chr9.24356_chr9_120389246_120389963_-_2.R 

fitVsDatCorrelation=0.910485827395228
cont.fitVsDatCorrelation=0.245169458445760

fstatistic=10182.2139931291,52,692
cont.fstatistic=1841.79156251241,52,692

residuals=-0.72383740633401,-0.0931478636661511,0.00112808587942244,0.0932602651029013,0.648924186541425
cont.residuals=-0.778915315121196,-0.249188070628294,-0.0869278701965047,0.203151307168793,1.5150280178736

predictedValues:
Include	Exclude	Both
chr9.24356_chr9_120389246_120389963_-_2.R.tl.Lung	64.1214134423782	57.3367165656366	88.2065441935687
chr9.24356_chr9_120389246_120389963_-_2.R.tl.cerebhem	63.0733588437063	58.2747522785672	85.9797914603844
chr9.24356_chr9_120389246_120389963_-_2.R.tl.cortex	61.2453338396898	57.1026234385246	75.9056526646634
chr9.24356_chr9_120389246_120389963_-_2.R.tl.heart	64.4939459801132	56.9247600007527	87.3395907182545
chr9.24356_chr9_120389246_120389963_-_2.R.tl.kidney	88.248372297353	64.2743072016227	128.390458735365
chr9.24356_chr9_120389246_120389963_-_2.R.tl.liver	131.948387377651	74.6462082422485	249.800204590852
chr9.24356_chr9_120389246_120389963_-_2.R.tl.stomach	71.7129093564155	58.7511150022696	97.1439225758802
chr9.24356_chr9_120389246_120389963_-_2.R.tl.testicle	102.205933441658	62.9953665589356	206.783881207215


diffExp=6.78469687674158,4.79860656513906,4.14271040116521,7.56918597936053,23.9740650957302,57.3021791354024,12.9617943541459,39.2105668827223
diffExpScore=0.99366060684184
diffExp1.5=0,0,0,0,0,1,0,1
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,0,0,0,1,0,1
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,0,1,1,0,1
diffExp1.3Score=0.75
diffExp1.2=0,0,0,0,1,1,1,1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	81.7673799283054	84.3452628207798	82.332788909689
cerebhem	76.5793465203177	82.111498744205	78.9417768345934
cortex	78.4110926514759	82.4570197638048	70.006164086941
heart	77.140733291318	85.0683014951341	90.2583613476789
kidney	75.1961127180339	75.3340375732105	80.3329762719464
liver	73.638597337627	84.8392808647985	107.761174831576
stomach	78.432427360362	82.4414407955534	77.5360660609533
testicle	76.5718779356496	88.7404553565157	74.3093714981679
cont.diffExp=-2.57788289247439,-5.5321522238872,-4.04592711232887,-7.92756820381614,-0.137924855176578,-11.2006835271715,-4.00901343519148,-12.1685774208662
cont.diffExpScore=0.97942375386095

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=0.967928829696575
cont.tran.correlation=0.160050258885645

tran.covariance=0.0250574037976527
cont.tran.covariance=0.00025027466741812

tran.mean=71.0847189917202
cont.tran.mean=80.1921790723182

weightedLogRatios:
wLogRatio
Lung	0.459075921711351
cerebhem	0.324805694193432
cortex	0.285744240051921
heart	0.51236614675754
kidney	1.36994505205244
liver	2.61901957259939
stomach	0.831930075366305
testicle	2.12203903757415

cont.weightedLogRatios:
wLogRatio
Lung	-0.137179282232166
cerebhem	-0.305033741112991
cortex	-0.220723880460227
heart	-0.429887474075957
kidney	-0.00791835580814447
liver	-0.618740336287199
stomach	-0.218703475027322
testicle	-0.650704217605612

varWeightedLogRatios=0.786756244973327
cont.varWeightedLogRatios=0.0516772972542184

