chr15.8631_chr15_38639213_38652556_-_2.R fitVsDatCorrelation=0.902438868654557 cont.fitVsDatCorrelation=0.236960312881478 fstatistic=11161.0770544661,66,1014 cont.fstatistic=2182.43633465328,66,1014 residuals=-0.575200711980162,-0.0921374600861065,-0.00145418006035213,0.0886389714428153,0.70359580342996 cont.residuals=-0.649855341375791,-0.250999251474108,-0.095222733609394,0.192687510250555,1.24011560518382 predictedValues: Include Exclude Both chr15.8631_chr15_38639213_38652556_-_2.R.tl.Lung 63.0611276164094 51.7648199849989 95.3284137343002 chr15.8631_chr15_38639213_38652556_-_2.R.tl.cerebhem 73.7060928501982 64.1938289196204 92.9599990650728 chr15.8631_chr15_38639213_38652556_-_2.R.tl.cortex 63.2037181842154 57.0472296960149 113.966412134227 chr15.8631_chr15_38639213_38652556_-_2.R.tl.heart 62.5024497688201 52.4017271868187 92.7053387009898 chr15.8631_chr15_38639213_38652556_-_2.R.tl.kidney 63.6110102819576 50.8675202098912 87.4879516956345 chr15.8631_chr15_38639213_38652556_-_2.R.tl.liver 66.294549364223 51.9064096734375 79.071999877933 chr15.8631_chr15_38639213_38652556_-_2.R.tl.stomach 66.8960844075536 51.6646138924437 99.3680575385996 chr15.8631_chr15_38639213_38652556_-_2.R.tl.testicle 65.0989227526695 53.6857979895879 92.7249328855862 diffExp=11.2963076314105,9.51226393057772,6.15648848820048,10.1007225820013,12.7434900720664,14.3881396907855,15.2314705151099,11.4131247630816 diffExpScore=0.989111736281311 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=1,0,0,0,1,1,1,1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 68.2189969494484 75.419660678685 68.1096725910749 cerebhem 70.9696129852315 72.1102229805428 73.0402598085745 cortex 64.9499339949789 70.702181322071 63.0302921242207 heart 68.471732416149 69.9739645139649 67.9558379130525 kidney 62.1781902766132 70.3407148962276 69.6556046087099 liver 67.6193892989582 65.6049324246186 73.6292540015697 stomach 66.6646851182605 87.864704980053 66.1286825090837 testicle 64.4510371680766 69.4742828480689 70.4755385439026 cont.diffExp=-7.20066372923661,-1.14060999531131,-5.75224732709216,-1.50223209781588,-8.16252461961436,2.01445687433964,-21.2000198617925,-5.02324567999221 cont.diffExpScore=1.06185611538490 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,-1,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,0,0,0,-1,0 cont.diffExp1.2Score=0.5 tran.correlation=0.766716991774477 cont.tran.correlation=0.0861036274487028 tran.covariance=0.00312035802482092 cont.tran.covariance=0.000343003002189136 tran.mean=59.8691189236788 cont.tran.mean=69.6883901782468 weightedLogRatios: wLogRatio Lung 0.798538259172263 cerebhem 0.584632225522823 cortex 0.419682839367992 heart 0.71336211718829 kidney 0.90341514233944 liver 0.996222451206502 stomach 1.05257737315759 testicle 0.786367843642688 cont.weightedLogRatios: wLogRatio Lung -0.428763356273344 cerebhem -0.0680845502740692 cortex -0.357772442443736 heart -0.0919583078842308 kidney -0.517028715805987 liver 0.126987195502905 stomach -1.19774816942279 testicle -0.315471008574998 varWeightedLogRatios=0.0441537634360163 cont.varWeightedLogRatios=0.161042857748232 