chr9.24622_chr9_79338860_79341474_-_0.R 

fitVsDatCorrelation=0.85636595609682
cont.fitVsDatCorrelation=0.289776811024622

fstatistic=7289.38156936096,38,370
cont.fstatistic=2114.88665300469,38,370

residuals=-0.639145893034782,-0.0979572439887861,-0.0120638809646035,0.0832802515329787,0.824321733330692
cont.residuals=-0.710129401035543,-0.230652451114395,-0.0213574915275265,0.199056679112485,1.35857750584432

predictedValues:
Include	Exclude	Both
chr9.24622_chr9_79338860_79341474_-_0.R.tl.Lung	64.662517907596	104.525758535561	79.5258556504573
chr9.24622_chr9_79338860_79341474_-_0.R.tl.cerebhem	61.665204989532	130.088805614691	79.8765282942283
chr9.24622_chr9_79338860_79341474_-_0.R.tl.cortex	64.1899312157241	94.2196597927178	78.6708490790586
chr9.24622_chr9_79338860_79341474_-_0.R.tl.heart	68.5933308187286	96.0734837349399	68.629399362902
chr9.24622_chr9_79338860_79341474_-_0.R.tl.kidney	71.0559960752006	102.614813628141	94.9434768113007
chr9.24622_chr9_79338860_79341474_-_0.R.tl.liver	67.5439453538319	104.222834601434	80.322700454884
chr9.24622_chr9_79338860_79341474_-_0.R.tl.stomach	64.11607625072	102.259414830444	71.805318585249
chr9.24622_chr9_79338860_79341474_-_0.R.tl.testicle	63.1427485867711	92.7457102249272	71.2058488266409


diffExp=-39.8632406279647,-68.4236006251585,-30.0297285769937,-27.4801529162113,-31.5588175529399,-36.6788892476021,-38.1433385797241,-29.6029616381561
diffExpScore=0.996697279906892
diffExp1.5=-1,-1,0,0,0,-1,-1,0
diffExp1.5Score=0.8
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	72.0111148630656	77.3354613889864	73.3377484972961
cerebhem	74.7746981424329	82.1131047389938	78.9768735562613
cortex	62.645742415209	76.4683230534888	80.509719605194
heart	74.1085650169433	74.8927373173673	77.3828491582204
kidney	77.5617190664379	82.903034839767	96.0041488774058
liver	69.7443301719818	79.7966407750504	73.0961072211707
stomach	84.912642914604	75.4573646350617	72.9771515767345
testicle	75.7054062772507	85.9088000308027	79.4904242581524
cont.diffExp=-5.3243465259208,-7.33840659656097,-13.8225806382797,-0.784172300423947,-5.34131577332911,-10.0523106030686,9.4552782795423,-10.2033937535520
cont.diffExpScore=1.40328874781313

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

tran.correlation=-0.347530200032629
cont.tran.correlation=0.100195617221105

tran.covariance=-0.00166000903477299
cont.tran.covariance=0.000531172333781623

tran.mean=84.48251451006
cont.tran.mean=76.6462303529652

weightedLogRatios:
wLogRatio
Lung	-2.11757801948311
cerebhem	-3.35548282989762
cortex	-1.67088830002011
heart	-1.48131217187490
kidney	-1.63441782213891
liver	-1.92137527327894
stomach	-2.051245677526
testicle	-1.66766002927724

cont.weightedLogRatios:
wLogRatio
Lung	-0.307618809039400
cerebhem	-0.408295356995694
cortex	-0.844813803848433
heart	-0.045374589592818
kidney	-0.291989038888539
liver	-0.580611818265544
stomach	0.517388209340746
testicle	-0.555065673493825

varWeightedLogRatios=0.354309574548140
cont.varWeightedLogRatios=0.169105456860384

