chr19.12481_chr19_40070272_40078896_-_2.R 

fitVsDatCorrelation=0.869888549027346
cont.fitVsDatCorrelation=0.214579222498885

fstatistic=5175.1046955909,58,830
cont.fstatistic=1309.18194289935,58,830

residuals=-0.715508176016015,-0.126051977041932,0.00648244018167108,0.128708565001132,0.863619768813714
cont.residuals=-0.932600683899833,-0.294495880076136,-0.139512707639579,0.189412129232647,1.97944315645442

predictedValues:
Include	Exclude	Both
chr19.12481_chr19_40070272_40078896_-_2.R.tl.Lung	61.2062231295847	61.703190387608	73.513575597743
chr19.12481_chr19_40070272_40078896_-_2.R.tl.cerebhem	54.0066780990934	64.5623760269616	69.7860166928447
chr19.12481_chr19_40070272_40078896_-_2.R.tl.cortex	58.9690015090613	76.2314158041194	87.2516556651914
chr19.12481_chr19_40070272_40078896_-_2.R.tl.heart	96.4850342918283	132.046204999901	143.664017814342
chr19.12481_chr19_40070272_40078896_-_2.R.tl.kidney	56.0390097539931	61.1741662138359	65.252034301951
chr19.12481_chr19_40070272_40078896_-_2.R.tl.liver	51.245199729919	58.2610589075508	62.4684348675147
chr19.12481_chr19_40070272_40078896_-_2.R.tl.stomach	64.845944310747	57.6931332212082	78.7924556043184
chr19.12481_chr19_40070272_40078896_-_2.R.tl.testicle	49.3377230198891	55.0552199207055	56.037128954223


diffExp=-0.496967258023311,-10.5556979278682,-17.262414295058,-35.5611707080723,-5.13515645984282,-7.0158591776318,7.15281108953878,-5.71749690081638
diffExpScore=1.17601903232815
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,-1,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,-1,-1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	73.1979766393873	67.6841624620306	70.2987822484283
cerebhem	64.3013286263574	67.9382620890666	71.0458290346331
cortex	74.6408015425438	68.3533610988549	68.3614679032011
heart	70.5426135535607	64.0151650039231	73.4111576838742
kidney	73.7985740052046	68.0283225504985	81.8232762190452
liver	63.9246376258595	70.639581442526	73.6381966024962
stomach	74.1566989341451	74.248670467735	73.9875221255019
testicle	71.2498799755876	71.7402610305646	74.32775536734
cont.diffExp=5.51381417735669,-3.63693346270912,6.28744044368885,6.5274485496376,5.77025145470614,-6.71494381666643,-0.091971533589799,-0.490381054976979
cont.diffExpScore=2.47326969589813

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=0.932636286800547
cont.tran.correlation=0.077741120806728

tran.covariance=0.0538548434429072
cont.tran.covariance=0.00018417976370894

tran.mean=66.1788487078754
cont.tran.mean=69.9037685654903

weightedLogRatios:
wLogRatio
Lung	-0.0333036746978596
cerebhem	-0.728087370664061
cortex	-1.079784014384
heart	-1.48293329362466
kidney	-0.356835149990936
liver	-0.513347727174085
stomach	0.480779207901395
testicle	-0.433493662237081

cont.weightedLogRatios:
wLogRatio
Lung	0.333155190247657
cerebhem	-0.230589942135284
cortex	0.375629942639379
heart	0.4085518457527
kidney	0.346880538749744
liver	-0.420284170913603
stomach	-0.00533812308807202
testicle	-0.029285239384146

