chr7.21607_chr7_32902239_32911879_-_2.R 

fitVsDatCorrelation=0.796325572991882
cont.fitVsDatCorrelation=0.278556715522501

fstatistic=9439.74032740201,63,945
cont.fstatistic=3735.15284455111,63,945

residuals=-0.576675939035007,-0.082255597089325,-0.00761194930900377,0.0764237658632204,1.64820056827062
cont.residuals=-0.514496131264096,-0.17282063525828,-0.0358527421272097,0.115904347665113,1.76158742402120

predictedValues:
Include	Exclude	Both
chr7.21607_chr7_32902239_32911879_-_2.R.tl.Lung	55.7231413400978	75.6504122589439	57.277245975128
chr7.21607_chr7_32902239_32911879_-_2.R.tl.cerebhem	72.1153095464007	130.933396727162	54.5853849867061
chr7.21607_chr7_32902239_32911879_-_2.R.tl.cortex	55.3235254231784	79.635136115424	58.4473621479939
chr7.21607_chr7_32902239_32911879_-_2.R.tl.heart	59.035788589188	77.6825661980459	59.1311998362245
chr7.21607_chr7_32902239_32911879_-_2.R.tl.kidney	55.1527905052337	72.6881997423356	54.5387762629775
chr7.21607_chr7_32902239_32911879_-_2.R.tl.liver	56.6438907673169	76.29406134622	56.8323697022197
chr7.21607_chr7_32902239_32911879_-_2.R.tl.stomach	61.2821589256334	78.1795840548882	56.8863613939043
chr7.21607_chr7_32902239_32911879_-_2.R.tl.testicle	59.8008200986075	80.909182005242	55.1952277857072


diffExp=-19.9272709188461,-58.8180871807612,-24.3116106922456,-18.6467776088579,-17.5354092371019,-19.6501705789031,-16.8974251292547,-21.1083619066345
diffExpScore=0.994946818122166
diffExp1.5=0,-1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,-1,-1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=-1,-1,-1,-1,-1,-1,0,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	62.5543012011159	57.0909687056986	59.4866670267475
cerebhem	57.9976016423151	61.0278759157279	62.236245867191
cortex	56.7078692548616	63.0514535872656	57.0093123721115
heart	58.0193085644454	60.5157931716079	59.782456927208
kidney	58.2889156300074	73.3213369439226	60.7287941149294
liver	60.8732927034918	70.9815561799275	57.9887683838586
stomach	63.2708581206318	66.1586773045033	59.9749625091887
testicle	62.8878764626108	58.8606286216734	63.1944153924592
cont.diffExp=5.46333249541724,-3.03027427341278,-6.34358433240403,-2.49648460716245,-15.0324213139153,-10.1082634764357,-2.88781918387148,4.02724784093743
cont.diffExpScore=1.57249770444511

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

tran.correlation=0.935825482894922
cont.tran.correlation=-0.180662520685871

tran.covariance=0.0156173466907815
cont.tran.covariance=-0.00071722304823536

tran.mean=71.6906227277449
cont.tran.mean=61.9755196256129

weightedLogRatios:
wLogRatio
Lung	-1.27587954174017
cerebhem	-2.72951379074030
cortex	-1.52817787972849
heart	-1.15706908169913
kidney	-1.14518500341689
liver	-1.24654425422373
stomach	-1.03185387830242
testicle	-1.28244268918285

cont.weightedLogRatios:
wLogRatio
Lung	0.373812374070213
cerebhem	-0.208089420139624
cortex	-0.433794776616757
heart	-0.171961857346365
kidney	-0.959088221577704
liver	-0.643016165888962
stomach	-0.186100571092818
testicle	0.271888863138305

varWeightedLogRatios=0.298779107027344
cont.varWeightedLogRatios=0.194892801959666

