chr1.1356_chr1_80601513_80630574_-_2.R 

fitVsDatCorrelation=0.854251657218727
cont.fitVsDatCorrelation=0.259035462373069

fstatistic=11204.6947161062,62,922
cont.fstatistic=3235.35054582827,62,922

residuals=-0.642397709389171,-0.0885239784598013,-0.00435313178463366,0.0865394147943733,0.872910251912048
cont.residuals=-0.919442913688366,-0.19302262376955,-0.00930816646240757,0.176197533762656,1.22786345853509

predictedValues:
Include	Exclude	Both
chr1.1356_chr1_80601513_80630574_-_2.R.tl.Lung	69.3911152686823	101.303950559015	96.4356664240204
chr1.1356_chr1_80601513_80630574_-_2.R.tl.cerebhem	61.2503090381308	81.0502636713246	88.1963612381414
chr1.1356_chr1_80601513_80630574_-_2.R.tl.cortex	64.1404957674141	82.8903622103936	89.3404948145594
chr1.1356_chr1_80601513_80630574_-_2.R.tl.heart	62.1387304618703	93.541837698635	90.5737657861214
chr1.1356_chr1_80601513_80630574_-_2.R.tl.kidney	71.5690879128523	105.111067455247	100.442880900691
chr1.1356_chr1_80601513_80630574_-_2.R.tl.liver	65.8332686739676	103.72752869065	109.707933347072
chr1.1356_chr1_80601513_80630574_-_2.R.tl.stomach	61.9972241265861	92.7358673296037	94.6761676866376
chr1.1356_chr1_80601513_80630574_-_2.R.tl.testicle	64.0260679170718	101.531377284311	103.289209717118


diffExp=-31.9128352903322,-19.7999546331939,-18.7498664429795,-31.4031072367648,-33.5419795423951,-37.8942600166823,-30.7386432030176,-37.5053093672391
diffExpScore=0.995877069988739
diffExp1.5=0,0,0,-1,0,-1,0,-1
diffExp1.5Score=0.75
diffExp1.4=-1,0,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.857142857142857
diffExp1.3=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	77.3926387183999	78.154722041376	91.6821070042671
cerebhem	76.5689900453933	73.7925725555154	83.4601129756772
cortex	79.6663416474875	73.2772994960542	85.0315105777457
heart	81.3939864065509	76.659904200349	74.6723974219321
kidney	82.8508426613438	83.1115839085942	79.5666601751736
liver	74.6973475736738	72.7027666716619	74.3360042494576
stomach	78.2364402667536	83.2701993907837	82.5122355901766
testicle	79.1642811471739	75.7590423939326	79.593644529086
cont.diffExp=-0.762083322976125,2.77641748987794,6.38904215143326,4.73408220620192,-0.260741247250365,1.99458090201189,-5.03375912403008,3.40523875324126
cont.diffExpScore=1.78026685088588

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=0.697214366786504
cont.tran.correlation=0.519926145874034

tran.covariance=0.00387583227845905
cont.tran.covariance=0.00093288628583385

tran.mean=80.1399096291097
cont.tran.mean=77.9186849453152

weightedLogRatios:
wLogRatio
Lung	-1.67576364807003
cerebhem	-1.19183345780399
cortex	-1.09995974117216
heart	-1.77273108572631
kidney	-1.71531203067410
liver	-2.00699377410200
stomach	-1.74290727945518
testicle	-2.02405175513929

cont.weightedLogRatios:
wLogRatio
Lung	-0.0426619661146399
cerebhem	0.159545079472428
cortex	0.362477723174821
heart	0.261822144586448
kidney	-0.0138840536019071
liver	0.116377737664057
stomach	-0.273796300164004
testicle	0.191237999301548

varWeightedLogRatios=0.115476439298418
cont.varWeightedLogRatios=0.0400108423565933

