chrX.25620_chrX_89269852_89271430_-_2.R fitVsDatCorrelation=0.728873193884032 cont.fitVsDatCorrelation=0.267207541212096 fstatistic=8607.00538943313,53,715 cont.fstatistic=4338.01066842971,53,715 residuals=-0.777079882577156,-0.0929756058541424,-0.0116225197265102,0.0689301830206606,0.922361582539976 cont.residuals=-0.422745514614352,-0.140372388294638,-0.0329691655884840,0.0849351777869884,1.16123716144364 predictedValues: Include Exclude Both chrX.25620_chrX_89269852_89271430_-_2.R.tl.Lung 51.8672871813888 42.1290183236331 57.9654274738839 chrX.25620_chrX_89269852_89271430_-_2.R.tl.cerebhem 64.7661657897399 52.0012796292444 54.9057903457187 chrX.25620_chrX_89269852_89271430_-_2.R.tl.cortex 49.5240711324982 42.9248339784278 57.431829677324 chrX.25620_chrX_89269852_89271430_-_2.R.tl.heart 52.2385642691206 44.1527729465193 56.6507679304261 chrX.25620_chrX_89269852_89271430_-_2.R.tl.kidney 52.6377325181361 42.1491313646928 61.2176743648955 chrX.25620_chrX_89269852_89271430_-_2.R.tl.liver 55.7320974984808 45.4374810697312 57.8302684474989 chrX.25620_chrX_89269852_89271430_-_2.R.tl.stomach 52.3599502535582 45.9204501483309 61.1306184380995 chrX.25620_chrX_89269852_89271430_-_2.R.tl.testicle 55.1160268042383 43.3538228320478 57.121653924345 diffExp=9.7382688577557,12.7648861604955,6.5992371540703,8.08579132260129,10.4886011534433,10.2946164287496,6.43950010522732,11.7622039721905 diffExpScore=0.987042117872573 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=1,1,0,0,1,1,0,1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 49.1091863527329 49.2680006056503 52.8464276341803 cerebhem 53.2676817804208 54.9729723733765 52.8002660562159 cortex 51.9668870089362 51.8947045239227 55.6725229424953 heart 49.7001607866581 48.0463452620698 48.9528482934876 kidney 54.866637423794 56.0484136028387 58.1550484302251 liver 49.9974633302486 49.9043830475467 52.6784412999515 stomach 51.0854200781272 56.0158045580815 52.1503677375147 testicle 52.6116928479747 49.8512221772853 49.1067301709696 cont.diffExp=-0.158814252917409,-1.70529059295564,0.0721824850135135,1.65381552458831,-1.18177617904474,0.0930802827019193,-4.93038447995427,2.76047067068938 cont.diffExpScore=2.85572525503303 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=0.889962950379065 cont.tran.correlation=0.705722880464936 tran.covariance=0.00491193980766105 cont.tran.covariance=0.00167295681705717 tran.mean=49.5194178587368 cont.tran.mean=51.787935984979 weightedLogRatios: wLogRatio Lung 0.799513784183185 cerebhem 0.891456115974634 cortex 0.547858308944504 heart 0.651091923007449 kidney 0.856060026947166 liver 0.800220888849634 stomach 0.51082181289405 testicle 0.93363782962619 cont.weightedLogRatios: wLogRatio Lung -0.0125778538939355 cerebhem -0.125766500247041 cortex 0.00549027771822291 heart 0.131614863009734 kidney -0.0855731205804136 liver 0.00728797072095729 stomach -0.366656325417611 testicle 0.212131711950466 varWeightedLogRatios=0.0253992200485690 cont.varWeightedLogRatios=0.0304704660764618 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.72624518376729 0.0828377171885583 44.9824706695559 1.11716242012860e-210 *** df.mm.trans1 0.264037238795014 0.0735614960454705 3.58934025256644 0.000354157134825954 *** df.mm.trans2 