chr19.11946_chr19_7979457_7979924_+_0.R fitVsDatCorrelation=0.951522728336655 cont.fitVsDatCorrelation=0.265698673227923 fstatistic=6089.52312220732,36,324 cont.fstatistic=611.771571591389,36,324 residuals=-0.743503764086716,-0.111459615036304,-0.00284451912617732,0.135755324898014,0.659010734034481 cont.residuals=-1.50329199614699,-0.461556843529868,0.0186308861035252,0.405906924490288,1.59441360442242 predictedValues: Include Exclude Both chr19.11946_chr19_7979457_7979924_+_0.R.tl.Lung 316.812299968158 174.377861847128 72.2403255195564 chr19.11946_chr19_7979457_7979924_+_0.R.tl.cerebhem 273.304447974919 120.160528665790 60.9801763132607 chr19.11946_chr19_7979457_7979924_+_0.R.tl.cortex 259.800954642547 145.259566334062 72.9638039150316 chr19.11946_chr19_7979457_7979924_+_0.R.tl.heart 297.797004715983 155.829190222244 75.631766842699 chr19.11946_chr19_7979457_7979924_+_0.R.tl.kidney 313.103022909518 266.851660430062 260.829551783218 chr19.11946_chr19_7979457_7979924_+_0.R.tl.liver 333.290762693092 178.970703553624 63.8729920597831 chr19.11946_chr19_7979457_7979924_+_0.R.tl.stomach 432.426173970963 179.818046898025 62.7011856457619 chr19.11946_chr19_7979457_7979924_+_0.R.tl.testicle 298.750232163169 158.888280292451 60.7076809514883 diffExp=142.434438121029,153.143919309129,114.541388308484,141.967814493739,46.2513624794567,154.320059139468,252.608127072938,139.861951870718 diffExpScore=0.999127497910832 diffExp1.5=1,1,1,1,0,1,1,1 diffExp1.5Score=0.875 diffExp1.4=1,1,1,1,0,1,1,1 diffExp1.4Score=0.875 diffExp1.3=1,1,1,1,0,1,1,1 diffExp1.3Score=0.875 diffExp1.2=1,1,1,1,0,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 182.772182284017 168.179760207205 157.976363841544 cerebhem 188.888059630200 180.627893249907 176.485896241325 cortex 177.084348437135 170.784881423733 277.081804864396 heart 232.019625522426 162.407575893344 133.461179002218 kidney 199.235476255067 131.962377765 154.127573236611 liver 184.443189816000 165.363465870789 126.881769683007 stomach 201.338855244282 172.565197781894 208.673651053959 testicle 177.50829436021 179.840346914474 192.669513894113 cont.diffExp=14.5924220768119,8.26016638029279,6.29946701340236,69.6120496290819,67.2730984900671,19.0797239452103,28.7736574623874,-2.33205255426392 cont.diffExpScore=1.01723809939039 cont.diffExp1.5=0,0,0,0,1,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,0,0,1,1,0,0,0 cont.diffExp1.4Score=0.666666666666667 cont.diffExp1.3=0,0,0,1,1,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,0,0,1,1,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.314661769510517 cont.tran.correlation=-0.340286299801370 tran.covariance=0.0150692077596949 cont.tran.covariance=-0.00303261806135715 tran.mean=244.090046080108 cont.tran.mean=179.688845665980 weightedLogRatios: wLogRatio Lung 3.25994455918875 cerebhem 4.27290007450842 cortex 3.06348981303632 heart 3.47956403461383 kidney 0.905747720464016 liver 3.41869251711543 stomach 4.94072970015201 testicle 3.39943270729503 cont.weightedLogRatios: wLogRatio Lung 0.429900686193526 cerebhem 0.233361034947207 cortex 0.186848666020505 heart 1.87932953268562 kidney 2.09631306720542 liver 0.563749034529286 stomach 0.80621374043156 testicle -0.0676825377248515 varWeightedLogRatios=1.35289134016744 cont.varWeightedLogRatios=0.639838280019157 