varWeightedLogRatios=0.36520866022116
cont.varWeightedLogRatios=0.09917528100213

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.25474282523502	0.107387033845113	39.6206382920656	1.51112492821583e-193	***
df.mm.trans1	-0.343366330492755	0.0915847718485767	-3.74916401015297	0.000189730317036259	***
df.mm.trans2	-0.117284179128308	0.0814064853472887	-1.44072279533948	0.150040129751250	   
df.mm.exp2	-0.0278085376281943	0.104093288868355	-0.267150148972267	0.789419920499983	   
df.mm.exp3	0.00287461336001200	0.104093288868355	0.0276157415263099	0.977975264858038	   
df.mm.exp4	0.545948030757652	0.104093288868355	5.24479567023866	1.98709693742219e-07	***
df.mm.exp5	0.0224013919845160	0.104093288868355	0.215204959205839	0.829660392448682	   
df.mm.exp6	-0.0722199262428216	0.104093288868355	-0.693800023305602	0.488001748867156	   
df.mm.exp7	-0.0787791397574203	0.104093288868355	-0.756812860981371	0.449376757115436	   
df.mm.exp8	-0.0581032626229973	0.104093288868355	-0.558184521352566	0.576868921477021	   
df.mm.trans1:exp2	-0.0973326244247523	0.0939314741776882	-1.03620884561686	0.300406514626545	   
df.mm.trans2:exp2	0.0731047266647716	0.0692714944888543	1.05533635738917	0.291578607214339	   
df.mm.trans1:exp3	-0.0401115751483987	0.0939314741776881	-0.427030188757823	0.669468087071048	   
df.mm.trans2:exp3	0.208563407622641	0.0692714944888543	3.01081143349951	0.00268445322024377	** 
df.mm.trans1:exp4	-0.0908089889710478	0.0939314741776881	-0.966757838797104	0.333946708661723	   
df.mm.trans2:exp4	0.214868230818302	0.0692714944888543	3.1018275613048	0.00198837004614153	** 
df.mm.trans1:exp5	-0.110602210487199	0.0939314741776881	-1.17747763947551	0.239342439507517	   
df.mm.trans2:exp5	-0.0310120499126957	0.0692714944888543	-0.447688477656354	0.654494857573785	   
df.mm.trans1:exp6	-0.105406993404480	0.0939314741776881	-1.12216905278293	0.26211508896182	   
df.mm.trans2:exp6	0.0148182155195833	0.0692714944888543	0.213915054510156	0.830665837842227	   
df.mm.trans1:exp7	0.136544639550267	0.0939314741776882	1.45366226545076	0.146417960048866	   
df.mm.trans2:exp7	0.0115816602519866	0.0692714944888543	0.167192296592505	0.867259469948813	   
df.mm.trans1:exp8	-0.157456645579239	0.0939314741776882	-1.67629271186974	0.0940574578138677	.  
df.mm.trans2:exp8	-0.0558956948951954	0.0692714944888543	-0.8069075931975	0.419950878992295	   
df.mm.trans1:probe2	0.060853126755522	0.0672878490444865	0.904370218689704	0.366061541134181	   
df.mm.trans1:probe3	0.0580045515511275	0.0672878490444865	0.862036049224556	0.388916664308281	   
df.mm.trans1:probe4	0.358340701795954	0.0672878490444865	5.32548902787844	1.29725284827922e-07	***
df.mm.trans1:probe5	0.239052884428676	0.0672878490444865	3.55269023788574	0.000402819641524319	***
df.mm.trans1:probe6	0.233522258384426	0.0672878490444865	3.4704967048365	0.000546297356858587	***
df.mm.trans1:probe7	0.0378808265377260	0.0672878490444865	0.562966822028767	0.57360951062289	   
df.mm.trans1:probe8	0.107523364688915	0.0672878490444865	1.59796109127857	0.110432238022551	   
df.mm.trans1:probe9	0.222011535915147	0.0672878490444865	3.29942982377645	0.00101010173464542	** 
df.mm.trans1:probe10	0.0974217154648724	0.0672878490444865	1.44783518641625	0.148040762615950	   
df.mm.trans1:probe11	0.121440894879615	0.0672878490444865	1.80479680364468	0.071468813786487	.  
df.mm.trans1:probe12	0.141285085601325	0.0672878490444865	2.09971172518706	0.0360555709467807	*  
df.mm.trans1:probe13	0.577574282402801	0.0672878490444865	8.58363420148777	4.49237797158176e-17	***
df.mm.trans1:probe14	0.74096353242931	0.0672878490444865	11.0118475022055	2.03767208391462e-26	***
df.mm.trans1:probe15	0.845290632738887	0.0672878490444865	12.5623072329156	2.92071365235942e-33	***
