chr3.15414_chr3_158650990_158654242_+_2.R 

fitVsDatCorrelation=0.860858859911426
cont.fitVsDatCorrelation=0.238222521441928

fstatistic=14082.7601327118,51,669
cont.fstatistic=3856.19915966501,51,669

residuals=-0.353839259101344,-0.0766749747097776,-0.00537025715386229,0.0722104981599185,0.838023866436389
cont.residuals=-0.465250395211861,-0.129497119189176,-0.0430118076131501,0.0498303828959253,1.05978507066204

predictedValues:
Include	Exclude	Both
chr3.15414_chr3_158650990_158654242_+_2.R.tl.Lung	43.8437309375588	41.7200881945267	81.5725727296354
chr3.15414_chr3_158650990_158654242_+_2.R.tl.cerebhem	44.472408146345	43.9685952336568	73.469065894886
chr3.15414_chr3_158650990_158654242_+_2.R.tl.cortex	44.4816434417233	44.5420235373338	75.8413148760195
chr3.15414_chr3_158650990_158654242_+_2.R.tl.heart	44.5579324876338	45.6557972103408	84.4265278883816
chr3.15414_chr3_158650990_158654242_+_2.R.tl.kidney	43.6336661880071	40.2683569542596	92.9401085987759
chr3.15414_chr3_158650990_158654242_+_2.R.tl.liver	45.1353096663735	47.1736520515562	88.845340426224
chr3.15414_chr3_158650990_158654242_+_2.R.tl.stomach	43.8825421438322	43.2911197481946	75.7021867753694
chr3.15414_chr3_158650990_158654242_+_2.R.tl.testicle	45.8368080039005	43.5862506713776	85.7093688479108


diffExp=2.12364274303204,0.503812912688254,-0.060380095610526,-1.09786472270699,3.36530923374757,-2.03834238518267,0.591422395637679,2.25055733252282
diffExpScore=1.81245051458436
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	47.7369474669081	47.2447527520519	41.2486393270984
cerebhem	47.4835696893475	49.2644837771892	46.7595553548558
cortex	46.3714850726772	46.5741440937056	44.739426227461
heart	49.594751464368	45.726592245634	42.0129643107052
kidney	49.1303324943396	48.0129609025621	45.0285030319255
liver	48.3322010842462	45.9151427092252	42.4648030377511
stomach	47.1285499357859	45.9068930477792	48.8778223208121
testicle	46.9549369899891	47.3016268377927	51.8077629663454
cont.diffExp=0.492194714856161,-1.78091408784169,-0.202659021028367,3.86815921873399,1.11737159177751,2.41705837502106,1.22165688800671,-0.346689847803646
cont.diffExpScore=1.47013130093613

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=0.604855921887253
cont.tran.correlation=-0.122150968549894

tran.covariance=0.000497634068890769
cont.tran.covariance=-7.25533154747509e-05

tran.mean=44.1281202885388
cont.tran.mean=47.4174606602251

weightedLogRatios:
wLogRatio
Lung	0.186472067919828
cerebhem	0.0431712555971914
cortex	-0.00514892462581354
heart	-0.0927116020012061
kidney	0.299838434974199
liver	-0.169250926597165
stomach	0.0512194916460207
testicle	0.191309312661114

cont.weightedLogRatios:
wLogRatio
Lung	0.040010788066708
cerebhem	-0.142815865902922
cortex	-0.0167405802587703
heart	0.313717837177960
kidney	0.0893302218857664
liver	0.197642287908361
stomach	0.100845935939960
testicle	-0.0283429753322419

varWeightedLogRatios=0.0244519722593700
cont.varWeightedLogRatios=0.0200566053845617

