chr16.9615_chr16_36305925_36318814_-_2.R 

fitVsDatCorrelation=0.84503456401502
cont.fitVsDatCorrelation=0.245496010140127

fstatistic=12528.4356948321,52,692
cont.fstatistic=3802.56061628462,52,692

residuals=-0.394250510348451,-0.0908358430258705,-0.00636654612347681,0.0780928268147692,0.631659982349875
cont.residuals=-0.535115205834323,-0.181848236367907,-0.00415104994273656,0.175467139956042,0.79356357286797

predictedValues:
Include	Exclude	Both
chr16.9615_chr16_36305925_36318814_-_2.R.tl.Lung	58.3581221813496	93.693295907683	54.6400422836453
chr16.9615_chr16_36305925_36318814_-_2.R.tl.cerebhem	57.3297920488247	76.5824613010311	53.498275000488
chr16.9615_chr16_36305925_36318814_-_2.R.tl.cortex	56.2071582529571	79.548866111912	49.2358544367214
chr16.9615_chr16_36305925_36318814_-_2.R.tl.heart	56.8012001525856	82.2491806872995	52.7533405560293
chr16.9615_chr16_36305925_36318814_-_2.R.tl.kidney	65.376938693	97.2119093786192	68.0290294686444
chr16.9615_chr16_36305925_36318814_-_2.R.tl.liver	58.9845292927675	87.7826336674106	51.9035886350198
chr16.9615_chr16_36305925_36318814_-_2.R.tl.stomach	60.0463040472544	85.3695398986885	50.1234716829661
chr16.9615_chr16_36305925_36318814_-_2.R.tl.testicle	55.9183149707586	85.2626017941826	51.9559031662812


diffExp=-35.3351737263335,-19.2526692522065,-23.3417078589549,-25.4479805347138,-31.8349706856192,-28.7981043746431,-25.3232358514342,-29.344286823424
diffExpScore=0.995447885485626
diffExp1.5=-1,0,0,0,0,0,0,-1
diffExp1.5Score=0.666666666666667
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	62.005163913907	60.3424417297554	56.004041041978
cerebhem	63.8644578194635	62.4963976084601	67.8533738087883
cortex	63.1288982356587	67.5872401963118	62.9077101096059
heart	65.0871972380494	66.9720610258605	66.4737870746126
kidney	62.44689884963	57.3168593638464	65.6301183183248
liver	62.2406765313283	62.8702248120693	60.3561521159147
stomach	65.0656711782369	64.330982286614	67.6876464126417
testicle	64.668917062243	58.0064239359257	69.828898042081
cont.diffExp=1.66272218415167,1.36806021100347,-4.45834196065309,-1.88486378781104,5.13003948578364,-0.629548280740991,0.734688891622909,6.66249312631734
cont.diffExpScore=2.35056552874721

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=0.741835006083664
cont.tran.correlation=0.323894189794039

tran.covariance=0.00293999221138515
cont.tran.covariance=0.000395361421448982

tran.mean=72.2951780241452
cont.tran.mean=63.02690698671

weightedLogRatios:
wLogRatio
Lung	-2.03730906793013
cerebhem	-1.21424542863058
cortex	-1.45971549716126
heart	-1.56395043348573
kidney	-1.73706696639256
liver	-1.70011600358016
stomach	-1.50286966925447
testicle	-1.78643069428544

cont.weightedLogRatios:
wLogRatio
Lung	0.111816482942057
cerebhem	0.0897764046239176
cortex	-0.285197744304588
heart	-0.119614768834234
kidney	0.350726811776024
liver	-0.0416248187721522
stomach	0.0473502093006231
testicle	0.447402224697114

varWeightedLogRatios=0.0612772635277453
cont.varWeightedLogRatios=0.0568950884856613

