chr5.18539_chr5_29968921_29970598_+_2.R 

fitVsDatCorrelation=0.898759347372797
cont.fitVsDatCorrelation=0.206706420658520

fstatistic=10154.1308661384,53,715
cont.fstatistic=2028.28812047987,53,715

residuals=-0.42796124068305,-0.0984156107331087,-0.00665059181905504,0.083962893650504,0.683071083064878
cont.residuals=-0.634942404928616,-0.263175425824438,-0.0930858327301919,0.208132100244832,1.41961854728860

predictedValues:
Include	Exclude	Both
chr5.18539_chr5_29968921_29970598_+_2.R.tl.Lung	72.7800601401155	57.1386353536895	65.9288209980366
chr5.18539_chr5_29968921_29970598_+_2.R.tl.cerebhem	80.5351556089835	61.9963476509976	58.672801191141
chr5.18539_chr5_29968921_29970598_+_2.R.tl.cortex	70.219622789643	58.1946879316932	57.5275252476764
chr5.18539_chr5_29968921_29970598_+_2.R.tl.heart	76.7484048060974	64.1305996509214	60.6550408472014
chr5.18539_chr5_29968921_29970598_+_2.R.tl.kidney	73.207191556617	60.5730336429721	68.8211239048799
chr5.18539_chr5_29968921_29970598_+_2.R.tl.liver	94.1958788162559	61.7225118582009	114.669985358038
chr5.18539_chr5_29968921_29970598_+_2.R.tl.stomach	83.7778885442713	56.5006282260848	57.6783193385503
chr5.18539_chr5_29968921_29970598_+_2.R.tl.testicle	73.0603890708588	62.9010196014278	58.8676163366502


diffExp=15.641424786426,18.5388079579859,12.0249348579499,12.6178051551760,12.6341579136450,32.473366958055,27.2772603181866,10.1593694694310
diffExpScore=0.992975906600461
diffExp1.5=0,0,0,0,0,1,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,1,1,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,0,0,1,1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,1,1,0,1,1,1,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	73.0846383631603	81.8485289793505	78.0084694593906
cerebhem	79.452222792052	71.5704712973954	73.7859400933798
cortex	75.3055793740689	80.677866146819	80.9448219264237
heart	76.2982735744856	72.9637280313944	68.9011437866122
kidney	79.1776708714558	76.8777875116958	86.249629176062
liver	82.0427350383694	76.5857305714304	59.8408138621404
stomach	75.2130695302309	75.1882867163144	71.3196054638205
testicle	77.5493034215667	80.6923252183183	79.8334778049907
cont.diffExp=-8.7638906161902,7.88175149465664,-5.37228677275002,3.33454554309127,2.29988335975997,5.45700446693907,0.0247828139165165,-3.14302179675164
cont.diffExpScore=13.3432349837214

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=0.123802100609911
cont.tran.correlation=-0.43288165227571

tran.covariance=0.000581642840976996
cont.tran.covariance=-0.000787133775768917

tran.mean=69.2301284530519
cont.tran.mean=77.1580135898817

weightedLogRatios:
wLogRatio
Lung	1.00812323641864
cerebhem	1.11394066992046
cortex	0.780958316273765
heart	0.763477255204816
kidney	0.795393622169896
liver	1.83210719923392
stomach	1.66674722587597
testicle	0.631299808138295

cont.weightedLogRatios:
wLogRatio
Lung	-0.492447934993957
cerebhem	0.451629606556978
cortex	-0.300172758515278
heart	0.192707786069074
kidney	0.128431462550348
liver	0.300979377190069
stomach	0.00142373354646417
testicle	-0.173649117886681

varWeightedLogRatios=0.198273368743684
cont.varWeightedLogRatios=0.101353007897744