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.30852363347646	0.0652072430783096	50.7385909492162	1.63763569559500e-231	***
df.mm.trans1	0.460147402302661	0.0585006754486405	7.86567674259826	1.47654305142104e-14	***
df.mm.trans2	0.417405265350380	0.0538868659948141	7.74595548738259	3.52473441780206e-14	***
df.mm.exp2	0.171358744500249	0.0738378087515593	2.32074525771500	0.0206003539376357	*  
df.mm.exp3	0.152745094897862	0.0738378087515593	2.06865693173264	0.0389619063185766	*  
df.mm.exp4	0.071917724343371	0.0738378087515593	0.973995918342475	0.330410603297999	   
df.mm.exp5	-0.170681705832581	0.0738378087515593	-2.31157598957020	0.0211039964419720	*  
df.mm.exp6	0.0664818673704361	0.0738378087515593	0.900377035755846	0.368243628334452	   
df.mm.exp7	0.112535647985709	0.0738378087515593	1.52409246547871	0.127958171691875	   
df.mm.exp8	0.0387456448777969	0.0738378087515593	0.524739906734823	0.599937844841712	   
df.mm.trans1:exp2	-0.157121531809917	0.0705544692529079	-2.22695363559044	0.0262824263573645	*  
df.mm.trans2:exp2	-0.11886585420172	0.061584989708359	-1.93011080727008	0.05401551565399	.  
df.mm.trans1:exp3	-0.138300240278993	0.0705544692529078	-1.96019106576006	0.0503881305079596	.  
df.mm.trans2:exp3	-0.0872947462823427	0.061584989708359	-1.41746790404179	0.156811728117298	   
df.mm.trans1:exp4	-0.0557592707145717	0.0705544692529078	-0.79030104407275	0.429632021195536	   
df.mm.trans2:exp4	0.0182301231011196	0.061584989708359	0.296015688034534	0.767309962182829	   
df.mm.trans1:exp5	0.165878975979219	0.0705544692529078	2.35107680258516	0.019008409320683	*  
df.mm.trans2:exp5	0.135264935031939	0.061584989708359	2.19639453822267	0.0284061088986772	*  
df.mm.trans1:exp6	-0.0374487502729345	0.0705544692529078	-0.530777860984209	0.595748873285909	   
df.mm.trans2:exp6	0.0563709060189071	0.061584989708359	0.91533515367716	0.360345512875774	   
df.mm.trans1:exp7	-0.111650822780161	0.0705544692529078	-1.58247697080591	0.114013334222202	   
df.mm.trans2:exp7	-0.0755708647502904	0.061584989708359	-1.22709876397094	0.220217117005486	   
df.mm.trans1:exp8	0.00571004916003136	0.0705544692529078	0.0809310766630992	0.935520970167394	   
df.mm.trans2:exp8	0.00501336007641451	0.0615849897083589	0.0814055519073027	0.935143781564733	   
df.mm.trans1:probe2	-0.0232518244790777	0.0352772346264539	-0.65911698366633	0.510047450387314	   
df.mm.trans1:probe3	-0.0142586286092234	0.0352772346264539	-0.404187821415317	0.686203786013141	   
df.mm.trans1:probe4	-0.0210876388787217	0.0352772346264539	-0.597769045732071	0.550196334892082	   
df.mm.trans1:probe5	-0.0301416888586591	0.0352772346264539	-0.85442323293834	0.393176380637425	   
df.mm.trans1:probe6	0.0691431270170149	0.0352772346264539	1.95999283246441	0.0504113485822793	.  
df.mm.trans1:probe7	0.0857548067220094	0.0352772346264539	2.43088234182911	0.0153238106477532	*  
df.mm.trans1:probe8	0.036303431791557	0.0352772346264539	1.02908950137303	0.303809530096284	   
df.mm.trans1:probe9	0.0542308199525833	0.0352772346264539	1.53727525773566	0.124698719670326	   
df.mm.trans1:probe10	0.00378617631852452	0.0352772346264539	0.107326335485643	0.914562269916045	   
df.mm.trans1:probe11	-0.00115819775345414	0.0352772346264539	-0.0328313079445752	0.973818904057587	   