df.mm.trans1:probe16	0.89535194655292	0.0672878490444865	13.3062946619228	9.2729462590474e-37	***
df.mm.trans1:probe17	0.694675020298525	0.0672878490444865	10.3239296568872	1.36719219348491e-23	***
df.mm.trans1:probe18	0.857851256215649	0.0672878490444865	12.7489772432537	3.98265682573685e-34	***
df.mm.trans2:probe2	0.0112484357958011	0.0672878490444865	0.167168901302883	0.86727787179262	   
df.mm.trans2:probe3	-0.0100552595000189	0.0672878490444865	-0.149436482854000	0.881245528094396	   
df.mm.trans2:probe4	-0.150095665947116	0.0672878490444865	-2.23065037861267	0.0259708354961786	*  
df.mm.trans2:probe5	-0.0629022334276667	0.0672878490444865	-0.934823067179331	0.350151376524693	   
df.mm.trans2:probe6	-0.0755324377982793	0.0672878490444865	-1.1225271556584	0.261962977384382	   
df.mm.trans3:probe2	0.209925796138934	0.0672878490444865	3.11981730906786	0.00187218716504155	** 
df.mm.trans3:probe3	0.558957555312023	0.0672878490444865	8.30696126045692	3.97790956014607e-16	***
df.mm.trans3:probe4	1.02433496106922	0.0672878490444865	15.2231788594103	2.45420412594452e-46	***
df.mm.trans3:probe5	0.243089381190091	0.0672878490444865	3.61267873237225	0.000321273113055284	***
df.mm.trans3:probe6	0.090867202493756	0.0672878490444865	1.35042513297877	0.177247740132245	   
df.mm.trans3:probe7	0.577559170860817	0.0672878490444865	8.58340962094019	4.50044405056682e-17	***
df.mm.trans3:probe8	0.0806527322585088	0.0672878490444865	1.19862253592303	0.231016864906710	   
df.mm.trans3:probe9	0.250625967766769	0.0672878490444865	3.72468389650962	0.000208784424252773	***
df.mm.trans3:probe10	1.07372916948329	0.0672878490444865	15.9572520853417	3.38969475143472e-50	***
df.mm.trans3:probe11	0.744194539737784	0.0672878490444865	11.0598651956577	1.27886591690740e-26	***
df.mm.trans3:probe12	1.10909401361304	0.0672878490444865	16.4828275738131	5.1107788383841e-53	***
df.mm.trans3:probe13	0.580880592350916	0.0672878490444865	8.63277100694472	3.03124884700919e-17	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29494883236729	0.212642633627316	20.1979666970018	4.23385374534143e-74	***
df.mm.trans1	-0.00241208978494929	0.181351755316450	-0.0133006144922078	0.989391154230825	   
df.mm.trans2	-0.080925275217984	0.161197202481246	-0.502026548676607	0.615781994100313	   
df.mm.exp2	-0.136410989988358	0.206120518421447	-0.661802090510193	0.50828175457188	   
df.mm.exp3	0.057303182795172	0.206120518421447	0.278008144138307	0.781075344122365	   
df.mm.exp4	-0.136004470949626	0.206120518421447	-0.659829850959055	0.509546072297106	   
df.mm.exp5	-0.138563736032244	0.206120518421447	-0.672246203790971	0.501614057952827	   
df.mm.exp6	-0.139133901308234	0.206120518421447	-0.675012378067826	0.499855888804659	   
df.mm.exp7	0.0544383017175515	0.206120518421447	0.264109085958360	0.791761424980263	   
df.mm.exp8	-0.0245047129181893	0.206120518421447	-0.118885364280355	0.905394943854318	   
df.mm.trans1:exp2	0.00682350490100465	0.185998582272499	0.0366857898465473	0.970744360244864	   
df.mm.trans2:exp2	0.140158156803879	0.137168077895286	1.02179864990800	0.307173932221955	   
df.mm.trans1:exp3	-0.0377836665804891	0.185998582272499	-0.203139540736572	0.83907575552223	   
df.mm.trans2:exp3	-0.0474646615209545	0.137168077895286	-0.346032854358352	0.729405716593556	   
df.mm.trans1:exp4	0.099053666684428	0.185998582272499	0.532550654280301	0.594487205412217	   
df.mm.trans2:exp4	0.0802722639212723	0.137168077895286	0.585210970022866	0.558564930963225	   
df.mm.trans1:exp5	0.146735365982483	0.185998582272499	0.788905830300938	0.430392327669001	   
df.mm.trans2:exp5	0.143635647083146	0.137168077895286	1.0471506875878	0.295334922693702	   
df.mm.trans1:exp6	0.00367097468361203	0.185998582272499	0.0197365734661021	0.98425825848161	   
