chr8.23138_chr8_84572768_84586224_+_2.R fitVsDatCorrelation=0.949161621281399 cont.fitVsDatCorrelation=0.292166311285129 fstatistic=9593.59284104448,54,738 cont.fstatistic=1027.18639188439,54,738 residuals=-0.717260801858044,-0.089659711635494,-0.00208883705563572,0.0848777994570143,0.961436396803087 cont.residuals=-0.936115245109397,-0.409178801870906,-0.137875448297087,0.399493549477621,1.24712168270799 predictedValues: Include Exclude Both chr8.23138_chr8_84572768_84586224_+_2.R.tl.Lung 66.8693993763533 102.044558086627 60.3191698954704 chr8.23138_chr8_84572768_84586224_+_2.R.tl.cerebhem 60.1888619744799 88.7612448890457 59.5158717673314 chr8.23138_chr8_84572768_84586224_+_2.R.tl.cortex 58.359789209052 86.1035529133162 58.0791692008935 chr8.23138_chr8_84572768_84586224_+_2.R.tl.heart 64.6840558285165 110.576573595748 58.0457312163257 chr8.23138_chr8_84572768_84586224_+_2.R.tl.kidney 67.4385373848865 120.518318849593 63.1761615452442 chr8.23138_chr8_84572768_84586224_+_2.R.tl.liver 69.8713360965718 125.338290389128 60.0531993666284 chr8.23138_chr8_84572768_84586224_+_2.R.tl.stomach 66.4973052922895 104.912559738093 56.1120873780912 chr8.23138_chr8_84572768_84586224_+_2.R.tl.testicle 67.4374880898058 118.078483856087 65.3438335569283 diffExp=-35.1751587102735,-28.5723829145659,-27.7437637042642,-45.892517767231,-53.0797814647064,-55.4669542925562,-38.4152544458039,-50.6409957662814 diffExpScore=0.997023692677755 diffExp1.5=-1,0,0,-1,-1,-1,-1,-1 diffExp1.5Score=0.857142857142857 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 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 81.3025369501324 60.5902795487617 72.8308512155106 cerebhem 75.3426939375554 78.0433360990666 82.8415843977235 cortex 75.8417169916703 70.5658172306033 82.9395185249856 heart 83.1142237753618 96.603121481373 92.7259899716763 kidney 79.4652646667931 68.2553476007468 76.4186175564178 liver 65.6822882436689 72.3345656460704 75.7623328661524 stomach 71.9947756676585 80.9031395762137 68.6378046710615 testicle 74.4212796019361 85.0540312733193 78.3327198843541 cont.diffExp=20.7122574013707,-2.70064216151114,5.27589976106708,-13.4888977060111,11.2099170660463,-6.65227740240154,-8.90836390855524,-10.6327516713832 cont.diffExpScore=12.867069718178 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=1,0,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=1,0,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.912510677111085 cont.tran.correlation=0.0964498815465408 tran.covariance=0.00790440989868484 cont.tran.covariance=0.000388240535550165 tran.mean=86.1050222230996 cont.tran.mean=76.2196511431832 weightedLogRatios: wLogRatio Lung -1.86568894271422 cerebhem -1.66717309313966 cortex -1.65723787820637 heart -2.37941851049629 kidney -2.61350934290250 liver -2.65231830572801 stomach -2.01771453689319 testicle -2.51578091395181 cont.weightedLogRatios: wLogRatio Lung 1.25002153155532 cerebhem -0.152830974062213 cortex 0.309507611520013 heart -0.676088616080569 kidney 0.653767858562403 liver -0.408375690307175 stomach -0.50570811923477 testicle -0.584460726874982 varWeightedLogRatios=0.174899748624951 cont.varWeightedLogRatios=0.47552725832257 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.37612654385351 0.0840682022592665 52.054479889525 3.06041204998391e-249 *** df.mm.trans1 -0.511897264227692 0.0740566007584647 -6.91224359456145 1.03295328221560e-11 *** df.mm.trans2 0.180517629951602 0.0668235323364963 2.70140807646307 0.00706311955038863 ** df.mm.exp2 -0.231306641928216 0.0889890546425876 -2.59927069522457 0.00952885487985472 ** df.mm.exp3 -0.268130332759399 0.0889890546425876 -3.01307092019696 0.00267464765113819 ** df.mm.exp4 0.085490770353342 0.0889890546425876 0.960688600375675 0.337023405436985 df.mm.exp5 0.128590297551192 0.0889890546425876 1.44501251381597 0.148878826216523 df.mm.exp6 0.253940020397391 0.0889890546425876 2.85360959746461 0.00444362965887694 ** df.mm.exp7 0.0944363591096404 0.0889890546425876 1.06121319626252 0.288940181463741 df.mm.exp8 0.074386457825329 0.0889890546425876 0.835905697887139 0.403478508248237 df.mm.trans1:exp2 0.126052506024184 0.0839382333040525 1.50172932002964 0.133594755510848 df.mm.trans2:exp2 0.091847203350507 0.0686767363396793 1.33738450959908 0.181509185252915 df.mm.trans1:exp3 0.132015990433684 0.0839382333040525 1.57277542351264 0.116199418437920 df.mm.trans2:exp3 0.0982714464224778 0.0686767363396793 1.43092772982719 0.152874217345172 df.mm.trans1:exp4 -0.118717485607614 0.0839382333040525 -1.41434339197465 0.157682837365540 df.mm.trans2:exp4 -0.00519207755603458 0.0686767363396793 -0.0756016932772438 0.939756478024705 df.mm.trans1:exp5 -0.120115125808561 0.0839382333040525 -1.43099420943807 0.15285516913973 df.mm.trans2:exp5 0.0378019055909149 0.0686767363396793 0.550432469649466 0.582189346101428 df.mm.trans1:exp6 -0.210025979474396 0.0839382333040525 -2.50214915429076 0.0125594941399289 * df.mm.trans2:exp6 -0.048333177362894 0.0686767363396793 -0.703778017694889 0.481793010001932 df.mm.trans1:exp7 -0.100016388255152 0.0839382333040525 -1.19154745481548 0.233821784877872 df.mm.trans2:exp7 -0.0667186821743796 0.0686767363396793 -0.97148882911944 0.331623204855605 df.mm.trans1:exp8 -0.0659268454836677 0.0839382333040525 -0.785420932614324 0.432458885902773 df.mm.trans2:exp8 0.0715535010899849 0.0686767363396793 1.04188848951815 0.297804566774148 df.mm.trans1:probe2 0.165213254053760 0.0490093695014913 3.37105446844675 0.000787793075887747 *** df.mm.trans1:probe3 0.678475050692524 0.0490093695014913 13.8437824765707 6.47584102720925e-39 *** df.mm.trans1:probe4 -0.0244356225990619 0.0490093695014913 -0.498590837784973 0.618216226092068 df.mm.trans1:probe5 0.0453994653471124 0.0490093695014913 0.926342571000244 0.354570803609996 df.mm.trans1:probe6 0.130347768738115 0.0490093695014913 2.65964998252320 0.00799190247245729 ** df.mm.trans1:probe7 -0.0113543780954282 0.0490093695014913 -0.231677701854187 0.816852573369619 df.mm.trans1:probe8 0.131587765292713 0.0490093695014913 2.6849511967035 0.00741688903091008 ** df.mm.trans1:probe9 0.0521448509221185 0.0490093695014913 1.06397718339412 0.287687035757054 df.mm.trans1:probe10 0.55034875358927 0.0490093695014913 11.2294599825962 4.00732999548832e-27 *** df.mm.trans1:probe11 1.21602376726680 0.0490093695014913 24.8120671544202 2.66952216411588e-99 *** df.mm.trans1:probe12 1.1413073121801 0.0490093695014913 23.2875330531516 2.32871363214103e-90 *** df.mm.trans1:probe13 1.18171411262079 0.0490093695014913 24.1120039829287 3.4691304858567e-95 *** df.mm.trans1:probe14 1.25472182051467 0.0490093695014913 25.6016723593331 5.94922801332825e-104 *** df.mm.trans1:probe15 1.47482908862568 0.0490093695014913 30.0927986551797 1.91881482297850e-130 *** df.mm.trans1:probe16 1.08891220667937 0.0490093695014913 22.2184496098492 3.86515183067031e-84 *** df.mm.trans1:probe17 0.0461223315945629 0.0490093695014913 0.94109212307168 0.346965587176561 df.mm.trans1:probe18 -0.0319068935131944 0.0490093695014913 -0.651036604586874 0.515225509308424 df.mm.trans1:probe19 -0.0303567398196597 0.0490093695014913 -0.619406862982311 0.535839529620927 df.mm.trans1:probe20 0.0547126928298078 0.0490093695014913 1.11637210162728 0.264626230920753 df.mm.trans1:probe21 -0.00309538051902426 0.0490093695014913 -0.0631589541042773 0.949657031394001 df.mm.trans1:probe22 0.0291174834792849 0.0490093695014913 0.594120752326733 0.552613422996612 df.mm.trans2:probe2 0.153283421908698 0.0490093695014914 3.12763505157996 0.00183142053691806 ** df.mm.trans2:probe3 -0.120314084717328 0.0490093695014913 -2.45492006816506 0.0143213253769746 * df.mm.trans2:probe4 0.151523083141640 0.0490093695014913 3.09171663873434 0.00206492082100047 ** df.mm.trans2:probe5 0.295368778618352 0.0490093695014913 6.02678185054724 2.64025567807746e-09 *** df.mm.trans2:probe6 0.276558069897242 0.0490093695014913 5.64296322744626 2.38328138199360e-08 *** df.mm.trans3:probe2 -0.107531847692911 0.0490093695014913 -2.19410795908400 0.0285386809824492 * df.mm.trans3:probe3 -0.235318638006306 0.0490093695014913 -4.80150306767659 1.90720419429563e-06 *** df.mm.trans3:probe4 0.415222473932421 0.0490093695014913 8.47230801285427 1.2985697483617e-16 *** df.mm.trans3:probe5 0.202143273505415 0.0490093695014913 4.12458424912534 4.13649568031107e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23529931186386 0.255409360684807 16.5823965907441 9.91024436164366e-53 *** df.mm.trans1 0.269470896984617 0.224992905116209 1.19768619746225 0.231423727659251 df.mm.trans2 -0.112771631958769 0.203017968912066 -0.555476111612635 0.578737383573412 df.mm.exp2 0.0482090080468197 0.270359505061301 0.178314455916350 0.858524962100713 df.mm.exp3 -0.0470893520310757 0.270359505061301 -0.174173096005627 0.861777194862166 df.mm.exp4 0.247006054479097 0.270359505061301 0.91362075257199 0.361214534168566 df.mm.exp5 0.0481773843691057 0.270359505061301 0.178197486928311 0.858616785756486 df.mm.exp6 -0.0756418878522482 0.270359505061301 -0.279782609585327 0.779722691579002 df.mm.exp7 0.226830664473956 0.270359505061301 0.838996448164547 0.401742977832509 df.mm.exp8 0.177891187776143 0.270359505061301 0.657980150303235 0.510756071245362 df.mm.trans1:exp2 -0.124339270219082 0.255014499288131 -0.487577257631125 0.625994111674316 df.mm.trans2:exp2 0.204920778649428 0.208648226690289 0.982135251758482 0.326355078060552 df.mm.trans1:exp3 -0.0224393714859126 0.255014499288131 -0.0879925319875996 0.929906482355743 df.mm.trans2:exp3 0.199500727352217 0.208648226690289 0.956158269431877 0.33930537912643 df.mm.trans1:exp4 -0.224967423565136 0.255014499288131 -0.882175030020365 0.377969400867744 df.mm.trans2:exp4 0.219470522965602 0.208648226690289 1.05186862331391 0.293204117538478 df.mm.trans1:exp5 -0.0710346014830932 0.255014499288131 -0.278551226229822 0.78066727641746 df.mm.trans2:exp5 0.0709439230275406 0.208648226690289 0.340016899030958 0.733940551912588 df.mm.trans1:exp6 -0.137706029199712 0.255014499288131 -0.539992939947008 0.58936480246027 df.mm.trans2:exp6 0.252809512437622 0.208648226690289 1.21165425869104 0.226032559764960 df.mm.trans1:exp7 -0.34841432912751 0.255014499288131 -1.36625301737785 0.172275706809979 df.mm.trans2:exp7 0.0622874902254189 0.208648226690289 0.298528730454424 0.765383685356974 df.mm.trans1:exp8 -0.266326491542446 0.255014499288131 -1.04435823173150 0.296661637451494 df.mm.trans2:exp8 0.161261052082212 0.208648226690289 0.772884843740288 0.439837995362838 df.mm.trans1:probe2 -0.281839252885298 0.148896388831267 -1.89285485764658 0.0587682031504326 . df.mm.trans1:probe3 -0.27312490896721 0.148896388831267 -1.8343286302042 0.0670076632266878 . df.mm.trans1:probe4 -0.0251235512890630 0.148896388831267 -0.168731770369082 0.866053861222453 df.mm.trans1:probe5 -0.187540878541314 0.148896388831267 -1.25953946911258 0.208233732825604 df.mm.trans1:probe6 -0.0796003341200308 0.148896388831267 -0.534602180380853 0.593085993057031 df.mm.trans1:probe7 -0.270027537643922 0.148896388831267 -1.81352643783674 0.070156720718142 . df.mm.trans1:probe8 -0.281099752593998 0.148896388831267 -1.88788831482372 0.0594330097312099 . df.mm.trans1:probe9 0.0856222608038478 0.148896388831267 0.575045919353201 0.565435506780092 df.mm.trans1:probe10 -0.237833654487119 0.148896388831267 -1.59730975582381 0.110624736067346 df.mm.trans1:probe11 -0.189988294408910 0.148896388831267 -1.27597650890116 0.202365256839663 df.mm.trans1:probe12 -0.0616571968438816 0.148896388831267 -0.414094642105477 0.678925116050181 df.mm.trans1:probe13 -0.150328026750828 0.148896388831267 -1.00961499423054 0.313010680915662 df.mm.trans1:probe14 -0.0150970061842077 0.148896388831267 -0.101392695301133 0.919266275012723 df.mm.trans1:probe15 -0.00657199901669374 0.148896388831267 -0.0441380685473929 0.964806287508624 df.mm.trans1:probe16 -0.0740111879236657 0.148896388831267 -0.497065029612886 0.619291236814309 df.mm.trans1:probe17 -0.235317617744344 0.148896388831267 -1.58041185277509 0.114441048974507 df.mm.trans1:probe18 -0.142749811178654 0.148896388831267 -0.958719095198619 0.338014247612968 df.mm.trans1:probe19 -0.250783851392652 0.148896388831267 -1.68428430911676 0.0925495714316893 . df.mm.trans1:probe20 -0.118448442580731 0.148896388831267 -0.795509169231495 0.426573161086548 df.mm.trans1:probe21 -0.116299346867723 0.148896388831267 -0.78107567134832 0.435008465509519 df.mm.trans1:probe22 0.0338097150783692 0.148896388831267 0.227068737823341 0.820433177082603 df.mm.trans2:probe2 -0.238356038375252 0.148896388831267 -1.60081812759987 0.109845155811969 df.mm.trans2:probe3 0.164384426941775 0.148896388831267 1.10401889684551 0.26994477491971 df.mm.trans2:probe4 -0.0728493371226901 0.148896388831267 -0.489261947146647 0.624801635563142 df.mm.trans2:probe5 -0.230821793536243 0.148896388831267 -1.55021753951209 0.121518016054455 df.mm.trans2:probe6 0.175317507094649 0.148896388831267 1.17744633345892 0.239396980824087 df.mm.trans3:probe2 -0.171432233840908 0.148896388831267 -1.15135252900713 0.249960105986095 df.mm.trans3:probe3 -0.0391276723721276 0.148896388831267 -0.262784562333932 0.79278997666191 df.mm.trans3:probe4 -0.0269491222330672 0.148896388831267 -0.180992450150061 0.856423194923957 df.mm.trans3:probe5 -0.281785626672868 0.148896388831267 -1.89249469973509 0.0588162038428359 .