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.33709092386072 0.0736687704991946 45.2985831207432 7.20642775042682e-246 *** df.mm.trans1 0.939397538818938 0.0629769855624305 14.9165211772115 1.22370734969288e-45 *** df.mm.trans2 0.596400753236022 0.0550063201991992 10.842404128766 5.42080213804566e-26 *** df.mm.exp2 0.396335880391682 0.0693163805439325 5.71778095280791 1.41894761797493e-08 *** df.mm.exp3 -0.0791485138641437 0.0693163805439325 -1.14184429774113 0.253788309959383 df.mm.exp4 0.0312318559263704 0.0693163805439325 0.450569629881002 0.652396081733214 df.mm.exp5 0.0770227074457038 0.0693163805439325 1.1111761295281 0.266755945741746 df.mm.exp6 0.239703750211868 0.0693163805439325 3.45811117561086 0.000566574010126298 *** df.mm.exp7 0.0155954368119737 0.0693163805439325 0.224989197208435 0.822033009897974 df.mm.exp8 0.09593171235898 0.0693163805439325 1.38396886286033 0.166672525803653 df.mm.trans1:exp2 -0.240354949247462 0.0632352654660541 -3.80096371029689 0.000152725280394308 *** df.mm.trans2:exp2 -0.181139564732465 0.0430023328456165 -4.21231948933508 2.75250324740997e-05 *** df.mm.trans1:exp3 0.0814071097150754 0.0632352654660541 1.28736882995733 0.198259523717147 df.mm.trans2:exp3 0.176317262095439 0.0430023328456165 4.1001790002519 4.45864019654278e-05 *** df.mm.trans1:exp4 -0.0401306392511175 0.0632352654660541 -0.634624350120905 0.525816598299004 df.mm.trans2:exp4 -0.0190030712313516 0.0430023328456165 -0.441907914614188 0.658650070355527 df.mm.trans1:exp5 -0.0683406700419825 0.0632352654660541 -1.08073666708443 0.280071237048210 df.mm.trans2:exp5 -0.0945088650625396 0.0430023328456165 -2.19776134940952 0.0281915920154021 * df.mm.trans1:exp6 -0.189700603700698 0.0632352654660541 -2.99991788288661 0.00276651836308176 ** df.mm.trans2:exp6 -0.236972234883255 0.0430023328456165 -5.51068323976779 4.53258259458506e-08 *** df.mm.trans1:exp7 0.0434404639741757 0.0632352654660541 0.686965788061653 0.492261362174815 df.mm.trans2:exp7 -0.0175331081333994 0.0430023328456165 -0.407724580811589 0.683561956046445 df.mm.trans1:exp8 -0.0641282464594787 0.0632352654660541 -1.01412156629443 0.310766663755705 df.mm.trans2:exp8 -0.0594939829592814 0.0430023328456165 -1.38350594077935 0.166814283006768 df.mm.trans1:probe2 -0.128793532901995 0.0470815248920376 -2.73554293743313 0.00633644342864908 ** df.mm.trans1:probe3 -0.363455114885336 0.0470815248920376 -7.71969717885674 2.7862699351135e-14 *** df.mm.trans1:probe4 -0.375227084764667 0.0470815248920376 -7.96973092152597 4.2588809962353e-15 *** df.mm.trans1:probe5 -0.472087897110497 0.0470815248920376 -10.0270307343069 1.26025533849811e-22 *** df.mm.trans1:probe6 -0.268610028421221 0.0470815248920376 -5.70521088765007 1.52422513997690e-08 *** df.mm.trans1:probe7 -0.284763099891566 0.0470815248920376 -6.04829814974249 2.05606500232257e-09 *** df.mm.trans1:probe8 0.221806917768239 0.0470815248920376 4.71112433755837 2.80653802457166e-06 *** df.mm.trans1:probe9 -0.388487317805481 0.0470815248920376 -8.2513750074221 4.83010278124735e-16 *** df.mm.trans1:probe10 -0.396272286134131 0.0470815248920376 -8.41672581852055 1.30622129071503e-16 *** df.mm.trans1:probe11 -0.128065185237990 0.0470815248920376 -2.72007301232609 0.0066383565141221 ** df.mm.trans1:probe12 -0.364101871224823 0.0470815248920376 -7.733434124314 2.51643167794682e-14 *** df.mm.trans1:probe13 -0.00647335316647835 0.0470815248920376 -0.137492427896555 0.890668873312696 df.mm.trans1:probe14 -0.143459653831631 0.0470815248920376 -3.04704773604078 0.00237082689669668 ** df.mm.trans1:probe15 -0.298793867210989 0.0470815248920376 -6.34630819405599 3.31959110144279e-10 *** df.mm.trans1:probe16 -0.305738909710925 0.0470815248920376 -6.49381918729295 1.30829601252425e-10 *** df.mm.trans1:probe17 -0.259360208633431 0.0470815248920376 -5.5087469921199 4.5812466008201e-08 *** df.mm.trans1:probe18 -0.404318146262049 0.0470815248920376 -8.58761790721921 3.30429155726782e-17 *** df.mm.trans1:probe19 -0.0792606645751641 0.0470815248920376 -1.68347700625492 0.0925905895961552 . df.mm.trans1:probe20 -0.165718870794745 0.0470815248920376 -3.51982802542513 0.000451009046802091 *** df.mm.trans1:probe21 -0.267288733503567 0.0470815248920376 -5.67714690882433 1.78741865850372e-08 *** df.mm.trans1:probe22 -0.284504247805673 0.0470815248920376 -6.0428001951523 2.12486771943446e-09 *** df.mm.trans2:probe2 0.120572451006574 0.0470815248920376 2.56092918152202 0.0105830559258621 * df.mm.trans2:probe3 0.0100404939799757 0.0470815248920376 0.213257620754626 0.83116889469344 df.mm.trans2:probe4 0.173240110404083 0.0470815248920376 3.67957730343991 0.000245912607679709 *** df.mm.trans2:probe5 0.0598238215080517 0.0470815248920376 1.27064324371892 0.204147038928416 df.mm.trans2:probe6 -0.059637793730648 0.0470815248920376 -1.26669205951598 0.205556297915591 df.mm.trans3:probe2 0.179726202539742 0.0470815248920376 3.81734030390628 0.000143072847192091 *** df.mm.trans3:probe3 -0.519734636683418 0.0470815248920376 -11.0390357550062 7.79049050381235e-27 *** df.mm.trans3:probe4 -0.0418681111331205 0.0470815248920376 -0.889268374996944 0.374069776743012 df.mm.trans3:probe5 -0.26543310830251 0.0470815248920376 -5.63773388629984 2.23289921109979e-08 *** df.mm.trans3:probe6 -0.875976803790773 0.0470815248920376 -18.6055316984628 1.05900469101782e-66 *** df.mm.trans3:probe7 -0.304185331589773 0.0470815248920376 -6.46082157039941 1.61388376504194e-10 *** df.mm.trans3:probe8 -0.683618528286523 0.0470815248920376 -14.5198892740650 1.53520726008566e-43 *** df.mm.trans3:probe9 -0.726084019356543 0.0470815248920376 -15.4218458518819 2.30573626372881e-48 *** df.mm.trans3:probe10 -0.283731026117082 0.0470815248920376 -6.02637715680841 2.34403787664576e-09 *** df.mm.trans3:probe11 0.267507149470779 0.0470815248920376 5.68178601020672 1.74105224410164e-08 *** df.mm.trans3:probe12 -0.550908869559484 0.0470815248920376 -11.7011687880282 9.3295827322189e-30 *** df.mm.trans3:probe13 -0.0478347006232687 0.0470815248920376 -1.01599726714371 0.309873043684646 df.mm.trans3:probe14 -0.675483040965108 0.0470815248920376 -14.3470935258375 1.22736078449468e-42 *** df.mm.trans3:probe15 -0.33113091799026 0.0470815248920376 -7.03313919312458 3.70882935973991e-12 *** df.mm.trans3:probe16 -0.220572232513939 