chr2.13068_chr2_85895316_85895982_+_1.R fitVsDatCorrelation=0.753295933107451 cont.fitVsDatCorrelation=0.285809445110905 fstatistic=12759.8247572766,58,830 cont.fstatistic=6002.58316691273,58,830 residuals=-0.357895897062868,-0.0831884278390477,-0.0076271411092513,0.0691405431464577,1.28096824896911 cont.residuals=-0.401436993877498,-0.128209561176987,-0.0222349323211049,0.101637505169606,1.57269932933922 predictedValues: Include Exclude Both chr2.13068_chr2_85895316_85895982_+_1.R.tl.Lung 48.1918454102679 45.3758774040128 55.6973800680236 chr2.13068_chr2_85895316_85895982_+_1.R.tl.cerebhem 55.5444801252518 49.4337148473651 50.9028705674348 chr2.13068_chr2_85895316_85895982_+_1.R.tl.cortex 46.4561815785901 42.9771948276058 52.8157263616331 chr2.13068_chr2_85895316_85895982_+_1.R.tl.heart 45.7376916676673 46.6352645965353 55.8544562184965 chr2.13068_chr2_85895316_85895982_+_1.R.tl.kidney 45.7673985378779 41.5364463623506 54.5905061460728 chr2.13068_chr2_85895316_85895982_+_1.R.tl.liver 46.9555121857758 47.1063011494369 57.693923623296 chr2.13068_chr2_85895316_85895982_+_1.R.tl.stomach 49.3323031818322 41.9909043799498 61.5588725855501 chr2.13068_chr2_85895316_85895982_+_1.R.tl.testicle 46.4475624206709 44.3365514487782 55.2846105621241 diffExp=2.8159680062551,6.11076527788673,3.47898675098434,-0.897572928867952,4.23095217552734,-0.150788963661086,7.34139880188232,2.11101097189272 diffExpScore=1.04211572418841 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 54.2477035425634 49.8229091685721 51.724299415783 cerebhem 49.9848399747069 52.3374641474294 50.5706797474755 cortex 50.8276947810935 54.9655055237554 53.3744954337587 heart 53.6099319223285 48.6446643126875 51.6371128176591 kidney 54.1555350353725 51.6595009716479 53.1057354008025 liver 50.1224179775123 47.3455486412975 49.8417757716842 stomach 50.9425856849264 52.5814867770152 55.0539741447886 testicle 52.4696359711245 48.6335443398697 52.985688635679 cont.diffExp=4.42479437399131,-2.35262417272258,-4.13781074266188,4.96526760964095,2.49603406372454,2.77686933621474,-1.63890109208874,3.83609163125486 cont.diffExpScore=2.34204454138128 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=0.567700393608132 cont.tran.correlation=-0.254673971192463 tran.covariance=0.00213044120368715 cont.tran.covariance=-0.000420294177315931 tran.mean=46.489076882748 cont.tran.mean=51.3969355482439 weightedLogRatios: wLogRatio Lung 0.231509487202870 cerebhem 0.461416576158577 cortex 0.295759728058312 heart -0.0744846154077448 kidney 0.36618477265128 liver -0.0123463321489318 stomach 0.615181927967083 testicle 0.177456150755183 cont.weightedLogRatios: wLogRatio Lung 0.336175345916216 cerebhem -0.180968167601593 cortex -0.310519915035301 heart 0.382270438169568 kidney 0.187246880951504 liver 0.221483097473815 stomach -0.124966402903444 testicle 0.297783496450668 varWeightedLogRatios=0.0532725447448619 cont.varWeightedLogRatios=0.070716345691981 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.80741888690323 0.0630215557905327 60.4145492624477 4.23629373938487e-306 *** df.mm.trans1 -0.038619248338053 0.0535500698270376 -0.721180167697074 0.471001757290359 df.mm.trans2 -0.000685367335219662 0.0477202230589235 -0.0143621989019915 0.988544468637846 df.mm.exp2 0.317660159314119 0.0609541307303807 5.21146238175769 2.36591307324241e-07 *** df.mm.exp3 -0.0378671794490799 0.0609541307303806 -0.621240578699718 0.534611916483021 df.mm.exp4 -0.0277069286692926 