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.90769444515586	0.0885817367837771	55.4030054427049	3.22083272804838e-181	***
df.mm.trans1	-0.661983206794103	0.0729505868485737	-9.07440550366245	6.88338044512475e-18	***
df.mm.trans2	-0.190537841308109	0.0729505868485737	-2.61187537399275	0.00937134733847204	** 
df.mm.exp2	0.166922075146334	0.099721428014417	1.67388372258570	0.0949985428735622	.  
df.mm.exp3	-0.100330504333728	0.099721428014417	-1.00610777775086	0.315021238110171	   
df.mm.exp4	0.122054611411587	0.099721428014417	1.22395571184502	0.221747744776643	   
df.mm.exp5	-0.101364234995969	0.099721428014417	-1.01647396165761	0.310067944517091	   
df.mm.exp6	0.0307243497313298	0.099721428014417	0.30810178256661	0.75817825974311	   
df.mm.exp7	0.071716401606103	0.099721428014417	0.719167415008686	0.472491731515106	   
df.mm.exp8	-0.0328485475750894	0.099721428014417	-0.329403100508553	0.742037414369749	   
df.mm.trans1:exp2	-0.214383954158517	0.0826846400705642	-2.59279055911181	0.00989758028611406	** 
df.mm.trans2:exp2	0.0518617280082367	0.0826846400705642	0.627223241994852	0.530899762309901	   
df.mm.trans1:exp3	0.0929951558147362	0.0826846400705642	1.12469686915699	0.261446596850742	   
df.mm.trans2:exp3	-0.00347416737022761	0.0826846400705642	-0.0420170828253314	0.966507744171365	   
df.mm.trans1:exp4	-0.0630410120606683	0.0826846400705642	-0.76242712076715	0.44629088838161	   
df.mm.trans2:exp4	-0.206374791376841	0.0826846400705642	-2.49592658564780	0.0129974585409629	*  
df.mm.trans1:exp5	0.195650766041389	0.0826846400705642	2.36622867166645	0.0184848919694515	*  
df.mm.trans2:exp5	0.0829130054573849	0.0826846400705642	1.00276188402859	0.316631097982416	   
df.mm.trans1:exp6	0.0128723663210329	0.0826846400705642	0.155680260687444	0.876369955528643	   
df.mm.trans2:exp6	-0.0336266365655410	0.0826846400705642	-0.406685407795736	0.684474199089987	   
df.mm.trans1:exp7	-0.08020298185024	0.0826846400705642	-0.9699864664319	0.332686882452283	   
df.mm.trans2:exp7	-0.093637068534298	0.0826846400705642	-1.13246025446064	0.258174330685619	   
df.mm.trans1:exp8	0.00906484911293891	0.0826846400705642	0.109631596693205	0.912760986447148	   
df.mm.trans2:exp8	-0.0867235371448316	0.0826846400705642	-1.04884700557226	0.294933151367511	   
df.mm.trans1:probe2	-0.227382897507554	0.0482774287449823	-4.70992145643603	3.5110011760577e-06	***
df.mm.trans1:probe3	-0.151975994940583	0.0482774287449823	-3.14797202111512	0.00177752637488388	** 
df.mm.trans1:probe4	-0.0964815222636575	0.0482774287449823	-1.99848096246603	0.046395845891645	*  
df.mm.trans1:probe5	-0.169264335752355	0.0482774287449823	-3.506076030819	0.000510614448447122	***
df.mm.trans1:probe6	-0.196720036555889	0.0482774287449823	-4.07478280575029	5.63886288384618e-05	***
df.mm.trans2:probe2	-0.297910216223108	0.0482774287449823	-6.17079707779738	1.78473684361428e-09	***
df.mm.trans2:probe3	-0.356538044507565	0.0482774287449823	-7.38519125347207	1.01796004129675e-12	***
df.mm.trans2:probe4	-0.141161738809296	0.0482774287449823	-2.92396969927625	0.00366869776024811	** 
df.mm.trans2:probe5	0.0441496756051114	0.0482774287449823	0.914499316819977	0.361050102980192	   
df.mm.trans2:probe6	0.00650655774004918	0.0482774287449823	0.134774322270124	0.892863592563278	   
df.mm.trans3:probe2	0.59956278667155	0.0482774287449823	12.4191118346141	7.68367030278041e-30	***
df.mm.trans3:probe3	0.447245256605367	0.0482774287449823	9.26406538690922	1.64164081603902e-18	***
df.mm.trans3:probe4	0.100299034286651	0.0482774287449823	2.07755543105795	0.0384395516891326	*  
