chr14.7324_chr14_46716880_46717276_-_0.R fitVsDatCorrelation=0.893555284466765 cont.fitVsDatCorrelation=0.303447905670957 fstatistic=9638.97350261072,44,508 cont.fstatistic=2130.87882905099,44,508 residuals=-0.684113975017282,-0.0873608469099123,0.00459872779497561,0.0832359662001965,0.668994561235057 cont.residuals=-0.705250037504787,-0.213888662314738,-0.0457094888359419,0.155751607424814,1.35500344640022 predictedValues: Include Exclude Both chr14.7324_chr14_46716880_46717276_-_0.R.tl.Lung 76.3938674896024 78.1781429182533 82.0564453967766 chr14.7324_chr14_46716880_46717276_-_0.R.tl.cerebhem 70.6061041827131 78.5016542843541 74.8079116829657 chr14.7324_chr14_46716880_46717276_-_0.R.tl.cortex 72.9902741377858 127.211923161412 85.2595531954681 chr14.7324_chr14_46716880_46717276_-_0.R.tl.heart 77.683127618851 93.2574093078848 77.9831538560314 chr14.7324_chr14_46716880_46717276_-_0.R.tl.kidney 76.9619417132367 76.6563068507154 80.9276186834884 chr14.7324_chr14_46716880_46717276_-_0.R.tl.liver 80.6093817628264 74.6198073559384 76.8089040644205 chr14.7324_chr14_46716880_46717276_-_0.R.tl.stomach 89.9712647741125 77.5688234456903 76.2439396190154 chr14.7324_chr14_46716880_46717276_-_0.R.tl.testicle 75.5167896981308 83.7640356621432 74.6429290623273 diffExp=-1.78427542865083,-7.895550101641,-54.2216490236259,-15.5742816890337,0.305634862521330,5.98957440688794,12.4024413284222,-8.24724596401244 diffExpScore=1.51974464046699 diffExp1.5=0,0,-1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,-1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,-1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,-1,-1,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 78.6236285657235 73.6842376198649 72.5746724396788 cerebhem 79.81363967543 91.3198201157543 77.1243302379026 cortex 74.0120262868389 75.1159055388407 75.477989939474 heart 81.7339366300058 93.8922654371395 73.0289576642855 kidney 77.4002185145458 74.0731729861796 76.7263021436504 liver 73.0052898492983 80.8031563041209 82.7701940830812 stomach 76.9811023370305 88.2215190677803 72.3228903937907 testicle 89.0632625477645 75.626617324409 73.813195657055 cont.diffExp=4.93939094585865,-11.5061804403244,-1.10387925200189,-12.1583288071336,3.3270455283662,-7.79786645482262,-11.2404167307497,13.4366452233555 cont.diffExpScore=2.83547939598098 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=-0.355787684851568 cont.tran.correlation=0.0687180080455773 tran.covariance=-0.00471785377962958 cont.tran.covariance=0.000470261104151231 tran.mean=81.9056783977281 cont.tran.mean=80.2106124250454 weightedLogRatios: wLogRatio Lung -0.100372455446877 cerebhem -0.456885867964092 cortex -2.53770288935107 heart -0.812031937882738 kidney 0.0172747827896223 liver 0.335937412946793 stomach 0.656385674033032 testicle -0.45358545686684 cont.weightedLogRatios: wLogRatio Lung 0.281089711546495 cerebhem -0.598896974773409 cortex -0.0638325192613466 heart -0.620283360970917 kidney 0.190112774567289 liver -0.440570392864568 stomach -0.60127550830861 testicle 0.720809205750659 varWeightedLogRatios=0.952035920589274 cont.varWeightedLogRatios=0.253920075423018 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.51489694432006 0.0783053781309508 57.657558804834 4.65055931571269e-225 *** df.mm.trans1 -0.142078467087876 0.0625000465644909 -2.27325377975954 0.0234275452702843 * df.mm.trans2 -0.112658192451553 0.0625000465644908 -1.80252973628278 0.0720546730300635 . df.mm.exp2 0.0178274882781016 0.0834992331861743 0.213504814329881 0.831018893566005 df.mm.exp3 0.402995250815066 0.0834992331861743 4.8263347510812 1.842234201049e-06 *** df.mm.exp4 0.244023608927001 0.0834992331861743 2.92246526842843 0.00362746440811741 ** df.mm.exp5 0.00160259891647822 0.0834992331861743 0.0191929776517226 0.984694695431896 