chr7.21362_chr7_13834264_13839012_-_2.R 

fitVsDatCorrelation=0.756153554229441
cont.fitVsDatCorrelation=0.261734545774145

fstatistic=11163.2588615701,61,899
cont.fstatistic=5124.06977726381,61,899

residuals=-0.457190054462834,-0.0866291035800554,-0.00752368079702982,0.0703124237857777,0.968208750639528
cont.residuals=-0.633081586026112,-0.145213640907938,-0.0283881120909451,0.115153769859451,1.16442673080934

predictedValues:
Include	Exclude	Both
chr7.21362_chr7_13834264_13839012_-_2.R.tl.Lung	57.0567849796842	51.4725754331404	52.6139552445667
chr7.21362_chr7_13834264_13839012_-_2.R.tl.cerebhem	61.3261939570531	52.8294986689535	56.6864113694435
chr7.21362_chr7_13834264_13839012_-_2.R.tl.cortex	56.9068520452071	52.9734064192476	49.1440418862971
chr7.21362_chr7_13834264_13839012_-_2.R.tl.heart	59.9371899030439	52.1601594295201	51.5732764381555
chr7.21362_chr7_13834264_13839012_-_2.R.tl.kidney	59.1153152119961	56.5837808163832	52.0681296406875
chr7.21362_chr7_13834264_13839012_-_2.R.tl.liver	62.0377978765949	58.6148385902199	53.2424467263754
chr7.21362_chr7_13834264_13839012_-_2.R.tl.stomach	65.8174664307996	51.3223038276403	51.6915682761101
chr7.21362_chr7_13834264_13839012_-_2.R.tl.testicle	58.1068946162714	54.6029497873495	50.7665650228155


diffExp=5.58420954654382,8.49669528809961,3.93344562595949,7.77703047352387,2.53153439561289,3.42295928637503,14.4951626031593,3.50394482892185
diffExpScore=0.980293618016256
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=0,0,0,0,0,0,1,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	56.7038238009891	55.1297038632626	56.5546248241224
cerebhem	55.6386742885769	57.3144091707518	58.8209162133378
cortex	53.7990943934285	54.1866525592654	55.9976799626266
heart	57.2564368694745	61.6179048596337	52.2607599025161
kidney	56.0097051715625	56.9488922076376	54.8175442304691
liver	55.699839983903	57.4081646705612	54.0497035654424
stomach	56.8029511581472	57.1457480763837	56.4817612499747
testicle	58.8926069014694	55.0502959013823	51.9858843618496
cont.diffExp=1.57411993772656,-1.67573488217486,-0.387558165836957,-4.36146799015918,-0.939187036075118,-1.70832468665821,-0.342796918236452,3.84231100008711
cont.diffExpScore=2.96710792366979

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=-0.00677278327649973
cont.tran.correlation=0.226408048920426

tran.covariance=-6.92222708189421e-06
cont.tran.covariance=0.000244081033908169

tran.mean=56.929000499569
cont.tran.mean=56.6003064922768

weightedLogRatios:
wLogRatio
Lung	0.411223866414809
cerebhem	0.602759189395847
cortex	0.286904249127738
heart	0.559221622928591
kidney	0.177591869536129
liver	0.232663443560679
stomach	1.01058809810400
testicle	0.250725337543707

cont.weightedLogRatios:
wLogRatio
Lung	0.113281006677100
cerebhem	-0.119694535172861
cortex	-0.0286318588390044
heart	-0.299834407842906
kidney	-0.067079748846458
liver	-0.121896841395480
stomach	-0.0243231183493062
testicle	0.272705714738659

varWeightedLogRatios=0.0768851182864192
cont.varWeightedLogRatios=0.0289589501743753