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.23046635031579	0.0757825590475806	55.8237463010409	5.74739305049375e-298	***
df.mm.trans1	-0.134514107423172	0.0648963064248397	-2.07275444218017	0.0384725017356144	*  
df.mm.trans2	0.414355573737442	0.0570470380033298	7.26340206678665	8.02852552977286e-13	***
df.mm.exp2	-0.258535439569415	0.072461986129793	-3.56787680517519	0.000378424898533629	***
df.mm.exp3	-0.202868262919167	0.072461986129793	-2.79965087564384	0.00522263438189664	** 
df.mm.exp4	-0.127394468073487	0.072461986129793	-1.75808689324772	0.0790645027821032	.  
df.mm.exp5	0.0270834778731593	0.072461986129793	0.373761185963737	0.708667926543383	   
df.mm.exp6	-0.157936954526188	0.072461986129793	-2.17958357149214	0.0295407637433304	*  
df.mm.exp7	-0.182625508893976	0.072461986129793	-2.52029400031706	0.0118935293182389	*  
df.mm.exp8	-0.146882846244928	0.072461986129793	-2.02703312578037	0.0429463411583298	*  
df.mm.trans1:exp2	0.133745497249887	0.0661484406081902	2.02189947367146	0.0434751383990833	*  
df.mm.trans2:exp2	0.0354795318367527	0.0467740109189669	0.758530883704175	0.448327083458411	   
df.mm.trans1:exp3	0.124185349359821	0.0661484406081902	1.87737380077324	0.0607824045912493	.  
df.mm.trans2:exp3	0.00226165118251463	0.0467740109189669	0.0483527313155334	0.961445601864395	   
df.mm.trans1:exp4	0.0170051042890116	0.0661484406081902	0.25707490808039	0.797178304544303	   
df.mm.trans2:exp4	0.0476778572018922	0.0467740109189669	1.01932368563584	0.308316711635552	   
df.mm.trans1:exp5	0.00382093255702872	0.0661484406081902	0.0577630027540759	0.953949924370433	   
df.mm.trans2:exp5	0.0098086894093359	0.0467740109189669	0.209703833744958	0.833945160348654	   
df.mm.trans1:exp6	0.105303430779053	0.0661484406081902	1.59192612570847	0.111744072485191	   
df.mm.trans2:exp6	0.181579090141848	0.0467740109189669	3.88205087770690	0.00011096195013655	***
df.mm.trans1:exp7	0.0699562835327894	0.0661484406081902	1.05756512004801	0.290530754380776	   
df.mm.trans2:exp7	0.0942554158641914	0.0467740109189669	2.01512365547361	0.0441815143891794	*  
df.mm.trans1:exp8	0.0664143206280162	0.0661484406081902	1.00401944501460	0.315632663291733	   
df.mm.trans2:exp8	0.149125323657711	0.0467740109189669	3.18820902308468	0.00147981664827830	** 
df.mm.trans1:probe2	0.245758534867363	0.047929098701311	5.12754342406694	3.57750003880326e-07	***
df.mm.trans1:probe3	0.158626624696452	0.047929098701311	3.30961000716904	0.000970474277124392	***
df.mm.trans1:probe4	-0.0675500986806126	0.047929098701311	-1.40937552574434	0.159061347134815	   
df.mm.trans1:probe5	-0.162316193234872	0.047929098701311	-3.38658972592849	0.000737582929527052	***
df.mm.trans1:probe6	0.234515045206905	0.047929098701311	4.89295754690439	1.17197187161662e-06	***
df.mm.trans1:probe7	0.230665763901295	0.047929098701311	4.81264555669572	1.73964832910478e-06	***
df.mm.trans1:probe8	0.298893976582165	0.047929098701311	6.2361693560073	6.82024623386205e-10	***
df.mm.trans1:probe9	0.274347072463724	0.047929098701311	5.72401901762071	1.40655641589470e-08	***
df.mm.trans1:probe10	0.538333093791089	0.047929098701311	11.2318634895666	1.58740788992089e-27	***
df.mm.trans1:probe11	0.330368129989866	0.047929098701311	6.89285087643072	1.01322944497305e-11	***
df.mm.trans1:probe12	0.364868855338206	0.047929098701311	7.61267925382926	6.63285091281906e-14	***
df.mm.trans1:probe13	0.307532895051077	0.047929098701311	6.41641306396326	2.22783611310091e-10	***
df.mm.trans1:probe14	0.27233237229215	0.047929098701311	5.68198400702871	1.7850135185781e-08	***
df.mm.trans1:probe15	0.168504926547726	0.047929098701311	3.51571239838727	0.000459936435484088	***
df.mm.trans1:probe16	0.287257272059919	0.047929098701311	5.99337938420406	2.94561035705176e-09	***
df.mm.trans1:probe17	0.264011212965944	0.047929098701311	5.50837007412206	4.69834582287064e-08	***
df.mm.trans1:probe18	0.240376174917455	0.047929098701311	5.01524504801254	6.35221463223574e-07	***
