chr5.18909_chr5_40608740_40611745_-_0.R 

fitVsDatCorrelation=0.868331363307392
cont.fitVsDatCorrelation=0.257393346127584

fstatistic=9208.8236803142,56,784
cont.fstatistic=2415.79791496348,56,784

residuals=-0.568242827934106,-0.0926374652853104,-0.00233738028171915,0.0975376147714898,0.75510984101271
cont.residuals=-0.664159356379255,-0.243289386738360,-0.0541024340254419,0.185091976625661,0.997729872153198

predictedValues:
Include	Exclude	Both
chr5.18909_chr5_40608740_40611745_-_0.R.tl.Lung	68.6080018768574	52.3801219306792	70.897690338758
chr5.18909_chr5_40608740_40611745_-_0.R.tl.cerebhem	81.4403598243504	56.0218656880517	70.7618849834397
chr5.18909_chr5_40608740_40611745_-_0.R.tl.cortex	83.1469633462977	50.3956720253344	104.153098051543
chr5.18909_chr5_40608740_40611745_-_0.R.tl.heart	81.5955043611129	51.1705694783514	110.870523135560
chr5.18909_chr5_40608740_40611745_-_0.R.tl.kidney	68.05055453135	54.6462262682556	67.3994516168166
chr5.18909_chr5_40608740_40611745_-_0.R.tl.liver	65.1011939170874	55.3811335547706	56.0568825554854
chr5.18909_chr5_40608740_40611745_-_0.R.tl.stomach	62.763590398396	51.4123444320359	64.8555898959241
chr5.18909_chr5_40608740_40611745_-_0.R.tl.testicle	69.9036160811238	54.3879045729307	66.047148432158


diffExp=16.2278799461781,25.4184941362987,32.7512913209634,30.4249348827615,13.4043282630944,9.7200603623168,11.3512459663601,15.5157115081931
diffExpScore=0.993582089259702
diffExp1.5=0,0,1,1,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,1,1,1,0,0,0,0
diffExp1.4Score=0.75
diffExp1.3=1,1,1,1,0,0,0,0
diffExp1.3Score=0.8
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	57.742993204957	66.9873298485794	72.1938232409426
cerebhem	61.6814985622114	65.250456173471	63.7337125916148
cortex	68.2932377838459	62.9393159535095	66.224877650408
heart	57.817982677988	63.1971019403809	65.1169048569201
kidney	62.7440553756153	77.7474551509117	66.8314712378464
liver	58.2877817608407	64.453761452613	63.6987102981684
stomach	71.5238254282439	65.4945861452221	68.4655612573248
testicle	62.7702648586615	68.9683504065555	67.8443925409752
cont.diffExp=-9.24433664362241,-3.56895761125968,5.35392183033645,-5.37911926239287,-15.0033997752965,-6.16597969177226,6.02923928302174,-6.19808554789395
cont.diffExpScore=1.61877070471147

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

tran.correlation=-0.230087176466866
cont.tran.correlation=-0.0225395702807552

tran.covariance=-0.00100842419843944
cont.tran.covariance=-4.53439965228485e-05

tran.mean=62.9003513929366
cont.tran.mean=64.7437497952254

weightedLogRatios:
wLogRatio
Lung	1.10475340320665
cerebhem	1.57613266089074
cortex	2.08806621651814
heart	1.94504788265798
kidney	0.901737711028875
liver	0.662192186038734
stomach	0.805892712899303
testicle	1.03442843037385

cont.weightedLogRatios:
wLogRatio
Lung	-0.613348657053691
cerebhem	-0.233439507165438
cortex	0.341498249979092
heart	-0.364888523896881
kidney	-0.91040759062283
liver	-0.413853670666361
stomach	0.372154712309345
testicle	-0.394233036714329

varWeightedLogRatios=0.288809248943130
cont.varWeightedLogRatios=0.194338844056909