df.mm.trans3:probe5	0.45743692263151	0.0482774287449823	9.4751716179386	3.26060865705170e-19	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31553187287816	0.164186863201650	26.2842701829191	1.20341401365079e-86	***
df.mm.trans1	-0.0802747129328501	0.135214418437328	-0.593684563085686	0.553085873333755	   
df.mm.trans2	0.0584743167386586	0.135214418437328	0.432456223341015	0.665661801616435	   
df.mm.exp2	0.0235245026393213	0.184834358121039	0.127273429455774	0.89879312931593	   
df.mm.exp3	-0.243903237578038	0.184834358121039	-1.31957737759079	0.18779207892182	   
df.mm.exp4	-0.0570747534037161	0.184834358121039	-0.308788658039111	0.757656089168085	   
df.mm.exp5	-0.125543361750232	0.184834358121039	-0.679220914479652	0.497422560101719	   
df.mm.exp6	0.00264480688283093	0.184834358121039	0.014309065206908	0.988591119734329	   
df.mm.exp7	0.145146769340612	0.184834358121039	0.785280241271822	0.432791819932903	   
df.mm.exp8	0.0746016608848942	0.184834358121039	0.4036136010819	0.686730037959269	   
df.mm.trans1:exp2	0.0141345847904584	0.153256553563417	0.0922282568791398	0.926566602355655	   
df.mm.trans2:exp2	0.0364205197332164	0.153256553563417	0.237644126051326	0.812288660047089	   
df.mm.trans1:exp3	0.104578478154122	0.153256553563417	0.682375244141506	0.495428675434096	   
df.mm.trans2:exp3	0.232627214343880	0.153256553563417	1.51789407327120	0.129895049226482	   
df.mm.trans1:exp4	0.0857853860993655	0.153256553563417	0.559750197330831	0.57598853696779	   
df.mm.trans2:exp4	0.0249790735896799	0.153256553563417	0.162988616205204	0.870616380067129	   
df.mm.trans1:exp5	0.199796876022102	0.153256553563417	1.30367590407433	0.193154654221206	   
df.mm.trans2:exp5	0.195062431081888	0.153256553563417	1.27278362031789	0.203894014968077	   
df.mm.trans1:exp6	-0.0346291575421716	0.153256553563417	-0.225955476206388	0.821360813109691	   
df.mm.trans2:exp6	0.0286840005555247	0.153256553563417	0.187163288541886	0.851635195150506	   
df.mm.trans1:exp7	0.0196557480563703	0.153256553563417	0.128253882782487	0.898017740716916	   
df.mm.trans2:exp7	-0.169731579993575	0.153256553563417	-1.10749965366630	0.268797413913004	   
df.mm.trans1:exp8	-0.0245725660547273	0.153256553563417	-0.160336151919007	0.872703780742342	   
df.mm.trans2:exp8	0.0305320073392909	0.153256553563417	0.199221544719502	0.842198879959162	   
df.mm.trans1:probe2	0.0870794893552984	0.0894825488512153	0.973144936898115	0.331117035823433	   
df.mm.trans1:probe3	0.148929727917961	0.0894825488512153	1.66434382826522	0.0968903246120253	.  
df.mm.trans1:probe4	0.142010420443277	0.0894825488512153	1.58701805286527	0.113362740886175	   
df.mm.trans1:probe5	0.0938114004793998	0.0894825488512153	1.04837648998334	0.295149501997771	   
df.mm.trans1:probe6	-0.0146345153955564	0.0894825488512153	-0.163546027504085	0.87017783056485	   
df.mm.trans2:probe2	-0.124288837619013	0.0894825488512153	-1.38897292505235	0.165676414483683	   
df.mm.trans2:probe3	-0.0653871096336562	0.0894825488512153	-0.73072471083023	0.465409772179728	   
df.mm.trans2:probe4	-0.0542917594913519	0.0894825488512153	-0.606730141109683	0.544402116178049	   
df.mm.trans2:probe5	-0.0427308237994939	0.0894825488512153	-0.477532483686215	0.633264961346079	   
df.mm.trans2:probe6	0.00230905114533168	0.0894825488512153	0.0258044856229006	0.979427195429045	   
df.mm.trans3:probe2	-0.0488459902810956	0.0894825488512153	-0.545871691275949	0.585483097113595	   
df.mm.trans3:probe3	0.0811592599805431	0.0894825488512153	0.90698422231455	0.365005438509639	   
df.mm.trans3:probe4	-0.003308425740038	0.0894825488512153	-0.0369728598761642	0.970526584945917	   
df.mm.trans3:probe5	-0.0222849141132100	0.0894825488512153	-0.249042013211578	0.803466468619836