0.0470815248920376 -4.68489992666406 3.18220658767228e-06 *** df.mm.trans3:probe17 -0.285214112878167 0.0470815248920376 -6.05787755456497 1.94134329793336e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27102238521846 0.166127335291055 25.7093293992564 1.22970550662899e-112 *** df.mm.trans1 -0.119106484952016 0.142016742308251 -0.838679179765242 0.401847024905823 df.mm.trans2 0.0608753696471692 0.124042431235627 0.490762467655383 0.623700644567226 df.mm.exp2 -0.0752349991538446 0.156312444387953 -0.481311641235154 0.630398870614672 df.mm.exp3 -0.0361941139470637 0.156312444387953 -0.231549791757035 0.81693442452047 df.mm.exp4 -0.0689856480100314 0.156312444387953 -0.441331771633072 0.659066916470162 df.mm.exp5 -0.184879903376710 0.156312444387953 -1.18275869909535 0.237182040476773 df.mm.exp6 -0.226168599474043 0.156312444387953 -1.44690079129409 0.148233766383974 df.mm.exp7 0.159199135198411 0.156312444387953 1.01846744078350 0.308698800836393 df.mm.exp8 -0.173075031874428 0.156312444387953 -1.10723770299997 0.268453757365668 df.mm.trans1:exp2 0.114763724751363 0.142599178418661 0.804799340529334 0.421124138502865 df.mm.trans2:exp2 0.030362829574687 0.0969727459618644 0.313106835054743 0.754263884416594 df.mm.trans1:exp3 -0.0129122328885293 0.142599178418661 -0.0905491394250524 0.92786872960475 df.mm.trans2:exp3 -0.0283974531453971 0.0969727459618645 -0.292839527887193 0.769704726642117 df.mm.trans1:exp4 0.072683568942615 0.142599178418661 0.509705383639878 0.610368793397984 df.mm.trans2:exp4 -0.0059591073713568 0.0969727459618644 -0.0614513625684096 0.951011857997717 df.mm.trans1:exp5 0.0921611292859886 0.142599178418661 0.646294952804076 0.518234599853828 df.mm.trans2:exp5 0.115162701078089 0.0969727459618645 1.18757801417088 0.235277813498498 df.mm.trans1:exp6 0.217340291637827 0.142599178418661 1.52413424851391 0.127786984804666 df.mm.trans2:exp6 0.0867514892663867 0.0969727459618645 0.894596604498573 0.371215059173785 df.mm.trans1:exp7 -0.182246854694415 0.142599178418661 -1.27803579736870 0.201529287991865 df.mm.trans2:exp7 -0.0064689399383689 0.0969727459618644 -0.0667088456060931 0.946826642469365 df.mm.trans1:exp8 0.116257780022147 0.142599178418661 0.815276646831883 0.415105422009403 df.mm.trans2:exp8 0.0909636922729956 0.0969727459618644 0.93803358222699 0.348450522408854 df.mm.trans1:probe2 0.0782983052428267 0.106171559790579 0.737469670759931 0.461007393550858 df.mm.trans1:probe3 0.0761398118106503 0.106171559790579 0.717139429436982 0.473453225030475 df.mm.trans1:probe4 0.0735370168755134 0.106171559790579 0.692624437472365 0.488703857985497 df.mm.trans1:probe5 0.165640868575441 0.106171559790579 1.56012465958081 0.119042455406143 df.mm.trans1:probe6 0.218663743939434 0.106171559790579 2.05953217952852 0.0396981331098234 * df.mm.trans1:probe7 0.0840655481758486 0.106171559790579 0.791789706600015 0.428668485787112 df.mm.trans1:probe8 0.0638529829683094 0.106171559790579 0.601413251291192 0.547699200109236 df.mm.trans1:probe9 0.151543842807912 0.106171559790579 1.42734874675317 0.153787264470487 df.mm.trans1:probe10 0.103192218966748 0.106171559790579 