mm.trans1:exp8	-0.0402338037124105	0.076727833105209	-0.524370389259423	0.600157179475644	   
df.mm.trans2:exp8	-0.0368463994445202	0.0566672602592015	-0.650223767233164	0.515722770437199	   
df.mm.trans1:probe2	-0.544088701796555	0.0525319562252683	-10.3572899410672	9.26119518222081e-24	***
df.mm.trans1:probe3	-0.469786746590239	0.0525319562252683	-8.94287554371082	2.31599084491672e-18	***
df.mm.trans1:probe4	-0.543825480143361	0.0525319562252683	-10.3522792452526	9.7021297370741e-24	***
df.mm.trans1:probe5	-0.649713058668708	0.0525319562252683	-12.3679585790142	1.95188324614263e-32	***
df.mm.trans1:probe6	-0.636035170842251	0.0525319562252683	-12.1075858685863	2.98766528307456e-31	***
df.mm.trans1:probe7	0.212437445581084	0.0525319562252683	4.04396601318456	5.73141311569654e-05	***
df.mm.trans1:probe8	-0.472683982071846	0.0525319562252683	-8.99802741106528	1.46561483803367e-18	***
df.mm.trans1:probe9	-0.531145580883907	0.0525319562252683	-10.1109042771269	8.9352659399481e-23	***
df.mm.trans1:probe10	-0.657329027011475	0.0525319562252683	-12.5129363961378	4.19989657444827e-33	***
df.mm.trans1:probe11	0.673114035325502	0.0525319562252683	12.8134203196059	1.67373403330875e-34	***
df.mm.trans1:probe12	-0.00558955899953196	0.0525319562252683	-0.106403024010047	0.915287611830576	   
df.mm.trans1:probe13	0.0663790566795989	0.0525319562252683	1.26359384742786	0.206721071866950	   
df.mm.trans1:probe14	0.422890944516009	0.0525319562252683	8.05016555451623	2.76781127967471e-15	***
df.mm.trans1:probe15	0.617233545164991	0.0525319562252683	11.7496775204442	1.18948898371578e-29	***
df.mm.trans1:probe16	0.316329398930322	0.0525319562252683	6.02165656222345	2.5635288539708e-09	***
df.mm.trans1:probe17	-0.14807132485027	0.0525319562252683	-2.81869047890218	0.00493361607879843	** 
df.mm.trans1:probe18	0.0367226138917121	0.0525319562252683	0.699052853357253	0.484709560265699	   
df.mm.trans1:probe19	0.0854746946181817	0.0525319562252683	1.62709902238645	0.104085379040167	   
df.mm.trans1:probe20	-0.0485678382524362	0.0525319562252683	-0.924538923396777	0.35546726169996	   
df.mm.trans1:probe21	-0.0652325333343987	0.0525319562252683	-1.24176859233392	0.21466328058884	   
df.mm.trans1:probe22	-0.453058343801031	0.0525319562252683	-8.62443313281957	3.11004987377377e-17	***
df.mm.trans2:probe2	-0.093079256515598	0.0525319562252683	-1.77185970605119	0.0767747940115383	.  
