chr14.7614_chr14_112172229_112181132_+_2.R 

fitVsDatCorrelation=0.885103594539738
cont.fitVsDatCorrelation=0.307807365854657

fstatistic=9000.50174350515,53,715
cont.fstatistic=2143.20126898091,53,715

residuals=-0.842007787536023,-0.0990936823189467,0.0076365632740031,0.113527014513244,0.769496095497825
cont.residuals=-1.05031486195915,-0.235853240604905,-0.014293422674699,0.225657282045363,1.21903238554978

predictedValues:
Include	Exclude	Both
chr14.7614_chr14_112172229_112181132_+_2.R.tl.Lung	81.923846724429	93.711094703253	75.5201027759929
chr14.7614_chr14_112172229_112181132_+_2.R.tl.cerebhem	83.6594251058409	74.0140924573761	82.4470131216445
chr14.7614_chr14_112172229_112181132_+_2.R.tl.cortex	126.308837605478	90.6845572992753	123.516695186914
chr14.7614_chr14_112172229_112181132_+_2.R.tl.heart	142.322167815106	95.100110832774	139.295474644935
chr14.7614_chr14_112172229_112181132_+_2.R.tl.kidney	80.4499292463077	95.7905617393025	66.5163206831218
chr14.7614_chr14_112172229_112181132_+_2.R.tl.liver	84.1509995472235	98.5712883352644	62.9024486348999
chr14.7614_chr14_112172229_112181132_+_2.R.tl.stomach	94.9410368888974	108.965490228169	83.8995347694735
chr14.7614_chr14_112172229_112181132_+_2.R.tl.testicle	128.374987986293	91.7101936110837	118.194551657761


diffExp=-11.7872479788239,9.64533264846474,35.6242803062028,47.2220569823323,-15.3406324929948,-14.4202887880409,-14.0244533392711,36.6647943752098
diffExpScore=2.47679769060416
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,1,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,1,0,0,0,1
diffExp1.3Score=0.75
diffExp1.2=0,0,1,1,0,0,0,1
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	99.0154943283399	92.4980429732272	118.114242037369
cerebhem	107.696300759443	94.3975742236379	105.309554897381
cortex	98.6252133549397	101.796709610762	90.318043258882
heart	106.825297667109	103.995466883139	81.6513205508644
kidney	104.035263549614	98.4371829655148	105.435552349633
liver	100.202318458531	99.8352328554132	109.913764651686
stomach	104.545248483770	92.9789729607183	128.078198476714
testicle	103.283972650007	117.612840174393	100.862607191344
cont.diffExp=6.51745135511266,13.2987265358051,-3.17149625582222,2.82983078397015,5.59808058409953,0.367085603117815,11.5662755230519,-14.3288675243857
cont.diffExpScore=2.43601821151884

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=0.0304785649263763
cont.tran.correlation=0.0200503168492809

tran.covariance=0.00185886482649190
cont.tran.covariance=5.86943242202795e-05

tran.mean=98.1674137578796
cont.tran.mean=101.61132074366

weightedLogRatios:
wLogRatio
Lung	-0.60129005826491
cerebhem	0.534768141700039
cortex	1.54838485619658
heart	1.91765019559295
kidney	-0.781000489700176
liver	-0.713602511149145
stomach	-0.636816736039383
testicle	1.57627230105112

cont.weightedLogRatios:
wLogRatio
Lung	0.310569076878539
cerebhem	0.608047490976491
cortex	-0.145820237846255
heart	0.125049309974491
kidney	0.255376406897975
liver	0.0169024451652322
stomach	0.538278783410015
testicle	-0.610922241520139

varWeightedLogRatios=1.38863754818743
cont.varWeightedLogRatios=0.154427218958774

