chr12.5931_chr12_51778953_51780407_+_2.R fitVsDatCorrelation=0.691242248959085 cont.fitVsDatCorrelation=0.270207541547915 fstatistic=16885.2903229521,58,830 cont.fstatistic=9505.4512536577,58,830 residuals=-0.323110242484661,-0.0750428937304737,-0.00516718502031335,0.0620141263788236,0.615389282598935 cont.residuals=-0.369959418302191,-0.0997453886187082,-0.0158018720352219,0.0803699990819065,0.628535018843319 predictedValues: Include Exclude Both chr12.5931_chr12_51778953_51780407_+_2.R.tl.Lung 42.729788733628 44.1153272072772 46.6082469971746 chr12.5931_chr12_51778953_51780407_+_2.R.tl.cerebhem 44.3642051104967 42.5004814872710 50.6328847035718 chr12.5931_chr12_51778953_51780407_+_2.R.tl.cortex 43.0370400428251 43.7956214901126 49.2386887821656 chr12.5931_chr12_51778953_51780407_+_2.R.tl.heart 44.1962855656658 45.4824219238782 49.5581919042974 chr12.5931_chr12_51778953_51780407_+_2.R.tl.kidney 43.5089300755021 45.9760887846605 49.5793374380593 chr12.5931_chr12_51778953_51780407_+_2.R.tl.liver 44.9617914367393 48.4837954616754 51.3357037667079 chr12.5931_chr12_51778953_51780407_+_2.R.tl.stomach 44.5207557064339 45.1626228155988 48.8163591955208 chr12.5931_chr12_51778953_51780407_+_2.R.tl.testicle 45.3540527620580 42.153191063061 49.9357084195499 diffExp=-1.38553847364922,1.86372362322569,-0.758581447287519,-1.28613635821242,-2.46715870915839,-3.52200402493618,-0.641867109164899,3.20086169899704 diffExpScore=2.52236553875799 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 46.7555984446843 45.8196242470839 46.4291436664318 cerebhem 45.3650546611057 45.6007052557026 43.4007824570057 cortex 47.038766653688 45.381040123895 45.1976141457878 heart 45.7550248757229 42.777497719311 47.4508602253326 kidney 44.5056614239986 49.125509488637 45.9722207977517 liver 46.2916691746415 49.9703339808697 48.5881278415123 stomach 46.5707472881512 46.8593414277201 45.14481213499 testicle 45.31031018455 48.7289980583237 44.9494893530376 cont.diffExp=0.935974197600423,-0.235650594596883,1.65772652979307,2.97752715641189,-4.61984806463834,-3.67866480622816,-0.288594139568957,-3.41868787377372 cont.diffExpScore=2.32231656299053 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=0.0581123432460876 cont.tran.correlation=-0.300163092035633 tran.covariance=4.18883459692355e-05 cont.tran.covariance=-0.000284116848086748 tran.mean=44.3963999791802 cont.tran.mean=46.3659926880053 weightedLogRatios: wLogRatio Lung -0.120331547305800 cerebhem 0.161840965741886 cortex -0.0658860328159886 heart -0.109089228744253 kidney -0.209619930574133 liver -0.289865226881023 stomach -0.0544389528920558 testicle 0.276501653031142 cont.weightedLogRatios: wLogRatio Lung 0.077545974117189 cerebhem -0.0197779630122705 cortex 0.137520358268806 heart 0.255003773831420 kidney -0.379739778939133 liver -0.296173579410334 stomach -0.0237477489747731 testicle -0.280040860357080 varWeightedLogRatios=0.0347412982796167 cont.varWeightedLogRatios=0.0523167003068902 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85639969138109 0.0539801815324113 71.4410285757486 0 *** df.mm.trans1 -0.170470007728961 0.0467332514739838 -3.64772410119722 0.000281083140559938 *** df.mm.trans2 -0.0447338652741399 0.041402895887257 -1.08045257017657 0.280254539841022 df.mm.exp2 -0.082578986020948 0.0535119108687762 -1.54318888412434 0.123166115338574 df.mm.exp3 -0.055010767741918 0.0535119108687761 -1.02800978041725 0.304244713295328 df.mm.exp4 0.00289301185831459 0.0535119108687761 