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31744827877432	0.0710811306089657	60.7397243373299	0	***
df.mm.trans1	-0.141613254706942	0.0620084129496929	-2.28377486167614	0.0226172345551928	*  
df.mm.trans2	-0.363924206246737	0.0546924749435636	-6.65400873926925	4.95185573892253e-11	***
df.mm.exp2	0.0236275010577287	0.0708878290951852	0.333308289438553	0.738979301810204	   
df.mm.exp3	0.094335406014676	0.0708878290951852	1.33077013669027	0.183602076910419	   
df.mm.exp4	0.0824977570466339	0.0708878290951852	1.16377886161332	0.244822412646937	   
df.mm.exp5	0.140544614950966	0.0708878290951852	1.98263392665403	0.0477123687806664	*  
df.mm.exp6	0.201761020009761	0.0708878290951852	2.84620114037975	0.00452506933615171	** 
df.mm.exp7	0.157601245538581	0.0708878290951852	2.22324830016957	0.0264469927760928	*  
df.mm.exp8	0.11301950633173	0.0708878290951852	1.59434289037082	0.111210737737686	   
df.mm.trans1:exp2	0.0485325585908649	0.0666306055627144	0.728382372949993	0.46656923197868	   
df.mm.trans2:exp2	0.00239307058764539	0.0498813446464725	0.0479752621868148	0.961746613363266	   
df.mm.trans1:exp3	-0.096966649188298	0.0666306055627144	-1.45528692662159	0.145938929732646	   
df.mm.trans2:exp3	-0.0655945340350608	0.0498813446464725	-1.31501134341814	0.188841414592056	   
df.mm.trans1:exp4	-0.0332475777310119	0.0666306055627144	-0.498983574443406	0.617913029469351	   
df.mm.trans2:exp4	-0.0692279328567718	0.0498813446464725	-1.38785217895419	0.165525924836944	   
df.mm.trans1:exp5	-0.105101583109947	0.0666306055627144	-1.57737697597575	0.115060602307031	   
df.mm.trans2:exp5	-0.0458713787932637	0.0498813446464725	-0.91960990864162	0.358023324458756	   
df.mm.trans1:exp6	-0.118064177193497	0.0666306055627144	-1.77192111937767	0.0767463251134973	.  
df.mm.trans2:exp6	-0.071822287189399	0.0498813446464725	-1.43986269212328	0.150254373017289	   
df.mm.trans1:exp7	-0.0147629947984231	0.0666306055627144	-0.221564770029409	0.824703026909932	   
df.mm.trans2:exp7	-0.160524965350811	0.0498813446464725	-3.21813628899764	0.00133645636555833	** 
df.mm.trans1:exp8	-0.0947821810630022	0.0666306055627144	-1.42250217092490	0.155227396096703	   
df.mm.trans2:exp8	-0.0539807496022686	0.0498813446464725	-1.08218312847920	0.279461347431438	   
df.mm.trans1:probe2	-0.349735879861234	0.0436199705131263	-8.01779267035474	3.33782993854837e-15	***
df.mm.trans1:probe3	-0.361058302872388	0.0436199705131263	-8.27736237840271	4.52546753347341e-16	***
df.mm.trans1:probe4	-0.290578109499184	0.0436199705131263	-6.66158427162948	4.71457092144862e-11	***
df.mm.trans1:probe5	-0.379884409356022	0.0436199705131263	-8.70895612461968	1.45266052930281e-17	***
df.mm.trans1:probe6	-0.232689944375396	0.0436199705131263	-5.3344819273863	1.21311403730475e-07	***
df.mm.trans1:probe7	-0.0567132323176531	0.0436199705131263	-1.30016668169426	0.193877075549879	   
df.mm.trans1:probe8	-0.354948167344383	0.0436199705131263	-8.13728581585286	1.33917541146145e-15	***
df.mm.trans1:probe9	0.081496880304124	0.0436199705131263	1.86833872983934	0.0620399533232067	.  
df.mm.trans1:probe10	0.095887245925009	0.0436199705131263	2.19824187859444	0.0281861046563950	*  
df.mm.trans1:probe11	0.0740368763728357	0.0436199705131263	1.69731605734479	0.0899830362281814	.  
df.mm.trans1:probe12	0.0275070839642217	0.0436199705131263	0.63060757814919	0.528457410234613	   
df.mm.trans1:probe13	0.0519532894038605	0.0436199705131263	1.19104366171514	0.233950812373544	   
df.mm.trans1:probe14	0.205651471965112	0.0436199705131263	4.71461739991839	2.80563279536420e-06	***
df.mm.trans1:probe15	-0.168035309572734	0.0436199705131263	-3.85225637697686	0.000125342613011937	***
df.mm.trans1:probe16	-0.161751029485533	0.0436199705131263	-3.70818750179710	0.000221538341597758	***
df.mm.trans1:probe17	-0.196176500494983	0.0436199705131263	-4.49740103414211	7.77940208029957e-06	***
df.mm.trans1:probe18	-0.202208769701642	0.0436199705131263	-4.63569248953967	4.08394598097453e-06	***
