chrX.25723_chrX_14724185_14735715_-_2.R fitVsDatCorrelation=0.903485036769058 cont.fitVsDatCorrelation=0.23313371917977 fstatistic=12824.0647496288,67,1037 cont.fstatistic=2478.90945234560,67,1037 residuals=-0.598030490395298,-0.0792776230246233,-0.00171708514107234,0.0763651135150377,0.964250485535618 cont.residuals=-0.70215443612939,-0.219978585867100,-0.0678164589837066,0.139756633848983,1.65539333637067 predictedValues: Include Exclude Both chrX.25723_chrX_14724185_14735715_-_2.R.tl.Lung 55.2835698506652 44.2393147970972 69.68701073845 chrX.25723_chrX_14724185_14735715_-_2.R.tl.cerebhem 55.8331413443868 46.8146376184853 82.4903109469542 chrX.25723_chrX_14724185_14735715_-_2.R.tl.cortex 68.4032736957803 45.8857152997185 74.5131403356283 chrX.25723_chrX_14724185_14735715_-_2.R.tl.heart 61.9802041205731 48.6995525295415 70.1189173200591 chrX.25723_chrX_14724185_14735715_-_2.R.tl.kidney 58.1794387165594 45.8912512346905 70.1513304806301 chrX.25723_chrX_14724185_14735715_-_2.R.tl.liver 52.9545865397293 48.5366665854545 65.3518167437014 chrX.25723_chrX_14724185_14735715_-_2.R.tl.stomach 55.1485687750964 46.6037520852162 69.2464379233713 chrX.25723_chrX_14724185_14735715_-_2.R.tl.testicle 57.3996496638775 44.3794859313264 70.0045837842286 diffExp=11.044255053568,9.01850372590154,22.5175583960618,13.2806515910316,12.2881874818689,4.41791995427479,8.54481668988016,13.0201637325511 diffExpScore=0.98948829621186 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 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=1,0,1,1,1,0,0,1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 58.5201827306168 69.0054110358232 61.8910459651192 cerebhem 64.5770115939659 70.271183419291 67.0607816908383 cortex 59.9378772939066 66.827141535443 62.5630673253568 heart 60.1672478796541 75.758544480346 58.111165527155 kidney 62.768563611993 61.3883056503158 56.7795134454387 liver 62.4589130139461 66.8790234928178 64.8901040070041 stomach 61.5058771933204 68.2389483079041 61.8008775170342 testicle 62.3944154351563 65.076393788569 63.4310301750021 cont.diffExp=-10.4852283052065,-5.69417182532509,-6.88926424153644,-15.5912966006920,1.38025796167719,-4.42011047887165,-6.73307111458371,-2.68197835341273 cont.diffExpScore=1.03378145548948 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,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.0251913700135389 cont.tran.correlation=-0.295746346313626 tran.covariance=-8.17039606844229e-05 cont.tran.covariance=-0.000574160744461031 tran.mean=52.2645505492624 cont.tran.mean=64.7359400289418 weightedLogRatios: wLogRatio Lung 0.869394204961928 cerebhem 0.693109109693826 cortex 1.60736331912944 heart 0.96608615113265 kidney 0.935957928830565 liver 0.342003428431654 stomach 0.660916820711433 testicle 1.00882475726709 cont.weightedLogRatios: wLogRatio Lung -0.684267967241808 cerebhem -0.355768269557327 cortex -0.451273478512409 heart -0.970620316938176 kidney 0.0917936255448767 liver -0.285040583563199 stomach -0.433302678635166 testicle -0.174847468421246 varWeightedLogRatios=0.132633648830321 cont.varWeightedLogRatios=0.102482759503368 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74353507507989 