0.0540629518054202 0.95689801898357 df.mm.exp5 -0.00241258815070974 0.0535119108687761 -0.0450850681939948 0.964050347676716 df.mm.exp6 0.048730104329277 0.0535119108687761 0.910640332930265 0.362749293172936 df.mm.exp7 0.0182337470328694 0.0535119108687761 0.340741841149774 0.733384165007916 df.mm.exp8 -0.0548525022167037 0.0535119108687761 -1.02505220475522 0.305637204266744 df.mm.trans1:exp2 0.120115633680016 0.049606869053327 2.42135083250048 0.0156763967433095 * df.mm.trans2:exp2 0.0452871132095326 0.0372427137902635 1.21599928148555 0.224330915945299 df.mm.trans1:exp3 0.062175603564146 0.0496068690533269 1.25336681694843 0.210425187283683 df.mm.trans2:exp3 0.04773733635835 0.0372427137902635 1.28178995298753 0.200274275417807 df.mm.trans1:exp4 0.0308514312909577 0.0496068690533269 0.62191853426171 0.534166227725064 df.mm.trans2:exp4 0.0276256303202013 0.0372427137902635 0.741772752538338 0.45843486988802 df.mm.trans1:exp5 0.0204824888412402 0.049606869053327 0.41289622248124 0.679789287018018 df.mm.trans2:exp5 0.0437267626732625 0.0372427137902635 1.17410248134748 0.240690749002532 df.mm.trans1:exp6 0.00218664015837880 0.049606869053327 0.0440793825554315 0.964851729272025 df.mm.trans2:exp6 0.0456922458034457 0.0372427137902635 1.22687745207738 0.220216553838836 df.mm.trans1:exp7 0.0228254483412681 0.0496068690533269 0.460126768265316 0.64554574288953 df.mm.trans2:exp7 0.00522879106273564 0.0372427137902635 0.140397692074271 0.888379845009098 df.mm.trans1:exp8 0.114455735665516 0.049606869053327 2.30725578634033 0.0212858399896227 * df.mm.trans2:exp8 0.00935561331772503 0.0372427137902635 0.251206541242193 0.80171660558812 df.mm.trans1:probe2 0.0811387300705397 0.0332772994062509 2.43826066171999 0.0149669090779264 * df.mm.trans1:probe3 0.0170636654146687 0.0332772994062509 0.512771941206965 0.608247295533269 df.mm.trans1:probe4 0.268932975149783 0.0332772994062509 8.0815745252232 2.25125240492730e-15 *** df.mm.trans1:probe5 0.180889201247790 0.0332772994062509 5.43581373715112 7.17518961050833e-08 *** df.mm.trans1:probe6 0.369547615248723 0.0332772994062509 11.1050963221885 8.23513649455572e-27 *** df.mm.trans1:probe7 0.0400174020655907 0.0332772994062509 1.20254355911086 0.229495940904209 df.mm.trans1:probe8 0.123857617137781 0.0332772994062509 3.72198523761563 0.000210991747407641 *** df.mm.trans1:probe9 0.225425832376028 0.0332772994062509 6.77416245903907 2.36945502640161e-11 *** df.mm.trans1:probe10 0.122732748562275 0.0332772994062509 3.6881823571062 0.000240566338999231 *** df.mm.trans1:probe11 0.0318801814461357 0.0332772994062509 0.958015885151643 0.338333715706337 df.mm.trans1:probe12 0.0706703604435766 0.0332772994062509 2.12368075849032 0.0339920558023143 * df.mm.trans1:probe13 0.0726138645752688 0.0332772994062509 2.18208405943028 0.0293830891211511 * df.mm.trans1:probe14 0.0977532288008603 0.0332772994062509 2.93753491253855 0.00339987740664348 ** df.mm.trans1:probe15 0.037557241293821 0.0332772994062509 1.12861445982501 0.259386614424496 df.mm.trans1:probe16 0.0609205263284237 0.0332772994062509 1.83069321776094 0.0675047452819166 . df.mm.trans1:probe17 0.0779614957077156 0.0332772994062509 2.34278313140612 0.0193758465041129 * df.mm.trans1:probe18 0.0505833847304302 0.0332772994062509 1.52005678444353 0.128877535456229 df.mm.trans1:probe19 0.0224529793446784 0.0332772994062509 0.674723602735047 0.500039279934835 df.mm.trans1:probe20 0.0257167745637187 0.0332772994062509 