df.mm.trans1:probe19	-0.214774325064417	0.0436199705131263	-4.92376135375392	1.00997061406214e-06	***
df.mm.trans1:probe20	-0.152093229243121	0.0436199705131263	-3.48677973538181	0.000512547462184957	***
df.mm.trans1:probe21	-0.357062038068274	0.0436199705131263	-8.18574689225948	9.21695697267511e-16	***
df.mm.trans1:probe22	-0.385282454544945	0.0436199705131263	-8.8327078173747	5.2776661039085e-18	***
df.mm.trans1:probe23	-0.235691520927905	0.0436199705131263	-5.40329390770633	8.3825747391018e-08	***
df.mm.trans1:probe24	-0.269446859282145	0.0436199705131263	-6.17714446187124	9.87793176926929e-10	***
df.mm.trans1:probe25	-0.354784717065332	0.0436199705131263	-8.13353867258044	1.3783185122108e-15	***
df.mm.trans1:probe26	-0.294410879293201	0.0436199705131263	-6.7494515890286	2.65800170064559e-11	***
df.mm.trans2:probe2	0.0168677197930593	0.0436199705131263	0.386697184675616	0.699071911813011	   
df.mm.trans2:probe3	-0.117190220035051	0.0436199705131263	-2.68661850653442	0.00735113392912942	** 
df.mm.trans2:probe4	-0.0628742832831091	0.0436199705131263	-1.44141049486012	0.149816972884194	   
df.mm.trans2:probe5	-0.037904040386372	0.0436199705131263	-0.868960706311475	0.385100547310973	   
df.mm.trans2:probe6	0.0264519081250665	0.0436199705131263	0.606417377496999	0.544390726239539	   
df.mm.trans3:probe2	0.371673953683861	0.0436199705131263	8.52072913648611	6.62537252509609e-17	***
df.mm.trans3:probe3	0.228115628221497	0.0436199705131263	5.22961445269321	2.11376412278437e-07	***
df.mm.trans3:probe4	0.0562592950492852	0.0436199705131263	1.28976004310584	0.197465674053032	   
df.mm.trans3:probe5	0.0837074979597205	0.0436199705131263	1.91901775666104	0.055298430659978	.  
df.mm.trans3:probe6	0.0319497475505521	0.0436199705131263	0.732456880981559	0.464080666781113	   
df.mm.trans3:probe7	0.0234293639007997	0.0436199705131263	0.537124707449061	0.591314384590861	   
df.mm.trans3:probe8	0.413429118169689	0.0436199705131263	9.47797793777229	2.23093649929108e-20	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03723477734732	0.104834719876510	38.5104742217367	2.00708572021767e-192	***
df.mm.trans1	0.0329615127223637	0.0914537310517688	0.360417364532731	0.718619705588401	   
df.mm.trans2	-0.0238945747859475	0.0806637463549055	-0.296224460996589	0.767127084429492	   
df.mm.exp2	-0.019390234799485	0.104549627196142	-0.185464408812359	0.852906628346979	   
df.mm.exp3	-0.059942337546212	0.104549627196142	-0.573338606303744	0.566558835409561	   
df.mm.exp4	0.199923434861526	0.104549627196142	1.91223479435710	0.0561636565784879	.  
df.mm.exp5	0.0513456006615349	0.104549627196142	0.49111223099062	0.623466936042758	   
df.mm.exp6	0.0679364228746902	0.104549627196142	0.649800718535678	0.515986951062369	   
df.mm.exp7	0.0389521667215264	0.104549627196142	0.372571072381249	0.7095555214575	   
df.mm.exp8	0.120667247949354	0.104549627196142	1.15416239335769	0.248740318286232	   
df.mm.trans1:exp2	0.000427127350997254	0.0982708182822335	0.00434643120372261	0.996533024832207	   
df.mm.trans2:exp2	0.0582536348763346	0.0735680024821805	0.791833853181548	0.428666436860207	   
df.mm.trans1:exp3	0.00735732410570431	0.0982708182822335	0.0748678420950368	0.940336518808692	   
df.mm.trans2:exp3	0.0426882919406077	0.0735680024821805	0.580256232333446	0.561887242901077	   
df.mm.trans1:exp4	-0.190225011976822	0.0982708182822335	-1.93572227546225	0.0532149731523724	.  
df.mm.trans2:exp4	-0.0886596042300818	0.0735680024821805	-1.20513812036091	0.228467114476924	   
df.mm.trans1:exp5	-0.0636622659378735	0.0982708182822335	-0.647824726105726	0.517263726171601	   
df.mm.trans2:exp5	-0.0188800239538118	0.0735680024821805	-0.256633635776435	0.797520324554498	   
df.mm.trans1:exp6	-0.0858007963966645	0.0982708182822335	-0.873105545435114	0.382838696988924	   
df.mm.trans2:exp6	-0.0274385489965225	0.0735680024821805	-0.372968519882929	0.709259792651284	   
df.mm.trans1:exp7	-0.0372055329651773	0.0982708182822335	-0.378602047032143	0.705072790989082	   