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29349817215006	0.0949381423287701	45.2241645647721	6.5505612723459e-212	***
df.mm.trans1	-0.283748800099142	0.0843069077529667	-3.36566489819047	0.000804397552069223	***
df.mm.trans2	0.250026670656203	0.0766880959090168	3.2603061491165	0.00116566444616295	** 
df.mm.exp2	-0.302754037336877	0.103386949358582	-2.92835835872109	0.00351590117887663	** 
df.mm.exp3	-0.0918670726967228	0.103386949358582	-0.888575137061987	0.374530288450941	   
df.mm.exp4	-0.0451818070703656	0.103386949358582	-0.437016541746089	0.662231303921835	   
df.mm.exp5	0.130743988850759	0.103386949358582	1.26460824757769	0.206423839633958	   
df.mm.exp6	0.260199909201574	0.103386949358582	2.51675778051164	0.0120617278578519	*  
df.mm.exp7	0.193059371458571	0.103386949358582	1.86734759712247	0.0622620270716792	.  
df.mm.exp8	-0.0203507226995459	0.103386949358582	-0.196840344219486	0.84400841642528	   
df.mm.trans1:exp2	0.323718014260772	0.0981707588406315	3.29749935809592	0.00102374789320042	** 
df.mm.trans2:exp2	0.0667929621366671	0.0826671769273382	0.807974393456982	0.419374049577369	   
df.mm.trans1:exp3	0.524806955454441	0.0981707588406315	5.34585819293098	1.21169903605865e-07	***
df.mm.trans2:exp3	0.0590375651262881	0.0826671769273382	0.71415968611315	0.475361511828687	   
df.mm.trans1:exp4	0.597484965015857	0.0981707588406315	6.08618057018183	1.88521452301737e-09	***
df.mm.trans2:exp4	0.0598953531685957	0.0826671769273382	0.724536090318431	0.468973696006864	   
df.mm.trans1:exp5	-0.148899112160929	0.0981707588406315	-1.51673587857917	0.129775361459963	   
df.mm.trans2:exp5	-0.108796418080416	0.0826671769273382	-1.31607757908613	0.188569775232883	   
df.mm.trans1:exp6	-0.23337722774866	0.0981707588406315	-2.37725806039169	0.0177039825994320	*  
df.mm.trans2:exp6	-0.209636472238758	0.0826671769273382	-2.53590941448287	0.0114273669550917	*  
df.mm.trans1:exp7	-0.045593454151329	0.0981707588406315	-0.464430087836486	0.642481018408814	   
df.mm.trans2:exp7	-0.0422447316566078	0.0826671769273382	-0.511021825430661	0.609493526651185	   
df.mm.trans1:exp8	0.469516180091286	0.0981707588406315	4.78264796601491	2.10169314702229e-06	***
df.mm.trans2:exp8	-0.00123233049534309	0.0826671769273382	-0.0149071317195974	0.988110428799732	   
df.mm.trans1:probe2	0.870631947909681	0.0537703391044136	16.1916767201161	1.81434289849665e-50	***
df.mm.trans1:probe3	0.252108312358031	0.0537703391044135	4.68861302638386	3.29465444235471e-06	***
df.mm.trans1:probe4	-0.139851787661031	0.0537703391044136	-2.60090953470576	0.00948988969322079	** 
df.mm.trans1:probe5	-0.241771885151456	0.0537703391044136	-4.49638014523161	8.0625752806939e-06	***
df.mm.trans1:probe6	0.901296392743188	0.0537703391044136	16.7619622222023	1.95123423074863e-53	***
df.mm.trans1:probe7	1.03714950095502	0.0537703391044136	19.2885058608435	4.59215488932170e-67	***
df.mm.trans1:probe8	0.562068525835845	0.0537703391044136	10.4531333667879	6.75287930433582e-24	***
df.mm.trans1:probe9	0.348523058800649	0.0537703391044136	6.48169724434641	1.68977179773831e-10	***
df.mm.trans1:probe10	0.688521648766688	0.0537703391044136	12.8048597095452	6.04384407258039e-34	***
df.mm.trans1:probe11	0.515058149052255	0.0537703391044136	9.57885253526286	1.57206575516834e-20	***
df.mm.trans1:probe12	0.570069076659843	0.0537703391044136	10.6019245211167	1.71909192218929e-24	***
