chr8.23277_chr8_104509066_104510332_-_2.R 

fitVsDatCorrelation=0.888076710632004
cont.fitVsDatCorrelation=0.243087291831814

fstatistic=4064.23253194765,59,853
cont.fstatistic=901.580166730569,59,853

residuals=-0.89892364894116,-0.120677984248857,0.00483282803378552,0.120504549192733,1.44081930154705
cont.residuals=-0.747248897794042,-0.324832884301995,-0.153846617382877,0.135182397435060,2.5361032371596

predictedValues:
Include	Exclude	Both
chr8.23277_chr8_104509066_104510332_-_2.R.tl.Lung	61.2098682447332	46.718863338436	73.0646052559404
chr8.23277_chr8_104509066_104510332_-_2.R.tl.cerebhem	56.6163380382661	44.1833882293504	60.770983699125
chr8.23277_chr8_104509066_104510332_-_2.R.tl.cortex	63.9005603491441	42.8437203122919	72.317420935714
chr8.23277_chr8_104509066_104510332_-_2.R.tl.heart	69.576765425212	47.9474784992037	81.5575820511574
chr8.23277_chr8_104509066_104510332_-_2.R.tl.kidney	162.705689856153	62.3823354806528	218.484437835319
chr8.23277_chr8_104509066_104510332_-_2.R.tl.liver	62.4229507560842	47.5497441835536	69.2118307875787
chr8.23277_chr8_104509066_104510332_-_2.R.tl.stomach	58.7166249153345	46.1818810868281	66.0023957182797
chr8.23277_chr8_104509066_104510332_-_2.R.tl.testicle	60.5311084901161	46.9907195302993	66.1949281733712


diffExp=14.4910049062972,12.4329498089157,21.0568400368523,21.6292869260082,100.323354375501,14.8732065725306,12.5347438285064,13.5403889598168
diffExpScore=0.995280386913674
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,1,1,0,0,0
diffExp1.4Score=0.75
diffExp1.3=1,0,1,1,1,1,0,0
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	55.6545023997741	56.6767479713558	61.7472055674079
cerebhem	62.9955852630419	59.6190103332456	71.9785364022699
cortex	62.8997362546237	62.2560946288255	56.3040972137995
heart	65.8205796247186	64.0627795247814	66.8562825078454
kidney	57.9586089871661	60.5643715947748	68.4485206821233
liver	58.7765332006374	72.5914832177713	73.4911778283898
stomach	65.5998935568464	61.9655612268848	69.0425930286126
testicle	67.0454426371431	68.4791076702872	61.3403259014849
cont.diffExp=-1.02224557158172,3.37657492979627,0.643641625798253,1.75780009993723,-2.60576260760876,-13.8149500171339,3.63433232996158,-1.43366503314408
cont.diffExpScore=2.70338597359002

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,-1,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.963331841064001
cont.tran.correlation=0.287853858592558

tran.covariance=0.0376346749049734
cont.tran.covariance=0.00172264778599340

tran.mean=61.2798772959787
cont.tran.mean=62.6853773807423

weightedLogRatios:
wLogRatio
Lung	1.0750299395727
cerebhem	0.970055520816487
cortex	1.58206345808605
heart	1.51024799025975
kidney	4.42193101563310
liver	1.08804254201403
stomach	0.949172316694146
testicle	1.00689243408775

cont.weightedLogRatios:
wLogRatio
Lung	-0.0733186570843707
cerebhem	0.226724632386898
cortex	0.0425450761791765
heart	0.112969782729266
kidney	-0.179504465943285
liver	-0.882269782822127
stomach	0.236819854937466
testicle	-0.0892013305372353

varWeightedLogRatios=1.38238848480497
cont.varWeightedLogRatios=0.128603660675561