df.mm.trans2:probe3	-0.12814853137972	0.0525319562252683	-2.43943954476379	0.0149128856407026	*  
df.mm.trans2:probe4	-0.297473259918536	0.0525319562252683	-5.66271049650058	2.03605147940934e-08	***
df.mm.trans2:probe5	-0.323178735793466	0.0525319562252683	-6.15204075796467	1.17502471993251e-09	***
df.mm.trans2:probe6	-0.378920815708399	0.0525319562252683	-7.21314877526177	1.20693569911153e-12	***
df.mm.trans3:probe2	-1.22950756138165	0.0525319562252683	-23.4049452890971	5.7663331267255e-94	***
df.mm.trans3:probe3	-0.897760770328735	0.0525319562252683	-17.0898027569913	1.55181721372183e-56	***
df.mm.trans3:probe4	-1.21365018481826	0.0525319562252683	-23.103083761318	4.26647264753491e-92	***
df.mm.trans3:probe5	-1.45719464844078	0.0525319562252683	-27.7392039655256	3.22054608109665e-121	***
df.mm.trans3:probe6	-1.40914299826261	0.0525319562252683	-26.8244912148312	2.00944266321508e-115	***
df.mm.trans3:probe7	-0.256384597987037	0.0525319562252683	-4.88054541292171	1.26253575335621e-06	***
df.mm.trans3:probe8	-0.68264566305747	0.0525319562252683	-12.9948646901733	2.33283903813378e-35	***
df.mm.trans3:probe9	-1.08750887753245	0.0525319562252683	-20.7018537986474	1.86775983369572e-77	***
df.mm.trans3:probe10	-1.07041817416183	0.0525319562252683	-20.3765146222929	1.65047606131353e-75	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.74335542821324	0.241054260303278	19.6775424016380	2.30512790239251e-71	***
df.mm.trans1	-0.235946078680029	0.208168562812254	-1.13343761177247	0.257348985807971	   
df.mm.trans2	-0.244326230688218	0.183915984339505	-1.32846653631365	0.18437932797073	   
df.mm.exp2	-0.130961217467670	0.236574761184739	-0.553572227281689	0.580016704640728	   
df.mm.exp3	-0.176158090664974	0.236574761184739	-0.744619120749797	0.456707095253247	   
df.mm.exp4	-0.206697056996798	0.236574761184739	-0.87370713579793	0.382523665534250	   
df.mm.exp5	0.109800448429857	0.236574761184739	0.46412579211742	0.642676047315787	   
df.mm.exp6	0.160272249461020	0.236574761184739	0.677469771747395	0.498291642641273	   
df.mm.exp7	-0.337265863397369	0.236574761184739	-1.42562064401283	0.154343699201095	   
df.mm.exp8	-0.365051377421318	0.236574761184739	-1.54306983379453	0.123184718576460	   
df.mm.trans1:exp2	0.0394122444580945	0.218670971331298	0.180235374719139	0.857010597183186	   
df.mm.trans2:exp2	0.25869768454558	0.161499215370410	1.60185103037335	0.109558788020192	   
df.mm.trans1:exp3	0.215101674668803	0.218670971331298	0.983677318298058	0.325553117620966	   
df.mm.trans2:exp3	0.179341683710466	0.161499215370410	1.11048021687990	0.267105079988086	   
df.mm.trans1:exp4	0.197363260165546	0.218670971331298	0.902558117174732	0.367015252878687	   
df.mm.trans2:exp4	0.0995018217201298	0.161499215370410	0.616113344525638	0.537984086972865	   
df.mm.trans1:exp5	0.0567609969755357	0.218670971331298	0.259572620133195	0.795256092522085	   
df.mm.trans2:exp5	-0.00762369291440365	0.161499215370410	-0.0472057582256246	0.962360287800413	   
df.mm.trans1:exp6	-0.0477050373535364	0.218670971331298	-0.218158985909752	0.827357418952256	   
df.mm.trans2:exp6	-0.00610367954109469	0.161499215370410	-0.0377938649862506	0.969860878883391	   
df.mm.trans1:exp7	0.2657690023756	0.218670971331298	1.21538309706845	0.224556291160007	   
df.mm.trans2:exp7	0.451962696960821	0.161499215370410	2.7985442277488	0.00524911687113972	** 
df.mm.trans1:exp8	0.339589238245836	0.218670971331298	1.55296899345337	0.120801465757562	   
df.mm.trans2:exp8	0.296955666831481	0.161499215370410	1.83874371247187	0.0663003710515154	.  