0.0235865274087594 0.0669137466229424 0.352491507338081 0.724573563874601 df.mm.exp2 0.486854540459977 0.0902096740243323 5.39692162426679 9.2293983343604e-08 *** df.mm.exp3 -0.0182676041510889 0.0902096740243324 -0.202501609152933 0.839582207280702 df.mm.exp4 0.0769928841519815 0.0902096740243324 0.853488109614653 0.393674511725068 df.mm.exp5 -0.0393669642338565 0.0902096740243323 -0.436394041544125 0.662682601323319 df.mm.exp6 0.149802955701001 0.0902096740243324 1.66060854693471 0.097230517247906 . df.mm.exp7 0.0424613785763913 0.0902096740243324 0.470696508280677 0.638001035899994 df.mm.exp8 0.104073846741067 0.0902096740243323 1.15368831410471 0.249013471978905 df.mm.trans1:exp2 -0.264759492991042 0.0856583176955843 -3.09087897257047 0.00207311821467855 ** df.mm.trans2:exp2 -0.276322988521776 0.0721307585666562 -3.83086209008083 0.000138916097363934 *** df.mm.trans1:exp3 -0.0279618456238059 0.0856583176955843 -0.326434681138354 0.744191027965442 df.mm.trans2:exp3 0.0369813686191970 0.0721307585666562 0.512699011546128 0.608320230939811 df.mm.trans1:exp4 -0.0698601695804832 0.0856583176955843 -0.815567845130404 0.415018941641205 df.mm.trans2:exp4 -0.0300739262737812 0.0721307585666562 -0.416936226256235 0.676850200712268 df.mm.trans1:exp5 0.0541118884037077 0.0856583176955843 0.631717851335961 0.527773177810427 df.mm.trans2:exp5 0.0398442656818814 0.0721307585666562 0.552389389403984 0.580854228827881 df.mm.trans1:exp6 -0.077935004690751 0.0856583176955843 -0.909835807979784 0.363215705317263 df.mm.trans2:exp6 -0.0742023916246427 0.0721307585666562 -1.02872052227306 0.303958894805622 df.mm.trans1:exp7 -0.0330076742760478 0.0856583176955843 -0.385341145659102 0.700099289273684 df.mm.trans2:exp7 0.0437124016734957 0.0721307585666562 0.606016109384195 0.544696389484635 df.mm.trans1:exp8 -0.0433215919415162 0.0856583176955843 -0.505748806502062 0.613188879409859 df.mm.trans2:exp8 -0.07541573671407 0.0721307585666562 -1.0455419880879 0.296125878886980 df.mm.trans1:probe2 0.0965818819484711 0.0469169928398155 2.05856931790625 0.0398973000596101 * df.mm.trans1:probe3 -0.0293242069583257 0.0469169928398155 -0.625023156502054 0.532155273030821 df.mm.trans1:probe4 0.486081512898085 0.0469169928398155 10.3604575544232 1.57231237122684e-23 *** df.mm.trans1:probe5 0.159335951400999 0.0469169928398155 3.39612455438066 0.000721263242228983 *** df.mm.trans1:probe6 -0.121804397670632 0.0469169928398155 -2.59616804696963 0.00962059058498169 ** df.mm.trans1:probe7 -0.0666012995032142 0.0469169928398155 -1.41955601738170 0.156172800143967 df.mm.trans1:probe8 -0.044977687786141 0.0469169928398155 -0.958665188532102 0.338051489715719 df.mm.trans1:probe9 -0.098617308178575 0.0469169928398155 -2.10195287910449 0.0359066261903223 * df.mm.trans1:probe10 -0.0225061379440324 0.0469169928398155 -0.479701203802066 0.631586571454811 df.mm.trans1:probe11 -0.137878165872254 0.0469169928398155 -2.93876818454668 0.00340132861876918 ** df.mm.trans1:probe12 -0.202690206000182 0.0469169928398155 -4.32018749991521 1.77959348352053e-05 *** df.mm.trans1:probe13 -0.0692644733650315 0.0469169928398155 -1.47631954165339 0.140298392364493 df.mm.trans1:probe14 -0.121944734176872 0.0469169928398155 -2.59915921280840 