df.mm.trans1:probe19	0.290575414679921	0.047929098701311	6.06260961614896	1.95097505731977e-09	***
df.mm.trans1:probe20	0.509262151282742	0.047929098701311	10.6253229266089	5.88966181217329e-25	***
df.mm.trans1:probe21	0.246867577864577	0.047929098701311	5.15068266572316	3.1739369829851e-07	***
df.mm.trans2:probe2	0.0670464206360314	0.047929098701311	1.39886671046867	0.162189250119868	   
df.mm.trans2:probe3	-0.215493012332699	0.047929098701311	-4.49607896187717	7.80321083833358e-06	***
df.mm.trans2:probe4	-0.190384362009942	0.047929098701311	-3.97220826530449	7.67594339902622e-05	***
df.mm.trans2:probe5	-0.0879285968487649	0.047929098701311	-1.83455560883225	0.0668936398281387	.  
df.mm.trans2:probe6	-0.107170748460348	0.047929098701311	-2.23602678465173	0.0255885936004052	*  
df.mm.trans3:probe2	0.446856566652382	0.047929098701311	9.32328332391861	8.11431122946514e-20	***
df.mm.trans3:probe3	0.219592708005979	0.047929098701311	4.58161563551314	5.24798349302676e-06	***
df.mm.trans3:probe4	0.144796907350005	0.047929098701311	3.02106468248785	0.00258839383277462	** 
df.mm.trans3:probe5	0.0880845603090718	0.047929098701311	1.83780965417283	0.0664120661870339	.  
df.mm.trans3:probe6	-0.26705135675812	0.047929098701311	-5.57180009627044	3.3091450297715e-08	***
df.mm.trans3:probe7	0.260011836235977	0.047929098701311	5.42492647016674	7.41087886751284e-08	***
df.mm.trans3:probe8	0.401058871480377	0.047929098701311	8.36775325110394	2.15925817460662e-16	***
df.mm.trans3:probe9	0.383091938923593	0.047929098701311	7.99288843946306	3.925738595596e-15	***
df.mm.trans3:probe10	-0.29514482997749	0.047929098701311	-6.15794659141831	1.09879153522972e-09	***
df.mm.trans3:probe11	-0.568441054640632	0.047929098701311	-11.8600405608104	2.6828770677217e-30	***
df.mm.trans3:probe12	0.282009594717132	0.047929098701311	5.8838910465349	5.60359229802392e-09	***
df.mm.trans3:probe13	-0.348417304726222	0.047929098701311	-7.26943160140608	7.69680164113026e-13	***
df.mm.trans3:probe14	0.0735077653823095	0.047929098701311	1.53367718930836	0.125452138317834	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09766699213677	0.140799401577166	29.1028722156252	1.31789039151838e-132	***
df.mm.trans1	0.184899952124173	0.120573404013038	1.53350528367084	0.125494453911330	   
df.mm.trans2	0.238023935973175	0.105989939025095	2.24572198231777	0.0249579904116967	*  
df.mm.exp2	0.0258265078844584	0.134629978353753	0.191833261805901	0.847915054932633	   
df.mm.exp3	0.0398212388242432	0.134629978353753	0.295782850975502	0.76746252202304	   
df.mm.exp4	0.236314780012898	0.134629978353753	1.75529093076104	0.0795416369006874	.  
df.mm.exp5	0.271375928967966	0.134629978353753	2.01571694719359	0.0441192788019062	*  
df.mm.exp6	0.101973697810178	0.134629978353753	0.757436783821155	0.448981754578497	   
df.mm.exp7	0.179624757253666	0.134629978353753	1.33421069697928	0.182464248398128	   
df.mm.exp8	0.132893851162633	0.134629978353753	0.98710445316601	0.323850400648331	   
df.mm.trans1:exp2	-0.0365260118734875	0.122899793434638	-0.297201572538958	0.76637956618555	   
df.mm.trans2:exp2	-0.0832589020584827	0.0869032773440585	-0.95806400636483	0.338281635503041	   
df.mm.trans1:exp3	-0.0108657243831221	0.122899793434638	-0.0884112501694389	0.929569019100029	   
df.mm.trans2:exp3	-0.104260848828991	0.0869032773440585	-1.19973437153828	0.230550717857219	   
df.mm.trans1:exp4	-0.185905055878156	0.122899793434638	-1.51265556013344	0.130709851777576	   
df.mm.trans2:exp4	-0.255626447567024	0.0869032773440585	-2.94150526170577	0.00334792400177685	** 
df.mm.trans1:exp5	-0.203225683294919	0.122899793434638	-1.65358848550873	0.098551557298973	.  
df.mm.trans2:exp5	-0.20988231744254	0.0869032773440585	-2.41512545737022	0.015923449200031	*  
df.mm.trans1:exp6	-0.137420783092339	0.122899793434638	-1.11815308432901	0.263792868713043	   
df.mm.trans2:exp6	-0.174284735824884	0.0869032773440585	-2.00550245228236	0.0452011731789613	*  
df.mm.trans1:exp7	-0.168780899250525	0.122899793434638	-1.37332126062757	0.169986352696779	   