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27382046258929	0.074921616463099	57.043890192816	8.50392797670266e-264	***
df.mm.trans1	-0.460172803775352	0.0672905680476686	-6.83859294291238	1.75976087313980e-11	***
df.mm.trans2	0.328571210328076	0.0618828162170192	5.30957106373112	1.48215000802363e-07	***
df.mm.exp2	-0.198319100717221	0.0847641328936239	-2.33965822509038	0.0195848251977693	*  
df.mm.exp3	-0.0970646818180713	0.084764132893624	-1.1451150209946	0.252557396835123	   
df.mm.exp4	-0.122174410885055	0.0847641328936239	-1.44134561062967	0.149939419673332	   
df.mm.exp5	-0.068729908447359	0.084764132893624	-0.810837156012824	0.417737967097402	   
df.mm.exp6	-0.00310724771971119	0.084764132893624	-0.0366575768976564	0.970768606458845	   
df.mm.exp7	0.0217577694184131	0.0847641328936239	0.256686037781078	0.797497446044735	   
df.mm.exp8	-0.0866256470465807	0.084764132893624	-1.02196110653657	0.307156438997499	   
df.mm.trans1:exp2	0.180540973695374	0.0811554785978162	2.22463075586165	0.0264277992365410	*  
df.mm.trans2:exp2	-0.00333945169858421	0.0706367774113533	-0.0472763880370266	0.96230658694879	   
df.mm.trans1:exp3	0.0595102549864821	0.0811554785978162	0.733286969834757	0.463631754980901	   
df.mm.trans2:exp3	-0.066590455485751	0.0706367774113533	-0.942716498771759	0.34615492675515	   
df.mm.trans1:exp4	0.0951333191665775	0.0811554785978162	1.17223532915173	0.241505987916923	   
df.mm.trans2:exp4	-0.0080987989751421	0.0706367774113533	-0.114654140122768	0.908752523435441	   
df.mm.trans1:exp5	0.182300938683304	0.0811554785978163	2.24631709199493	0.0249983107091806	*  
df.mm.trans2:exp5	0.105596498672207	0.0706367774113533	1.49492236964982	0.135390565339086	   
df.mm.trans1:exp6	0.0137838951948558	0.0811554785978162	0.169845529014313	0.865181260441163	   
df.mm.trans2:exp6	-0.0620557036313371	0.0706367774113533	-0.878518328631495	0.379967427277505	   
df.mm.trans1:exp7	0.00675968262164739	0.0811554785978162	0.0832929919019575	0.933642660519733	   
df.mm.trans2:exp7	-0.114795046114593	0.0706367774113533	-1.62514557319177	0.104586868729169	   
df.mm.trans1:exp8	0.0439190649940256	0.0811554785978162	0.541171905493603	0.588563267426299	   
df.mm.trans2:exp8	-0.00766506428912004	0.0706367774113533	-0.108513788001434	0.913619600090559	   
df.mm.trans1:probe2	0.606980847175086	0.0405777392989081	14.9584687974822	4.86138987194096e-44	***
df.mm.trans1:probe3	0.0869866488424808	0.0405777392989081	2.14370367461110	0.0324045164016833	*  
df.mm.trans1:probe4	0.337940904595236	0.0405777392989081	8.32823391431099	4.39126566100095e-16	***
df.mm.trans1:probe5	0.140903638020257	0.0405777392989081	3.47243686944502	0.000547839556740062	***
df.mm.trans1:probe6	0.0799499175046864	0.0405777392989081	1.97029008727546	0.0492034100377079	*  
df.mm.trans1:probe7	0.0972217709153629	0.0405777392989081	2.39593857605515	0.0168424638924908	*  
df.mm.trans1:probe8	0.279183550510123	0.0405777392989081	6.88021450513965	1.33964842719289e-11	***
df.mm.trans1:probe9	0.123620760117042	0.0405777392989081	3.04651669247549	0.00240312826874055	** 
df.mm.trans1:probe10	0.45388131358642	0.0405777392989081	11.1854756186142	8.17663187718422e-27	***
df.mm.trans1:probe11	0.177714665277732	0.0405777392989081	4.37960981435242	1.37296407995232e-05	***
df.mm.trans1:probe12	0.222041375082173	0.0405777392989081	5.47199964607559	6.22530443281349e-08	***
