chr1.919_chr1_36941563_36945564_-_1.R 

fitVsDatCorrelation=0.89872009066316
cont.fitVsDatCorrelation=0.204862835759098

fstatistic=15094.2616018457,75,1221
cont.fstatistic=3016.80526534923,75,1221

residuals=-0.502134344671547,-0.0811803749655245,-0.000607176819343898,0.0805745500106353,0.729821784734267
cont.residuals=-0.694292618165463,-0.236575172320668,-0.0212271561147302,0.201895112754451,1.27355245910532

predictedValues:
Include	Exclude	Both
chr1.919_chr1_36941563_36945564_-_1.R.tl.Lung	63.4417885046837	46.6102954951113	67.523717736161
chr1.919_chr1_36941563_36945564_-_1.R.tl.cerebhem	65.6858151793331	50.6292686469779	69.676909412216
chr1.919_chr1_36941563_36945564_-_1.R.tl.cortex	70.0111691947099	45.2694990291685	77.638930776007
chr1.919_chr1_36941563_36945564_-_1.R.tl.heart	65.0890799871566	45.1656661709739	73.5293309055738
chr1.919_chr1_36941563_36945564_-_1.R.tl.kidney	65.6222844034749	45.6844932521394	71.7270377222204
chr1.919_chr1_36941563_36945564_-_1.R.tl.liver	58.4936152327372	49.6042031501857	58.7168151571364
chr1.919_chr1_36941563_36945564_-_1.R.tl.stomach	60.5723013934458	46.3505810039967	64.3941953605015
chr1.919_chr1_36941563_36945564_-_1.R.tl.testicle	60.9483729638069	49.6131301233975	66.5096751409029


diffExp=16.8314930095724,15.0565465323553,24.7416701655413,19.9234138161826,19.9377911513355,8.88941208255146,14.2217203894491,11.3352428404094
diffExpScore=0.99242064165411
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,1,1,0,0,0
diffExp1.4Score=0.75
diffExp1.3=1,0,1,1,1,0,1,0
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	69.8318815988698	66.7705638653717	68.1894566816713
cerebhem	70.2187777483342	60.806058480939	65.2477509877668
cortex	68.4090822345144	63.2978886844499	67.166889506704
heart	70.1614265332214	64.3682488635245	69.4857572722494
kidney	71.0512651271931	61.2069581934526	65.8469214591268
liver	71.0749785766116	64.5193704542531	69.2800391864775
stomach	69.8560141000745	59.5962139261277	69.2025301480339
testicle	73.3712375076415	58.4236593775028	66.567756386743
cont.diffExp=3.06131773349811,9.41271926739523,5.11119355006451,5.79317766969683,9.84430693374048,6.55560812235848,10.2598001739468,14.9475781301388
cont.diffExpScore=0.984845201671836

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=0,0,0,0,0,0,0,1
cont.diffExp1.2Score=0.5

tran.correlation=-0.48408428175568
cont.tran.correlation=-0.497421132086241

tran.covariance=-0.00130949320835544
cont.tran.covariance=-0.000458403797445477

tran.mean=55.5494727332062
cont.tran.mean=66.4352265795051

weightedLogRatios:
wLogRatio
Lung	1.23196347163959
cerebhem	1.05565562306160
cortex	1.75744667511294
heart	1.45913760753802
kidney	1.44965314695646
liver	0.657142213797974
stomach	1.06239701108764
testicle	0.82455605339613

cont.weightedLogRatios:
wLogRatio
Lung	0.18934040243737
cerebhem	0.601562077729511
cortex	0.325110774135065
heart	0.362612833463174
kidney	0.624725569193335
liver	0.407918871760519
stomach	0.66190575212749
testicle	0.952620779371698

varWeightedLogRatios=0.130766905429743
cont.varWeightedLogRatios=0.0584888639682416