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.69187651332228	0.087328007589273	42.2759732557528	6.22232933771745e-194	***
df.mm.trans1	0.411474978259224	0.078433321577459	5.24617560475059	2.06669078875523e-07	***
df.mm.trans2	0.312857374230824	0.0721300914123644	4.33740437735248	1.65634552732569e-05	***
df.mm.exp2	0.0253167134433016	0.0988003621662163	0.256241099609636	0.79784083025264	   
df.mm.exp3	0.100208157756748	0.0988003621662163	1.01424889099256	0.310818490350145	   
df.mm.exp4	0.00845948252407053	0.0988003621662163	0.0856219788935466	0.93179169225662	   
df.mm.exp5	0.0582007362668132	0.0988003621662163	0.589074118664661	0.556003799847006	   
df.mm.exp6	-0.055529256420502	0.0988003621662163	-0.56203494808129	0.574274202822363	   
df.mm.exp7	0.0397488830622276	0.0988003621662163	0.402315155437955	0.68757634100339	   
df.mm.exp8	-0.291661695484937	0.0988003621662163	-2.95203063116572	0.0032637616739316	** 
df.mm.trans1:exp2	-0.0417966095830406	0.0945941449940792	-0.441851972821962	0.658734370222679	   
df.mm.trans2:exp2	-0.00908897570463788	0.0823336351385136	-0.110392012806760	0.912130494394383	   
df.mm.trans1:exp3	-0.146098864646674	0.0945941449940792	-1.54448105277360	0.122929002823203	   
df.mm.trans2:exp3	-0.104299293787634	0.0823336351385136	-1.26678839835223	0.205657157851557	   
df.mm.trans1:exp4	-0.00266649510458574	0.0945941449940792	-0.0281887965132793	0.97751970002422	   
df.mm.trans2:exp4	-0.0156702829137987	0.0823336351385136	-0.190326625168873	0.849109009909905	   
df.mm.trans1:exp5	0.261176143950261	0.0945941449940792	2.76101807322862	0.00591483849581678	** 
df.mm.trans2:exp5	0.0560180418968749	0.0823336351385136	0.680378581640823	0.496492344723391	   
df.mm.trans1:exp6	0.777161726626226	0.0945941449940792	8.21574873027152	1.03643068793449e-15	***
df.mm.trans2:exp6	0.319347789037778	0.0823336351385136	3.87870386750840	0.000115033182806545	***
df.mm.trans1:exp7	0.0721435239451916	0.0945941449940792	0.762663735157258	0.445923851911748	   
df.mm.trans2:exp7	-0.0153799477261029	0.0823336351385136	-0.186800299783054	0.851871962574445	   
df.mm.trans1:exp8	0.757873057524763	0.0945941449940792	8.01183897346077	4.80069040576149e-15	***
df.mm.trans2:exp8	0.385781676218133	0.0823336351385136	4.68559022772547	3.36183544556174e-06	***
df.mm.trans1:probe2	-0.107625339143994	0.0472970724970396	-2.27551798582737	0.0231797757618031	*  
df.mm.trans1:probe3	0.133863928463151	0.0472970724970396	2.83027936816871	0.00478561159532413	** 
df.mm.trans1:probe4	-0.2524749533699	0.0472970724970396	-5.33806724265445	1.274987115148e-07	***
df.mm.trans1:probe5	0.121680761195023	0.0472970724970396	2.57269117877515	0.0102984228374312	*  
df.mm.trans1:probe6	-0.184529508006961	0.0472970724970396	-3.9014995699472	0.000104930983934568	***
df.mm.trans1:probe7	-0.21136915897239	0.0472970724970396	-4.46896917321088	9.18075916851844e-06	***
df.mm.trans1:probe8	0.373488096165323	0.0472970724970396	7.89664299389143	1.12586731474530e-14	***
df.mm.trans1:probe9	-0.116199349234428	0.0472970724970396	-2.45679791791979	0.0142625875119172	*  
df.mm.trans1:probe10	-0.281043870811524	0.0472970724970396	-5.94209865376161	4.45852677897369e-09	***
df.mm.trans1:probe11	0.131323134865958	0.0472970724970396	2.77655947678743	0.00564233579971635	** 
df.mm.trans1:probe12	0.237386987266104	0.0472970724970396	5.01906301454413	6.61040489900726e-07	***