df.mm.trans1:probe13	0.459516274497542	0.0537703391044136	8.54590620314358	7.67873773613994e-17	***
df.mm.trans1:probe14	0.501563340327169	0.0537703391044136	9.3278812944291	1.32543568716859e-19	***
df.mm.trans1:probe15	0.600165085282807	0.0537703391044135	11.1616384660952	8.85813210550983e-27	***
df.mm.trans1:probe16	0.494285049909355	0.0537703391044136	9.19252246019002	4.11108103633361e-19	***
df.mm.trans1:probe17	0.615240285451741	0.0537703391044136	11.4420012166381	5.8999662788201e-28	***
df.mm.trans1:probe18	0.334347897703174	0.0537703391044136	6.21807307285013	8.55612940829358e-10	***
df.mm.trans1:probe19	0.347029809898608	0.0537703391044136	6.45392637797449	2.0100401090148e-10	***
df.mm.trans1:probe20	0.696811137989064	0.0537703391044135	12.9590244286161	1.19438043853060e-34	***
df.mm.trans1:probe21	0.569710866724942	0.0537703391044136	10.5952626710918	1.82823029531529e-24	***
df.mm.trans1:probe22	0.314586687748672	0.0537703391044135	5.850561722101	7.4573447723391e-09	***
df.mm.trans2:probe2	0.0755606013923505	0.0537703391044136	1.40524688240525	0.160382210157838	   
df.mm.trans2:probe3	0.0694027964762282	0.0537703391044136	1.29072640478348	0.197215763104364	   
df.mm.trans2:probe4	-0.0773003524327294	0.0537703391044136	-1.43760210034429	0.150984473983896	   
df.mm.trans2:probe5	-0.148921161284547	0.0537703391044136	-2.76957824266955	0.00575844122078495	** 
df.mm.trans2:probe6	0.0481755766504672	0.0537703391044136	0.895950768636921	0.370580592842347	   
df.mm.trans3:probe2	0.132936741323815	0.0537703391044136	2.47230617358899	0.0136560810058336	*  
df.mm.trans3:probe3	0.0731675531434533	0.0537703391044136	1.36074189529237	0.174024030105169	   
df.mm.trans3:probe4	0.0523870656932113	0.0537703391044136	0.97427441533303	0.33024979760363	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.50091504533727	0.194090944789095	23.1897219637325	3.8345889942805e-89	***
df.mm.trans1	0.199154655639456	0.172356515270277	1.15548086666266	0.248279543648741	   
df.mm.trans2	-0.0741595710790246	0.156780663956042	-0.473014778785587	0.636347000431718	   
df.mm.exp2	0.219114932646363	0.211363633073622	1.03667281575370	0.300238832545670	   
df.mm.exp3	0.360155944352692	0.211363633073622	1.70396363421347	0.0888223734028362	.  
df.mm.exp4	0.56227256141462	0.211363633073622	2.6602143104664	0.00798413351142442	** 
df.mm.exp5	0.225237110020331	0.211363633073622	1.06563795646849	0.286947045062679	   
df.mm.exp6	0.160204857168725	0.211363633073622	0.757958475822195	0.448725581975753	   
df.mm.exp7	-0.0214591795917072	0.211363633073622	-0.101527302874438	0.91916034895729	   
df.mm.exp8	0.440309634854024	0.211363633073622	2.08318540162798	0.0375890493851790	*  
df.mm.trans1:exp2	-0.1350760429669	0.200699685781258	-0.67302568233275	0.501148416816238	   
df.mm.trans2:exp2	-0.198787043856947	0.169004259819102	-1.17622504941428	0.239896422146044	   
df.mm.trans1:exp3	-0.364105348149796	0.200699685781258	-1.81417996113174	0.0700690305288455	.  
df.mm.trans2:exp3	-0.264365650101690	0.169004259819102	-1.56425435894137	0.118200308126123	   
df.mm.trans1:exp4	-0.486354139650969	0.200699685781258	-2.42329298004504	0.015627771307473	*  
df.mm.trans2:exp4	-0.445112738133717	0.169004259819102	-2.63373679817393	0.00862769695776373	** 
df.mm.trans1:exp5	-0.175783542014137	0.200699685781258	-0.875853598523927	0.381403768977627	   
df.mm.trans2:exp5	-0.163005988921726	0.169004259819102	-0.964508167404797	0.335117310773618	   