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20163260504761	0.120225643546432	34.9478903261176	8.91514684846e-167	***
df.mm.trans1	0.0823264080242393	0.103450863985302	0.795802034441546	0.426368508256153	   
df.mm.trans2	-0.390091256523807	0.091416648880417	-4.26717957069383	2.20134125659604e-05	***
df.mm.exp2	0.0504216766618849	0.117224338045796	0.430129762321086	0.667209894764575	   
df.mm.exp3	-0.0332902173470918	0.117224338045796	-0.283987249593906	0.77648907011899	   
df.mm.exp4	0.0441152183297536	0.117224338045796	0.376331562755502	0.706764001084124	   
df.mm.exp5	0.171397967772673	0.117224338045796	1.46213636715708	0.144072154030705	   
df.mm.exp6	0.0914252261389142	0.117224338045796	0.779916761851939	0.435656217688485	   
df.mm.exp7	0.0485070237464061	0.117224338045796	0.413796525150399	0.6791271459839	   
df.mm.exp8	0.0933913238296533	0.117224338045796	0.796688856482756	0.425853438296722	   
df.mm.trans1:exp2	-0.128432497535076	0.107691571221132	-1.19259563286857	0.233359417275143	   
df.mm.trans2:exp2	-0.106220799131226	0.0789765611848145	-1.34496612080458	0.178993653336919	   
df.mm.trans1:exp3	0.0763099255007737	0.107691571221132	0.708597011219015	0.47876819043443	   
df.mm.trans2:exp3	-0.0532987078156201	0.0789765611848145	-0.6748674165604	0.499942877852439	   
df.mm.trans1:exp4	0.0840070408308766	0.107691571221132	0.780070713782958	0.435565648614794	   
df.mm.trans2:exp4	-0.0181570134220484	0.0789765611848145	-0.229903823991005	0.818221623933078	   
df.mm.trans1:exp5	0.8062365949499	0.107691571221132	7.48653386525849	1.76353117307617e-13	***
df.mm.trans2:exp5	0.117736173285237	0.0789765611848145	1.49077361078967	0.136390625646529	   
df.mm.trans1:exp6	-0.071800640155854	0.107691571221132	-0.666724789523406	0.50512823559448	   
df.mm.trans2:exp6	-0.073796826103613	0.0789765611848145	-0.934414274267015	0.350354672648795	   
df.mm.trans1:exp7	-0.0900925410404792	0.107691571221132	-0.836579316458155	0.403063373338106	   
df.mm.trans2:exp7	-0.060067495846195	0.0789765611848145	-0.760573706237094	0.447121910362646	   
df.mm.trans1:exp8	-0.104542323372464	0.107691571221132	-0.970756784277932	0.331944586667236	   
df.mm.trans2:exp8	-0.087589207414229	0.0789765611848145	-1.10905319376036	0.267719816082487	   
df.mm.trans1:probe2	0.707731273036683	0.0750212380614465	9.43374558091153	3.64640370275572e-20	***
df.mm.trans1:probe3	0.119048065983271	0.0750212380614465	1.58685818922056	0.112915365151151	   
df.mm.trans1:probe4	-0.261132974330357	0.0750212380614465	-3.48078732207104	0.000525311710366146	***
df.mm.trans1:probe5	-0.203586567897572	0.0750212380614465	-2.71371911685626	0.00678745743550044	** 
df.mm.trans1:probe6	-0.320190836150454	0.0750212380614465	-4.26800256066423	2.19340730699690e-05	***
df.mm.trans1:probe7	-0.461316595427579	0.0750212380614465	-6.14914665963971	1.19573635355425e-09	***
df.mm.trans1:probe8	-0.360497765024305	0.0750212380614465	-4.80527613699253	1.82560327474612e-06	***
df.mm.trans1:probe9	-0.325917676888069	0.0750212380614465	-4.34433882071	1.56504784515313e-05	***
df.mm.trans1:probe10	-0.517285532467689	0.0750212380614465	-6.8951878939135	1.04676737852729e-11	***
df.mm.trans1:probe11	-0.443842721573497	0.0750212380614465	-5.91622763156701	4.76612034661979e-09	***
df.mm.trans1:probe12	-0.160490315668445	0.0750212380614465	-2.13926509099990	0.0326974912741634	*  
df.mm.trans1:probe13	-0.190848193506896	0.0750212380614465	-2.54392220707662	0.0111369125667166	*  
df.mm.trans1:probe14	0.188333424794276	0.0750212380614465	2.51040144978707	0.0122434344874827	*  
df.mm.trans1:probe15	-0.113049308897833	0.0750212380614465	-1.50689740424224	0.132207190947386	   
df.mm.trans1:probe16	-0.494762992651306	0.0750212380614465	-6.5949723763032	7.45285142829535e-11	***