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.80779946779673	0.0836086530660806	45.543126556377	1.56851805963964e-213	***
df.mm.trans1	0.236738940869060	0.0742461020248527	3.18857063755099	0.00149211529688278	** 
df.mm.trans2	0.122374270937974	0.067536484787656	1.81197276291082	0.0704097941350317	.  
df.mm.exp2	0.299446170333773	0.0910492176110616	3.28883847868884	0.00105528500969199	** 
df.mm.exp3	0.118811501395958	0.0910492176110617	1.30491512737088	0.192341559244242	   
df.mm.exp4	0.251904637786368	0.0910492176110617	2.76668646250688	0.00580920914272355	** 
df.mm.exp5	0.0212858925920978	0.0910492176110616	0.233784464607105	0.815219230364629	   
df.mm.exp6	-0.218379996920454	0.0910492176110616	-2.39848295954960	0.0167183832382455	*  
df.mm.exp7	0.263192672891368	0.0910492176110616	2.89066375084801	0.00396082930261804	** 
df.mm.exp8	0.213210746046294	0.0910492176110616	2.34170871140348	0.0194693179363113	*  
df.mm.trans1:exp2	-0.198194384113403	0.0864555037185817	-2.29244380737794	0.0221696504088433	*  
df.mm.trans2:exp2	-0.217851209891632	0.0728020492726199	-2.99237744085816	0.00286344016946391	** 
df.mm.trans1:exp3	-0.154625721017744	0.0864555037185817	-1.78850060860278	0.0741185598686704	.  
df.mm.trans2:exp3	-0.100497937488364	0.07280204927262	-1.38042731616018	0.167886513499874	   
df.mm.trans1:exp4	-0.198814054331606	0.0864555037185817	-2.29961131195024	0.0217575338058283	*  
df.mm.trans2:exp4	-0.136463528129853	0.0728020492726199	-1.87444624833075	0.0612764524900605	.  
df.mm.trans1:exp5	-0.0154342496527840	0.0864555037185817	-0.178522465186526	0.85836324998504	   
df.mm.trans2:exp5	0.0370833980432336	0.0728020492726199	0.509372997240343	0.610647965928211	   
df.mm.trans1:exp6	0.476314409737923	0.0864555037185817	5.50935902575222	5.02859618539175e-08	***
df.mm.trans2:exp6	0.295548207212143	0.07280204927262	4.05961384555827	5.45790702917938e-05	***
df.mm.trans1:exp7	-0.122465578585205	0.0864555037185817	-1.41651570250331	0.157060088343323	   
df.mm.trans2:exp7	-0.274421429767918	0.07280204927262	-3.76941902748231	0.00017712936110982	***
df.mm.trans1:exp8	-0.209366417843727	0.0864555037185817	-2.42166673998256	0.0156972882103329	*  
df.mm.trans2:exp8	-0.117128886604540	0.0728020492726199	-1.60886798894810	0.108086627734968	   
df.mm.trans1:probe2	0.0431214275750976	0.0473536296071389	0.910625604264064	0.36279955986283	   
df.mm.trans1:probe3	-0.158940007633923	0.0473536296071389	-3.35644825861378	0.000831257274434386	***
df.mm.trans1:probe4	0.151144448631881	0.0473536296071389	3.19182394012507	0.00147564729993147	** 
df.mm.trans1:probe5	0.101950578298895	0.0473536296071389	2.15296227859851	0.0316554833752497	*  
df.mm.trans1:probe6	-0.0443956138309763	0.0473536296071389	-0.937533494249474	0.348800772970944	   
df.mm.trans1:probe7	0.00870642517225108	0.0473536296071389	0.183859721936468	0.854175632451325	   
df.mm.trans1:probe8	0.00450553331355216	0.0473536296071389	0.0951465252174232	0.924225102566217	   
df.mm.trans1:probe9	-0.111838169156897	0.0473536296071390	-2.36176550952362	0.0184552638728318	*  
df.mm.trans1:probe10	-0.0658092471329625	0.0473536296071389	-1.38974029401626	0.165040305887325	   
df.mm.trans1:probe11	0.721758790995305	0.047353629607139	15.2418895232161	1.24630319251384e-45	***
df.mm.trans1:probe12	0.985663656429373	0.0473536296071389	20.8149547269504	1.36456865150147e-75	***