df.mm.trans1:exp6	-0.148289876732457	0.200699685781258	-0.738864518672326	0.460231741029339	   
df.mm.trans2:exp6	-0.0838711887783614	0.169004259819102	-0.496266714626809	0.619858768839881	   
df.mm.trans1:exp7	0.0758028108683797	0.200699685781258	0.377692723201354	0.705770963601106	   
df.mm.trans2:exp7	0.0266450627361875	0.169004259819102	0.157659119153018	0.874769928133826	   
df.mm.trans1:exp8	-0.398103770457996	0.200699685781258	-1.98357944063693	0.0476848221668626	*  
df.mm.trans2:exp8	-0.200098907441734	0.169004259819102	-1.18398736017610	0.236811522719268	   
df.mm.trans1:probe2	0.0311091888834046	0.109927745186594	0.282996697790891	0.777261279443726	   
df.mm.trans1:probe3	-0.085518122496733	0.109927745186594	-0.77794848199217	0.436856727716192	   
df.mm.trans1:probe4	0.0068078724568374	0.109927745186594	0.0619304293495836	0.950635542270949	   
df.mm.trans1:probe5	-0.224051950840126	0.109927745186594	-2.03817471612662	0.0418999601473585	*  
df.mm.trans1:probe6	-0.102004816479782	0.109927745186594	-0.92792603274666	0.353759071051913	   
df.mm.trans1:probe7	-0.295030850542641	0.109927745186594	-2.68386156781301	0.00744613971562332	** 
df.mm.trans1:probe8	-0.265073790813174	0.109927745186594	-2.41134565585086	0.0161448724257753	*  
df.mm.trans1:probe9	-0.148134753744668	0.109927745186594	-1.34756474348874	0.178225258143694	   
df.mm.trans1:probe10	-0.178557920110154	0.109927745186594	-1.62432077367789	0.104748087779652	   
df.mm.trans1:probe11	-0.184698295551673	0.109927745186594	-1.68017905978296	0.0933592905420064	.  
df.mm.trans1:probe12	-0.190930119601633	0.109927745186594	-1.73686924331563	0.0828409227148064	.  
df.mm.trans1:probe13	-0.132311369207890	0.109927745186594	-1.20362124214684	0.229134345974460	   
df.mm.trans1:probe14	-0.112214226401614	0.109927745186594	-1.02079985549725	0.30769453494948	   
df.mm.trans1:probe15	-0.14568769272502	0.109927745186594	-1.32530411205766	0.185493632012883	   
df.mm.trans1:probe16	-0.0180918172521883	0.109927745186594	-0.164579171723015	0.869321741581306	   
df.mm.trans1:probe17	-0.134847961655419	0.109927745186594	-1.22669633063541	0.220340540040483	   
df.mm.trans1:probe18	-0.155949471601197	0.109927745186594	-1.41865432913669	0.156435551245123	   
df.mm.trans1:probe19	-0.116484080418423	0.109927745186594	-1.05964222426922	0.289665222160071	   
df.mm.trans1:probe20	-0.151630840405793	0.109927745186594	-1.37936824000539	0.168212510455647	   
df.mm.trans1:probe21	-0.0504612789514712	0.109927745186594	-0.459040425743446	0.646344651028652	   
df.mm.trans1:probe22	-0.0708649252925367	0.109927745186594	-0.644650039644212	0.51936076636756	   
df.mm.trans2:probe2	0.250161297189004	0.109927745186594	2.27568842392222	0.023159684145793	*  
df.mm.trans2:probe3	0.296291270952047	0.109927745186594	2.69532746668382	0.00719720088981843	** 
df.mm.trans2:probe4	0.218945193061799	0.109927745186594	1.99171913050846	0.0467818588530649	*  
df.mm.trans2:probe5	0.0985954307927934	0.109927745186594	0.896911244976735	0.370068165951821	   
df.mm.trans2:probe6	0.140326937905603	0.109927745186594	1.27653794469638	0.202179875671297	   
df.mm.trans3:probe2	-0.0234944415106044	0.109927745186594	-0.213726220534446	0.830821492920304	   
df.mm.trans3:probe3	0.284089123987868	0.109927745186594	2.58432594524383	0.00995407313921918	** 
df.mm.trans3:probe4	0.32237403379997	0.109927745186594	2.93259934744194	0.00346880578765179	**