0.0668748804869184 55.9781946199092 0 *** df.mm.trans1 0.231060596079671 0.057119670274682 4.04520185373141 5.61556651769243e-05 *** df.mm.trans2 0.040128723773852 0.0498404273045626 0.805144055620456 0.42092111817004 df.mm.exp2 -0.102192978683866 0.0626898583234968 -1.63013574151855 0.103376487604669 df.mm.exp3 0.182523366534386 0.0626898583234968 2.91152941505332 0.00367363895974521 ** df.mm.exp4 0.204216584940650 0.0626898583234968 3.25756973140435 0.00116015516971264 ** df.mm.exp5 0.081076044119763 0.0626898583234968 1.29328804192519 0.196199473158841 df.mm.exp6 0.113893290049085 0.0626898583234968 1.81677376684063 0.0695403344298668 . df.mm.exp7 0.0559644598565373 0.0626898583234968 0.892719514020042 0.372214507238740 df.mm.exp8 0.0361791285115201 0.0626898583234968 0.577112941057004 0.563988424654332 df.mm.trans1:exp2 0.112084847675643 0.057119670274682 1.96228106949218 0.0499968734913065 * df.mm.trans2:exp2 0.158775033548410 0.0385100738431363 4.12294804198898 4.03944378656363e-05 *** df.mm.trans1:exp3 0.0304215630069092 0.057119670274682 0.532593463173288 0.594429127623315 df.mm.trans2:exp3 -0.14598338020121 0.0385100738431363 -3.79078421910709 0.000158830840216053 *** df.mm.trans1:exp4 -0.0898772942481234 0.057119670274682 -1.57349112513980 0.115910167257102 df.mm.trans2:exp4 -0.108160611982009 0.0385100738431363 -2.80863164330928 0.00506866926385721 ** df.mm.trans1:exp5 -0.0300197934511831 0.057119670274682 -0.525559641833038 0.599306561108029 df.mm.trans2:exp5 -0.0444154189119793 0.0385100738431363 -1.15334546209642 0.249034378721286 df.mm.trans1:exp6 -0.156934356541676 0.057119670274682 -2.74746607932078 0.00610988552890136 ** df.mm.trans2:exp6 -0.0211876344501826 0.0385100738431363 -0.550184207293045 0.582311517364684 df.mm.trans1:exp7 -0.0584094209792693 0.057119670274682 -1.02257979953990 0.306745050985556 df.mm.trans2:exp7 -0.0038972738987005 0.0385100738431363 -0.101201413286698 0.919410137884128 df.mm.trans1:exp8 0.0013833163872824 0.057119670274682 0.0242178636646568 0.980683488372467 df.mm.trans2:exp8 -0.0330156631977489 0.0385100738431363 -0.85732536718138 0.391463177292742 df.mm.trans1:probe2 0.0170620709555967 0.0428397527060115 0.398276597735879 0.690508292041833 df.mm.trans1:probe3 0.633966002665936 0.0428397527060115 14.7985448706144 4.23615157802437e-45 *** df.mm.trans1:probe4 0.570156838999984 0.0428397527060115 13.3090599965106 1.99114033948938e-37 *** df.mm.trans1:probe5 -0.140507953890767 0.0428397527060115 -3.27984978940016 0.00107318299320829 ** df.mm.trans1:probe6 0.239840226745796 0.0428397527060115 5.59854367955163 2.76688527414886e-08 *** df.mm.trans1:probe7 0.221073193891159 0.0428397527060115 5.16046848842191 2.95087919951709e-07 *** df.mm.trans1:probe8 0.0350271217005231 0.0428397527060115 0.817631276746562 0.413755578977741 df.mm.trans1:probe9 -0.176680993406313 0.0428397527060115 -4.12423000241831 4.01737616813125e-05 *** df.mm.trans1:probe10 0.0361217891557815 0.0428397527060115 0.843183885856389 0.399320050466635 df.mm.trans1:probe11 -0.0957842783434759 0.0428397527060115 -2.23587374560253 0.0255721655018107 * df.mm.trans1:probe12 -0.077456171388472 0.0428397527060115 -1.80804431622228 0.0708892196099211 . df.mm.trans1:probe13 -0.139011661419231 0.0428397527060115 -3.24492212579285 0.00121238420287840 ** df.mm.trans1:probe14 -0.15986957466453 0.0428397527060115 -3.73180433046936 0.00020043304755142 *** df.mm.trans1:probe15 -0.0983925169938176 0.0428397527060115 -2.29675735219664 0.0218312086435786 * df.mm.trans1:probe16 -0.144608597408795 0.0428397527060115 -3.37557031202244 0.000763935061468557 *** df.mm.trans1:probe17 0.103362088032016 0.0428397527060115 2.41276108060986 0.0160048604625096 * df.mm.trans1:probe18 0.0626357057180265 0.0428397527060115 1.46209307387615 0.144018676523749 df.mm.trans1:probe19 0.120045067208126 0.0428397527060115 2.80218861280404 0.00517022908843385 ** df.mm.trans1:probe20 0.0897349725825975 0.0428397527060115 2.09466597994637 0.0364428848822048 * df.mm.trans1:probe21 0.123364473163955 0.0428397527060115 2.87967285923767 0.00406274502163483 ** df.mm.trans1:probe22 0.295125549966599 0.0428397527060115 6.88905820703269 9.72619975016996e-12 *** df.mm.trans2:probe2 -0.0054293875877715 0.0428397527060115 -0.126737136533695 0.89917302095825 df.mm.trans2:probe3 0.0431691458479068 0.0428397527060115 1.00768895992831 0.313838874066683 df.mm.trans2:probe4 0.0471729213875413 0.0428397527060115 1.10114831220587 0.271087635242412 df.mm.trans2:probe5 0.0430281875795834 0.0428397527060115 1.00439859853685 0.315420829927092 df.mm.trans2:probe6 0.0148608110651113 0.0428397527060115 0.346893017032424 0.728742074100135 df.mm.trans3:probe2 0.344120005236114 0.0428397527060115 8.03272622971574 2.57559438014167e-15 *** df.mm.trans3:probe3 -0.0831371250793002 0.0428397527060115 -1.94065371128144 0.0525710344367459 . df.mm.trans3:probe4 0.535087282449155 0.0428397527060115 12.4904381713219 1.87968643945695e-33 *** df.mm.trans3:probe5 0.406102645561571 0.0428397527060115 9.4795749253843 1.67740296823990e-20 *** df.mm.trans3:probe6 0.401429630604812 0.0428397527060115 9.37049364779553 4.36943456320546e-20 *** df.mm.trans3:probe7 -0.215393772974645 0.0428397527060115 -5.02789487261489 5.83957579340705e-07 *** df.mm.trans3:probe8 -0.112019505121227 0.0428397527060115 -2.6148494808073 0.00905619067965794 ** df.mm.trans3:probe9 -0.0670127142718432 0.0428397527060115 -1.56426473167852 0.118060504910485 df.mm.trans3:probe10 0.194082773613719 0.0428397527060115 4.53043636702609 6.56975614656818e-06 *** df.mm.trans3:probe11 0.304470823150278 0.0428397527060115 7.10720309801305 2.20001959249005e-12 *** df.mm.trans3:probe12 0.545995252537846 0.0428397527060115 12.7450607916611 1.14031664382769e-34 *** df.mm.trans3:probe13 0.179184883664827 0.0428397527060115 4.18267782483448 3.12454981714074e-05 *** df.mm.trans3:probe14 -0.0166371323771533 0.0428397527060115 -0.388357339299456 0.697831323172686 df.mm.trans3:probe15 0.068770027395572 0.0428397527060115 1.60528534950954 0.108735485679540 df.mm.trans3:probe16 1.30933161750442 0.0428397527060115 30.5634728213707 8.46307515319757e-147 *** df.mm.trans3:probe17 -0.0422147878085169 0.0428397527060115 -0.985411566173517 0.324651804617064 df.mm.trans3:probe18 0.37505192216546 0.0428397527060115 