df.mm.trans2:exp7	-0.116224500335993	0.0869032773440585	-1.33740065838771	0.181421714966336	   
df.mm.trans1:exp8	-0.110260318763162	0.122899793434638	-0.897156257807725	0.369869738686594	   
df.mm.trans2:exp8	-0.164026520196726	0.0869032773440585	-1.88746069434562	0.0594122606351304	.  
df.mm.trans1:probe2	0.0860393344119363	0.08904936043451	0.966198229747115	0.334198358438488	   
df.mm.trans1:probe3	0.0820098425757479	0.08904936043451	0.920948136803979	0.357318405159272	   
df.mm.trans1:probe4	0.240827950548923	0.08904936043451	2.70443211915078	0.00696819564492587	** 
df.mm.trans1:probe5	0.156894272919609	0.08904936043451	1.76187984005786	0.0784209620194151	.  
df.mm.trans1:probe6	0.126187846780118	0.08904936043451	1.41705505984988	0.156804636719704	   
df.mm.trans1:probe7	0.112923952056099	0.08904936043451	1.26810514421547	0.205080522046212	   
df.mm.trans1:probe8	0.0952517084656462	0.08904936043451	1.06965067464687	0.285056522610443	   
df.mm.trans1:probe9	-0.00236097108405083	0.08904936043451	-0.0265130605377809	0.978853853692954	   
df.mm.trans1:probe10	0.07912021346222	0.08904936043451	0.888498390961581	0.374504455131245	   
df.mm.trans1:probe11	0.124382797718299	0.08904936043451	1.39678485181008	0.162814387115376	   
df.mm.trans1:probe12	0.108077805719833	0.08904936043451	1.21368424424920	0.225179131866707	   
df.mm.trans1:probe13	0.057079407286365	0.0890493604345099	0.640986156529931	0.5216909644563	   
df.mm.trans1:probe14	0.112870609017219	0.08904936043451	1.26750611645581	0.205294400675034	   
df.mm.trans1:probe15	0.203783052500378	0.08904936043451	2.28842803031974	0.0223381002931404	*  
df.mm.trans1:probe16	0.148794738378621	0.08904936043451	1.67092427899075	0.0950760008741865	.  
df.mm.trans1:probe17	0.188499449291335	0.08904936043451	2.11679733994232	0.0345443799218012	*  
df.mm.trans1:probe18	0.0936655234891588	0.08904936043451	1.05183825051774	0.293149358123965	   
df.mm.trans1:probe19	0.181274294392262	0.08904936043451	2.03566082347753	0.0420699038007958	*  
df.mm.trans1:probe20	-0.00561867088724176	0.08904936043451	-0.0630961397120188	0.949703624075396	   
df.mm.trans1:probe21	0.131662211460230	0.08904936043451	1.47853068026310	0.139607328781612	   
df.mm.trans2:probe2	0.0775503574354857	0.08904936043451	0.870869336479053	0.384052189717703	   
df.mm.trans2:probe3	0.054628773698467	0.08904936043451	0.613466210559063	0.539719426591523	   
df.mm.trans2:probe4	-0.0194419615449824	0.08904936043451	-0.218327918921784	0.827221936614033	   
df.mm.trans2:probe5	0.250354163017819	0.08904936043451	2.8114088837498	0.0050371036503047	** 
df.mm.trans2:probe6	0.0968996619227034	0.08904936043451	1.08815674194501	0.276810275998298	   
df.mm.trans3:probe2	0.0101575053619289	0.08904936043451	0.114066011393749	0.909210298626397	   
df.mm.trans3:probe3	0.0546203103711268	0.08904936043451	0.613371169704206	0.539782225485933	   
df.mm.trans3:probe4	-0.0257082810054419	0.08904936043451	-0.288696975250582	0.772878170521469	   
df.mm.trans3:probe5	0.069883379320125	0.08904936043451	0.78477126594884	0.432789278844049	   
df.mm.trans3:probe6	-0.107804500446447	0.08904936043451	-1.21061510066353	0.226353196076089	   
df.mm.trans3:probe7	0.0380158953222501	0.08904936043451	0.426908123053937	0.669545915292907	   
df.mm.trans3:probe8	-0.0784214922213104	0.08904936043451	-0.880651942233592	0.37873572915971	   
df.mm.trans3:probe9	0.0061663288710729	0.08904936043451	0.069246189315507	0.944808672449475	   
df.mm.trans3:probe10	-0.0121757229209752	0.08904936043451	-0.136730043445171	0.891274030875338	   
df.mm.trans3:probe11	0.0140868618331745	0.08904936043451	0.158191611533633	0.874340453153282	   
df.mm.trans3:probe12	-0.0202972633999906	0.08904936043451	-0.227932725186926	0.819749078256346	   
df.mm.trans3:probe13	0.0678888994468877	0.08904936043451	0.762373801626746	0.446031923723509	   
df.mm.trans3:probe14	-0.0481029557619388	0.08904936043451	-0.540183057208091	0.589201246350435