chr2.14408_chr2_61370663_61375039_-_2.R 

fitVsDatCorrelation=0.897313415530377
cont.fitVsDatCorrelation=0.246678516027653

fstatistic=6994.16540154255,60,876
cont.fstatistic=1439.38351000734,60,876

residuals=-1.09977743092009,-0.121911653468499,-0.00818724252638072,0.109549438330283,0.845796336914204
cont.residuals=-0.880606401400014,-0.331275986658485,-0.0767421370259189,0.243866623836090,1.90049748997702

predictedValues:
Include	Exclude	Both
chr2.14408_chr2_61370663_61375039_-_2.R.tl.Lung	103.481913952715	79.7255804252427	74.3612639249
chr2.14408_chr2_61370663_61375039_-_2.R.tl.cerebhem	76.1396402237273	48.8343956055486	61.0348472640141
chr2.14408_chr2_61370663_61375039_-_2.R.tl.cortex	74.6352103987138	51.5436024084028	57.2608492882752
chr2.14408_chr2_61370663_61375039_-_2.R.tl.heart	107.742762596503	98.5442254853167	71.8275899820082
chr2.14408_chr2_61370663_61375039_-_2.R.tl.kidney	131.170541493366	71.2715781465053	86.5841541019436
chr2.14408_chr2_61370663_61375039_-_2.R.tl.liver	68.9829147865036	67.3278289829635	68.6474976879476
chr2.14408_chr2_61370663_61375039_-_2.R.tl.stomach	105.099160375834	76.756063331439	66.7133814047584
chr2.14408_chr2_61370663_61375039_-_2.R.tl.testicle	78.311960004456	64.2517692258419	68.0599564180144


diffExp=23.7563335274719,27.3052446181787,23.0916079903110,9.1985371111861,59.8989633468609,1.65508580354009,28.3430970443946,14.0601907786141
diffExpScore=0.994689581059835
diffExp1.5=0,1,0,0,1,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,1,1,0,1,0,0,0
diffExp1.4Score=0.75
diffExp1.3=0,1,1,0,1,0,1,0
diffExp1.3Score=0.8
diffExp1.2=1,1,1,0,1,0,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	68.1188053270495	79.738266521456	75.6529638724906
cerebhem	83.8799995840148	65.8154302546966	69.9914248387993
cortex	75.6308883339322	58.1811957278621	73.2947740313171
heart	78.8303216840267	57.535166774242	71.6850588939605
kidney	65.1824666224864	70.3368428782224	75.943787745728
liver	70.2664825494934	64.0701656124016	78.2805601511454
stomach	77.099083843103	63.6337698822032	68.4004284960064
testicle	72.2670437067607	55.7940920097219	72.3059787417321
cont.diffExp=-11.6194611944065,18.0645693293182,17.4496926060702,21.2951549097847,-5.15437625573601,6.19631693709184,13.4653139608997,16.4729516970388
cont.diffExpScore=1.42176501980749

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=0,0,0,1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,1,1,1,0,0,1,1
cont.diffExp1.2Score=0.833333333333333

tran.correlation=0.613014526313665
cont.tran.correlation=-0.469009365102227

tran.covariance=0.0347066318771233
cont.tran.covariance=-0.00464838000992372

tran.mean=81.4886967151924
cont.tran.mean=69.1487513319795

weightedLogRatios:
wLogRatio
Lung	1.1759742262746
cerebhem	1.82561422457402
cortex	1.52794313750423
heart	0.413643840653746
kidney	2.78861694263751
liver	0.102525242850646
stomach	1.41352222177004
testicle	0.84336324808677

cont.weightedLogRatios:
wLogRatio
Lung	-0.67723425424647
cerebhem	1.04486096857834
cortex	1.10028413109146
heart	1.32568655236863
kidney	-0.320802032548785
liver	0.388294097599758
stomach	0.815605829207512
testicle	1.07386926211782

varWeightedLogRatios=0.712664170404373
cont.varWeightedLogRatios=0.538508863743992