8.75476393944802 8.17516509349926e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16752332011684 0.151724553577211 27.4676920897714 2.97015261491422e-125 *** df.mm.trans1 -0.0940146773054402 0.129592104087556 -0.725466091992159 0.468329818269826 df.mm.trans2 0.0780611761391776 0.113077085563710 0.690335939859328 0.490137423977845 df.mm.exp2 0.0364397932995266 0.142229648841193 0.256203918074871 0.797844198060574 df.mm.exp3 -0.0189382743406719 0.142229648841193 -0.133152788430333 0.894098381021993 df.mm.exp4 0.184140457401252 0.142229648841193 1.29466998548843 0.195722294279821 df.mm.exp5 0.0393170371047457 0.142229648841193 0.276433482224549 0.78227022679534 df.mm.exp6 -0.0134819198914624 0.142229648841193 -0.0947897994637932 0.924500101577875 df.mm.exp7 0.0400495570833193 0.142229648841193 0.28158374438537 0.77831888510012 df.mm.exp8 -0.0190966376811648 0.142229648841193 -0.134266222526411 0.893218115002871 df.mm.trans1:exp2 0.0620469977983117 0.129592104087556 0.478786869270917 0.632191192859587 df.mm.trans2:exp2 -0.0182629095371629 0.0873709787521523 -0.20902718268694 0.834468070633162 df.mm.trans1:exp3 0.0428752230735636 0.129592104087556 0.330847495497071 0.740826519655598 df.mm.trans2:exp3 -0.0131373395591669 0.0873709787521523 -0.150362737682428 0.880507678758429 df.mm.trans1:exp4 -0.156384006973235 0.129592104087556 -1.20674024142379 0.227807400757 df.mm.trans2:exp4 -0.0907741432841676 0.0873709787521523 -1.03895074291967 0.299069825128538 df.mm.trans1:exp5 0.0307656329310333 0.129592104087556 0.237403606860548 0.812390584794666 df.mm.trans2:exp5 -0.156282604144568 0.0873709787521523 -1.78872442974341 0.0739510211827342 . df.mm.trans1:exp6 0.0786191700004323 0.129592104087556 0.606666359451307 0.544205139912493 df.mm.trans2:exp6 -0.0178176346551286 0.0873709787521523 -0.203930812148419 0.838447578865441 df.mm.trans1:exp7 0.0097114786024212 0.129592104087556 0.074938814141484 0.940277847484472 df.mm.trans2:exp7 -0.0512189880163166 0.0873709787521523 -0.586224267460834 0.557852388958946 df.mm.trans1:exp8 0.0832007141402476 0.129592104087556 0.642019934208607 0.52100213340746 df.mm.trans2:exp8 -0.0395264158113447 0.0873709787521523 -0.452397539501879 0.651077239205842 df.mm.trans1:probe2 -0.112553689085479 0.0971940780656669 -1.15803031754090 0.247118298003385 df.mm.trans1:probe3 -0.056856302141382 0.0971940780656669 -0.584977019926753 0.558690424845216 df.mm.trans1:probe4 0.0704040902805865 0.0971940780656669 0.724366048649791 0.469004427759286 df.mm.trans1:probe5 -0.0138023165769489 0.0971940780656669 -0.142007793598533 0.887101433358856 df.mm.trans1:probe6 0.0870523454165785 0.0971940780656669 0.895654829482138 0.370644993656305 df.mm.trans1:probe7 -0.0543240339461799 0.0971940780656669 -0.558923290670828 0.576334747407972 df.mm.trans1:probe8 0.0447313094935873 0.0971940780656669 0.460226696768148 0.645449993466141 df.mm.trans1:probe9 0.0117097393153862 0.097194078065667 0.120477909235116 0.904127915978165 df.mm.trans1:probe10 -0.142140360120192 0.0971940780656669 -1.46243848338330 0.143924076430764 df.mm.trans1:probe11 0.106810878679353 0.097194078065667 1.09894430612520 0.272047428808492 