df.mm.trans2:exp6	0.181872316861977	0.137168077895286	1.3259084741336	0.185234792794641	   
df.mm.trans1:exp7	-0.0414256732517261	0.185998582272499	-0.222720371013554	0.823807941692481	   
df.mm.trans2:exp7	0.0381293541411365	0.137168077895286	0.277975420565742	0.78110045544045	   
df.mm.trans1:exp8	-0.00246993153443124	0.185998582272499	-0.0132793030153995	0.98940815170595	   
df.mm.trans2:exp8	0.0827046080741217	0.137168077895286	0.602943551758876	0.546710950142851	   
df.mm.trans1:probe2	0.069080630721136	0.133240158700855	0.51846704022796	0.60427063717616	   
df.mm.trans1:probe3	0.0526409566454232	0.133240158700855	0.395083262874299	0.692882951601843	   
df.mm.trans1:probe4	-0.0790391242428122	0.133240158700855	-0.593207971331431	0.553203581876677	   
df.mm.trans1:probe5	0.0482706950519305	0.133240158700855	0.362283380045394	0.717232498856505	   
df.mm.trans1:probe6	-0.122733787545153	0.133240158700855	-0.921147113166607	0.357241282219173	   
df.mm.trans1:probe7	0.00307081750457548	0.133240158700855	0.0230472369180372	0.981618132830553	   
df.mm.trans1:probe8	-0.0601948724713047	0.133240158700855	-0.451777249878932	0.651547490398064	   
df.mm.trans1:probe9	0.0801420092259933	0.133240158700855	0.601485393048237	0.547680980331252	   
df.mm.trans1:probe10	0.0341537096731581	0.133240158700855	0.256331949812807	0.797758048046773	   
df.mm.trans1:probe11	-0.0760297075199534	0.133240158700855	-0.570621562307292	0.56841064399041	   
df.mm.trans1:probe12	-0.0247641075459836	0.133240158700855	-0.185860687854499	0.852599423489485	   
df.mm.trans1:probe13	0.157285268898253	0.133240158700855	1.18046443678728	0.238153730836217	   
df.mm.trans1:probe14	0.0311624871879695	0.133240158700855	0.233882092995207	0.815134189669337	   
df.mm.trans1:probe15	-0.140946050364015	0.133240158700855	-1.05783460285769	0.290438624957731	   
df.mm.trans1:probe16	-0.0177947033377382	0.133240158700855	-0.133553603592518	0.893787948359447	   
df.mm.trans1:probe17	0.107049063517651	0.133240158700855	0.803429420689841	0.421956597305753	   
df.mm.trans1:probe18	-0.0417911550006885	0.133240158700855	-0.313652846170171	0.753863525859441	   
df.mm.trans2:probe2	-0.0138665844364246	0.133240158700855	-0.104072109877603	0.917137265574435	   
df.mm.trans2:probe3	0.145136102683739	0.133240158700855	1.08928197098288	0.276345721886815	   
df.mm.trans2:probe4	-0.0422563664324721	0.133240158700855	-0.317144371820692	0.751213814556188	   
df.mm.trans2:probe5	0.0368117824775398	0.133240158700855	0.276281436741516	0.782400691471052	   
df.mm.trans2:probe6	-0.110080425527525	0.133240158700855	-0.826180534463886	0.408939109559346	   
df.mm.trans3:probe2	0.0186154404185926	0.133240158700855	0.139713436250006	0.888920304562341	   
df.mm.trans3:probe3	-0.00367017183580708	0.133240158700855	-0.0275455378588012	0.97803124109136	   
df.mm.trans3:probe4	-0.00493117876245825	0.133240158700855	-0.0370097034598218	0.970486167960139	   
df.mm.trans3:probe5	-0.00467269984369975	0.133240158700855	-0.0350697559148867	0.972032549957391	   
df.mm.trans3:probe6	-0.0394650960322378	0.133240158700855	-0.296195204336578	0.767155104655402	   
df.mm.trans3:probe7	0.199485764864495	0.133240158700855	1.49718948708528	0.134724130118445	   
df.mm.trans3:probe8	0.0541784141431051	0.133240158700855	0.406622257668906	0.684390272959194	   
df.mm.trans3:probe9	0.180889576478839	0.133240158700855	1.35762054205417	0.174953110997872	   
df.mm.trans3:probe10	0.0113787040228515	0.133240158700855	0.085399958494484	0.931964021359513	   
df.mm.trans3:probe11	-0.0782402709606926	0.133240158700855	-0.587212381939248	0.557220774372234	   
df.mm.trans3:probe12	0.16276491402679	0.133240158700855	1.22159051455517	0.222209365395311	   
df.mm.trans3:probe13	0.0385257216406358	0.133240158700855	0.289144969626853	0.772542646263871