df.mm.trans1:probe13	0.297059970117663	0.0472970724970396	6.28072636284743	5.9536439325733e-10	***
df.mm.trans1:probe14	-0.0343387889399726	0.0472970724970396	-0.726023559748269	0.468069805434053	   
df.mm.trans1:probe15	-0.0163355660374031	0.0472970724970396	-0.345382180650305	0.729912032337674	   
df.mm.trans1:probe16	0.0821122406106483	0.0472970724970396	1.73609562443400	0.0829920126723081	.  
df.mm.trans1:probe17	0.177637980259780	0.0472970724970396	3.75579229075751	0.000187332597693652	***
df.mm.trans1:probe18	0.240436404932785	0.0472970724970396	5.08353672307805	4.77352662853585e-07	***
df.mm.trans1:probe19	0.334015978676144	0.0472970724970396	7.06208568610775	4.00260081797103e-12	***
df.mm.trans1:probe20	0.251639118942632	0.0472970724970396	5.32039523076999	1.39988023590002e-07	***
df.mm.trans1:probe21	0.223975698592738	0.0472970724970396	4.73550870631068	2.65162065730761e-06	***
df.mm.trans1:probe22	0.0349682249811668	0.0472970724970396	0.739331699300321	0.459956317195932	   
df.mm.trans2:probe2	0.128807002488821	0.0472970724970396	2.72336099653701	0.00662506059272638	** 
df.mm.trans2:probe3	0.158664477733410	0.0472970724970396	3.35463633068074	0.000838051376002513	***
df.mm.trans2:probe4	0.0172365303731930	0.0472970724970396	0.364431231431330	0.715647349249559	   
df.mm.trans2:probe5	0.103752164528157	0.0472970724970396	2.19362761901702	0.0285940852306764	*  
df.mm.trans2:probe6	-0.010594396670726	0.0472970724970396	-0.223996879963112	0.822825846086056	   
df.mm.trans3:probe2	0.270240961849916	0.0472970724970396	5.71369320726627	1.64279361041039e-08	***
df.mm.trans3:probe3	-0.0798240868881026	0.0472970724970396	-1.68771728721897	0.091916121184119	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.56361407703254	0.204726802070320	22.2912390116123	2.22234066996771e-83	***
df.mm.trans1	-0.191258980501619	0.183874607306152	-1.04015983122222	0.298628929925396	   
df.mm.trans2	2.28712881116043e-05	0.169097673879681	0.000135254894918768	0.99989212109609	   
df.mm.exp2	-0.0503326981145291	0.231621936055294	-0.217305402811733	0.82803440107872	   
df.mm.exp3	0.0976316971033014	0.231621936055295	0.42151317256926	0.67351129380349	   
df.mm.exp4	-0.141617896513319	0.231621936055294	-0.611418326455535	0.541123471893829	   
df.mm.exp5	-0.172176200763417	0.231621936055295	-0.743350149367172	0.457522009788409	   
df.mm.exp6	-0.368017069042611	0.231621936055295	-1.58886966973091	0.112546573479557	   
df.mm.exp7	-0.00444510435304940	0.231621936055294	-0.0191912062767157	0.984694104628774	   
df.mm.exp8	0.0876810869411023	0.231621936055295	0.37855260358488	0.705136230066849	   
df.mm.trans1:exp2	-0.0152182748599765	0.221761120330344	-0.0686246301304159	0.945308237147366	   
df.mm.trans2:exp2	0.0234921161231388	0.193018280046079	0.121709281201400	0.9031645961393	   
df.mm.trans1:exp3	-0.139544677546812	0.221761120330344	-0.629256730570898	0.529388668412334	   
df.mm.trans2:exp3	-0.120273158504247	0.193018280046079	-0.623117968285359	0.533412306749513	   
df.mm.trans1:exp4	0.0833709695356062	0.221761120330344	0.375949442406376	0.707069705268831	   
df.mm.trans2:exp4	0.150153730905880	0.193018280046079	0.777924924364856	0.43687915777669	   
df.mm.trans1:exp5	0.088397352110884	0.221761120330344	0.398615194490377	0.690299728234322	   