df.mm.trans1:probe17	-0.548426773930607	0.0750212380614465	-7.3102869014427	6.1362639168912e-13	***
df.mm.trans1:probe18	-0.526790398297403	0.0750212380614465	-7.02188356137142	4.47045295222923e-12	***
df.mm.trans1:probe19	-0.496503968130459	0.0750212380614465	-6.61817881122936	6.42083317493635e-11	***
df.mm.trans1:probe20	-0.532676924623301	0.0750212380614465	-7.10034835984724	2.62202106979809e-12	***
df.mm.trans1:probe21	-0.486612121494147	0.0750212380614465	-6.486324860376	1.48857287360215e-10	***
df.mm.trans2:probe2	0.0181743783316154	0.0750212380614465	0.242256443658389	0.808639693847095	   
df.mm.trans2:probe3	0.096080333731337	0.0750212380614465	1.28070845288693	0.200644200429388	   
df.mm.trans2:probe4	0.0619144567221325	0.0750212380614465	0.825292388155753	0.409436352787651	   
df.mm.trans2:probe5	0.129392972079490	0.0750212380614465	1.72475122276055	0.0849345601327465	.  
df.mm.trans2:probe6	0.248751087151402	0.0750212380614465	3.31574222952254	0.000952598536938708	***
df.mm.trans3:probe2	0.0213128319166973	0.0750212380614465	0.284090645094939	0.776409860909719	   
df.mm.trans3:probe3	0.242421880554016	0.0750212380614465	3.23137669836185	0.00127918263447009	** 
df.mm.trans3:probe4	-0.117949609180384	0.0750212380614465	-1.57221624473562	0.116271234352080	   
df.mm.trans3:probe5	1.24320608216651	0.0750212380614465	16.5713885066553	1.11001178415083e-53	***
df.mm.trans3:probe6	0.316512631889827	0.0750212380614465	4.21897372088935	2.71692234332432e-05	***
df.mm.trans3:probe7	0.0202102107240101	0.0750212380614465	0.269393191131514	0.787692275802956	   
df.mm.trans3:probe8	1.09928550862933	0.0750212380614465	14.652990766814	1.57414417831296e-43	***
df.mm.trans3:probe9	0.209707249491543	0.0750212380614465	2.79530510173375	0.00530151367509828	** 
df.mm.trans3:probe10	0.511432022608866	0.0750212380614465	6.81716319037517	1.75604466539864e-11	***
df.mm.trans3:probe11	0.82610045513808	0.0750212380614465	11.0115545475464	1.84641412395085e-26	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.78499861997029	0.253688494929702	14.9198670638144	6.71047687579097e-45	***
df.mm.trans1	0.100089398089742	0.21829198172246	0.458511564648296	0.646701750526328	   
df.mm.trans2	0.253308149417016	0.192898547946082	1.31316773565252	0.189479567727776	   
df.mm.exp2	0.0211928707062622	0.247355431093895	0.0856778062747186	0.931742650898491	   
df.mm.exp3	0.308552802152204	0.247355431093895	1.24740661964717	0.212590799253647	   
df.mm.exp4	0.210772506489865	0.247355431093895	0.852103814974884	0.39439564407366	   
df.mm.exp5	0.00387567296185866	0.247355431093895	0.0156684368914765	0.987502571540024	   
df.mm.exp6	0.127946616922038	0.247355431093895	0.517258167149239	0.605110051368841	   
df.mm.exp7	0.141950938027274	0.247355431093895	0.573874353190937	0.56620410991321	   
df.mm.exp8	0.381983569801435	0.247355431093895	1.54426999282840	0.122893832156200	   
df.mm.trans1:exp2	0.102708797934892	0.227240310917134	0.451983178162196	0.651396008294393	   
df.mm.trans2:exp2	0.0294175798935962	0.166648681185568	0.176524528633016	0.859923781810172	   
df.mm.trans1:exp3	-0.186173811650029	0.227240310917134	-0.819281627008158	0.412854594314221	   
df.mm.trans2:exp3	-0.214660403851627	0.166648681185568	-1.28810142585285	0.198059965471152	   
df.mm.trans1:exp4	-0.043002936908989	0.227240310917134	-0.189239914060276	0.849949803551246	   
df.mm.trans2:exp4	-0.0882730117887311	0.166648681185568	-0.529695231673849	0.596461088177843	   
df.mm.trans1:exp5	0.0366904645661474	0.227240310917134	0.161461073601184	0.871768501824667	   
df.mm.trans2:exp5	0.0624670818015816	0.166648681185568	0.374842941193292	0.707870450660449	   
df.mm.trans1:exp6	-0.0733669170095941	0.227240310917134	-0.322860485067494	0.746880026670873	   
df.mm.trans2:exp6	0.119536949041517	0.166648681185568	0.717299100065536	0.473385935167449	   