df.mm.trans1:probe12	-0.033828078487749	0.0352772346264539	-0.958920925802438	0.337945000147685	   
df.mm.trans1:probe13	-0.00924124827315473	0.0352772346264539	-0.261960677218867	0.793432394577696	   
df.mm.trans1:probe14	-0.026873153066393	0.0352772346264539	-0.761770398132091	0.446465508082473	   
df.mm.trans1:probe15	0.0345848577795526	0.0352772346264539	0.980373267512809	0.327256293229328	   
df.mm.trans1:probe16	0.0125280903756552	0.0352772346264539	0.355132439044997	0.72260230714295	   
df.mm.trans1:probe17	0.0337979089427293	0.0352772346264539	0.95806571293388	0.338375720738335	   
df.mm.trans1:probe18	0.0169009877209620	0.0352772346264539	0.47909049277599	0.63203080897385	   
df.mm.trans1:probe19	0.0172974990663922	0.0352772346264539	0.490330357511101	0.624060918897489	   
df.mm.trans1:probe20	0.0335136599214051	0.0352772346264539	0.95000813630311	0.342451181630252	   
df.mm.trans1:probe21	0.0490560546120543	0.0352772346264539	1.39058673763696	0.164813171654895	   
df.mm.trans2:probe2	-0.00110371293953596	0.0352772346264539	-0.0312868327470401	0.975050122608928	   
df.mm.trans2:probe3	0.0158268204956489	0.0352772346264539	0.448641189232576	0.653835791631709	   
df.mm.trans2:probe4	0.00192417921182952	0.0352772346264539	0.054544502487352	0.956517637724575	   
df.mm.trans2:probe5	0.00849619965349612	0.0352772346264539	0.240840863618174	0.809752245967988	   
df.mm.trans2:probe6	0.0203411209749445	0.0352772346264539	0.576607582491485	0.564398525579804	   
df.mm.trans3:probe2	0.467446594903494	0.0352772346264539	13.250658671328	9.36298489088075e-36	***
df.mm.trans3:probe3	0.178803762588612	0.0352772346264539	5.06853114995952	5.19440825892136e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01904504860234	0.124458555676664	32.2922359716561	1.29495155748106e-138	***
df.mm.trans1	-0.134494937486771	0.111657988111891	-1.20452588982703	0.228812335591432	   
df.mm.trans2	-0.142460082246437	0.102851787547623	-1.38510069336883	0.166483357192688	   
df.mm.exp2	-0.088860613071479	0.140931384271415	-0.630523949870139	0.528567157361943	   
df.mm.exp3	-0.124554014295553	0.140931384271415	-0.883791888793762	0.377126055843621	   
df.mm.exp4	-0.0128423647145519	0.140931384271415	-0.0911249455253999	0.92742058850827	   
df.mm.exp5	-0.0427772296624525	0.140931384271415	-0.303532317401136	0.761578594111134	   
df.mm.exp6	-0.0452117189428196	0.140931384271415	-0.320806605118899	0.748457138862494	   
df.mm.exp7	-0.211258654835576	0.140931384271415	-1.49901780875664	0.134340726341046	   
df.mm.exp8	-0.2432361057401	0.140931384271415	-1.72591865891035	0.0848237722667433	.  
df.mm.trans1:exp2	0.0835386849164845	0.134664600513857	0.620346286980508	0.535241081969106	   
df.mm.trans2:exp2	0.130722429394697	0.117544900054426	1.11210634688676	0.266491901851099	   
df.mm.trans1:exp3	0.0955330606885406	0.134664600513857	0.709414800355869	0.478314268670251	   
df.mm.trans2:exp3	0.110257958761308	0.117544900054426	0.938007167561125	0.348579236497557	   
df.mm.trans1:exp4	0.0510216975146982	0.134664600513857	0.378879804492111	0.704897333788804	   
df.mm.trans2:exp4	-0.0198192143093106	0.117544900054426	-0.168609733813494	0.866154586683429	   
df.mm.trans1:exp5	0.0715481653828542	0.134664600513857	0.531306409478355	0.595382817544893	   
df.mm.trans2:exp5	0.0589066280189054	0.117544900054426	0.501141504153989	0.616436387614421	   