df.mm.trans1:probe2	-0.0808936088625424	0.149713779587147	-0.540321733147183	0.589116215731603	   
df.mm.trans1:probe3	0.125586611300185	0.149713779587147	0.838844705186824	0.401791470295017	   
df.mm.trans1:probe4	-0.13874386786987	0.149713779587147	-0.926727441204628	0.354330191798518	   
df.mm.trans1:probe5	0.0701778176436128	0.149713779587147	0.46874654983086	0.639370577636982	   
df.mm.trans1:probe6	0.102223214930303	0.149713779587147	0.682790957600536	0.494924333082663	   
df.mm.trans1:probe7	0.0166704854090149	0.149713779587147	0.111349038511924	0.911365764679957	   
df.mm.trans1:probe8	-0.093970563781927	0.149713779587147	-0.627668101366901	0.530389372827735	   
df.mm.trans1:probe9	-0.135911427730071	0.149713779587147	-0.90780840684714	0.364235848017171	   
df.mm.trans1:probe10	-0.221939128447778	0.149713779587147	-1.48242285419419	0.138597151535624	   
df.mm.trans1:probe11	0.0947682546393201	0.149713779587147	0.632996207167134	0.526905778052355	   
df.mm.trans1:probe12	-0.125346045574612	0.149713779587147	-0.837237867618256	0.402693380709004	   
df.mm.trans1:probe13	-0.0361779211321189	0.149713779587147	-0.241647236693134	0.809111595191876	   
df.mm.trans1:probe14	0.135246647411732	0.149713779587147	0.903368065282237	0.366585619781294	   
df.mm.trans1:probe15	0.0355949499396996	0.149713779587147	0.237753331976902	0.812129511357917	   
df.mm.trans1:probe16	-0.0866156902580283	0.149713779587147	-0.578541871675949	0.563051053281802	   
df.mm.trans1:probe17	-0.137645781379289	0.149713779587147	-0.919392869239314	0.358150028630872	   
df.mm.trans1:probe18	-0.10482057354442	0.149713779587147	-0.700139785619434	0.484030916750662	   
df.mm.trans1:probe19	-0.030335139295319	0.149713779587147	-0.202620890201101	0.839479678596871	   
df.mm.trans1:probe20	-0.152852297619057	0.149713779587147	-1.02096345467041	0.307561220353393	   
df.mm.trans1:probe21	-0.177418607855284	0.149713779587147	-1.18505195944245	0.236326952793264	   
df.mm.trans1:probe22	-0.144922692615340	0.149713779587147	-0.967998356697572	0.333319565043664	   
df.mm.trans2:probe2	0.0326471989872695	0.149713779587147	0.218064089206070	0.827431332119681	   
df.mm.trans2:probe3	0.101932281783496	0.149713779587147	0.680847695279526	0.496152638790929	   
df.mm.trans2:probe4	-0.0942665940874676	0.149713779587147	-0.629645409710574	0.529095213310565	   
df.mm.trans2:probe5	-0.111197210410457	0.149713779587147	-0.742731969743175	0.457848457677075	   
df.mm.trans2:probe6	-0.0277334134888366	0.149713779587147	-0.185242891905573	0.853082564419512	   
df.mm.trans3:probe2	-0.0117296141934240	0.149713779587147	-0.0783469245500966	0.937570466179095	   
df.mm.trans3:probe3	-0.164217255517523	0.149713779587147	-1.09687468962691	0.273005729226415	   
df.mm.trans3:probe4	-0.180287024872137	0.149713779587147	-1.20421129818043	0.228841957016491	   
df.mm.trans3:probe5	-0.0302289334868692	0.149713779587147	-0.201911497860979	0.84003405561434	   
df.mm.trans3:probe6	-0.0101693562521759	0.149713779587147	-0.0679253191003465	0.945861001845484	   
df.mm.trans3:probe7	-0.182265372098108	0.149713779587147	-1.21742549417112	0.223779050725418	   
df.mm.trans3:probe8	0.0584179052338721	0.149713779587147	0.390197251014344	0.696488110104069	   
df.mm.trans3:probe9	0.0491054763714073	0.149713779587147	0.327995702912728	0.742995402119108	   
df.mm.trans3:probe10	0.0802649814781266	0.149713779587147	0.536122871919116	0.59201340655706