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.46284895277045	0.0785907444070746	56.7859356269021	4.99703476143695e-280	***
df.mm.trans1	-0.098627032565266	0.0650371010170567	-1.51647338246826	0.129802779503564	   
df.mm.trans2	-0.475240938122767	0.059991297672807	-7.92183127484147	8.01339583040124e-15	***
df.mm.exp2	0.240594052696139	0.0772023786049568	3.11640725381344	0.00189745355259737	** 
df.mm.exp3	-0.231045413224610	0.0772023786049568	-2.99272402482396	0.00285178128232076	** 
df.mm.exp4	-0.297122827059507	0.0772023786049568	-3.84862270345166	0.000128451239927112	***
df.mm.exp5	0.0847956722505476	0.0772023786049568	1.09835569554723	0.272386393936976	   
df.mm.exp6	0.238116456301320	0.0772023786049568	3.08431502505586	0.00211180835784922	** 
df.mm.exp7	-0.0186081456396409	0.0772023786049568	-0.241030729569336	0.809594319072917	   
df.mm.exp8	0.127191826101772	0.0772023786049568	1.64751175287760	0.0998535996912688	.  
df.mm.trans1:exp2	-0.0691322549927915	0.0650371010170567	-1.06296642857222	0.288124474805121	   
df.mm.trans2:exp2	-0.17337914634164	0.0527999894009077	-3.28369661261067	0.00106998532704589	** 
df.mm.trans1:exp3	0.423245924643298	0.0650371010170567	6.50776123204349	1.35989550791874e-10	***
df.mm.trans2:exp3	0.192423545214778	0.0527999894009077	3.64438605761291	0.000285696050169316	***
df.mm.trans1:exp4	0.470487820533519	0.0650371010170567	7.2341450214721	1.11633813594295e-12	***
df.mm.trans2:exp4	0.273760212072921	0.0527999894009077	5.18485354219057	2.75353461318939e-07	***
df.mm.trans1:exp5	-0.0929539676698328	0.0650371010170567	-1.42924524949928	0.153332076316705	   
df.mm.trans2:exp5	-0.0424426796298548	0.0527999894009077	-0.803838790716219	0.421733739166639	   
df.mm.trans1:exp6	-0.290582740879290	0.0650371010170567	-4.46795346556239	9.05942048956084e-06	***
df.mm.trans2:exp6	-0.182404636987819	0.0527999894009077	-3.45463396976900	0.000580644118832858	***
df.mm.trans1:exp7	-0.0704258931089483	0.0650371010170567	-1.08285720008458	0.279204734452153	   
df.mm.trans2:exp7	-4.07135733096867e-05	0.0527999894009077	-0.000771090558381567	0.999384954966215	   
df.mm.trans1:exp8	-0.108483619359683	0.0650371010170567	-1.66802667497790	0.0957097441490892	.  
df.mm.trans2:exp8	-0.0895772062576678	0.0527999894009077	-1.69653833786807	0.0901809271950458	.  
df.mm.trans1:probe2	-0.146699637150279	0.0487778257627926	-3.00750668682287	0.00271819401294079	** 
df.mm.trans1:probe3	-0.096521921657025	0.0487778257627926	-1.97880738117383	0.0481872450774777	*  
df.mm.trans1:probe4	-0.235824693278071	0.0487778257627926	-4.83467004915083	1.60514586199094e-06	***
df.mm.trans1:probe5	0.0269466334132496	0.0487778257627926	0.552436132440415	0.580807036393341	   
df.mm.trans1:probe6	-0.430664783264591	0.0487778257627926	-8.8291098778966	6.81355333549165e-18	***
df.mm.trans1:probe7	-0.557854872638024	0.0487778257627926	-11.4366490083195	4.03988220884292e-28	***
df.mm.trans1:probe8	-0.565767856390042	0.0487778257627926	-11.5988740281574	8.07605247443353e-29	***
df.mm.trans1:probe9	-0.505412405051154	0.0487778257627926	-10.3615197509824	1.15121979094890e-23	***
df.mm.trans1:probe10	-0.344519023985376	0.0487778257627926	-7.06302543415482	3.59689744240771e-12	***
df.mm.trans1:probe11	-0.403187364174521	0.0487778257627926	-8.26579204524672	5.90908346310449e-16	***
df.mm.trans2:probe2	-0.20424391356192	0.0487778257627925	-4.18722873289109	3.14406174175928e-05	***
df.mm.trans2:probe3	-0.0990274438383659	0.0487778257627926	-2.03017338903005	0.0426760583919203	*  
df.mm.trans2:probe4	-0.15321463434595	0.0487778257627925	-3.14107141821031	0.00174652452662354	** 