df.mm.trans1:probe13	0.607002566414834	0.0473536296071389	12.8185013788959	5.23855111727749e-34	***
df.mm.trans1:probe14	0.563429301280475	0.0473536296071389	11.8983340021635	6.51849111399169e-30	***
df.mm.trans1:probe15	0.835352497252292	0.0473536296071390	17.6407279480506	4.28581035495288e-58	***
df.mm.trans1:probe16	0.59656709434101	0.0473536296071389	12.5981281538569	5.21699871295027e-33	***
df.mm.trans1:probe17	0.32379169090978	0.0473536296071389	6.83773754189617	1.72711203203063e-11	***
df.mm.trans1:probe18	0.0735932275419041	0.0473536296071389	1.55412009918685	0.120598269801117	   
df.mm.trans1:probe19	0.155469655351798	0.0473536296071389	3.28316238146104	0.00107643962009961	** 
df.mm.trans1:probe20	0.220375668753142	0.0473536296071389	4.65382845161924	3.88312084131739e-06	***
df.mm.trans1:probe21	0.605392170907009	0.0473536296071389	12.7844935209727	7.48087091806299e-34	***
df.mm.trans1:probe22	0.698652160566132	0.0473536296071389	14.7539305088623	3.34090475302573e-43	***
df.mm.trans2:probe2	0.196699484643896	0.047353629607139	4.15384177043616	3.66468110882265e-05	***
df.mm.trans2:probe3	0.121365002443688	0.0473536296071390	2.56295036833652	0.0105822347562695	*  
df.mm.trans2:probe4	0.127161058593616	0.047353629607139	2.68534977463365	0.00741339771098373	** 
df.mm.trans2:probe5	0.311891348506295	0.0473536296071390	6.58642961677587	8.7317077885876e-11	***
df.mm.trans2:probe6	0.395950858594248	0.047353629607139	8.36157358747756	3.22782621403510e-16	***
df.mm.trans3:probe2	-0.00899858692814903	0.0473536296071390	-0.190029507828739	0.849339944307516	   
df.mm.trans3:probe3	-0.0675054915958548	0.0473536296071389	-1.42556108488203	0.154431479404831	   
df.mm.trans3:probe4	0.163156121440036	0.0473536296071389	3.44548290793402	0.000603335227418161	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43047519437949	0.186576343658110	23.7461786822132	2.36770833036874e-92	***
df.mm.trans1	-0.0488325697096597	0.165683404033737	-0.294734225159426	0.768282469364985	   
df.mm.trans2	-0.0254366667122422	0.150710601512063	-0.168778217703592	0.866018826016971	   
df.mm.exp2	0.00499911981562245	0.203180286870273	0.0246043545494858	0.980377410936158	   
df.mm.exp3	-0.0214203470986800	0.203180286870274	-0.105425321662020	0.916067892733102	   
df.mm.exp4	0.0522688469922304	0.203180286870274	0.257253534766406	0.797057073648124	   
df.mm.exp5	-0.08300557213896	0.203180286870273	-0.408531621928251	0.683005741590386	   
df.mm.exp6	0.314291983669332	0.203180286870274	1.54686258450851	0.122338862568964	   
df.mm.exp7	0.0334780817501277	0.203180286870274	0.164770324256422	0.869171333353422	   
df.mm.exp8	0.0219433104944403	0.203180286870273	0.107999210122440	0.91402661729506	   
df.mm.trans1:exp2	0.0785385508586831	0.192929214637439	0.407084800538250	0.68406754367551	   
df.mm.trans2:exp2	-0.139186874465352	0.16246093754635	-0.856740559099887	0.391875328567421	   
df.mm.trans1:exp3	0.0513563752715005	0.192929214637439	0.266192838487481	0.790167440612079	   
df.mm.trans2:exp3	0.00701427997762921	0.16246093754635	0.0431751784986963	0.965573945456446	   
df.mm.trans1:exp4	-0.00923673492791593	0.192929214637439	-0.0478762894737015	0.961828205467664	   
df.mm.trans2:exp4	-0.167176737113576	0.16246093754635	-1.02902728273300	0.303814826556908	   
df.mm.trans1:exp5	0.163081698606153	0.192929214637439	0.845292916952068	0.398230085240532	   
df.mm.trans2:exp5	0.0203522264194528	0.16246093754635	0.125274584320593	0.900341386401763	   