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.66731065529983	0.0643884962996554	56.9559916142887	0	***
df.mm.trans1	0.373551424520509	0.0535684783128594	6.97334395684778	5.05712850059102e-12	***
df.mm.trans2	0.182968126550345	0.047982694184577	3.81321077650441	0.000144024220790158	***
df.mm.exp2	0.0860785534913187	0.0603472860183658	1.42638649010863	0.154012442715769	   
df.mm.exp3	-0.0702460113665248	0.0603472860183659	-1.16402933754380	0.244639522793309	   
df.mm.exp4	-0.0910557023998834	0.0603472860183659	-1.50886159772242	0.131592860694560	   
df.mm.exp5	-0.0466588170279861	0.0603472860183659	-0.773171754795836	0.439570390581561	   
df.mm.exp6	0.120801723918139	0.0603472860183659	2.00177558741208	0.0455297771662928	*  
df.mm.exp7	-0.00441726101131657	0.0603472860183658	-0.0731973432901727	0.941661069171475	   
df.mm.exp8	0.03746994290826	0.0603472860183658	0.620905186968251	0.534777892870056	   
df.mm.trans1:exp2	-0.0513183232086913	0.0516660283219356	-0.993270140466811	0.320775120816909	   
df.mm.trans2:exp2	-0.00337016276751387	0.0370516291086351	-0.0909585583303929	0.927540439343683	   
df.mm.trans1:exp3	0.168778031616513	0.0516660283219357	3.26671194009421	0.00111828920327863	** 
df.mm.trans2:exp3	0.0410580563648358	0.0370516291086351	1.10813093385054	0.268023506375430	   
df.mm.trans1:exp4	0.116689726415883	0.0516660283219356	2.25853873823587	0.0240879573616852	*  
df.mm.trans2:exp4	0.059571452509134	0.0370516291086351	1.60779576883032	0.108138446700540	   
df.mm.trans1:exp5	0.0804513875696312	0.0516660283219356	1.55714286897246	0.119695712536095	   
df.mm.trans2:exp5	0.0265962911007447	0.0370516291086351	0.717817049899872	0.473007411446969	   
df.mm.trans1:exp6	-0.202006885952875	0.0516660283219356	-3.90985900240196	9.74293175006558e-05	***
df.mm.trans2:exp6	-0.0585476029473084	0.0370516291086351	-1.58016271769447	0.114328607740338	   
df.mm.trans1:exp7	-0.0418677921138724	0.0516660283219356	-0.810354375470677	0.417894440629204	   
df.mm.trans2:exp7	-0.00117036201072895	0.0370516291086351	-0.0315873293262614	0.974806310452305	   
df.mm.trans1:exp8	-0.0775655509380549	0.0516660283219356	-1.50128727632666	0.133539819836279	   
df.mm.trans2:exp8	0.0249641259192873	0.0370516291086351	0.67376594551599	0.500587650343394	   
df.mm.trans1:probe2	-0.114710729708446	0.041252015497839	-2.78073030672781	0.00550703506822706	** 
df.mm.trans1:probe3	0.272926172299060	0.041252015497839	6.61606879095052	5.50636667764684e-11	***
df.mm.trans1:probe4	-0.151480800172753	0.041252015497839	-3.67208240239047	0.000250982455036393	***
df.mm.trans1:probe5	0.188357936114618	0.041252015497839	4.56602989796912	5.47302843710941e-06	***
df.mm.trans1:probe6	0.169898919119073	0.041252015497839	4.11856044047043	4.06904815323449e-05	***
df.mm.trans1:probe7	0.0113563593875478	0.041252015497839	0.275292231191533	0.783138310535104	   
df.mm.trans1:probe8	0.304304498749072	0.041252015497839	7.37671832701151	2.98731452626216e-13	***
df.mm.trans1:probe9	0.472232490253695	0.041252015497839	11.4475010385476	6.7888038360097e-29	***
df.mm.trans1:probe10	0.526723375329422	0.041252015497839	12.7684276506930	3.89373100745077e-35	***
df.mm.trans1:probe11	0.48191684196028	0.041252015497839	11.6822617305941	5.77150467524674e-30	***
df.mm.trans1:probe12	1.00519349669592	0.041252015497839	24.3671366008426	3.4005640376239e-107	***
df.mm.trans1:probe13	0.618523925770401	0.041252015497839	14.9937868078907	8.95514914629477e-47	***
df.mm.trans1:probe14	0.585185081207106	0.041252015497839	14.1856118821094	2.15047709235502e-42	***
df.mm.trans2:probe2	-0.00373893227992636	0.041252015497839	-0.09063635400123	0.927796410392954	   