0.00953795204524638 ** df.mm.trans1:probe15 -0.10423846676459 0.0469169928398155 -2.22176359683755 0.0266115942347762 * df.mm.trans1:probe16 -0.199098722707422 0.0469169928398155 -4.24363776653775 2.48889612284137e-05 *** df.mm.trans1:probe17 -0.0699844359967402 0.0469169928398155 -1.49166499727896 0.136228045324403 df.mm.trans1:probe18 -0.190899158185046 0.0469169928398155 -4.06887028835835 5.25029678307406e-05 *** df.mm.trans1:probe19 -0.172813521680710 0.0469169928398155 -3.6833887088785 0.000247519310740114 *** df.mm.trans1:probe20 -0.0503416168247777 0.0469169928398155 -1.07299325420652 0.283636145496873 df.mm.trans1:probe21 -0.157373126611391 0.0469169928398155 -3.35428843763914 0.000837671005191547 *** df.mm.trans1:probe22 0.0369107864163506 0.0469169928398155 0.786725324497498 0.431703351663599 df.mm.trans2:probe2 -0.0674096326853468 0.0469169928398155 -1.43678502404230 0.151216506004349 df.mm.trans2:probe3 0.0246377460120005 0.0469169928398155 0.525134807682985 0.599652274244624 df.mm.trans2:probe4 0.0173889710751728 0.0469169928398155 0.370632685998066 0.711020908743452 df.mm.trans2:probe5 -0.0176557018418879 0.0469169928398155 -0.376317849316732 0.706792249095131 df.mm.trans2:probe6 -0.0479107483689807 0.0469169928398155 -1.02118114288693 0.307514012935225 df.mm.trans3:probe2 0.160723846483827 0.0469169928398155 3.42570648192591 0.000648243273940272 *** df.mm.trans3:probe3 -0.0608027783332946 0.0469169928398155 -1.29596495114016 0.195405737966853 df.mm.trans3:probe4 0.083990151104381 0.0469169928398155 1.79018615688225 0.0738470033612041 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.71901735095704 0.116593594885369 31.8972697823877 1.52849076395040e-139 *** df.mm.trans1 0.112458904199609 0.103537368727393 1.08616729961243 0.277771030209181 df.mm.trans2 0.153614644695102 0.0941807009029357 1.63106287405336 0.103317418702833 df.mm.exp2 0.191724433266012 0.126969580342165 1.51000289005714 0.131484463665145 df.mm.exp3 0.0564059675177322 0.126969580342165 0.44424788493217 0.656997799639598 df.mm.exp4 0.0633858926891501 0.126969580342165 0.499221093102253 0.617777202286298 df.mm.exp5 0.144077828057662 0.126969580342165 1.13474288620465 0.256863330566794 df.mm.exp6 0.0339440260778998 0.126969580342165 0.267339830425724 0.789284615690664 df.mm.exp7 0.181070985507969 0.126969580342165 1.42609737718285 0.154276689832782 df.mm.exp8 0.154054517799540 0.126969580342165 1.21331831911537 0.225408811982128 df.mm.trans1:exp2 -0.110440743639915 0.120563573344591 -0.916037411434853 0.359956119338348 df.mm.trans2:exp2 -0.0821575755810588 0.101523614224568 -0.809245969113431 0.418642885267512 df.mm.trans1:exp3 0.000154647877453104 0.120563573344591 0.00128270814445002 0.998976905055337 df.mm.trans2:exp3 -0.00446401021811106 0.101523614224568 -0.0439701664702042 0.96494046107865 df.mm.trans1:exp4 -0.0514238365085514 0.120563573344591 -0.426528802041837 0.669850951913703 df.mm.trans2:exp4 -0.0884946166810462 0.101523614224568 -0.871665349554024 0.383683560063848 df.mm.trans1:exp5 -0.0332184735243901 0.120563573344591 -0.275526617226633 0.782991286096425 df.mm.trans2:exp5 -0.0151367775622289 0.101523614224568 -0.149096125840700 0.88151982496464 df.mm.trans1:exp6 -0.0160178673925403 