df.mm.trans1:exp6	-0.198669912318030	0.192929214637439	-1.02975546078586	0.303473024068324	   
df.mm.trans2:exp6	-0.380751540747799	0.16246093754635	-2.34364978128463	0.0193690911138987	*  
df.mm.trans1:exp7	-0.00477126811198205	0.192929214637439	-0.0247306667419365	0.980276694310585	   
df.mm.trans2:exp7	-0.118352956181136	0.16246093754635	-0.72850100441757	0.466545480398965	   
df.mm.trans1:exp8	0.0373523976127216	0.192929214637439	0.193606746821189	0.84653880171857	   
df.mm.trans2:exp8	-0.0361701738676891	0.16246093754635	-0.222639204315621	0.823879904948899	   
df.mm.trans1:probe2	-0.147291432161423	0.105671682858681	-1.39385905643617	0.163793211823845	   
df.mm.trans1:probe3	-0.130234622678637	0.105671682858681	-1.23244580908969	0.218187699203717	   
df.mm.trans1:probe4	-0.08620278486357	0.105671682858681	-0.815760500179147	0.414908796838294	   
df.mm.trans1:probe5	-0.199699325020041	0.105671682858681	-1.88980926221366	0.0591877428645394	.  
df.mm.trans1:probe6	-0.0931166920848912	0.105671682858681	-0.881188692806374	0.378511809524664	   
df.mm.trans1:probe7	-0.109130809284193	0.105671682858681	-1.03273465825407	0.302077274337416	   
df.mm.trans1:probe8	-0.0573991099713323	0.105671682858681	-0.543183456708022	0.58717278898033	   
df.mm.trans1:probe9	-0.151237513644314	0.105671682858681	-1.43120190341408	0.152809283520337	   
df.mm.trans1:probe10	-0.106767574198091	0.105671682858681	-1.01037071909677	0.312659502379359	   
df.mm.trans1:probe11	-0.0816178859711045	0.105671682858681	-0.772372349556078	0.440149148935051	   
df.mm.trans1:probe12	-0.106896129376906	0.105671682858681	-1.01158727186981	0.312077632435228	   
df.mm.trans1:probe13	-0.0489967361856191	0.105671682858681	-0.4636694983948	0.643025670502874	   
df.mm.trans1:probe14	-0.138918305598207	0.105671682858681	-1.31462187257856	0.189058534354136	   
df.mm.trans1:probe15	-0.0914966256246822	0.105671682858681	-0.865857561358647	0.386858716018483	   
df.mm.trans1:probe16	-0.0598459061814144	0.105671682858681	-0.566338157606978	0.57134163882771	   
df.mm.trans1:probe17	-0.111501419136649	0.105671682858681	-1.05516838683987	0.291704728964447	   
df.mm.trans1:probe18	-0.244769804813190	0.105671682858681	-2.31632352387659	0.0208224765178129	*  
df.mm.trans1:probe19	-0.0654032340823395	0.105671682858681	-0.618928669564255	0.53616048885399	   
df.mm.trans1:probe20	-0.104296037953080	0.105671682858681	-0.98698189648933	0.323985384650804	   
df.mm.trans1:probe21	-0.108819780249717	0.105671682858681	-1.02979130554063	0.30345620536735	   
df.mm.trans1:probe22	-0.0969933341432713	0.105671682858681	-0.917874415542185	0.358994127867209	   
df.mm.trans2:probe2	-0.108729622642315	0.105671682858681	-1.02893811947448	0.3038566968706	   
df.mm.trans2:probe3	0.0927766964169804	0.105671682858681	0.87797122092826	0.380254260579242	   
df.mm.trans2:probe4	0.0109090186386996	0.105671682858681	0.103235023268141	0.917805391561304	   
df.mm.trans2:probe5	0.0355486423237356	0.105671682858681	0.336406512719932	0.736663044622556	   
df.mm.trans2:probe6	-0.0321866963741871	0.105671682858681	-0.304591499855563	0.760765927669905	   
df.mm.trans3:probe2	-0.00787599492148476	0.105671682858681	-0.0745326913362175	0.940607376598368	   
df.mm.trans3:probe3	-0.0727832768992478	0.105671682858681	-0.68876803066138	0.49119268296894	   
df.mm.trans3:probe4	0.0831050544207866	0.105671682858681	0.786445830827981	0.431866911925046