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.33116097024748	0.0937146516346122	56.8871662782609	2.17302194349073e-296	***
df.mm.trans1	-0.455964648380092	0.0802311519801063	-5.68313724939598	1.80030213126391e-08	***
df.mm.trans2	-0.98241272021291	0.0708393719977862	-13.8681737642114	1.10959323807857e-39	***
df.mm.exp2	-0.599493235513484	0.0903382844823209	-6.63609275899859	5.63828529232828e-11	***
df.mm.exp3	-0.501628834554379	0.0903382844823209	-5.55278238267351	3.72822282699742e-08	***
df.mm.exp4	0.286931145384654	0.0903382844823208	3.17618545701746	0.00154447305615016	** 
df.mm.exp5	-0.0271730624560279	0.0903382844823208	-0.300792323119061	0.76364423766853	   
df.mm.exp6	-0.494604347648334	0.0903382844823208	-5.47502479688031	5.71517214937119e-08	***
df.mm.exp7	0.0860789337419316	0.0903382844823208	0.95285110000929	0.340928357722578	   
df.mm.exp8	-0.405931617646220	0.0903382844823208	-4.49346165883481	7.94670559564753e-06	***
df.mm.trans1:exp2	0.292665408245029	0.0822869373188557	3.5566448063436	0.000395761050783895	***
df.mm.trans2:exp2	0.109337634869222	0.0593979826557316	1.84076343977773	0.065994277160731	.  
df.mm.trans1:exp3	0.174844366653476	0.0822869373188557	2.12481315200695	0.0338816209404796	*  
df.mm.trans2:exp3	0.0654664395147425	0.0593979826557316	1.10216604314971	0.270692208626296	   
df.mm.trans1:exp4	-0.246581440152254	0.0822869373188557	-2.99660490700692	0.00280703233986167	** 
df.mm.trans2:exp4	-0.0750162014850056	0.0593979826557316	-1.26294190696335	0.206946115353649	   
df.mm.trans1:exp5	0.264274529436905	0.0822869373188557	3.21162189343445	0.00136801889651454	** 
df.mm.trans2:exp5	-0.0849198062776798	0.0593979826557316	-1.42967492296618	0.153166871381724	   
df.mm.trans1:exp6	0.0890663567773079	0.0822869373188557	1.08238755359411	0.279378207886902	   
df.mm.trans2:exp6	0.325587512050800	0.0593979826557315	5.48145740803847	5.5178294218758e-08	***
df.mm.trans1:exp7	-0.0705714977207993	0.0822869373188557	-0.857626982121597	0.3913330437971	   
df.mm.trans2:exp7	-0.124037042545278	0.0593979826557316	-2.08823662015917	0.0370647708391235	*  
df.mm.trans1:exp8	0.127235101867093	0.0822869373188557	1.54623693641758	0.12240852384002	   
df.mm.trans2:exp8	0.190150384420076	0.0593979826557316	3.20129364531082	0.00141742209562958	** 
df.mm.trans1:probe2	-0.176776070251575	0.0589462804147014	-2.99893511529333	0.00278587231905707	** 
df.mm.trans1:probe3	-0.0233150515956592	0.0589462804147014	-0.395530497117582	0.692547721916613	   
df.mm.trans1:probe4	-0.226091853266484	0.0589462804147014	-3.83555759033264	0.000134259869395623	***
df.mm.trans1:probe5	-0.650840961858099	0.0589462804147014	-11.0412558227470	1.25461552083549e-26	***
df.mm.trans1:probe6	-0.0550748890048259	0.0589462804147014	-0.934323397801535	0.350394566147997	   
df.mm.trans1:probe7	-0.463133581186164	0.0589462804147014	-7.85687541144083	1.15136902647688e-14	***
df.mm.trans1:probe8	-0.564208051245572	0.0589462804147014	-9.57156324837176	1.03973523097504e-20	***
df.mm.trans1:probe9	-0.584538993512977	0.0589462804147014	-9.9164695278583	4.85910026719875e-22	***
df.mm.trans1:probe10	-0.63142775224459	0.0589462804147014	-10.7119185095708	3.02296478134228e-25	***
df.mm.trans1:probe11	-0.517258953679239	0.0589462804147014	-8.77509064253412	8.81189172890046e-18	***
df.mm.trans1:probe12	-0.67595027556844	0.0589462804147014	-11.4672252568435	1.84278797079967e-28	***
df.mm.trans1:probe13	-0.55048129119628	0.0589462804147014	-9.33869427084305	7.83562269908644e-20	***
df.mm.trans1:probe14	-0.568949804810231	0.0589462804147014	-9.65200519536654	5.12805705124479e-21	***
df.mm.trans1:probe15	-0.433136312930966	0.0589462804147014	-7.3479837893714	4.60974281300399e-13	***
df.mm.trans1:probe16	-0.328790700789228	0.0589462804147014	-5.57780233928426	3.24577078245869e-08	***
df.mm.trans1:probe17	-0.336470088714188	0.0589462804147014	-5.70808007472293	1.56357593688151e-08	***