df.mm.trans2:exp5	0.0591896132125669	0.193018280046079	0.306652889034327	0.759199838377155	   
df.mm.trans1:exp6	0.263307991972456	0.221761120330344	1.18734966517224	0.235497134473219	   
df.mm.trans2:exp6	0.373857075905229	0.193018280046079	1.93689984086470	0.0531642955460222	.  
df.mm.trans1:exp7	-0.0371958252079517	0.221761120330344	-0.167729244659945	0.866845293484971	   
df.mm.trans2:exp7	-0.0183853093066970	0.193018280046079	-0.0952516481978182	0.92414252237779	   
df.mm.trans1:exp8	-0.153329592073128	0.221761120330344	-0.691417827636884	0.489534892306817	   
df.mm.trans2:exp8	-0.0368838560759654	0.193018280046079	-0.191089963433309	0.848511161041644	   
df.mm.trans1:probe2	-0.0515352255914652	0.110880560165172	-0.464781432513475	0.642234190367304	   
df.mm.trans1:probe3	0.0740955269871746	0.110880560165172	0.668246326288386	0.504199192561203	   
df.mm.trans1:probe4	-0.0116042769539203	0.110880560165172	-0.104655648714564	0.91667938146253	   
df.mm.trans1:probe5	0.0402445870742452	0.110880560165172	0.362954398988382	0.716749784254867	   
df.mm.trans1:probe6	0.0420659321759381	0.110880560165172	0.379380588565525	0.704521650613485	   
df.mm.trans1:probe7	-0.0951761153428966	0.110880560165172	-0.85836611215815	0.390987542910305	   
df.mm.trans1:probe8	-0.0214747545099292	0.110880560165172	-0.193674657468717	0.84648747115087	   
df.mm.trans1:probe9	-0.0263727833273639	0.110880560165172	-0.237848575873696	0.812068947062457	   
df.mm.trans1:probe10	0.195951633559308	0.110880560165172	1.76723163435873	0.0776301465944695	.  
df.mm.trans1:probe11	0.0691955974230681	0.110880560165172	0.624055265593821	0.532796956591774	   
df.mm.trans1:probe12	0.0341409250022389	0.110880560165172	0.307907219727076	0.758245585030027	   
df.mm.trans1:probe13	0.087500430861007	0.110880560165172	0.789141313235277	0.430299702335191	   
df.mm.trans1:probe14	0.0525334711139235	0.110880560165172	0.473784322839528	0.635803154490247	   
df.mm.trans1:probe15	0.123504241458466	0.110880560165172	1.11384936434745	0.265730542039234	   
df.mm.trans1:probe16	0.0392065775756133	0.110880560165172	0.353592888755338	0.723751701689038	   
df.mm.trans1:probe17	-0.0180604672693442	0.110880560165172	-0.162882179188494	0.870658755429861	   
df.mm.trans1:probe18	0.165654842918762	0.110880560165172	1.49399356092714	0.135633121916851	   
df.mm.trans1:probe19	0.00972808990867727	0.110880560165172	0.0877348553631576	0.930112797853133	   
df.mm.trans1:probe20	0.115855617193869	0.110880560165172	1.04486861377041	0.296448546986907	   
df.mm.trans1:probe21	0.0290857159681016	0.110880560165172	0.262315737986662	0.793156097767985	   
df.mm.trans1:probe22	-0.0664573371764084	0.110880560165172	-0.599359681060512	0.54912923164847	   
df.mm.trans2:probe2	-0.292025572756045	0.110880560165172	-2.63369496258887	0.00863484776676608	** 
df.mm.trans2:probe3	-0.297400289422749	0.110880560165172	-2.68216799211448	0.00748920060795712	** 
df.mm.trans2:probe4	-0.152523612195748	0.110880560165172	-1.37556675370816	0.169400891091694	   
df.mm.trans2:probe5	-0.249057382219045	0.110880560165172	-2.2461771644014	0.0250073148076143	*  
df.mm.trans2:probe6	-0.167457860437713	0.110880560165172	-1.51025445928719	0.131434971911698	   
df.mm.trans3:probe2	0.126574188559658	0.110880560165172	1.14153633757900	0.254041660267536	   
df.mm.trans3:probe3	-0.0113997855015219	0.110880560165172	-0.102811398901127	0.918142443857585