df.mm.trans1:probe13	0.365326728963131	0.0405777392989081	9.00313164989361	2.09845008938337e-18	***
df.mm.trans1:probe14	0.132222736701482	0.0405777392989081	3.25850426825133	0.00117476744123229	** 
df.mm.trans1:probe15	0.28134074378226	0.0405777392989081	6.93337649270743	9.43649902159625e-12	***
df.mm.trans1:probe16	0.452810063006522	0.0405777392989081	11.1590756614355	1.05183562415579e-26	***
df.mm.trans1:probe17	0.553901112519655	0.0405777392989081	13.6503689483401	9.70107845555594e-38	***
df.mm.trans1:probe18	0.355492678170008	0.0405777392989081	8.76078077074081	1.48393199721205e-17	***
df.mm.trans1:probe19	0.262644527504478	0.0405777392989081	6.47262592846185	1.82420670161089e-10	***
df.mm.trans1:probe20	0.301825263385253	0.0405777392989081	7.43819810073487	3.03090473631916e-13	***
df.mm.trans1:probe21	0.467951358136758	0.0405777392989081	11.5322185568221	2.88358318149504e-28	***
df.mm.trans1:probe22	0.543831592587552	0.0405777392989081	13.4022151550021	1.39942292181738e-36	***
df.mm.trans2:probe2	-0.183120651298134	0.0405777392989081	-4.51283522596493	7.51565056552572e-06	***
df.mm.trans2:probe3	-0.228590167341408	0.0405777392989081	-5.63338843639223	2.57157382271338e-08	***
df.mm.trans2:probe4	-0.093859784653	0.0405777392989081	-2.3130856049323	0.0210102723599726	*  
df.mm.trans2:probe5	0.117808839096258	0.0405777392989081	2.90328739677787	0.00381023129099329	** 
df.mm.trans2:probe6	-0.173523548193198	0.0405777392989081	-4.27632369844388	2.16711857464479e-05	***
df.mm.trans3:probe2	-0.183187093553084	0.0405777392989081	-4.51447263248629	7.45947396328338e-06	***
df.mm.trans3:probe3	-0.241168546509838	0.0405777392989081	-5.94337069232262	4.42572043864045e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17351598853157	0.135828031001827	30.726470506485	1.98472750022449e-131	***
df.mm.trans1	-0.0413376235483495	0.121993435197851	-0.338851213438719	0.734824612648098	   
df.mm.trans2	-0.123416357128881	0.112189531892248	-1.10007016739686	0.271684084585703	   
df.mm.exp2	-0.127306721784250	0.153671874874574	-0.82843215056859	0.407711433679896	   
df.mm.exp3	0.0150995971886086	0.153671874874574	0.0982586904788717	0.921755333545296	   
df.mm.exp4	-0.018633810966256	0.153671874874574	-0.121257132975470	0.903522572788225	   
df.mm.exp5	-0.202952843922168	0.153671874874574	-1.32068958023592	0.187041484059095	   
df.mm.exp6	-0.0300109752314001	0.153671874874574	-0.195292569026668	0.845221240270736	   
df.mm.exp7	-0.0772947673610643	0.153671874874574	-0.502985776832306	0.61513431792828	   
df.mm.exp8	-0.218043024517577	0.153671874874574	-1.41888699344329	0.156382199253344	   
df.mm.trans1:exp2	0.156852042253833	0.147129618704657	1.06608066842539	0.286758982901398	   
df.mm.trans2:exp2	0.162379939501110	0.128059895728811	1.26799993531915	0.205224425283905	   
df.mm.trans1:exp3	0.00286137221771274	0.147129618704657	0.0194479686884567	0.984489350135574	   
df.mm.trans2:exp3	0.0982839144518015	0.128059895728811	0.767483948760464	0.443055615978384	   
df.mm.trans1:exp4	0.0671440073193356	0.147129618704657	0.45635955499972	0.648274638782511	   
df.mm.trans2:exp4	0.122873644674974	0.128059895728811	0.959501364386397	0.337641351338012	   
df.mm.trans1:exp5	0.210051750617578	0.147129618704657	1.42766461618601	0.153839522548319	   
df.mm.trans2:exp5	0.151511955001613	0.128059895728811	1.18313351841599	0.237162539702124	   