df.mm.trans1:probe18	-0.347827273099867	0.0589462804147014	-5.90075015171131	5.16761609728724e-09	***
df.mm.trans1:probe19	-0.257202175058221	0.0589462804147014	-4.36333171913039	1.43352224750674e-05	***
df.mm.trans1:probe20	-0.389908392029599	0.0589462804147014	-6.61463945284585	6.47458343475193e-11	***
df.mm.trans2:probe2	-0.126434815282177	0.0589462804147014	-2.14491591993045	0.0322340039544813	*  
df.mm.trans2:probe3	0.220657976321037	0.0589462804147014	3.74337404783905	0.000193411781506597	***
df.mm.trans2:probe4	-0.0713274069076181	0.0589462804147014	-1.21004084406705	0.226589636690120	   
df.mm.trans2:probe5	0.361452439235759	0.0589462804147014	6.1318956292552	1.31250912020670e-09	***
df.mm.trans2:probe6	0.182654426991434	0.0589462804147014	3.09865907918897	0.00200603636408485	** 
df.mm.trans3:probe2	0.0735246121230029	0.0589462804147014	1.24731554910232	0.212615174736096	   
df.mm.trans3:probe3	0.162051689068144	0.0589462804147014	2.74914189543548	0.00609813214625584	** 
df.mm.trans3:probe4	0.0231369251803564	0.0589462804147014	0.392508653940207	0.694777902286561	   
df.mm.trans3:probe5	0.132108465740943	0.0589462804147014	2.24116712388852	0.0252650309258086	*  
df.mm.trans3:probe6	0.0848823810717165	0.0589462804147014	1.43999554296808	0.1502259299894	   
df.mm.trans3:probe7	0.0754587272283095	0.0589462804147014	1.28012703596290	0.200839342523946	   
df.mm.trans3:probe8	0.0940731296545972	0.0589462804147014	1.59591290566207	0.110868910202130	   
df.mm.trans3:probe9	0.0457872811293105	0.0589462804147014	0.776762856064638	0.437508383889271	   
df.mm.trans3:probe10	0.667108159959527	0.0589462804147014	11.3172223126932	8.25572303625315e-28	***
df.mm.trans3:probe11	1.40959947436638	0.0589462804147014	23.9132895994371	1.12721774350118e-97	***
df.mm.trans3:probe12	1.34994180317923	0.0589462804147014	22.9012211403682	2.46855007278522e-91	***
df.mm.trans3:probe13	1.29229761089614	0.0589462804147014	21.9233105431676	2.84101364635647e-85	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.08195499086423	0.205754108655152	19.8389962540465	1.24911761508996e-72	***
df.mm.trans1	0.106469053091207	0.176150568498148	0.604420718019579	0.545720473034675	   
df.mm.trans2	0.241462093241813	0.155530555669397	1.55250582242550	0.120902426633022	   
df.mm.exp2	0.0940224797876894	0.198341165195458	0.474044204061388	0.635586515171884	   
df.mm.exp3	-0.178908632312198	0.198341165195458	-0.90202471149088	0.367291683381302	   
df.mm.exp4	-0.126434571776153	0.198341165195458	-0.637460063580629	0.523991785533884	   
df.mm.exp5	-0.173353475691497	0.198341165195458	-0.874016624439322	0.382348740826385	   
df.mm.exp6	-0.221871872228668	0.198341165195458	-1.11863753553137	0.263601458056233	   
df.mm.exp7	-0.000989691195882999	0.198341165195458	-0.00498984260230443	0.996019834219377	   
df.mm.exp8	-0.252716937456163	0.198341165195458	-1.27415273176962	0.202947210282005	   
df.mm.trans1:exp2	0.114111403416893	0.180664123983686	0.631621823418561	0.527798807335904	   
df.mm.trans2:exp2	-0.285917769179710	0.130410546953689	-2.19244360106276	0.0286098940332737	*  
df.mm.trans1:exp3	0.283520090029120	0.180664123983686	1.56932147776458	0.116934169236436	   
df.mm.trans2:exp3	-0.136278765199980	0.130410546953689	-1.04499803415727	0.296312148929010	   
df.mm.trans1:exp4	0.272478969700822	0.180664123983686	1.50820740550253	0.131862004681096	   
df.mm.trans2:exp4	-0.199918673878351	0.130410546953689	-1.53299467373099	0.125638219184541	   
df.mm.trans1:exp5	0.129290673756611	0.180664123983686	0.715641107408164	0.474403687210473	   
df.mm.trans2:exp5	0.047899615414125	0.130410546953689	0.36729863138397	0.71348498880561	   
df.mm.trans1:exp6	0.25291346209111	0.180664123983686	1.39990971375119	0.161894401031456	   
df.mm.trans2:exp6	0.00310109015990916	0.130410546953689	0.0237794429388477	0.98103395306782	   