df.mm.trans2:probe3	-0.119108271786662	0.041252015497839	-2.88733217878534	0.0039537041854335	** 
df.mm.trans2:probe4	-0.0920753341922015	0.041252015497839	-2.23202025600482	0.0257944715116891	*  
df.mm.trans2:probe5	-0.100482118545307	0.041252015497839	-2.43581113147238	0.0150007086752466	*  
df.mm.trans2:probe6	-0.0228886131668164	0.041252015497839	-0.554848360512601	0.579100008224087	   
df.mm.trans3:probe2	0.320433531659248	0.041252015497839	7.76770608156185	1.6845937029208e-14	***
df.mm.trans3:probe3	-0.0319959410283462	0.041252015497839	-0.775621279159618	0.43812282855569	   
df.mm.trans3:probe4	-0.264402562974840	0.041252015497839	-6.40944593334332	2.08159764000978e-10	***
df.mm.trans3:probe5	0.0472071004866137	0.041252015497839	1.14435864325433	0.252699213634976	   
df.mm.trans3:probe6	0.481014471246005	0.041252015497839	11.6603871457191	7.27376170418487e-30	***
df.mm.trans3:probe7	0.247756973157065	0.041252015497839	6.00593619892449	2.50611412459364e-09	***
df.mm.trans3:probe8	-0.299986564620981	0.041252015497839	-7.27204625036312	6.30893148719237e-13	***
df.mm.trans3:probe9	-0.178970770022510	0.041252015497839	-4.33847335366887	1.55318120373761e-05	***
df.mm.trans3:probe10	0.310995717255114	0.041252015497839	7.53892175938423	9.20641609275261e-14	***
df.mm.trans3:probe11	-0.194105851321401	0.041252015497839	-4.70536648885845	2.82313231891785e-06	***
df.mm.trans3:probe12	-0.308816581029847	0.041252015497839	-7.48609679558626	1.35407984751997e-13	***
df.mm.trans3:probe13	0.0788203209776588	0.041252015497839	1.91070230209207	0.0562768541528762	.  
df.mm.trans3:probe14	-0.345586651494154	0.041252015497839	-8.37744889124892	1.46906504608511e-16	***
df.mm.trans3:probe15	-0.0057479152067824	0.041252015497839	-0.139336590889323	0.889207154925935	   
df.mm.trans3:probe16	-0.0242069322023279	0.041252015497839	-0.586806048388011	0.557442450735853	   
df.mm.trans3:probe17	-0.182749491933853	0.041252015497839	-4.43007425766691	1.02645619182778e-05	***
df.mm.trans3:probe18	0.110198647427671	0.041252015497839	2.67135183815306	0.0076550999737372	** 
df.mm.trans3:probe19	-0.335794101532944	0.041252015497839	-8.14006533936591	9.65828960967312e-16	***
df.mm.trans3:probe20	-0.0500983843449316	0.041252015497839	-1.21444694859954	0.224811916715658	   
df.mm.trans3:probe21	0.141681848560757	0.041252015497839	3.43454366655561	0.000613464278853662	***
df.mm.trans3:probe22	-0.0201376353468945	0.041252015497839	-0.48816124748982	0.625523281443676	   
df.mm.trans3:probe23	-0.110884779422721	0.041252015497839	-2.68798452838093	0.00728621931616908	** 
df.mm.trans3:probe24	-0.0608696379852356	0.041252015497839	-1.47555549106259	0.140321077944635	   
df.mm.trans3:probe25	-0.0331956375992307	0.041252015497839	-0.804703411424096	0.421147507515638	   
df.mm.trans3:probe26	-0.334603615015632	0.041252015497839	-8.11120647021867	1.21042004628383e-15	***
df.mm.trans3:probe27	0.200089093944259	0.041252015497839	4.85040770807285	1.39094968033534e-06	***
df.mm.trans3:probe28	-0.0909496853379104	0.041252015497839	-2.20473313219508	0.0276587544215836	*  
df.mm.trans3:probe29	0.0132507122778045	0.041252015497839	0.321213693873906	0.748103428569751	   
df.mm.trans3:probe30	-0.0565077778414034	0.041252015497839	-1.36981859333306	0.170995381148383	   
df.mm.trans3:probe31	0.0569265821504871	0.041252015497839	1.37997092901968	0.167848219073336	   
df.mm.trans3:probe32	0.286558258717150	0.041252015497839	6.94652746681339	6.07316074879699e-12	***
df.mm.trans3:probe33	0.205867409978631	0.041252015497839	4.99048125271394	6.89507540584444e-07	***