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 6.72531191168064 0.119174931241230 56.4322701228791 1.15829906233188e-169 *** df.mm.trans1 -0.750509368724892 0.100939921843296 -7.43520853810465 9.40553671580307e-13 *** df.mm.trans2 -1.50068436846406 0.100939921843296 -14.8671045217748 1.77201682042124e-38 *** df.mm.exp2 -0.35066951145964 0.140571079894659 -2.4946063708298 0.0131073553728375 * df.mm.exp3 -0.391061082042563 0.140571079894659 -2.78194549217105 0.005719991188644 ** df.mm.exp4 -0.220239490329237 0.140571079894659 -1.56674822797321 0.118149667286194 df.mm.exp5 -0.87017756165876 0.140571079894659 -6.19030288670225 1.81516785918958e-09 *** df.mm.exp6 0.199805148690681 0.140571079894659 1.42138161590856 0.156167632356458 df.mm.exp7 0.483441113441762 0.140571079894659 3.43912214236416 0.000659896359315088 *** df.mm.exp8 0.0222033321926721 0.140571079894659 0.157950925676255 0.874593863258697 df.mm.trans1:exp2 0.202946394481323 0.121738126226186 1.66707342040286 0.0964660840978477 . df.mm.trans2:exp2 -0.0217264653197014 0.121738126226186 -0.178468865861581 0.858466308939774 df.mm.trans1:exp3 0.192667375957336 0.121738126226186 1.58263792888815 0.114479626294752 df.mm.trans2:exp3 0.208358772302277 0.121738126226186 1.71153260495526 0.087939825022157 . df.mm.trans1:exp4 0.158342067645916 0.121738126226186 1.30067771333789 0.194293125007432 df.mm.trans2:exp4 0.107775398759804 0.121738126226186 0.885305221139682 0.376648583411252 df.mm.trans1:exp5 0.858400359874168 0.121738126226186 7.05120397762063 1.07631006534448e-11 *** df.mm.trans2:exp5 1.29564592228673 0.121738126226186 10.6428935819125 6.95936835018297e-23 *** df.mm.trans1:exp6 -0.149099363296877 0.121738126226186 -1.22475487276561 0.221557288849189 df.mm.trans2:exp6 -0.173807588010566 0.121738126226186 -1.42771696426176 0.154336461207973 df.mm.trans1:exp7 -0.172338982504584 0.121738126226186 -1.41565331952278 0.157837553418331 df.mm.trans2:exp7 -0.452720188839573 0.121738126226186 -3.71880365563070 0.000235822890754239 *** df.mm.trans1:exp8 -0.0809049366756815 0.121738126226186 -0.664581747589594 0.506790758809784 df.mm.trans2:exp8 -0.115226581077005 0.121738126226186 -0.946511866487223 0.344592933811197 df.mm.trans1:probe2 0.0731000579948754 0.0608690631130932 1.20093943057834 0.230651909344142 df.mm.trans1:probe3 -0.765776607751266 0.0608690631130932 -12.5807194753182 7.94699713791198e-30 *** df.mm.trans1:probe4 0.0559377595880761 0.0608690631130932 0.918985059522686 0.358787025306483 df.mm.trans1:probe5 -0.271784026638594 0.0608690631130932 -4.46506012641638 1.10672989057175e-05 *** df.mm.trans1:probe6 -1.03991470620207 0.0608690631130932 -17.0844539576686 4.14094403884748e-47 *** df.mm.trans2:probe2 0.0793322401621439 0.0608690631130932 1.30332612504231 0.193388836394456 df.mm.trans2:probe3 -0.386744250431716 0.0608690631130932 -6.3537079536308 7.1192649904399e-10 *** df.mm.trans2:probe4 0.119533749692908 0.0608690631130932 1.96378494393477 0.050409806143818 . df.mm.trans2:probe5 0.0385196497891362 0.0608690631130932 0.632828038071946 0.527292316689485 df.mm.trans2:probe6 -0.421268197954159 0.0608690631130932 -6.92089177011733 2.41147611386183e-11 *** df.mm.trans3:probe2 -0.293624864229841 0.0608690631130932 -4.82387684667158 2.17072775943653e-06 *** df.mm.trans3:probe3 -0.288734413695468 0.0608690631130932 -4.74353306800544 3.15351647683649e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.33817965110175 0.373535031128439 14.2909746241879 2.89260724361595e-36 *** df.mm.trans1 -0.0224170889466457 0.316380269366528 -0.0708548892493527 0.943556960279897 df.mm.trans2 -0.259402226437593 0.316380269366528 -0.819906459264923 0.412872181319569 df.mm.exp2 -0.00647578041051777 0.44059788545566 -0.0146977110519280 0.988282391619864 df.mm.exp3 -0.578110316368228 0.44059788545566 -1.31210415540319 0.190413823215385 df.mm.exp4 0.372291941078521 0.44059788545566 0.844969876996805 0.398751174230018 df.mm.exp5 -0.131604823596374 0.44059788545566 -0.298695994558101 0.765363494622548 df.mm.exp6 0.211403216562707 0.44059788545566 0.479809875492428 0.631686325972458 df.mm.exp7 -0.155835549004199 0.44059788545566 -0.353691096004776 0.72380043388889 df.mm.exp8 -0.160717980005302 0.44059788545566 -0.364772472385086 0.715519228980281 df.mm.trans1:exp2 0.0393898711649396 0.381568961658308 0.103231329387355 0.917843241037584 df.mm.trans2:exp2 0.0778814485481681 0.381568961658308 0.204108447945277 0.83839689801653 df.mm.trans1:exp3 0.546496008522225 0.381568961658308 1.43223391689707 0.153040920993371 df.mm.trans2:exp3 0.593481668957085 0.381568961658308 1.55537197359502 0.120833729645772 df.mm.trans1:exp4 -0.133710451971874 0.381568961658308 -0.350422768641258 0.726249149314798 df.mm.trans2:exp4 -0.407216273442775 0.381568961658308 -1.06721540366649 0.2866691247743 df.mm.trans1:exp5 0.217851774789044 0.381568961658308 0.570936833651917 0.568438238806175 df.mm.trans2:exp5 -0.110911719967446 0.381568961658308 -0.290672803902657 0.771487541589397 df.mm.trans1:exp6 -0.202302186524855 0.381568961658308 -0.530185122096004 0.596346739728054 df.mm.trans2:exp6 -0.22829075045458 0.381568961658308 -0.598294865133744 0.550061204073944 df.mm.trans1:exp7 0.252584412853837 0.381568961658308 0.661962681021275 0.508465670778836 df.mm.trans2:exp7 0.181577263630006 0.381568961658308 0.475870109667376 0.63448761666284 df.mm.trans1:exp8 0.131494844867961 0.381568961658308 0.344616198069364 0.730606507519898 df.mm.trans2:exp8 0.227754067132756 0.381568961658308 0.596888347896357 0.550998780748639 df.mm.trans1:probe2 -0.210364578942516 0.190784480829154 -1.10262940690074 0.271006474297798 df.mm.trans1:probe3 -0.00648479647862138 0.190784480829154 -0.0339901675987392 0.972905921357378 df.mm.trans1:probe4 -0.40998542541816 0.190784480829154 -2.14894536304187 0.0323792604610706 * df.mm.trans1:probe5 -0.269011172942158 0.190784480829154 -1.41002649572454 0.159491089517894 df.mm.trans1:probe6 -0.0718528398556958 0.190784480829154 -0.376617844089947 0.706704145262886 df.mm.trans2:probe2 -0.00669788913682746 0.190784480829154 -0.035107096278053 0.972015961557267 df.mm.trans2:probe3 0.135447081701736 0.190784480829154 0.709948110627655 0.478246951051927 df.mm.trans2:probe4 0.0371987586065601 0.190784480829154 0.194977906194956 0.845532451139959 df.mm.trans2:probe5 0.187370526472776 0.190784480829154 0.982105701986133 0.326780560076382 df.mm.trans2:probe6 0.0629853777183073 0.190784480829154 0.330138895179374 0.741508373490797 df.mm.trans3:probe2 0.0532890407311747 0.190784480829154 0.279315385085722 0.78018101196463 df.mm.trans3:probe3 -0.035033770193342 0.190784480829154 -0.183630083752538 0.854418525894958