0.0609541307303806 -0.454553749471863 0.649549168648925 df.mm.exp5 -0.119954048928506 0.0609541307303806 -1.96793962100946 0.0494075185693945 * df.mm.exp6 -0.0237818236594960 0.0609541307303807 -0.390159343994102 0.696518826904323 df.mm.exp7 -0.154199203014619 0.0609541307303807 -2.52975805194715 0.0115981066788155 * df.mm.exp8 -0.0525985115619894 0.0609541307303807 -0.862919558227303 0.388430963122168 df.mm.trans1:exp2 -0.175665840232029 0.0545832853772769 -3.21830829745472 0.00133954531816156 ** df.mm.trans2:exp2 -0.232008110245291 0.0403716673335677 -5.74680526143104 1.27687803788814e-08 *** df.mm.trans1:exp3 0.00118689174156439 0.0545832853772769 0.0217446006293072 0.982656912268699 df.mm.trans2:exp3 -0.0164438275161663 0.0403716673335677 -0.407311082306819 0.683884548632683 df.mm.trans1:exp4 -0.0245601747759966 0.0545832853772769 -0.449957795802102 0.652858363061097 df.mm.trans2:exp4 0.0550833054822254 0.0403716673335677 1.3644050176849 0.172809942103342 df.mm.trans1:exp5 0.0683362398817586 0.0545832853772769 1.25196274664345 0.210936126151398 df.mm.trans2:exp5 0.0315446872238881 0.0403716673335677 0.78135705823722 0.434815330963949 df.mm.trans1:exp6 -0.00220739651389471 0.0545832853772769 -0.040440887693683 0.96775135872362 df.mm.trans2:exp6 0.0612079689741524 0.0403716673335677 1.51611199181907 0.129871777062928 df.mm.trans1:exp7 0.177588482059239 0.0545832853772769 3.25353230080888 0.00118588232985028 ** df.mm.trans2:exp7 0.0766716063372056 0.0403716673335677 1.89914391456049 0.0578920490224178 . df.mm.trans1:exp8 0.0157326735725810 0.0545832853772769 0.288232440825751 0.773240778380219 df.mm.trans2:exp8 0.0294273084257426 0.0403716673335677 0.728909910571732 0.466262391479268 df.mm.trans1:probe2 0.175642480300439 0.0395493476223446 4.44109677807188 1.01609126320421e-05 *** df.mm.trans1:probe3 0.100241579532579 0.0395493476223446 2.53459502011975 0.0114404422002856 * df.mm.trans1:probe4 0.26631756625482 0.0395493476223446 6.73380427909653 3.08473432195029e-11 *** df.mm.trans1:probe5 0.406302935802334 0.0395493476223446 10.2733157492788 2.17977250928709e-23 *** df.mm.trans1:probe6 0.328057659835243 0.0395493476223446 8.29489434232532 4.3690592850384e-16 *** df.mm.trans1:probe7 0.133335907917808 0.0395493476223446 3.3713807163402 0.000782497792471908 *** df.mm.trans1:probe8 0.0587108351424291 0.0395493476223446 1.4844956660994 0.138057132005889 df.mm.trans1:probe9 0.144136067914393 0.0395493476223446 3.64446132691602 0.000284615532288517 *** df.mm.trans1:probe10 0.396164458538943 0.0395493476223446 10.0169656986989 2.25331331421856e-22 *** df.mm.trans1:probe11 0.159405303983645 0.0395493476223446 4.03054193221595 6.07649896245511e-05 *** df.mm.trans1:probe12 0.197554867685539 0.0395493476223446 4.99514858176636 7.16942295760847e-07 *** df.mm.trans1:probe13 0.0923889587722463 0.0395493476223446 2.3360425475147 0.0197262508036945 * df.mm.trans1:probe14 0.250648468507661 0.0395493476223446 6.33761322439739 3.82495190227888e-10 *** df.mm.trans1:probe15 0.0865261262592659 0.0395493476223446 2.18780160637543 0.0289622751162551 * df.mm.trans1:probe16 0.107046700954572 0.0395493476223446 2.70666161112839 0.00693593529784154 ** df.mm.trans1:probe17 0.182835471344344 0.0395493476223446 4.6229706009372 4.38516481409199e-06 *** df.mm.trans2:probe2 0.0279510019979254 0.0395493476223446 0.706737371873452 0.479928054077933 df.mm.trans2:probe3 0.101299439805628 0.0395493476223446 2.56134287657366 0.0106025079388078 * df.mm.trans2:probe4 -0.00996437968476829 0.0395493476223446 -0.251948016435513 0.801143616516365 df.mm.trans2:probe5 0.0360245323098140 0.0395493476223446 0.910875513138955 0.362625423848384 df.mm.trans2:probe6 0.0096315967234627 0.0395493476223446 0.243533643473326 0.807652203536198 df.mm.trans3:probe2 0.100241579532580 0.0395493476223446 2.53459502011975 0.0114404422002856 * df.mm.trans3:probe3 0.208750695694732 0.0395493476223446 5.27823360547145 1.66639298757681e-07 *** df.mm.trans3:probe4 0.090422903480599 0.0395493476223446 2.28633110068071 0.0224860099591141 * df.mm.trans3:probe5 0.165217165544795 0.0395493476223446 4.17749407961033 3.25953344432258e-05 *** df.mm.trans3:probe6 0.316027657957886 0.0395493476223446 7.99071734319422 4.47758072872291e-15 *** df.mm.trans3:probe7 0.236674209771197 0.0395493476223446 5.98427594890289 3.2308860006089e-09 *** df.mm.trans3:probe8 0.233580793257168 0.0395493476223446 5.90605932334519 5.10763716175678e-09 *** df.mm.trans3:probe9 0.265825960615144 0.0395493476223446 6.72137409581334 3.34492664376814e-11 *** df.mm.trans3:probe10 0.209435950752572 0.0395493476223446 5.29556018856414 1.52054984279485e-07 *** df.mm.trans3:probe11 0.659812799941623 0.0395493476223446 16.6832789820493 4.17085354088165e-54 *** df.mm.trans3:probe12 0.204597326208850 0.0395493476223446 5.1732162098486 2.88686885366875e-07 *** df.mm.trans3:probe13 0.435367425992459 0.0395493476223446 11.008207522151 2.11078590380697e-26 *** df.mm.trans3:probe14 0.399509264878628 0.0395493476223446 10.1015386825980 1.04788397934144e-22 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97793533849805 0.0918266435157618 43.3200559902339 3.02807346488886e-215 *** df.mm.trans1 0.0149690159163261 0.078026051730545 0.19184638443606 0.847909460031963 df.mm.trans2 -0.0768954244214795 0.069531573068253 -1.10590658356022 0.269087397258553 df.mm.exp2 -0.0100476345412647 0.0888142662170304 -0.113130862520576 0.909954163822828 df.mm.exp3 0.00170629370569586 0.0888142662170304 0.0192119327037651 0.98467665573344 df.mm.exp4 -0.0340720620857164 0.0888142662170304 -0.38363276010699 0.701348901456677 df.mm.exp5 0.0081414958825807 0.0888142662170304 0.0916687850878122 0.926983317908943 df.mm.exp6 -0.0930202082296639 0.0888142662170304 -1.04735660375052 0.295240033656098 df.mm.exp7 -0.0713586121436587 0.0888142662170304 -0.80345889442225 0.421939577420966 df.mm.exp8 -0.0815815890383415 0.0888142662170304 -0.918564015819093 0.358590514177964 df.mm.trans1:exp2 -0.0717932670229491 0.0795315162468783 -0.902702103655254 0.366945912641458 df.mm.trans2:exp2 0.0592851791423109 0.058824233357523 1.00783598456756 0.313826925041603 df.mm.trans1:exp3 -0.0668255753297513 0.0795315162468783 -0.840240177520497 0.401015706933912 df.mm.trans2:exp3 0.0965246207590496 0.058824233357523 1.64089891613870 0.101197260391751 df.mm.trans1:exp4 0.0222457495435298 0.0795315162468783 0.279709863376369 0.779769799623249 df.mm.trans2:exp4 0.0101392880149839 0.058824233357523 0.172365833539371 0.863191950414442 df.mm.trans1:exp5 -0.00984197145157098 0.0795315162468783 -0.123749324997400 0.901543716893378 df.mm.trans2:exp5 0.0280577301612029 0.058824233357523 0.476975704735038 0.633504978844142 df.mm.trans1:exp6 0.0139279203471173 0.0795315162468783 0.175124541871965 0.861024485800013 df.mm.trans2:exp6 0.0420181120375224 0.058824233357523 0.714299356561844 0.475242920245352 df.mm.trans1:exp7 0.00849717931622945 0.0795315162468783 