df.mm.trans3:probe34	0.0428657728467561	0.041252015497839	1.03911947887738	0.298954896840183	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29857648475498	0.143716287096613	29.9101554291148	6.3893251599906e-148	***
df.mm.trans1	-0.029436133365654	0.119565811456615	-0.246191892206034	0.805575071112822	   
df.mm.trans2	-0.112470196223949	0.107098240359692	-1.05015914216905	0.293852755735886	   
df.mm.exp2	-0.0439494092117493	0.134696232733164	-0.326285363146079	0.744264376816067	   
df.mm.exp3	-0.0588858647874876	0.134696232733164	-0.437175291339748	0.662061535222384	   
df.mm.exp4	-0.0507656641992431	0.134696232733164	-0.376890007754046	0.706320836389927	   
df.mm.exp5	-0.0347331625758318	0.134696232733164	-0.257862910276331	0.796556163943421	   
df.mm.exp6	-0.0325190037800228	0.134696232733164	-0.241424746039063	0.809266471807484	   
df.mm.exp7	-0.128072221106236	0.134696232733164	-0.950822591749459	0.341882609661735	   
df.mm.exp8	-0.060030414456699	0.134696232733164	-0.445672556972104	0.655912800391846	   
df.mm.trans1:exp2	0.0494745118494821	0.115319508703867	0.429021181286241	0.667983493021862	   
df.mm.trans2:exp2	-0.0496234834375077	0.0826999056103492	-0.600042806231423	0.548589049191339	   
df.mm.trans1:exp3	0.0383007998900384	0.115319508703867	0.332127671375987	0.739849861031525	   
df.mm.trans2:exp3	0.00547551654012726	0.0826999056103493	0.0662094654125221	0.947221899962569	   
df.mm.trans1:exp4	0.0554736827103761	0.115319508703867	0.481043349333276	0.63057187752104	   
df.mm.trans2:exp4	0.0141238229356544	0.0826999056103492	0.170784027278103	0.86442188451338	   
df.mm.trans1:exp5	0.052044161196069	0.115319508703867	0.451304048907414	0.651850569558658	   
df.mm.trans2:exp5	-0.05226828112245	0.0826999056103492	-0.632023467701626	0.527489861208425	   
df.mm.trans1:exp6	0.0501636979799158	0.115319508703867	0.43499749993501	0.663641114996474	   
df.mm.trans2:exp6	-0.00177782310932065	0.0826999056103493	-0.0214972809968742	0.982852484812195	   
df.mm.trans1:exp7	0.128417741400948	0.115319508703867	1.11358210630879	0.265677673975219	   
df.mm.trans2:exp7	0.0144019457052444	0.0826999056103492	0.174147063396916	0.861778755543715	   
df.mm.trans1:exp8	0.109471751793568	0.115319508703867	0.949290827059313	0.342660568021475	   
df.mm.trans2:exp8	-0.0735109741486441	0.0826999056103492	-0.888888247285312	0.374238223698423	   
df.mm.trans1:probe2	-0.0617318834061126	0.092075243922618	-0.670450392268235	0.502697507558689	   
df.mm.trans1:probe3	-0.0818585891690043	0.0920752439226179	-0.889040155438525	0.374156613387704	   
df.mm.trans1:probe4	0.0321066739094315	0.0920752439226179	0.34870039482507	0.727374365106251	   
df.mm.trans1:probe5	-0.069294957114439	0.092075243922618	-0.752590535330821	0.451841071619039	   
df.mm.trans1:probe6	-0.0549231223563333	0.0920752439226179	-0.596502599574886	0.550950091615693	   
df.mm.trans1:probe7	-0.0626556831202789	0.092075243922618	-0.680483487754169	0.496327384338696	   
df.mm.trans1:probe8	-0.102966131598791	0.092075243922618	-1.11828247433508	0.263666336100236	   
df.mm.trans1:probe9	-0.127601950139820	0.092075243922618	-1.38584428021781	0.166047512001823	   
df.mm.trans1:probe10	-0.0506322933226306	0.0920752439226179	-0.549901267328524	0.582487665176744	   
df.mm.trans1:probe11	-0.0280903025549843	0.092075243922618	-0.305079860321543	0.760357325491884	   
df.mm.trans1:probe12	-0.0930393064045453	0.0920752439226179	-1.01047037662738	0.312470181979419	   
df.mm.trans1:probe13	-0.128360901067491	0.092075243922618	-1.39408700535584	0.163544966970293	   
df.mm.trans1:probe14	-0.0929391325216532	0.092075243922618	-1.00938241988000	0.312991249844641	   
df.mm.trans2:probe2	0.174723639593974	0.0920752439226179	1.89761799317975	0.057982130601381	.  