df.mm.exp6 0.0732152944331994 0.0834992331861743 0.876837925804117 0.380989132725375 df.mm.exp7 0.229232832183334 0.0834992331861743 2.74532859088926 0.00625922988029308 ** df.mm.exp8 0.152157798303854 0.0834992331861743 1.82226581607755 0.0690024398879956 . df.mm.trans1:exp2 -0.0966133104950578 0.065062078330058 -1.48494042881540 0.138179849587924 df.mm.trans2:exp2 -0.0136978961848301 0.065062078330058 -0.210535791914624 0.833333939115259 df.mm.trans1:exp3 -0.448571474022036 0.065062078330058 -6.89451498531059 1.60986714378046e-11 *** df.mm.trans2:exp3 0.0838690250846598 0.065062078330058 1.28906157376644 0.197963346887936 df.mm.trans1:exp4 -0.227287947386012 0.065062078330058 -3.49340127490221 0.000518554949566164 *** df.mm.trans2:exp4 -0.0676502033338281 0.065062078330058 -1.03977931646512 0.298937062308657 df.mm.trans1:exp5 0.00580601279420858 0.065062078330058 0.0892380468504994 0.928927899117062 df.mm.trans2:exp5 -0.0212608219852391 0.065062078330058 -0.326777479769145 0.743970788096444 df.mm.trans1:exp6 -0.0195026771420365 0.065062078330058 -0.299754905508858 0.764486724199172 df.mm.trans2:exp6 -0.119799414470542 0.065062078330058 -1.84130937014958 0.0661590910455019 . df.mm.trans1:exp7 -0.0656449176115993 0.065062078330058 -1.00895820263510 0.31347501003349 df.mm.trans2:exp7 -0.237057351600789 0.065062078330058 -3.64355639545058 0.000296537528465835 *** df.mm.trans1:exp8 -0.163705211154062 0.065062078330058 -2.51613866872788 0.0121719741169356 * df.mm.trans2:exp8 -0.0831441578036566 0.065062078330058 -1.27792040982565 0.201861121916743 df.mm.trans1:probe2 -0.0939819889918056 0.0453242311522146 -2.07354844423463 0.0386246421116588 * df.mm.trans1:probe3 -0.0873666740227581 0.0453242311522146 -1.92759307332429 0.054462948939298 . df.mm.trans1:probe4 -0.282821778645933 0.0453242311522146 -6.23996858757778 9.23354218139859e-10 *** df.mm.trans1:probe5 -0.13135344791776 0.0453242311522146 -2.89808441486032 0.00391670190928163 ** df.mm.trans1:probe6 -0.0320490070373466 0.0453242311522146 -0.70710536555413 0.479825232845251 df.mm.trans2:probe2 -0.154157943935552 0.0453242311522146 -3.40122579063361 0.000723707407085558 *** df.mm.trans2:probe3 -0.36524570489292 0.0453242311522146 -8.05850856391358 5.55795253065033e-15 *** df.mm.trans2:probe4 0.0117114601674078 0.0453242311522146 0.258392914114232 0.796208338640712 df.mm.trans2:probe5 -0.120789694663690 0.0453242311522146 -2.66501364927814 0.00794346156285431 ** df.mm.trans2:probe6 -0.106745093735442 0.0453242311522146 -2.35514405918888 0.0188948779783177 * df.mm.trans3:probe2 0.681776101379955 0.0453242311522146 15.0421989308613 1.46174247870144e-42 *** df.mm.trans3:probe3 0.0250853456363641 0.0453242311522146 0.553464339022513 0.580188993647214 df.mm.trans3:probe4 0.173025387709744 0.0453242311522146 3.81750298485294 0.000151449221715430 *** df.mm.trans3:probe5 -0.123868431990563 0.0453242311522146 -2.73294061127192 0.0064959580646535 ** df.mm.trans3:probe6 -0.269636032791387 0.0453242311522146 -5.94904813466895 5.02602637408664e-09 *** df.mm.trans3:probe7 -0.0154341813865864 0.0453242311522146 -0.340528255951061 0.733599520973424 df.mm.trans3:probe8 0.545272456209887 0.0453242311522146 12.0304844086306 1.65791795245519e-29 *** df.mm.trans3:probe9 -0.323042813067888 0.0453242311522146 -7.12737546463828 3.52953861189679e-12 *** df.mm.trans3:probe10 0.965205526708082 0.0453242311522146 21.2955741812053 2.14350858463704e-72 *** df.mm.trans3:probe11 -0.0386654325281968 0.0453242311522146 -0.853085238188481 0.394014078860183 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.41699196563834 0.16619355738738 26.5773958694607 3.39749755218121e-98 *** df.mm.trans1 -0.058787947671642 0.132648680376196 -0.443185318579255 0.657820368666677 df.mm.trans2 -0.0861636277499317 0.132648680376196 -0.649562645520249 0.51626835843263 df.mm.exp2 0.168798287079193 0.177216877480906 0.952495549400369 0.341298696156196 df.mm.exp3 -0.0804263443577638 0.177216877480906 -0.453830049942221 0.650144970299677 df.mm.exp4 0.274916086651197 0.177216877480906 1.55129743035235 0.121453164897203 df.mm.exp5 -0.0660467191588372 0.177216877480906 -0.372688651880538 0.709535624622287 df.mm.exp6 -0.113365271378781 0.177216877480906 -0.639697939554294 0.522657369418999 df.mm.exp7 0.162425019356916 0.177216877480906 0.916532452584357 0.35982258307673 df.mm.exp8 0.133772536250093 0.177216877480906 0.754852123294557 0.450687515514699 df.mm.trans1:exp2 -0.153776146121324 0.138086278449562 -1.11362365506500 0.265967309225570 df.mm.trans2:exp2 0.045780660622351 0.138086278449562 0.331536638805665 0.740375897267207 df.mm.trans1:exp3 0.0199816696039465 0.138086278449562 0.144704237294983 0.885001784249329 df.mm.trans2:exp3 0.0996697680307924 0.138086278449562 0.72179342618172 0.470753583263477 df.mm.trans1:exp4 -0.236119062147014 0.138086278449562 -1.70993863255761 0.0878878481028877 . df.mm.trans2:exp4 -0.0325569781761068 0.138086278449562 -0.235772725151679 0.81370405528824 df.mm.trans1:exp5 0.0503640507926989 0.138086278449562 0.364728859074112 0.715465596330051 df.mm.trans2:exp5 0.0713112438010169 0.138086278449562 0.516425271226817 0.605782143278912 df.mm.trans1:exp6 0.0392249014476425 0.138086278449562 0.284060819714031 0.776479514650133 df.mm.trans2:exp6 0.205592395177691 0.138086278449562 1.48886911491924 0.137142357173638 df.mm.trans1:exp7 -0.183537323962268 0.138086278449562 -1.3291496158998 0.184395238415718 df.mm.trans2:exp7 0.0176369901227744 0.138086278449562 0.127724422156953 0.898417606020787 df.mm.trans1:exp8 -0.0090978760271456 0.138086278449562 -0.0658854458914883 0.947494948016068 df.mm.trans2:exp8 -0.107753138154886 0.138086278449562 -0.78033197334842 0.435558933947404 df.mm.trans1:probe2 0.0518972191956092 0.0961951195540827 0.5394995030536 0.58977862125944 df.mm.trans1:probe3 -0.0569485072036903 0.0961951195540827 -0.592010358401527 0.554107032090781 df.mm.trans1:probe4 -0.105491494221358 0.0961951195540827 -1.09664081411166 0.273318014977129 df.mm.trans1:probe5 0.106150918541524 0.0961951195540827 1.10349588454791 0.270334326662639 df.mm.trans1:probe6 0.114352184688145 0.0961951195540827 1.18875245665508 0.235092485605197 df.mm.trans2:probe2 -0.128344032055251 0.0961951195540827 -1.33420523463349 0.182734376208131 df.mm.trans2:probe3 -0.189660105373534 0.0961951195540827 -1.97161879160515 0.0491953178192754 * df.mm.trans2:probe4 0.0228167294754096 0.0961951195540827 0.237192173378210 0.812603330138216 df.mm.trans2:probe5 -0.0549663837503523 0.0961951195540827 -0.571405119148994 0.567977772902105 df.mm.trans2:probe6 -0.177516582104047 0.0961951195540827 -1.84538033662138 0.0655639982871035 . df.mm.trans3:probe2 0.0316804585748994 0.0961951195540827 0.329335404142702 0.742037926328929 df.mm.trans3:probe3 0.06466949477267 0.0961951195540827 0.672274176407792 0.50171481149232 df.mm.trans3:probe4 0.0595968756064366 0.0961951195540827 0.619541572199307 0.535837406142099 df.mm.trans3:probe5 -0.0472869918763374 0.0961951195540827 -0.491573710761405 0.623233055531457 df.mm.trans3:probe6 -0.057498933861786 0.0961951195540827 -0.597732339523307 0.550284900801099 df.mm.trans3:probe7 0.168592771376917 0.0961951195540827 1.75261252502661 0.0802717843964445 . df.mm.trans3:probe8 0.0369600220103495 0.0961951195540827 0.384219305321097 0.70097668580776 df.mm.trans3:probe9 -0.0984539668838067 0.0961951195540827 -1.02348193276535 0.306567164320831 df.mm.trans3:probe10 -0.0346724182201604 0.0961951195540827 -0.360438433684434 0.718669107393086 df.mm.trans3:probe11 0.0147443575740815 0.0961951195540827 0.153275526268169 0.878241864275174