0.120563573344591 -0.132858266789742 0.89434285357647 df.mm.trans2:exp6 -0.021109985925867 0.101523614224568 -0.207931781064966 0.835341445846266 df.mm.trans1:exp7 -0.141617962438346 0.120563573344591 -1.17463308783640 0.240532592175511 df.mm.trans2:exp7 -0.0527119057113497 0.101523614224568 -0.519208325215373 0.603776146430779 df.mm.trans1:exp8 -0.0851622373310654 0.120563573344591 -0.706367893456986 0.480189489435515 df.mm.trans2:exp8 -0.142286299967447 0.101523614224568 -1.40150940305093 0.161495716892276 df.mm.trans1:probe2 0.0518885441806298 0.0660353887342609 0.785768739689552 0.432263296161137 df.mm.trans1:probe3 0.0854650205757482 0.0660353887342609 1.2942305968649 0.1960036364518 df.mm.trans1:probe4 0.061154008373923 0.0660353887342608 0.926079327253126 0.354717224491436 df.mm.trans1:probe5 0.0248067914986594 0.0660353887342609 0.375659051519886 0.707281805711261 df.mm.trans1:probe6 0.116543034040427 0.0660353887342608 1.76485724206787 0.078014631670319 . df.mm.trans1:probe7 0.137698084738272 0.0660353887342609 2.08521653885307 0.0374037808251609 * df.mm.trans1:probe8 0.177660379431152 0.0660353887342608 2.69038136727099 0.00730365398854767 ** df.mm.trans1:probe9 0.00290304732656616 0.0660353887342609 0.043961993443373 0.964946973624566 df.mm.trans1:probe10 0.142291319111872 0.0660353887342608 2.15477370299855 0.0315128305904336 * df.mm.trans1:probe11 0.0714322608233278 0.0660353887342609 1.08172696780487 0.27973855981914 df.mm.trans1:probe12 0.102579333078144 0.0660353887342608 1.55339939757064 0.120770244510784 df.mm.trans1:probe13 0.0465735934645465 0.0660353887342608 0.70528233962501 0.480864243639645 df.mm.trans1:probe14 0.0403576691011466 0.0660353887342608 0.611152139401399 0.541293106974737 df.mm.trans1:probe15 0.0527702138621999 0.0660353887342609 0.799120212263115 0.424486086146223 df.mm.trans1:probe16 0.128985738762456 0.0660353887342608 1.95328203914298 0.0511758589751123 . df.mm.trans1:probe17 0.097786069855168 0.0660353887342608 1.48081311747369 0.13909689232016 df.mm.trans1:probe18 -0.0146850192017262 0.0660353887342608 -0.222381051784544 0.824080767423269 df.mm.trans1:probe19 0.0421480855347773 0.0660353887342608 0.63826512333242 0.523505481187549 df.mm.trans1:probe20 0.102724834365622 0.0660353887342609 1.55560278109373 0.120245074502316 df.mm.trans1:probe21 0.0833507056863574 0.0660353887342608 1.26221269055864 0.207283813284278 df.mm.trans1:probe22 0.072382564898117 0.0660353887342608 1.09611779813091 0.273396279179837 df.mm.trans2:probe2 0.0240213002077032 0.0660353887342609 0.363764046341418 0.716141746882958 df.mm.trans2:probe3 0.0791227524115197 0.0660353887342609 1.19818712251282 0.231241188502679 df.mm.trans2:probe4 0.0206191795293890 0.0660353887342609 0.312244387814003 0.754945837761537 df.mm.trans2:probe5 0.0775679377858775 0.0660353887342608 1.17464194991001 0.24052904747391 df.mm.trans2:probe6 0.0450968272662485 0.0660353887342609 0.682919085215456 0.494879182715637 df.mm.trans3:probe2 -0.00523449785704183 0.0660353887342608 -0.0792680706114483 0.93684158182793 df.mm.trans3:probe3 -0.060633680162798 0.0660353887342608 -0.91819979143001 0.358823905917880 df.mm.trans3:probe4 -0.00493495899529419 0.0660353887342608 -0.0747320351993901 0.940448821619184