df.mm.trans2:probe3	0.155387407793984	0.092075243922618	1.68761331682787	0.0917408776088521	.  
df.mm.trans2:probe4	0.0752501841586678	0.092075243922618	0.817268366097512	0.413934518418004	   
df.mm.trans2:probe5	0.171057386540462	0.0920752439226179	1.8577999824168	0.063438004343308	.  
df.mm.trans2:probe6	0.02982274967624	0.092075243922618	0.323895418635042	0.74607268366117	   
df.mm.trans3:probe2	0.0424044401999321	0.092075243922618	0.46054116604426	0.645209839276027	   
df.mm.trans3:probe3	0.0200163341157479	0.092075243922618	0.217391051742096	0.827939924648626	   
df.mm.trans3:probe4	0.0295655937808404	0.0920752439226179	0.321102530075163	0.748187645616107	   
df.mm.trans3:probe5	0.0747245543467651	0.092075243922618	0.811559667542834	0.417202517600624	   
df.mm.trans3:probe6	0.00136796149783207	0.0920752439226179	0.0148569956434949	0.988148695800274	   
df.mm.trans3:probe7	0.0203308789161489	0.092075243922618	0.22080722298423	0.825279455613019	   
df.mm.trans3:probe8	0.0579032283644012	0.0920752439226179	0.628868585057069	0.529552721383416	   
df.mm.trans3:probe9	0.187817109874610	0.092075243922618	2.03982201809267	0.0415829187904592	*  
df.mm.trans3:probe10	0.101706512345120	0.0920752439226179	1.10460214941811	0.269549643189456	   
df.mm.trans3:probe11	0.0947945523539538	0.092075243922618	1.02953354577720	0.303432919278705	   
df.mm.trans3:probe12	0.0128106113950177	0.092075243922618	0.139131984334291	0.889368798189696	   
df.mm.trans3:probe13	0.00994724225053651	0.0920752439226179	0.108033840875799	0.913986597588837	   
df.mm.trans3:probe14	0.0496560065550994	0.0920752439226179	0.539298126615134	0.58977948398628	   
df.mm.trans3:probe15	0.00107221265399803	0.0920752439226179	0.0116449613198868	0.99071077747542	   
df.mm.trans3:probe16	-0.00779816465301304	0.0920752439226179	-0.084693391196083	0.932519034186137	   
df.mm.trans3:probe17	0.00965400815531157	0.0920752439226179	0.104849118438665	0.916512754422661	   
df.mm.trans3:probe18	0.0752776505585616	0.0920752439226179	0.817566669949054	0.41376416952664	   
df.mm.trans3:probe19	0.117729648027275	0.092075243922618	1.27862434039509	0.201272350809081	   
df.mm.trans3:probe20	0.195109914820278	0.0920752439226179	2.11902685790605	0.0342896284695494	*  
df.mm.trans3:probe21	0.143603408474213	0.092075243922618	1.55963104040104	0.119106235853764	   
df.mm.trans3:probe22	0.084295625041051	0.0920752439226179	0.915508028541254	0.360105757909835	   
df.mm.trans3:probe23	0.111443582562445	0.0920752439226179	1.21035337854879	0.22637757298945	   
df.mm.trans3:probe24	0.176452658500838	0.0920752439226179	1.91639631874484	0.0555479182803178	.  
df.mm.trans3:probe25	0.0675071336554969	0.0920752439226179	0.733173552189896	0.463593319237452	   
df.mm.trans3:probe26	0.19666450218312	0.0920752439226179	2.13591073783526	0.0328851342167151	*  
df.mm.trans3:probe27	0.131848507232438	0.0920752439226179	1.43196478896376	0.152409830151821	   
df.mm.trans3:probe28	-0.0127882717247901	0.092075243922618	-0.138889360266454	0.889560482015383	   
df.mm.trans3:probe29	0.0285255165707461	0.0920752439226179	0.309806581611878	0.756760911269118	   
df.mm.trans3:probe30	0.0523261963105645	0.0920752439226179	0.56829821004374	0.569936990089373	   
df.mm.trans3:probe31	0.00934951586148885	0.092075243922618	0.101542124279860	0.919136791780478	   
df.mm.trans3:probe32	0.0441262809844069	0.092075243922618	0.479241532300382	0.631852629660636	   
df.mm.trans3:probe33	0.132625258824226	0.0920752439226179	1.44040084146491	0.150010385834972	   
df.mm.trans3:probe34	-0.0289968868613879	0.092075243922618	-0.314925984727855	0.75287166097776