0.772802331396159 0.439859384003692 df.mm.trans1:probe21 0.077299387138935 0.0332772994062509 2.32288642762925 0.0204261606199504 * df.mm.trans1:probe22 0.0829500633799616 0.0332772994062509 2.49269216132305 0.0128717898528758 * df.mm.trans2:probe2 -0.0970535902312925 0.0332772994062509 -2.91651041289311 0.0036351113644873 ** df.mm.trans2:probe3 -0.117863460477541 0.0332772994062509 -3.54185774027688 0.000419469200379182 *** df.mm.trans2:probe4 -0.0529024878143166 0.0332772994062509 -1.58974702750005 0.112272643700099 df.mm.trans2:probe5 -0.0516914058067547 0.0332772994062509 -1.55335338891848 0.120719932135573 df.mm.trans2:probe6 -0.0533672804263222 0.0332772994062509 -1.60371428506898 0.109157479545011 df.mm.trans3:probe2 0.0254027088718997 0.0332772994062509 0.763364495471288 0.445462966283996 df.mm.trans3:probe3 0.185277918319380 0.0332772994062509 5.56769694732431 3.48620708448214e-08 *** df.mm.trans3:probe4 0.214833709813773 0.0332772994062509 6.4558637163152 1.82916538115867e-10 *** df.mm.trans3:probe5 0.368942758178525 0.0332772994062509 11.0869200554544 9.8300295773783e-27 *** df.mm.trans3:probe6 0.168647745936346 0.0332772994062509 5.06795169516271 4.95937022433061e-07 *** df.mm.trans3:probe7 0.377128022474544 0.0332772994062509 11.3328914666586 8.79852990439217e-28 *** df.mm.trans3:probe8 0.265393737701292 0.0332772994062509 7.97521861558993 5.03157421296697e-15 *** df.mm.trans3:probe9 0.199608530258596 0.0332772994062509 5.99833922283671 2.97384959777042e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81969337343692 0.0719216306953838 53.1091041249442 3.43171935177776e-269 *** df.mm.trans1 0.0229655508624563 0.0622660309448615 0.368829528941599 0.712348853501994 df.mm.trans2 0.0195078533802374 0.0551640195195488 0.353633646535207 0.72370326067818 df.mm.exp2 0.0324686807349492 0.0712977204235143 0.455395776219528 0.648943637259397 df.mm.exp3 0.0233030630627001 0.0712977204235143 0.326841628656260 0.743870076307781 df.mm.exp4 -0.112099944265003 0.0712977204235143 -1.57227950065051 0.116266843304107 df.mm.exp5 0.0302384076958576 0.0712977204235143 0.424114649335756 0.671592099278236 df.mm.exp6 0.0312931850312925 0.0712977204235143 0.438908633339305 0.660841970137473 df.mm.exp7 0.0465284925509534 0.0712977204235143 0.65259439256361 0.514198500317028 df.mm.exp8 0.0625503800927678 0.0712977204235143 0.877312482379708 0.380570796576445 df.mm.trans1:exp2 -0.0626605945884383 0.0660947557922796 -0.948041850481541 0.343384086633459 df.mm.trans2:exp2 -0.0372579745610218 0.0496211133656327 -0.750849225943135 0.452956215131704 df.mm.trans1:exp3 -0.0172649803051934 0.0660947557922796 -0.261215585082926 0.793991061073027 df.mm.trans2:exp3 -0.0329211400193087 0.0496211133656326 -0.663450249024636 0.507226456893267 df.mm.trans1:exp4 0.0904675619120568 0.0660947557922796 1.368755521185 0.171446034144429 df.mm.trans2:exp4 0.0433996780131926 0.0496211133656327 0.874621205965342 0.382033050763115 df.mm.trans1:exp5 -0.0795560049969891 0.0660947557922796 -1.20366591938119 0.229061905885102 df.mm.trans2:exp5 0.0394275573491067 0.0496211133656327 0.794572202735259 0.427089524479836 df.mm.trans1:exp6 -0.0412651729543045 0.0660947557922796 -0.624333541438467 0.532580127891183 df.mm.trans2:exp6 0.0554238475694129 0.0496211133656327 1.11694083042883 0.264342846869083 df.mm.trans1:exp7 -0.0504898905209605 0.0660947557922796 -0.763901612400815 0.445142970843745 df.mm.trans2:exp7 -0.0240905899699685 0.0496211133656327 -0.48549071828472 0.627456420302011 df.mm.trans1:exp8 -0.0939497769996857 0.0660947557922796 -1.42144071603726 0.155564290747846 df.mm.trans2:exp8 -0.000988560894465891 0.0496211133656327 -0.0199221828656219 0.984110237347455 df.mm.trans1:probe2 0.00898697833597192 0.0443377100723355 0.202693786424918 0.839424055281072 df.mm.trans1:probe3 0.000244643737833060 0.0443377100723355 0.00551773507097979 0.99559883259461 df.mm.trans1:probe4 -0.0220175347655212 0.0443377100723355 -0.496587097745921 0.619611744166468 df.mm.trans1:probe5 0.0109221614390378 0.0443377100723355 0.246340224184304 0.805479783128944 df.mm.trans1:probe6 0.00377299536874076 0.0443377100723355 0.0850967576490812 0.932204989535901 df.mm.trans1:probe7 -0.0302101879899569 0.0443377100723355 -0.681365545055665 0.495830278498276 df.mm.trans1:probe8 -0.037021898076627 0.0443377100723355 -0.834997973874315 0.403959126159184 df.mm.trans1:probe9 0.0331840406868857 0.0443377100723355 0.74843830754333 0.454407847743499 df.mm.trans1:probe10 0.0281518682617071 0.0443377100723355 0.634941863614026 0.525641318778758 df.mm.trans1:probe11 0.0639305227823772 0.0443377100723355 1.44189951799668 0.149707923890578 df.mm.trans1:probe12 0.0303818957032941 0.0443377100723355 0.685238269042922 0.493384902375782 df.mm.trans1:probe13 -0.0436193848239485 0.0443377100723355 -0.983798774289084 0.325501147090296 df.mm.trans1:probe14 -0.0019639974424513 0.0443377100723355 -0.0442963211056029 0.96467885853793 df.mm.trans1:probe15 0.0382198465326155 0.0443377100723355 0.86201670023691 0.388927305384029 df.mm.trans1:probe16 0.0498187384437626 0.0443377100723355 1.12362001471174 0.261499140803195 df.mm.trans1:probe17 -0.0436274089652467 0.0443377100723355 -0.983979752090714 0.325412207088457 df.mm.trans1:probe18 -0.0179480551544477 0.0443377100723355 -0.404803385767241 0.685726341486224 df.mm.trans1:probe19 0.000115642876946195 0.0443377100723355 0.00260822845287967 0.997919563877339 df.mm.trans1:probe20 0.0539685330265967 0.0443377100723355 1.21721516376351 0.223868329062442 df.mm.trans1:probe21 -0.00363145316981269 0.0443377100723355 -0.0819043916315951 0.934742488667331 df.mm.trans1:probe22 -0.0511305569249389 0.0443377100723355 -1.15320698433728 0.249157425687143 df.mm.trans2:probe2 -0.0324155736151728 0.0443377100723355 -0.731106174908172 0.464920642539579 df.mm.trans2:probe3 -0.0770264306743021 0.0443377100723355 -1.73726677694080 0.0827110473280727 . df.mm.trans2:probe4 -0.0640202418965733 0.0443377100723355 -1.44392305764385 0.149137964278653 df.mm.trans2:probe5 -0.0342246936894709 0.0443377100723355 -0.771909366397915 0.440387827528178 df.mm.trans2:probe6 -0.00964431697219705 0.0443377100723355 -0.217519510061811 0.827856964921908 df.mm.trans3:probe2 -0.0532768381066427 0.0443377100723355 -1.20161456285684 0.229855642072381 df.mm.trans3:probe3 -0.0396870843098773 0.0443377100723355 -0.895109022209967 0.37098831388205 df.mm.trans3:probe4 -0.00946549850877274 0.0443377100723355 -0.213486409048417 0.831000016816264 df.mm.trans3:probe5 -0.0313778458684272 0.0443377100723355 -0.70770109275457 0.479329567725773 df.mm.trans3:probe6 -0.046582309055375 0.0443377100723355 -1.05062505436969 0.293736625534636 df.mm.trans3:probe7 -0.0497347942625436 0.0443377100723355 -1.12172672385207 0.262303061666032 df.mm.trans3:probe8 -0.0181106666163979 0.0443377100723355 -0.408470951405721 0.683033311784349 df.mm.trans3:probe9 0.0300802625060657 0.0443377100723355 0.67843518433836 0.49768491082504