df.mm.trans1:exp7	0.124827771115291	0.180664123983686	0.690938346600361	0.489787329959791	   
df.mm.trans2:exp7	-0.224615608966629	0.130410546953689	-1.72237303050644	0.08535492945207	.  
df.mm.trans1:exp8	0.311831817814563	0.180664123983686	1.72603066363481	0.0846945051766619	.  
df.mm.trans2:exp8	-0.104364679306937	0.130410546953689	-0.800277905007164	0.423766773352795	   
df.mm.trans1:probe2	0.131533467238143	0.129418817375020	1.01633958574196	0.309748321038739	   
df.mm.trans1:probe3	0.117033175647245	0.129418817375020	0.904297984025888	0.366086034572014	   
df.mm.trans1:probe4	0.0671905078198923	0.129418817375020	0.519171084875491	0.603772577815099	   
df.mm.trans1:probe5	-0.0084329085744734	0.129418817375020	-0.0651598333651678	0.94806160806623	   
df.mm.trans1:probe6	0.0371277462392402	0.129418817375020	0.286880586550672	0.774271595942499	   
df.mm.trans1:probe7	0.0481424973810698	0.129418817375020	0.371989934366082	0.709990309095965	   
df.mm.trans1:probe8	0.0182449439328391	0.129418817375020	0.140975974768571	0.887921332880834	   
df.mm.trans1:probe9	0.0815696049598784	0.129418817375020	0.630276235050982	0.528678242253405	   
df.mm.trans1:probe10	0.143443579913326	0.129418817375020	1.10836725928089	0.268007450677603	   
df.mm.trans1:probe11	-0.0288913774897896	0.129418817375020	-0.223239387252862	0.823401244102345	   
df.mm.trans1:probe12	0.100398761724205	0.129418817375020	0.77576633568886	0.438096344069179	   
df.mm.trans1:probe13	-0.0262883074471881	0.129418817375020	-0.203125851250918	0.839083837727078	   
df.mm.trans1:probe14	0.0453393056025262	0.129418817375020	0.350330087402556	0.726175206731033	   
df.mm.trans1:probe15	-0.0139811825327509	0.129418817375020	-0.108030523043935	0.913996218013906	   
df.mm.trans1:probe16	-0.071851762478184	0.129418817375020	-0.555187908030231	0.578907877539296	   
df.mm.trans1:probe17	0.0427719446647917	0.129418817375020	0.330492470355763	0.741106907413845	   
df.mm.trans1:probe18	0.0547969783325888	0.129418817375020	0.423408121353807	0.67210145249652	   
df.mm.trans1:probe19	0.150103616789360	0.129418817375020	1.15982837607301	0.246434705247258	   
df.mm.trans1:probe20	0.195115447452726	0.129418817375020	1.50762811320811	0.132010269395494	   
df.mm.trans2:probe2	0.186403221095398	0.129418817375020	1.44031003277717	0.150136995662330	   
df.mm.trans2:probe3	0.198427975014345	0.129418817375020	1.53322352219736	0.125581845322345	   
df.mm.trans2:probe4	0.290333743795905	0.129418817375020	2.2433657615229	0.0251224424069644	*  
df.mm.trans2:probe5	0.18013530039522	0.129418817375020	1.39187873949765	0.164312404815860	   
df.mm.trans2:probe6	0.196017610759888	0.129418817375020	1.51459899522866	0.130234703167008	   
df.mm.trans3:probe2	0.140017045902270	0.129418817375020	1.08189093937197	0.279598718793265	   
df.mm.trans3:probe3	-0.0593387619158509	0.129418817375020	-0.458501809237705	0.646705688821417	   
df.mm.trans3:probe4	-0.203137834279424	0.129418817375020	-1.56961590593728	0.116865612153198	   
df.mm.trans3:probe5	-0.103488911294113	0.129418817375020	-0.79964346293036	0.424134173798645	   
df.mm.trans3:probe6	-0.133753659520880	0.129418817375020	-1.03349468210097	0.301657687170188	   
df.mm.trans3:probe7	-0.110997626849402	0.129418817375020	-0.857662193958718	0.391313605151869	   
df.mm.trans3:probe8	-0.165985232408157	0.129418817375020	-1.28254326360577	0.199991403777413	   
df.mm.trans3:probe9	-0.222998590485332	0.129418817375020	-1.72307702240195	0.0852274931985535	.  
df.mm.trans3:probe10	-0.0777910485843662	0.129418817375020	-0.601079890561425	0.547942273419239	   
df.mm.trans3:probe11	-0.0780326714411283	0.129418817375020	-0.602946874526067	0.546700093242307	   
df.mm.trans3:probe12	-0.160818996493689	0.129418817375020	-1.24262452520857	0.214338749253323	   
df.mm.trans3:probe13	-0.172157459365404	0.129418817375020	-1.33023514553173	0.183787013525413