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.4977244114994	0.0771308974307897	58.3128754016541	0	***
df.mm.trans1	-0.468518533429823	0.0638814470857274	-7.33418785584307	4.79394544201632e-13	***
df.mm.trans2	-0.144554975636652	0.0582452139242622	-2.48183440144319	0.0132436448337446	*  
df.mm.exp2	0.854573985833002	0.0742069720004001	11.5160875426691	8.26589479285146e-29	***
df.mm.exp3	0.0239121068028272	0.0742069720004001	0.322235312373322	0.747345706092078	   
df.mm.exp4	0.0524010311925344	0.0742069720004001	0.706147007214523	0.480270771824193	   
df.mm.exp5	-0.00124051279669848	0.0742069720004001	-0.0167169305424805	0.986665969072605	   
df.mm.exp6	0.0326582168515387	0.0742069720004001	0.440096340965949	0.659967969481245	   
df.mm.exp7	0.134826704336481	0.0742069720004001	1.81690076689497	0.0695489629278368	.  
df.mm.exp8	0.174855221442292	0.0742069720004001	2.35631796755363	0.0186604390022578	*  
df.mm.trans1:exp2	-0.596703150974674	0.062716338116658	-9.5143174632542	1.47010364562979e-20	***
df.mm.trans2:exp2	-0.306008103628113	0.048579866612186	-6.29907253700347	4.58256120629469e-10	***
df.mm.trans1:exp3	-0.0311093987812027	0.062716338116658	-0.496033405575057	0.619986186230581	   
df.mm.trans2:exp3	0.0274204073647237	0.048579866612186	0.564439741747777	0.572588841400634	   
df.mm.trans1:exp4	0.00534729050394463	0.062716338116658	0.0852615229862143	0.93207154001977	   
df.mm.trans2:exp4	-0.0258930620258468	0.048579866612186	-0.532999858409485	0.594158994727753	   
df.mm.trans1:exp5	-0.00904766888844554	0.062716338116658	-0.144263347640228	0.885323278858078	   
df.mm.trans2:exp5	-0.0387033200249738	0.048579866612186	-0.796694654062004	0.425828495216062	   
df.mm.trans1:exp6	-0.0162696014671168	0.062716338116658	-0.259415679481380	0.795371034183244	   
df.mm.trans2:exp6	-0.0241860043122179	0.048579866612186	-0.497860657076216	0.618698091432334	   
df.mm.trans1:exp7	-0.0397334735826240	0.0627163381166581	-0.633542626623324	0.526532652875342	   
df.mm.trans2:exp7	-0.101941054113675	0.048579866612186	-2.09842186120997	0.0361328448404563	*  
df.mm.trans1:exp8	-0.104231371123789	0.062716338116658	-1.66194925044746	0.0968546009617601	.  
df.mm.trans2:exp8	-0.107650795383572	0.048579866612186	-2.2159549395833	0.0269321908539992	*  
df.mm.trans1:probe2	-0.147636224669428	0.048579866612186	-3.03904137588543	0.00243863304098383	** 
df.mm.trans1:probe3	-0.153209833182625	0.048579866612186	-3.15377220785108	0.00166263258709057	** 
df.mm.trans1:probe4	0.140770879741255	0.048579866612186	2.89772058999322	0.00384568057071659	** 
df.mm.trans1:probe5	-0.096552634555737	0.048579866612186	-1.98750308078280	0.0471549530310533	*  
df.mm.trans1:probe6	0.242684985059154	0.048579866612186	4.99558771942526	6.98572770342525e-07	***
df.mm.trans1:probe7	-0.165674862460909	0.048579866612186	-3.41036058792615	0.000676232407043205	***
df.mm.trans1:probe8	-0.113876422461867	0.048579866612186	-2.34410735152785	0.0192790082242750	*  
df.mm.trans1:probe9	-0.126861985114382	0.048579866612186	-2.61141073373305	0.00915987772801463	** 
df.mm.trans1:probe10	-0.127803084864056	0.048579866612186	-2.63078295138828	0.00865749599907595	** 
df.mm.trans1:probe11	-0.0827511461479815	0.048579866612186	-1.70340414494312	0.0888212536436274	.  
