chr9.25151_chr9_106162000_106164666_-_0.R fitVsDatCorrelation=0.92706569880259 cont.fitVsDatCorrelation=0.320358822173842 fstatistic=5790.84038559359,39,393 cont.fstatistic=898.48256199141,39,393 residuals=-0.852694117816225,-0.100191742866948,-0.00107605008652892,0.0961534527749225,0.752715359640317 cont.residuals=-0.806924461993427,-0.314178061664640,-0.0989345240664686,0.199283684859343,1.65924745679149 predictedValues: Include Exclude Both chr9.25151_chr9_106162000_106164666_-_0.R.tl.Lung 58.4954042082646 70.9474822851123 67.647391375678 chr9.25151_chr9_106162000_106164666_-_0.R.tl.cerebhem 59.518354079105 59.3051895142467 63.6278323235764 chr9.25151_chr9_106162000_106164666_-_0.R.tl.cortex 60.3945834855498 63.935663268734 59.6220872379951 chr9.25151_chr9_106162000_106164666_-_0.R.tl.heart 64.5365932899346 67.9452386252742 60.2589114082614 chr9.25151_chr9_106162000_106164666_-_0.R.tl.kidney 58.6612219887555 73.3919911633683 66.2234496983162 chr9.25151_chr9_106162000_106164666_-_0.R.tl.liver 61.9185998099387 63.3448376896678 65.3692262405825 chr9.25151_chr9_106162000_106164666_-_0.R.tl.stomach 65.1245668306377 67.3646989371096 60.0247188556926 chr9.25151_chr9_106162000_106164666_-_0.R.tl.testicle 59.2347306677434 62.9550030649853 62.7062043439423 diffExp=-12.4520780768477,0.213164564858324,-3.54107978318422,-3.40864533533961,-14.7307691746128,-1.42623787972914,-2.24013210647182,-3.72027239724193 diffExpScore=0.986439980387525 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=-1,0,0,0,-1,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 78.8515719877094 68.974242227342 68.2967781418144 cerebhem 57.9594566863114 89.0012730036848 75.0064252003367 cortex 57.2521009357472 101.951460800966 67.9554138096568 heart 77.7584386594436 81.8066850903931 77.129539925932 kidney 67.7948553550883 71.50837633962 70.4682931046398 liver 68.3895921651535 83.959248179702 74.3950187540806 stomach 75.2543647115363 63.3615557067286 77.4741524852856 testicle 65.3778253410872 83.6472669827756 56.5190019223284 cont.diffExp=9.87732976036737,-31.0418163173734,-44.699359865219,-4.04824643094958,-3.7135209845316,-15.5696560145486,11.8928090048077,-18.2694416416883 cont.diffExpScore=1.44050367067311 cont.diffExp1.5=0,-1,-1,0,0,0,0,0 cont.diffExp1.5Score=0.666666666666667 cont.diffExp1.4=0,-1,-1,0,0,0,0,0 cont.diffExp1.4Score=0.666666666666667 cont.diffExp1.3=0,-1,-1,0,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,-1,-1,0,0,-1,0,-1 cont.diffExp1.2Score=0.8 tran.correlation=-0.0360128180728739 cont.tran.correlation=-0.775572602296022 tran.covariance=-6.90752285800906e-05 cont.tran.covariance=-0.0146328401943670 tran.mean=63.5671349317767 cont.tran.mean=74.5530196358306 weightedLogRatios: wLogRatio Lung -0.803896240037099 cerebhem 0.0146548336570849 cortex -0.235283847836161 heart -0.215810621581998 kidney -0.937320787817434 liver -0.0942157820909447 stomach -0.141811382363553 testicle -0.250468771877955 cont.weightedLogRatios: wLogRatio Lung 0.575573715746712 cerebhem -1.83323280099320 cortex -2.50200165555251 heart -0.222241638897392 kidney -0.226279924339351 liver -0.88767400108927 stomach 0.728467199802315 testicle -1.06046699684463 varWeightedLogRatios=0.118782136368981 cont.varWeightedLogRatios=1.25902156443816 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.63963876275364 0.098090429671939 37.1049324070282 1.74129057305583e-130 *** df.mm.trans1 0.200336052929412 0.0800905004488697 2.50137097167107 0.0127772692933987 * df.mm.trans2 0.436110159996936 