chr17.10431_chr17_56296611_56296879_-_0.R fitVsDatCorrelation=0.851670963704312 cont.fitVsDatCorrelation=0.283173932685805 fstatistic=12414.9055675013,44,508 cont.fstatistic=3699.00025049748,44,508 residuals=-0.40160417891014,-0.0762283380158724,-0.00670627309853928,0.0764697344481848,0.59267498268568 cont.residuals=-0.479840205462128,-0.145672355110172,-0.0466041472492797,0.124284756129798,0.829512070256182 predictedValues: Include Exclude Both chr17.10431_chr17_56296611_56296879_-_0.R.tl.Lung 56.2620008433455 50.8454466038353 70.6339217891844 chr17.10431_chr17_56296611_56296879_-_0.R.tl.cerebhem 63.6401525421733 54.237791679112 62.4516452588604 chr17.10431_chr17_56296611_56296879_-_0.R.tl.cortex 47.0685572116 45.9939002766862 63.6739505839086 chr17.10431_chr17_56296611_56296879_-_0.R.tl.heart 53.5237804615675 49.2043367077827 65.8038162854953 chr17.10431_chr17_56296611_56296879_-_0.R.tl.kidney 49.5379997276586 51.7901621578757 61.2207375631662 chr17.10431_chr17_56296611_56296879_-_0.R.tl.liver 48.3191730645223 55.4901843602251 61.0696919193765 chr17.10431_chr17_56296611_56296879_-_0.R.tl.stomach 54.5265553279346 49.2946980776123 72.8784968504968 chr17.10431_chr17_56296611_56296879_-_0.R.tl.testicle 60.6294920202756 53.1463014853017 80.7175557635574 diffExp=5.41655423951021,9.40236086306138,1.07465693491383,4.31944375378479,-2.25216243021704,-7.1710112957028,5.23185725032233,7.48319053497394 diffExpScore=1.72827699127033 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 67.1284372945849 59.5845769741549 57.3191541394933 cerebhem 57.4487454283496 61.774512044149 58.9705812894515 cortex 61.9137668943813 61.7148081217794 57.9855518772511 heart 65.074968400398 59.6577655964127 57.8882607346164 kidney 63.2929847193051 56.6967798532538 61.867358174146 liver 65.8616344287171 56.5247522968802 57.3957788566452 stomach 61.6405232634371 60.331225878955 60.1026678733403 testicle 66.8919540036294 57.8995962488708 60.7222052657099 cont.diffExp=7.54386032042996,-4.32576661579939,0.198958772601920,5.41720280398535,6.59620486605127,9.33688213183692,1.30929738448211,8.99235775475859 cont.diffExpScore=1.21213601095845 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=0.396164045554233 cont.tran.correlation=-0.632332327139964 tran.covariance=0.00261107524260033 cont.tran.covariance=-0.00114859514762135 tran.mean=52.7194082842193 cont.tran.mean=61.4648144654536 weightedLogRatios: wLogRatio Lung 0.402830365198112 cerebhem 0.651186713860072 cortex 0.0886915612995401 heart 0.331364732932721 kidney -0.174504820197901 liver -0.546179202479269 stomach 0.398265352328356 testicle 0.532058086735069 cont.weightedLogRatios: wLogRatio Lung 0.494368261368936 cerebhem -0.296720684153907 cortex 0.0132741673063703 heart 0.359142560908629 kidney 0.450435671963743 liver 0.628497169724027 stomach 0.088252968554544 testicle 0.596370139517697 varWeightedLogRatios=0.160012696566334 cont.varWeightedLogRatios=0.106032386370547 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.5159274127931 0.0648372946448927 54.2269296097173 2.05418651838005e-213 *** df.mm.trans1 0.50843249695381 0.0517503909839328 9.82470832175287 5.66224620048973e-21 *** df.mm.trans2 0.404194685858217 0.0517503909839328 7.81046632060634 3.28898789242013e-14 *** df.mm.exp2 0.310930516114848 0.0691378359179996 4.4972555473623 8.53723541561597e-06 *** df.mm.exp3 -0.174960983712488 0.0691378359179996 -2.53061122595737 0.0116872301288759 * df.mm.exp4 -0.0118694528624355 0.0691378359179996 -0.171678108011845 0.86375896303167 df.mm.exp5 0.0341548448483123 0.0691378359179996 0.494010904374001 0.621512010859322 df.mm.exp6 0.0807194521903544 0.0691378359179996 1.16751487978436 0.243550197082991 df.mm.exp7 -0.093588691608215 0.0691378359179996 -1.35365376086135 0.176448819780186 df.mm.exp8 -0.0144255692359956 0.0691378359179996 -0.208649418143561 0.834805562169678 df.mm.trans1:exp2 -0.187705282348962 0.0538717677327413 -3.4842978103145 0.000536087481102926 *** df.mm.trans2:exp2 -0.246343160030048 0.0538717677327413 -4.57276919614295 6.05342845744132e-06 *** df.mm.trans1:exp3 -0.00345317997305166 0.0538717677327413 -0.0640999937143876 0.948915821649229 df.mm.trans2:exp3 0.0746791958905084 0.0538717677327413 1.38623993667691 0.166281773822970 df.mm.trans1:exp4 -0.0380238640404840 0.0538717677327413 -0.705821725196042 0.480622554342199 df.mm.trans2:exp4 -0.0209393558944968 0.0538717677327413 -0.388688858297304 0.697669178475089 df.mm.trans1:exp5 -0.161434165171862 0.0538717677327413 -2.99663760752643 0.00286321133503753 ** df.mm.trans2:exp5 -0.0157452061898678 0.0538717677327413 -0.292271942290441 0.770198036509951 df.mm.trans1:exp6 -0.232910379096302 0.0538717677327413 -4.32342187566174 1.84951832924512e-05 *** df.mm.trans2:exp6 0.0066961217085974 0.0538717677327413 0.124297419416734 0.90112895747258 df.mm.trans1:exp7 0.0622571616762815 0.0538717677327413 1.15565470183084 0.248365666189121 df.mm.trans2:exp7 0.0626146499710953 0.0538717677327413 1.16229061354971 0.245663184633443 df.mm.trans1:exp8 0.089187644273381 0.0538717677327413 1.65555444023746 0.0984296565926395 . df.mm.trans2:exp8 0.0586835124111992 0.0538717677327413 1.08931848500552 0.276529938424045 df.mm.trans1:probe2 0.0859038726509432 0.0375287189706810 2.28901691843132 0.0224878654148147 * df.mm.trans1:probe3 0.0165442616655234 0.0375287189706810 0.44084269645464 0.659514424461241 df.mm.trans1:probe4 0.0728296591033136 0.0375287189706810 1.94063802604643 0.0528550630238056 . df.mm.trans1:probe5 0.041362665206292 0.0375287189706811 1.10216032789731 0.270913867556169 df.mm.trans1:probe6 -0.120429691185897 0.0375287189706811 -3.20900085291965 0.00141611447105305 ** df.mm.trans2:probe2 -0.00422219419298061 0.0375287189706810 -0.112505683881167 0.910466905600121 df.mm.trans2:probe3 0.0804056373776871 0.0375287189706811 2.14250951226188 0.0326271124933763 * df.mm.trans2:probe4 0.0903121457818708 0.0375287189706810 2.40648091005788 0.0164630509195610 * df.mm.trans2:probe5 0.0530655813421525 0.0375287189706810 1.41399927302633 0.157974655353262 df.mm.trans2:probe6 -0.0721971091380304 0.0375287189706811 -1.92378293526176 0.0549402272137487 . df.mm.trans3:probe2 -0.370686814663406 0.0375287189706811 -9.87741721088325 3.64543542428798e-21 *** df.mm.trans3:probe3 0.0865782428807076 0.0375287189706810 2.3069863628531 0.0214568965102316 * df.mm.trans3:probe4 -0.252137163111673 0.0375287189706811 -6.71851238270757 4.93656812042718e-11 *** df.mm.trans3:probe5 -0.0918358463348905 0.0375287189706810 -2.44708183102750 0.0147395142936368 * df.mm.trans3:probe6 -0.403062423951376 0.0375287189706811 -10.7401061109031 2.18255118900195e-24 *** df.mm.trans3:probe7 -0.456578225889566 0.0375287189706810 -12.1661020789509 4.59908261703742e-30 *** df.mm.trans3:probe8 0.0548892672348275 0.0375287189706811 1.46259368132734 0.144196970196412 df.mm.trans3:probe9 -0.360759116917093 0.0375287189706811 -9.61288119636943 3.27096666672582e-20 *** df.mm.trans3:probe10 0.0214234878727405 0.0375287189706811 0.570855826160156 0.568349806761813 df.mm.trans3:probe11 -0.109316374003402 0.0375287189706811 -2.91287251474810 0.00373885828640109 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.39049487641845 0.118653225581978 37.0027435401243 2.7197383844499e-146 *** df.mm.trans1 -0.154278232706482 0.094703994807344 -1.62905728549603 0.103920813264312 df.mm.trans2 -0.304269473618802 0.094703994807344 -3.21284729580602 0.00139766238186828 ** df.mm.exp2 -0.148024355817027 0.126523280873416 -1.16993769680319 0.242574632684534 df.mm.exp3 -0.0572970873259792 0.126523280873416 -0.452858058457271 0.650844299546325 df.mm.exp4 -0.0397199997653231 0.126523280873416 -0.313934316997851 0.753699798325233 df.mm.exp5 -0.184870473336296 0.126523280873416 -1.46115775737158 0.144590392028546 df.mm.exp6 -0.0731057100472873 0.126523280873416 -0.577804413090015 0.563652191829754 df.mm.exp7 -0.120254597712337 0.126523280873416 -0.950454310718118 0.342333419051521 df.mm.exp8 -0.089890026659883 0.126523280873416 -0.710462343683738 0.477743501098227 df.mm.trans1:exp2 -0.0076902366393844 0.0985861462033779 -0.0780052465335227 0.937854594253969 df.mm.trans2:exp2 0.184118443757711 0.0985861462033779 1.86758942151856 0.0623949271056788 . df.mm.trans1:exp3 -0.0235681111960185 0.0985861462033778 -0.239061086203722 0.811154629711087 df.mm.trans2:exp3 0.0924242264334962 0.0985861462033779 0.937497102714919 0.348948400394581 df.mm.trans1:exp4 0.00865220617231098 0.0985861462033778 0.0877629008284993 0.930099709124964 df.mm.trans2:exp4 0.0409475608794488 0.0985861462033779 0.415348022580943 0.678062523695194 df.mm.trans1:exp5 0.126037211539335 0.0985861462033779 1.27844749382258 0.201675464218012 df.mm.trans2:exp5 0.135191124763969 0.0985861462033779 1.37129941650297 0.170887063010143 df.mm.trans1:exp6 0.0540540445144669 0.0985861462033779 0.548292499464948 0.583731935354844 df.mm.trans2:exp6 0.0203875810330329 0.0985861462033779 0.206799655105439 0.836249187636657 df.mm.trans1:exp7 0.0349663382660910 0.098586146203378 0.354678011187873 0.722978015012746 df.mm.trans2:exp7 0.132707644535221 0.0985861462033779 1.34610845079036 0.178867918446715 df.mm.trans1:exp8 0.0863609586710302 0.0985861462033779 0.875994873487318 0.38144683883458 df.mm.trans2:exp8 0.0612036729836168 0.0985861462033779 0.620814134040263 0.535000352727359 df.mm.trans1:probe2 -0.140187226942215 0.0686781208595914 -2.04122106411182 0.0417450828653069 * df.mm.trans1:probe3 -0.143275260183151 0.0686781208595914 -2.08618492162983 0.0374601082777677 * df.mm.trans1:probe4 -0.0437036357248370 0.0686781208595914 -0.63635456500312 0.524831957241707 df.mm.trans1:probe5 -0.100626849522087 0.0686781208595914 -1.46519514894435 0.143486303637467 df.mm.trans1:probe6 -0.0755580696675258 0.0686781208595914 -1.10017671890004 0.271776194146156 df.mm.trans2:probe2 0.0675484655731063 0.0686781208595914 0.98355145317976 0.32580429931083 df.mm.trans2:probe3 0.0535976674195349 0.0686781208595914 0.78041837412984 0.435508138645643 df.mm.trans2:probe4 -0.0523155383854478 0.0686781208595914 -0.761749706175043 0.446562896969785 df.mm.trans2:probe5 0.00531918658558739 0.0686781208595914 0.0774509628250047 0.938295295154953 df.mm.trans2:probe6 -0.0542366240979717 0.0686781208595914 -0.789722016548115 0.430058583101334 df.mm.trans3:probe2 0.0930647329185027 0.0686781208595914 1.35508560446446 0.17599253103945 df.mm.trans3:probe3 0.135283482413999 0.0686781208595914 1.96981921929079 0.0494019069190498 * df.mm.trans3:probe4 0.206661683711035 0.0686781208595914 3.00913422097764 0.00275002116944474 ** df.mm.trans3:probe5 0.171627242379251 0.0686781208595914 2.49900900361169 0.0127687439351347 * df.mm.trans3:probe6 0.0363335976785015 0.0686781208595914 0.5290418145363 0.597007524742514 df.mm.trans3:probe7 0.0688535685946 0.0686781208595914 1.00255463796639 0.316553091912912 df.mm.trans3:probe8 0.128362656913535 0.0686781208595914 1.8690473080352 0.0621914205762212 . df.mm.trans3:probe9 0.0932075359563979 0.0686781208595914 1.35716491350944 0.175331485085961 df.mm.trans3:probe10 0.217098941620111 0.0686781208595914 3.16110777206554 0.00166552434464057 ** df.mm.trans3:probe11 0.133070993211023 0.0686781208595914 1.93760387653994 0.053225449010484 .