0.106840403870245 0.914941411223869 df.mm.trans2:exp7 0.125247805802300 0.058824233357523 2.12918721848981 0.0335325516638613 * df.mm.trans1:exp8 0.048255568353138 0.0795315162468783 0.606747747689673 0.544184254080093 df.mm.trans2:exp8 0.0574201928944792 0.058824233357523 0.976131597763285 0.329283662672399 df.mm.trans1:probe2 -0.0447970175905266 0.05762605825646 -0.777374315473066 0.437159469012235 df.mm.trans1:probe3 -0.0480900489548677 0.05762605825646 -0.83451914654386 0.404228624744571 df.mm.trans1:probe4 0.118764853094046 0.05762605825646 2.06095743292893 0.0396181387573178 * df.mm.trans1:probe5 0.0440540508183393 0.05762605825646 0.764481419539063 0.444797689481614 df.mm.trans1:probe6 0.048204407697745 0.05762605825646 0.836503643598445 0.403112391767384 df.mm.trans1:probe7 0.0812523351201613 0.05762605825646 1.4099929368508 0.158916379633150 df.mm.trans1:probe8 -0.0347286784767706 0.05762605825646 -0.602655804119267 0.546902304784563 df.mm.trans1:probe9 -0.0613895782604127 0.05762605825646 -1.06530934299208 0.287045764582683 df.mm.trans1:probe10 0.0482838635712391 0.05762605825646 0.837882462068737 0.402337929156739 df.mm.trans1:probe11 -0.0339908245976894 0.05762605825646 -0.589851633551198 0.555450658160613 df.mm.trans1:probe12 -0.0676556208003409 0.05762605825646 -1.17404561143581 0.240713513243094 df.mm.trans1:probe13 -0.0593802611268061 0.05762605825646 -1.03044113936336 0.303103142452289 df.mm.trans1:probe14 0.0189071478197634 0.05762605825646 0.328100661260202 0.742918303360593 df.mm.trans1:probe15 0.0101393772031207 0.05762605825646 0.175951253823335 0.860375157339663 df.mm.trans1:probe16 -0.0432549029151643 0.05762605825646 -0.750613597804347 0.453097972897354 df.mm.trans1:probe17 0.0427137742273144 0.05762605825646 0.741223250724877 0.458767745797157 df.mm.trans2:probe2 0.00377109926873863 0.05762605825646 0.0654408679482408 0.947838744250315 df.mm.trans2:probe3 -0.00143243123708947 0.05762605825646 -0.0248573523928107 0.980174719400855 df.mm.trans2:probe4 0.0335992686105242 0.05762605825646 0.583056860509068 0.560013401315858 df.mm.trans2:probe5 0.100752326099266 0.05762605825646 1.74838135988542 0.0807677158051226 . df.mm.trans2:probe6 0.0120094900783088 0.05762605825646 0.208403809694246 0.834964811610004 df.mm.trans3:probe2 0.107625416585824 0.05762605825646 1.86765188947760 0.0621628425087092 . df.mm.trans3:probe3 0.085157466538997 0.05762605825646 1.47775969961386 0.139851456465644 df.mm.trans3:probe4 -0.0390672091711717 0.05762605825646 -0.677943457407868 0.497996488075259 df.mm.trans3:probe5 0.0364470664364702 0.05762605825646 0.632475438008715 0.527250443922434 df.mm.trans3:probe6 0.0685149150593098 0.05762605825646 1.18895716855021 0.234796563977302 df.mm.trans3:probe7 0.00441375549255365 0.05762605825646 0.0765930488063334 0.938965747272238 df.mm.trans3:probe8 0.0123870288818618 0.05762605825646 0.214955338897801 0.82985494289218 df.mm.trans3:probe9 -0.00780188545578776 0.05762605825646 -0.135388150636056 0.892337818790723 df.mm.trans3:probe10 0.0218196047194276 0.05762605825646 0.378641284509194 0.705051090472876 df.mm.trans3:probe11 0.0117359949144099 0.05762605825646 0.203657776872050 0.83867086022155 df.mm.trans3:probe12 0.0108923994356017 0.05762605825646 0.189018644779173 0.850124412812086 df.mm.trans3:probe13 0.0619082689462537 0.05762605825646 1.07431031757779 0.282995835147302 df.mm.trans3:probe14 0.0448296656397007 0.05762605825646 0.777940865574911 0.4368255684536