df.mm.trans1:probe12 -0.0131372209346245 0.0971940780656669 -0.135164828928658 0.892507784862577 df.mm.trans1:probe13 -0.0254299976826068 0.0971940780656669 -0.261641431131489 0.793649855955835 df.mm.trans1:probe14 -0.0123441693010109 0.0971940780656669 -0.127005364387230 0.898960774417488 df.mm.trans1:probe15 -0.0823509507541932 0.0971940780656669 -0.847283624610901 0.3970325511607 df.mm.trans1:probe16 -0.00496995801871111 0.097194078065667 -0.0511343707108707 0.959228295486088 df.mm.trans1:probe17 0.0131661990943835 0.097194078065667 0.135462976308990 0.892272124454508 df.mm.trans1:probe18 0.0510915978104187 0.0971940780656669 0.525665748646743 0.599232849175992 df.mm.trans1:probe19 -0.0270866259658321 0.0971940780656669 -0.278685970430541 0.780541392380755 df.mm.trans1:probe20 -0.0732153588156837 0.0971940780656669 -0.753290326661852 0.451446453421084 df.mm.trans1:probe21 0.0537166485432799 0.0971940780656669 0.552674088919157 0.580605705439502 df.mm.trans1:probe22 0.0140504094453934 0.0971940780656669 0.144560344879248 0.885086097817987 df.mm.trans2:probe2 -0.128476807172039 0.0971940780656669 -1.32185838611728 0.186506827327800 df.mm.trans2:probe3 -0.0887590526957303 0.0971940780656669 -0.91321461618024 0.361341941046772 df.mm.trans2:probe4 0.0443958565074055 0.0971940780656669 0.456775324083125 0.647928204445389 df.mm.trans2:probe5 -0.0614433337671326 0.0971940780656669 -0.632171578659555 0.527414113951493 df.mm.trans2:probe6 -0.0393064370599576 0.097194078065667 -0.404411851444294 0.685993280763119 df.mm.trans3:probe2 -0.0572569403143397 0.0971940780656669 -0.589099062966114 0.555923126025544 df.mm.trans3:probe3 0.0439490724732867 0.0971940780656669 0.452178500459602 0.65123496322517 df.mm.trans3:probe4 -0.0196248513138496 0.0971940780656669 -0.201914064153070 0.840023508768367 df.mm.trans3:probe5 -0.0386906849822429 0.0971940780656669 -0.398076567546661 0.690655683671703 df.mm.trans3:probe6 -0.112867117966958 0.0971940780656669 -1.16125509098098 0.245805405877465 df.mm.trans3:probe7 -0.0446410077973436 0.0971940780656669 -0.45929761036658 0.646116725035818 df.mm.trans3:probe8 0.133030917593809 0.0971940780656669 1.36871422869950 0.171385038028631 df.mm.trans3:probe9 -0.0277909621803049 0.0971940780656669 -0.285932669288025 0.774986797916418 df.mm.trans3:probe10 -0.0149782114851244 0.0971940780656669 -0.15410621493837 0.877555973045403 df.mm.trans3:probe11 0.0560335828563945 0.0971940780656669 0.576512314037659 0.564394058778682 df.mm.trans3:probe12 -0.0645326992706429 0.0971940780656669 -0.663957110916191 0.506865350596532 df.mm.trans3:probe13 -0.0723968451576679 0.0971940780656669 -0.744868891176216 0.456519755199602 df.mm.trans3:probe14 0.0207297305199837 0.0971940780656669 0.213281826758809 0.831149069663529 df.mm.trans3:probe15 0.0120781019838520 0.0971940780656669 0.124267879527513 0.901127261485586 df.mm.trans3:probe16 -0.142643668813100 0.097194078065667 -1.46761687185022 0.142511542868643 df.mm.trans3:probe17 -0.088621062181017 0.0971940780656669 -0.911794874180937 0.362088616992951 df.mm.trans3:probe18 -0.053274807108046 0.097194078065667 -0.548128118176626 0.583721903365032