df.mm.trans2:exp7	-0.00303583959121794	0.0735680024821805	-0.0412657607762733	0.967093191352405	   
df.mm.trans1:exp8	-0.082793332300386	0.0982708182822335	-0.842501708519448	0.399731261998776	   
df.mm.trans2:exp8	-0.122108670635843	0.0735680024821805	-1.65980679800869	0.097302074531266	.  
df.mm.trans1:probe2	-0.0152885450885408	0.0643333519119408	-0.237645709949448	0.812210042623232	   
df.mm.trans1:probe3	0.0559521324125698	0.0643333519119408	0.869722014316258	0.384684487112502	   
df.mm.trans1:probe4	-0.07060410449144	0.0643333519119408	-1.09747280987446	0.272728622889424	   
df.mm.trans1:probe5	-0.0687654584622218	0.0643333519119408	-1.0688928280365	0.285404882483341	   
df.mm.trans1:probe6	-0.0253804943504364	0.0643333519119408	-0.394515342293638	0.69329417341273	   
df.mm.trans1:probe7	-0.0774023151967132	0.0643333519119408	-1.20314444835178	0.229237171219581	   
df.mm.trans1:probe8	-0.0867775108879673	0.0643333519119408	-1.34887283670137	0.177717474017747	   
df.mm.trans1:probe9	-0.130389541965676	0.0643333519119408	-2.02677985975536	0.042979657246613	*  
df.mm.trans1:probe10	-0.0655519063779564	0.0643333519119408	-1.01894125565979	0.308504982964972	   
df.mm.trans1:probe11	-0.100331867168205	0.0643333519119408	-1.55956225171570	0.119215302535277	   
df.mm.trans1:probe12	-0.0091703878699481	0.0643333519119408	-0.142544848005130	0.886681597387758	   
df.mm.trans1:probe13	-0.0805086766531438	0.0643333519119408	-1.25142984564746	0.211103229250708	   
df.mm.trans1:probe14	-0.059496603049669	0.0643333519119408	-0.924817396909579	0.355309070171319	   
df.mm.trans1:probe15	0.0266618116518106	0.0643333519119408	0.414432185786078	0.678656487054993	   
df.mm.trans1:probe16	-0.0194881959081649	0.0643333519119408	-0.30292523751662	0.762016917566814	   
df.mm.trans1:probe17	-0.0672120345104698	0.0643333519119408	-1.04474634871302	0.296421050685448	   
df.mm.trans1:probe18	-0.0360736178654289	0.0643333519119408	-0.56072964944849	0.575121569905007	   
df.mm.trans1:probe19	0.0133973989251470	0.0643333519119408	0.208249664085362	0.835081201824966	   
df.mm.trans1:probe20	-0.0359876813768971	0.0643333519119408	-0.559393849494379	0.576032288967517	   
df.mm.trans1:probe21	0.0663383783229301	0.0643333519119408	1.03116620464194	0.302740214969698	   
df.mm.trans1:probe22	-0.0823404806852795	0.0643333519119408	-1.27990347522987	0.200909263970841	   
df.mm.trans1:probe23	0.0169464925918733	0.0643333519119408	0.263416907222082	0.792289623215002	   
df.mm.trans1:probe24	-0.065742942511791	0.0643333519119408	-1.02191072838517	0.307098063036773	   
df.mm.trans1:probe25	-0.0397824104068427	0.0643333519119408	-0.618379257796123	0.536482001850779	   
df.mm.trans1:probe26	-0.143059282003995	0.0643333519119408	-2.22371876720824	0.0264151838474099	*  
df.mm.trans2:probe2	-0.0181023462656243	0.0643333519119408	-0.281383539449378	0.778480994108122	   
df.mm.trans2:probe3	-0.0346045843129656	0.0643333519119408	-0.537894937610779	0.590782714022291	   
df.mm.trans2:probe4	-0.0275697830248936	0.0643333519119408	-0.428545726369597	0.668356507556836	   
df.mm.trans2:probe5	-0.00478064102014796	0.0643333519119408	-0.0743104607185972	0.940779884711426	   
df.mm.trans2:probe6	0.0339357724756373	0.0643333519119408	0.527498901690813	0.597977333802154	   
df.mm.trans3:probe2	0.00863758452302673	0.0643333519119408	0.134262933087177	0.893224713988097	   
df.mm.trans3:probe3	-0.00721780625395846	0.0643333519119408	-0.112193847195125	0.910694752977141	   
df.mm.trans3:probe4	-0.0759448113536315	0.0643333519119408	-1.18048895474286	0.238118041370675	   
df.mm.trans3:probe5	0.0607743770274004	0.0643333519119408	0.944679162848348	0.345076514565036	   
df.mm.trans3:probe6	-0.10973483970011	0.0643333519119408	-1.70572240430305	0.0884051950088634	.  
df.mm.trans3:probe7	0.0704324967316176	0.0643333519119408	1.09480533251284	0.273895156589718	   
df.mm.trans3:probe8	-0.0357048661400665	0.0643333519119408	-0.554997758999704	0.579034251344208