0.0800905004488697 5.44521706760157 9.1356321020131e-08 *** df.mm.exp2 -0.10064875235236 0.108821561173683 -0.924897155185277 0.355586964242305 df.mm.exp3 0.0541713286412072 0.108821561173683 0.497799590972122 0.618903728834028 df.mm.exp4 0.170704683987011 0.108821561173683 1.56866600833414 0.117530555085484 df.mm.exp5 0.0579797726894075 0.108821561173683 0.532796736823778 0.594475421680845 df.mm.exp6 -0.0222168846213428 0.108821561173683 -0.204158848501390 0.838335049124635 df.mm.exp7 0.175087201912183 0.108821561173683 1.60893852306288 0.108432750631761 df.mm.exp8 -0.0311114394505001 0.108821561173683 -0.285894073885277 0.775109884897903 df.mm.trans1:exp2 0.117985298644710 0.0888524326295296 1.32787921673062 0.184988728502648 df.mm.trans2:exp2 -0.078594349798564 0.0888524326295296 -0.884549217985548 0.376940643695317 df.mm.trans1:exp3 -0.0222200957225401 0.0888524326295297 -0.250078642361846 0.80265720460651 df.mm.trans2:exp3 -0.158233929535087 0.0888524326295297 -1.78086209743793 0.0757073064220672 . df.mm.trans1:exp4 -0.0724204738413879 0.0888524326295296 -0.815064615544576 0.415528981603535 df.mm.trans2:exp4 -0.213942534798268 0.0888524326295297 -2.40784105135648 0.0165070565947772 * df.mm.trans1:exp5 -0.055149068245337 0.0888524326295296 -0.620681579707346 0.535168930058518 df.mm.trans2:exp5 -0.0241048724511763 0.0888524326295297 -0.271291080478140 0.786309607669412 df.mm.trans1:exp6 0.0790893101492662 0.0888524326295296 0.890119806612715 0.373946121122183 df.mm.trans2:exp6 -0.091129617864012 0.0888524326295296 -1.02562884512096 0.305697429509814 df.mm.trans1:exp7 -0.0677335438230293 0.0888524326295297 -0.76231501849189 0.446329284303881 df.mm.trans2:exp7 -0.226905993056963 0.0888524326295296 -2.55373979464409 0.0110338658512402 * df.mm.trans1:exp8 0.0436712853944065 0.0888524326295297 0.491503542469051 0.62334473189083 df.mm.trans2:exp8 -0.0884082436238776 0.0888524326295296 -0.995000823359513 0.320348091281919 df.mm.trans1:probe2 1.63059706317729 0.0544107805868416 29.9682718312559 1.50488176468415e-103 *** df.mm.trans1:probe3 0.0427316202628573 0.0544107805868415 0.785352090192054 0.432720358831857 df.mm.trans1:probe4 0.422328725975702 0.0544107805868416 7.76185751096972 7.31471916833698e-14 *** df.mm.trans1:probe5 0.652903715347662 0.0544107805868415 11.9995285549268 1.76734757388415e-28 *** df.mm.trans1:probe6 -0.000880625648756105 0.0544107805868416 -0.0161847641084765 0.987095203548504 df.mm.trans2:probe2 0.30175154692641 0.0544107805868416 5.54580440993313 5.38114703836969e-08 *** df.mm.trans2:probe3 -0.00163196138397349 0.0544107805868416 -0.0299933462885874 0.97608758508536 df.mm.trans2:probe4 1.20321385331757 0.0544107805868416 22.1135194228136 5.5523497510312e-71 *** df.mm.trans2:probe5 0.518740687199399 0.0544107805868416 9.53378506252947 1.61371412525843e-19 *** df.mm.trans2:probe6 0.212217807494157 0.0544107805868416 3.90028970739447 0.000112984532848960 *** df.mm.trans3:probe2 -0.4118039166791 0.0544107805868416 -7.56842508483859 2.71365561345543e-13 *** df.mm.trans3:probe3 -0.0842328440160336 0.0544107805868416 -1.54809107878162 0.122405239331222 df.mm.trans3:probe4 -0.26146936953694 0.0544107805868415 -4.80546992189582 2.20092595362683e-06 *** df.mm.trans3:probe5 0.211768925058802 0.0544107805868416 3.89203982693855 0.000116748537475231 *** df.mm.trans3:probe6 0.175080110757001 0.0544107805868416 3.21774671983555 0.00139917190349623 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.4010564892861 0.247855346162151 