df.mm.trans2:probe5	-0.0974378944260739	0.0487778257627926	-1.99758584771523	0.0461066953279303	*  
df.mm.trans2:probe6	-0.144016460091542	0.0487778257627926	-2.95249855522255	0.00324625967410040	** 
df.mm.trans3:probe2	0.503297138803758	0.0487778257627926	10.3181544263843	1.714325855316e-23	***
df.mm.trans3:probe3	0.506830618110194	0.0487778257627926	10.3905947053672	8.8086664234497e-24	***
df.mm.trans3:probe4	0.559416550753493	0.0487778257627926	11.4686651568675	2.94392511061515e-28	***
df.mm.trans3:probe5	0.313628887810244	0.0487778257627926	6.42974308316708	2.21987020890329e-10	***
df.mm.trans3:probe6	0.506793178708769	0.0487778257627926	10.3898271557513	8.87119440586584e-24	***
df.mm.trans3:probe7	0.403865482563093	0.0487778257627926	8.2796942308806	5.30800087660488e-16	***
df.mm.trans3:probe8	0.414955717308687	0.0487778257627926	8.50705645074514	8.99519185006012e-17	***
df.mm.trans3:probe9	0.426949580252138	0.0487778257627926	8.75294405963073	1.26301185433557e-17	***
df.mm.trans3:probe10	0.477948516310591	0.0487778257627926	9.79847930563496	1.83672672220352e-21	***
df.mm.trans3:probe11	0.331248879160312	0.0487778257627926	6.7909726188121	2.20075722294805e-11	***
df.mm.trans3:probe12	0.381426594653566	0.0487778257627926	7.81967192446113	1.70805908863580e-14	***
df.mm.trans3:probe13	0.24212382303252	0.0487778257627926	4.96380925648414	8.48393572221806e-07	***
df.mm.trans3:probe14	0.50489514972384	0.0487778257627926	10.3509154380754	1.26910202666080e-23	***
df.mm.trans3:probe15	0.211319452340228	0.0487778257627926	4.33228519384767	1.66746519076422e-05	***
df.mm.trans3:probe16	0.492892057141781	0.0487778257627926	10.1048386112723	1.19367450435165e-22	***
df.mm.trans3:probe17	-0.00909644579781243	0.0487778257627926	-0.186487315815358	0.852110868236878	   
df.mm.trans3:probe18	0.432128561976251	0.0487778257627926	8.85911897913818	5.33656876970375e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93776302231874	0.153115190092219	25.7176510047573	3.02426641614519e-106	***
df.mm.trans1	0.100225682389236	0.126709171167706	0.790989961228471	0.429189064953730	   
df.mm.trans2	0.285049310495152	0.116878635217811	2.43884872512366	0.0149550268967079	*  
df.mm.exp2	0.164352383954246	0.150410292774950	1.09269373074193	0.274863938142595	   
df.mm.exp3	0.191774574323426	0.150410292774951	1.27500964718130	0.202683540225416	   
df.mm.exp4	0.0462230848213126	0.150410292774950	0.30731330927248	0.758686518498466	   
df.mm.exp5	0.309204546440144	0.150410292774951	2.05574060614846	0.0401381752170739	*  
df.mm.exp6	0.0960252547819015	0.150410292774950	0.638422098716197	0.52338539066072	   
df.mm.exp7	0.24451622297584	0.150410292774950	1.62566150537114	0.104423560366631	   
df.mm.exp8	0.174761428042515	0.150410292774950	1.16189806440972	0.245630465346320	   
df.mm.trans1:exp2	-0.0983703714951679	0.126709171167706	-0.776347683349373	0.437777870772901	   
df.mm.trans2:exp2	-0.190622841381987	0.102868098209022	-1.85308025229214	0.0642464160224115	.  
df.mm.trans1:exp3	-0.0239658324619691	0.126709171167706	-0.189140472162422	0.850031674142688	   
df.mm.trans2:exp3	-0.254107045748135	0.102868098209022	-2.47022206274100	0.0137147087996772	*  
df.mm.trans1:exp4	-0.0449252507906828	0.126709171167706	-0.35455405774237	0.723019044515257	   
df.mm.trans2:exp4	-0.104468134864022	0.102868098209022	-1.01555425523420	0.310154995504005	   
df.mm.trans1:exp5	-0.226142720222410	0.126709171167706	-1.78473837480239	0.074690330632126	.  
df.mm.trans2:exp5	-0.160242221864874	0.102868098209022	-1.55774457440897	0.119697367518210	   
df.mm.trans1:exp6	-0.0866347709317297	0.126709171167706	-0.683729284418285	0.494348153119614	   