0.971938428429346 0.331312967696422 df.mm.trans1:probe11 0.0217659301712824 0.106171559790579 0.205007162127176 0.8376076735394 df.mm.trans1:probe12 0.289944060555764 0.106171559790579 2.73090139325137 0.00642570228198644 ** df.mm.trans1:probe13 0.119334738576907 0.106171559790579 1.12398027129197 0.261287410555883 df.mm.trans1:probe14 0.141493997529299 0.106171559790579 1.33269208635902 0.18293225176842 df.mm.trans1:probe15 0.160453156152048 0.106171559790579 1.51126305828546 0.131033004736338 df.mm.trans1:probe16 0.140407700327834 0.106171559790579 1.32246055916278 0.186313031893835 df.mm.trans1:probe17 0.109820179046316 0.106171559790579 1.03436531650221 0.301212033336268 df.mm.trans1:probe18 0.195193456553272 0.106171559790579 1.83847215712274 0.0662851971986208 . df.mm.trans1:probe19 0.0366314573888371 0.106171559790579 0.34502137353065 0.730149855100103 df.mm.trans1:probe20 0.261696874728387 0.106171559790579 2.46484911067124 0.0138719397314328 * df.mm.trans1:probe21 0.081514325373661 0.106171559790579 0.767760458021396 0.442808254040634 df.mm.trans1:probe22 0.188289548458138 0.106171559790579 1.77344619245996 0.0764549010970279 . df.mm.trans2:probe2 -0.0277392021498758 0.106171559790579 -0.261267727483619 0.793939107162037 df.mm.trans2:probe3 -0.0560759860187389 0.106171559790579 -0.528163908765656 0.59750114118919 df.mm.trans2:probe4 -0.0591558947764558 0.106171559790579 -0.557172701363146 0.577532400371178 df.mm.trans2:probe5 0.00734438793884184 0.106171559790579 0.0691747201729773 0.944864183876509 df.mm.trans2:probe6 -0.0674578340950493 0.106171559790579 -0.635366328121281 0.525332874816269 df.mm.trans3:probe2 0.0391187015348188 0.106171559790579 0.368448025177171 0.712616099943645 df.mm.trans3:probe3 -0.138496491004001 0.106171559790579 -1.30445941716578 0.192372981841762 df.mm.trans3:probe4 0.133243298706207 0.106171559790579 1.25498107938723 0.209774787224165 df.mm.trans3:probe5 0.0250762944112474 0.106171559790579 0.236186549964131 0.813335614217606 df.mm.trans3:probe6 -0.0485266309233001 0.106171559790579 -0.457058660709306 0.647726781734484 df.mm.trans3:probe7 -0.12025778782374 0.106171559790579 -1.13267421201069 0.257618847673011 df.mm.trans3:probe8 0.00418275656088023 0.106171559790579 0.0393962052467781 0.96858226029263 df.mm.trans3:probe9 -0.0247139847266376 0.106171559790579 -0.232774057152267 0.815983834500371 df.mm.trans3:probe10 -0.0173467928150954 0.106171559790579 -0.163384552787127 0.870248221450874 df.mm.trans3:probe11 -0.0403513046620552 0.106171559790579 -0.380057566655771 0.703982273744964 df.mm.trans3:probe12 0.0401523840278551 0.106171559790579 0.378183989262800 0.705373074359913 df.mm.trans3:probe13 -0.0447762150580312 0.106171559790579 -0.421734550630613 0.67330817302259 df.mm.trans3:probe14 0.153769598711826 0.106171559790579 1.44831251434125 0.147838802278974 df.mm.trans3:probe15 0.0400317090957648 0.106171559790579 0.377047386086503 0.706217282754948 df.mm.trans3:probe16 0.021925480734138 0.106171559790579 0.206509923913574 0.836434071257293 df.mm.trans3:probe17 0.118544443770041 0.106171559790579 1.11653670722995 0.264456969580585