17.7565525917963 3.23509788861106e-52 *** df.mm.trans1 -0.0496483219776031 0.202373042706054 -0.245330708644417 0.80632843434089 df.mm.trans2 -0.154382821319172 0.202373042706054 -0.762862579199408 0.446003006034808 df.mm.exp2 -0.146617152880172 0.274970818303234 -0.533209864904591 0.594189712647945 df.mm.exp3 0.0756715638098365 0.274970818303234 0.275198525708233 0.783308345583858 df.mm.exp4 0.0350419108786659 0.274970818303234 0.127438653653866 0.898658405057815 df.mm.exp5 -0.146299748850470 0.274970818303234 -0.532055545942089 0.594988168636569 df.mm.exp6 -0.0312745854221206 0.274970818303234 -0.113737834491336 0.90950369940824 df.mm.exp7 -0.257650949180425 0.274970818303234 -0.937011973744395 0.349327850542103 df.mm.exp8 0.194777204748282 0.274970818303234 0.708355911911658 0.479144189799622 df.mm.trans1:exp2 -0.161206353296364 0.224512732999490 -0.718027664367394 0.473166844177293 df.mm.trans2:exp2 0.401534692072146 0.224512732999490 1.78847180161073 0.074470086733233 . df.mm.trans1:exp3 -0.395774474124095 0.224512732999490 -1.76281527037041 0.0787088397763461 . df.mm.trans2:exp3 0.315092127859835 0.224512732999490 1.40344880956285 0.161272667933141 df.mm.trans1:exp4 -0.0490020795390335 0.224512732999490 -0.218259690149266 0.827340067997877 df.mm.trans2:exp4 0.135583920362114 0.224512732999490 0.603903032806706 0.546256536775482 df.mm.trans1:exp5 -0.00478118844355794 0.224512732999490 -0.0212958453611128 0.983020465932494 df.mm.trans2:exp5 0.182381209441733 0.224512732999490 0.812342387022421 0.417086982405532 df.mm.trans1:exp6 -0.111072012498155 0.224512732999490 -0.494724780257375 0.621070856901759 df.mm.trans2:exp6 0.227872991978706 0.224512732999490 1.01496689713017 0.310746052219938 df.mm.trans1:exp7 0.21095760429552 0.224512732999490 0.939624231896013 0.347987487411929 df.mm.trans2:exp7 0.172775115987258 0.224512732999490 0.7695559787589 0.442025607413872 df.mm.trans1:exp8 -0.382161315485459 0.224512732999490 -1.70218103169377 0.089512231487919 . df.mm.trans2:exp8 -0.00190158375214729 0.224512732999490 -0.00846982586128697 0.993246435241294 df.mm.trans1:probe2 -0.0371886623638691 0.137485409151617 -0.270491702307536 0.786923995041916 df.mm.trans1:probe3 -0.0840499127346558 0.137485409151617 -0.611336964797237 0.541329971171102 df.mm.trans1:probe4 0.280963058126765 0.137485409151617 2.04358455097532 0.0416609149820745 * df.mm.trans1:probe5 -0.0415291131791777 0.137485409151617 -0.302061967414884 0.76276461216236 df.mm.trans1:probe6 0.0757136181638434 0.137485409151617 0.55070293372257 0.582150233349948 df.mm.trans2:probe2 -0.0270109505475766 0.137485409151617 -0.196464124551495 0.844348463283922 df.mm.trans2:probe3 -0.00950929060219454 0.137485409151617 -0.0691658166555538 0.944892810497904 df.mm.trans2:probe4 -0.0448740918573917 0.137485409151617 -0.326391666827024 0.744301784314291 df.mm.trans2:probe5 -0.0328623517553168 0.137485409151617 -0.239024285981334 0.811211348192369 df.mm.trans2:probe6 -0.0410297246399105 0.137485409151617 -0.298429665322983 0.765532972924482 df.mm.trans3:probe2 -0.0501479284179925 0.137485409151617 -0.364750912314557 0.71549354469787 df.mm.trans3:probe3 -0.0119962280518981 0.137485409151617 -0.0872545539626598 0.930513617174624 df.mm.trans3:probe4 0.0394644638393849 0.137485409151617 0.287044742295991 0.774229342331949 df.mm.trans3:probe5 0.0630624406771211 0.137485409151617 0.458684605633873 0.646714227908395 df.mm.trans3:probe6 0.120640735872135 0.137485409151617 0.877480284028496 0.380761919894594