df.mm.trans1:exp7	0.0224601552072174	0.227240310917134	0.098838780481196	0.921289498612391	   
df.mm.trans2:exp7	-0.0527362093277455	0.166648681185568	-0.316451405151068	0.751737327776755	   
df.mm.trans1:exp8	-0.195775912106525	0.227240310917134	-0.861536895968765	0.389184525567827	   
df.mm.trans2:exp8	-0.192818906504247	0.166648681185568	-1.15703829836816	0.247580620718774	   
df.mm.trans1:probe2	0.338683755148531	0.158302541871784	2.13947136378167	0.0326807461215936	*  
df.mm.trans1:probe3	0.362275673206345	0.158302541871784	2.28850193384619	0.0223520511498674	*  
df.mm.trans1:probe4	0.102707171307228	0.158302541871784	0.648803045692185	0.516640320141446	   
df.mm.trans1:probe5	0.435636957082193	0.158302541871784	2.75192648160403	0.00605028596768907	** 
df.mm.trans1:probe6	0.109517327359141	0.158302541871784	0.691822923777459	0.489236797750943	   
df.mm.trans1:probe7	0.134399020350196	0.158302541871784	0.849001025258656	0.396118919480231	   
df.mm.trans1:probe8	0.324122997041173	0.158302541871784	2.0474907933171	0.0409157488081038	*  
df.mm.trans1:probe9	0.0765098453741384	0.158302541871784	0.483314067288363	0.628996784308517	   
df.mm.trans1:probe10	0.145720047132521	0.158302541871784	0.920516154759822	0.35756334570075	   
df.mm.trans1:probe11	0.264204586614349	0.158302541871784	1.66898511856075	0.0954872781509274	.  
df.mm.trans1:probe12	0.238123738128623	0.158302541871784	1.50423193028378	0.132891812985999	   
df.mm.trans1:probe13	-0.00859836832033322	0.158302541871784	-0.0543160470998463	0.95669608313582	   
df.mm.trans1:probe14	0.127328009662463	0.158302541871784	0.804333323754154	0.421428604785142	   
df.mm.trans1:probe15	0.138735857132221	0.158302541871784	0.876396901097072	0.381061037970432	   
df.mm.trans1:probe16	0.219948319655464	0.158302541871784	1.38941748537184	0.16506839624854	   
df.mm.trans1:probe17	0.271242193858132	0.158302541871784	1.71344180990992	0.0869946290569512	.  
df.mm.trans1:probe18	0.207329407907493	0.158302541871784	1.30970359323363	0.190648753894663	   
df.mm.trans1:probe19	0.141679595759580	0.158302541871784	0.894992550873452	0.371043541434832	   
df.mm.trans1:probe20	0.301901276318780	0.158302541871784	1.90711578442817	0.0568412625718084	.  
df.mm.trans1:probe21	0.35893137472654	0.158302541871784	2.26737594028814	0.0236167339863732	*  
df.mm.trans2:probe2	-0.0175999696306236	0.158302541871784	-0.111179324239016	0.911500301076075	   
df.mm.trans2:probe3	0.0490853441666286	0.158302541871784	0.310073000636875	0.756581181964009	   
df.mm.trans2:probe4	0.0281170075591878	0.158302541871784	0.177615641711937	0.859067007833527	   
df.mm.trans2:probe5	-0.102568215943330	0.158302541871784	-0.647925262162903	0.517207645490408	   
df.mm.trans2:probe6	0.0269393991713521	0.158302541871784	0.170176668377008	0.864911553016471	   
df.mm.trans3:probe2	-0.078290304559451	0.158302541871784	-0.494561259937705	0.621037200390768	   
df.mm.trans3:probe3	-0.152549005373807	0.158302541871784	-0.96365480661304	0.335492123267624	   
df.mm.trans3:probe4	-0.0461126709607406	0.158302541871784	-0.291294570608279	0.770896857030254	   
df.mm.trans3:probe5	0.0455748487795176	0.158302541871784	0.287897138230607	0.773495410965194	   
df.mm.trans3:probe6	0.128043582079418	0.158302541871784	0.808853607563204	0.418824897468589	   
df.mm.trans3:probe7	0.187493854282972	0.158302541871784	1.18440204475574	0.236583861895864	   
df.mm.trans3:probe8	0.0958101322319292	0.158302541871784	0.605234325987828	0.54518429154764	   
df.mm.trans3:probe9	-0.215261922183003	0.158302541871784	-1.35981342837408	0.174248276974314	   
df.mm.trans3:probe10	-0.0617580171679469	0.158302541871784	-0.390126503577986	0.696540401732443	   
df.mm.trans3:probe11	-0.0717724137115422	0.158302541871784	-0.453387626394992	0.650384957509164