df.mm.trans1:exp6	0.0338020542510007	0.147129618704657	0.229743368796828	0.818359083724516	   
df.mm.trans2:exp6	0.0710479543444974	0.128059895728811	0.554802531582202	0.579209008043553	   
df.mm.trans1:exp7	0.125474182529361	0.147129618704657	0.852813890459634	0.39405758992554	   
df.mm.trans2:exp7	0.141300423062698	0.128059895728811	1.10339323844153	0.270239985203043	   
df.mm.trans1:exp8	0.26010602372814	0.147129618704657	1.76786989606945	0.0775232628880883	.  
df.mm.trans2:exp8	0.178561087347087	0.128059895728811	1.39435602638019	0.163657609743657	   
df.mm.trans1:probe2	-0.0097936421486426	0.0735648093523285	-0.133129443749896	0.894129713107344	   
df.mm.trans1:probe3	0.0365721149859018	0.0735648093523285	0.497141436345532	0.619247225408953	   
df.mm.trans1:probe4	-0.0289906490199220	0.0735648093523285	-0.394083112226599	0.69364108600966	   
df.mm.trans1:probe5	0.00387220480932951	0.0735648093523285	0.0526366457470735	0.958036609242012	   
df.mm.trans1:probe6	0.0344272495089127	0.0735648093523285	0.467985301831317	0.639942454593059	   
df.mm.trans1:probe7	0.0495910332621751	0.0735648093523285	0.674113529264592	0.500464254396975	   
df.mm.trans1:probe8	-0.0166182903401063	0.0735648093523285	-0.225899998741454	0.821345901419975	   
df.mm.trans1:probe9	0.0246795283082581	0.0735648093523285	0.335480082467948	0.737364646585885	   
df.mm.trans1:probe10	-0.0689130381901248	0.0735648093523285	-0.936766353326294	0.349205561642555	   
df.mm.trans1:probe11	-0.0243923986581555	0.0735648093523285	-0.331576998199390	0.740309082758171	   
df.mm.trans1:probe12	-0.0162463091137184	0.0735648093523285	-0.220843488303068	0.825279443657638	   
df.mm.trans1:probe13	-0.0794095849302267	0.0735648093523285	-1.07945069972119	0.280762948876998	   
df.mm.trans1:probe14	0.115098661844727	0.0735648093523285	1.56458859688575	0.118136566684537	   
df.mm.trans1:probe15	-0.0540054372731981	0.0735648093523285	-0.734120536009909	0.463123940753472	   
df.mm.trans1:probe16	-0.02960399134155	0.0735648093523285	-0.402420554096263	0.687498820703302	   
df.mm.trans1:probe17	0.0389497208352626	0.0735648093523285	0.529461316873918	0.59665527984602	   
df.mm.trans1:probe18	0.0128130964025009	0.0735648093523285	0.174174262331522	0.86177945325287	   
df.mm.trans1:probe19	-0.0845067457688268	0.0735648093523285	-1.14873873136942	0.251060640723773	   
df.mm.trans1:probe20	0.0844628287214046	0.0735648093523285	1.14814174691708	0.251306794523060	   
df.mm.trans1:probe21	-0.0723870578641084	0.0735648093523285	-0.983990286951205	0.325464192440359	   
df.mm.trans1:probe22	-0.0396166558419149	0.0735648093523285	-0.538527268550054	0.590386265282915	   
df.mm.trans2:probe2	0.11901624627772	0.0735648093523285	1.61784210855095	0.106152319301298	   
df.mm.trans2:probe3	0.113844444153137	0.0735648093523285	1.54753944386500	0.122190405064331	   
df.mm.trans2:probe4	0.0803233291507821	0.0735648093523285	1.09187164159000	0.275269519173319	   
df.mm.trans2:probe5	0.110097395572928	0.0735648093523285	1.49660410381317	0.134952238429494	   
df.mm.trans2:probe6	0.0261431941185725	0.0735648093523285	0.355376359277481	0.722415950661749	   
df.mm.trans3:probe2	-0.0343068456081593	0.0735648093523285	-0.466348596702689	0.641112765563056	   
df.mm.trans3:probe3	0.084292370831715	0.0735648093523285	1.14582463509160	0.252263804512523