df.mm.trans1:exp6	0.0576040684966637	0.134664600513857	0.427759546880595	0.668963850038411	   
df.mm.trans2:exp6	0.0166650932148965	0.117544900054426	0.141776403801272	0.88729930568694	   
df.mm.trans1:exp7	0.198431949887878	0.134664600513857	1.47352718628872	0.141079288783424	   
df.mm.trans2:exp7	0.182532341053470	0.117544900054426	1.55287333579724	0.120926313581492	   
df.mm.trans1:exp8	0.226718781991376	0.134664600513857	1.68358114252934	0.0927291569125046	.  
df.mm.trans2:exp8	0.244439199773899	0.117544900054426	2.07953896477616	0.0379481076752222	*  
df.mm.trans1:probe2	0.0295153712337365	0.0673323002569284	0.438353823070220	0.661271325800674	   
df.mm.trans1:probe3	-0.0755435777926946	0.0673323002569283	-1.12195153744092	0.262285567368075	   
df.mm.trans1:probe4	-0.00635790817237316	0.0673323002569284	-0.0944258275465488	0.924799173700039	   
df.mm.trans1:probe5	-0.115423290157986	0.0673323002569284	-1.71423358057798	0.086949008567058	.  
df.mm.trans1:probe6	0.0501782440594282	0.0673323002569284	0.745232880325739	0.456392678970423	   
df.mm.trans1:probe7	-0.00208389272011465	0.0673323002569283	-0.0309493766314663	0.97531914255669	   
df.mm.trans1:probe8	0.0170828941752165	0.0673323002569284	0.253710241741797	0.799797372066455	   
df.mm.trans1:probe9	-0.0538680813120125	0.0673323002569284	-0.800033284270124	0.423975499439649	   
df.mm.trans1:probe10	-0.000719623071251374	0.0673323002569284	-0.0106876353326029	0.991475849361023	   
df.mm.trans1:probe11	0.0306496807648305	0.0673323002569284	0.455200262695269	0.6491128601796	   
df.mm.trans1:probe12	-0.00641286051459778	0.0673323002569283	-0.0952419639627254	0.9241511592338	   
df.mm.trans1:probe13	-0.107506757055931	0.0673323002569284	-1.59665950287906	0.110813848354130	   
df.mm.trans1:probe14	0.00838120491435111	0.0673323002569284	0.124475250100916	0.900976386570712	   
df.mm.trans1:probe15	-0.00393986081396043	0.0673323002569284	-0.0585136821247248	0.95335693754364	   
df.mm.trans1:probe16	0.0374672724051470	0.0673323002569283	0.556453177185071	0.57808717102719	   
df.mm.trans1:probe17	-0.0362043071206818	0.0673323002569284	-0.537695979233332	0.590965768090505	   
df.mm.trans1:probe18	-0.070677352618969	0.0673323002569284	-1.04967975769841	0.294244410603863	   
df.mm.trans1:probe19	-0.0447951031041313	0.0673323002569283	-0.66528401574283	0.506098139075216	   
df.mm.trans1:probe20	-0.0371650213100149	0.0673323002569284	-0.55196423066195	0.581157192377336	   
df.mm.trans1:probe21	-0.0648434246164098	0.0673323002569284	-0.963035933259054	0.335877449886806	   
df.mm.trans2:probe2	-0.0806635986378235	0.0673323002569284	-1.19799261765936	0.231344138000279	   
df.mm.trans2:probe3	-0.00204892397650544	0.0673323002569284	-0.0304300308869161	0.97573317036725	   
df.mm.trans2:probe4	-0.0519686812927977	0.0673323002569284	-0.771823940285631	0.440491254489781	   
df.mm.trans2:probe5	-0.0083024343076393	0.0673323002569283	-0.123305371656080	0.901902321758322	   
df.mm.trans2:probe6	-0.0482067048316515	0.0673323002569284	-0.715952145518616	0.474270717731907	   
df.mm.trans3:probe2	0.000770309783263131	0.0673323002569283	0.0114404198330335	0.990875475458661	   
df.mm.trans3:probe3	-0.0681860177099416	0.0673323002569283	-1.01267916660734	0.311579540590141