df.mm.trans2:exp6	-0.134580659533236	0.102868098209022	-1.30828373301678	0.191160512498854	   
df.mm.trans1:exp7	-0.0304876182661695	0.126709171167706	-0.240610983287213	0.809919540721766	   
df.mm.trans2:exp7	-0.267052232792516	0.102868098209022	-2.59606464435535	0.00960635228188326	** 
df.mm.trans1:exp8	-0.0912819684682311	0.126709171167706	-0.720405378924109	0.471490168225245	   
df.mm.trans2:exp8	-0.145617213206061	0.102868098209022	-1.41557213306476	0.157297927581363	   
df.mm.trans1:probe2	0.0389888198423455	0.0950318783757797	0.410270958637416	0.681719283324762	   
df.mm.trans1:probe3	0.00181835341375386	0.0950318783757797	0.0191341415620939	0.984738963746616	   
df.mm.trans1:probe4	-0.000844733200428297	0.0950318783757797	-0.00888894563451658	0.992910002235765	   
df.mm.trans1:probe5	0.101571280446942	0.0950318783757797	1.06881272035163	0.285483034312165	   
df.mm.trans1:probe6	0.0613512630850084	0.0950318783757797	0.645586135237802	0.518736313160455	   
df.mm.trans1:probe7	0.0741237896358664	0.0950318783757797	0.779988682774032	0.435632943319920	   
df.mm.trans1:probe8	-0.0322167437281770	0.0950318783757797	-0.339009859415637	0.7346930829278	   
df.mm.trans1:probe9	0.0671153194444696	0.0950318783757797	0.706240059562739	0.480248678434422	   
df.mm.trans1:probe10	0.0989398498446188	0.0950318783757797	1.04112274255367	0.298139533612378	   
df.mm.trans1:probe11	0.0214721823813811	0.0950318783757797	0.225947153190793	0.821301412459102	   
df.mm.trans2:probe2	-0.0270735415966331	0.0950318783757797	-0.284889050488696	0.775804382179984	   
df.mm.trans2:probe3	-0.120114922354845	0.0950318783757797	-1.26394347252488	0.206625984764708	   
df.mm.trans2:probe4	-0.063711795301448	0.0950318783757797	-0.670425507633509	0.502784012171019	   
df.mm.trans2:probe5	-0.147416500185276	0.0950318783757797	-1.55123209921574	0.121249631206671	   
df.mm.trans2:probe6	-0.0810953536054266	0.0950318783757797	-0.853348949757211	0.393726459000508	   
df.mm.trans3:probe2	-0.0766897185861046	0.0950318783757797	-0.806989400786695	0.419917332166022	   
df.mm.trans3:probe3	-0.066741830863091	0.0950318783757797	-0.702309919616417	0.482694362925538	   
df.mm.trans3:probe4	-0.0155206056611871	0.0950318783757797	-0.16331999247468	0.870308582941447	   
df.mm.trans3:probe5	-0.0372885250744282	0.0950318783757797	-0.392379122792671	0.69488475678413	   
df.mm.trans3:probe6	0.0187711502476106	0.0950318783757797	0.197524773459542	0.843468096227151	   
df.mm.trans3:probe7	-0.150594064875309	0.0950318783757797	-1.58466892846022	0.113444830136347	   
df.mm.trans3:probe8	-0.0682286955817292	0.0950318783757797	-0.717955876994622	0.47299837214819	   
df.mm.trans3:probe9	-0.0195170879980288	0.0950318783757797	-0.205374115839881	0.83733315336627	   
df.mm.trans3:probe10	0.0895532690037325	0.0950318783757797	0.942349772879545	0.346303874512815	   
df.mm.trans3:probe11	0.0674643439525805	0.0950318783757797	0.709912769332094	0.477969320933727	   
df.mm.trans3:probe12	-0.119992852340663	0.0950318783757797	-1.26265895604190	0.207087196291418	   
df.mm.trans3:probe13	-0.192732271877478	0.0950318783757797	-2.0280802102571	0.0428897255884018	*  
df.mm.trans3:probe14	-0.0401104658892328	0.0950318783757797	-0.422073798548168	0.673086866323462	   
df.mm.trans3:probe15	-0.00586690817926478	0.0950318783757797	-0.0617362118852956	0.950788636759587	   
df.mm.trans3:probe16	-0.0824411570642232	0.0950318783757797	-0.867510549862335	0.385927776893151	   
df.mm.trans3:probe17	-0.0717478014999748	0.0950318783757797	-0.754986671064905	0.450483714479011	   
df.mm.trans3:probe18	-0.0146202032959473	0.0950318783757797	-0.153845252201955	0.877771343526848