df.mm.trans1:probe12	0.313737602584887	0.048579866612186	6.45818163910289	1.69289399158419e-10	***
df.mm.trans2:probe2	-0.10350682922761	0.048579866612186	-2.13065280837238	0.0333754160210819	*  
df.mm.trans2:probe3	-0.285428729747987	0.048579866612186	-5.87545313836636	5.83930106478826e-09	***
df.mm.trans2:probe4	-0.254901663813751	0.048579866612186	-5.24706388859886	1.90821442580011e-07	***
df.mm.trans2:probe5	-0.091477852002446	0.048579866612186	-1.88304041122046	0.060001834325192	.  
df.mm.trans2:probe6	-0.0760813078121741	0.048579866612186	-1.56610779563338	0.117658163345523	   
df.mm.trans3:probe2	0.238799775268216	0.048579866612186	4.91561199981381	1.04310139362062e-06	***
df.mm.trans3:probe3	0.0857423130779567	0.048579866612186	1.76497629691821	0.0778907079206023	.  
df.mm.trans3:probe4	-0.0379121096239053	0.048579866612186	-0.780407857571087	0.435346273900464	   
df.mm.trans3:probe5	0.107944061694352	0.048579866612186	2.22199172665646	0.0265200154169044	*  
df.mm.trans3:probe6	0.128583756927686	0.048579866612186	2.64685281979411	0.00825962690631642	** 
df.mm.trans3:probe7	0.440617342732747	0.048579866612186	9.06995785414982	6.7445429546945e-19	***
df.mm.trans3:probe8	0.287366699644468	0.048579866612186	5.91534558829735	4.62551473489315e-09	***
df.mm.trans3:probe9	0.21074533719993	0.048579866612186	4.33812095208729	1.59160677711839e-05	***
df.mm.trans3:probe10	0.862004998599681	0.048579866612186	17.744079156929	5.06227096281468e-61	***
df.mm.trans3:probe11	0.365423359981253	0.048579866612186	7.52211534252316	1.25277284031135e-13	***
df.mm.trans3:probe12	0.0331604472328251	0.048579866612186	0.682596506440529	0.495029130418679	   
df.mm.trans3:probe13	0.00112846683014545	0.048579866612186	0.0232291051590161	0.981472426193285	   
df.mm.trans3:probe14	0.0383143708779724	0.048579866612186	0.788688268410383	0.430492034740417	   
df.mm.trans3:probe15	0.0691374718091047	0.048579866612186	1.42317129771126	0.155016546410994	   
df.mm.trans3:probe16	-0.0100763841562588	0.048579866612186	-0.207418934199608	0.835727384555025	   
df.mm.trans3:probe17	0.130483080252460	0.048579866612186	2.68594974321584	0.00735922794524753	** 
df.mm.trans3:probe18	0.194795957935290	0.048579866612186	4.00980841487997	6.55589938445346e-05	***
df.mm.trans3:probe19	0.177077044210294	0.048579866612186	3.64507061379776	0.000281893005702330	***
df.mm.trans3:probe20	-0.0188932189555677	0.048579866612186	-0.388910474093983	0.697429984854153	   
df.mm.trans3:probe21	0.0536963030045733	0.048579866612186	1.10532009964605	0.26930223816809	   
df.mm.trans3:probe22	0.225341233765394	0.048579866612186	4.63857250914864	4.00212517640811e-06	***
df.mm.trans3:probe23	0.351484704153887	0.048579866612186	7.23519286209236	9.60760787925065e-13	***
df.mm.trans3:probe24	-0.0169040759722304	0.048579866612186	-0.347964643607936	0.727944229813912	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07802285931532	0.122469713804888	33.2982149840914	1.81244004942882e-161	***
df.mm.trans1	0.0804694648539276	0.101432017552386	0.79333396688445	0.427782413727932	   
df.mm.trans2	-0.0188390314754348	0.0924827133796757	-0.203703273692821	0.838629240270895	   
df.mm.exp2	-0.0541338930720944	0.117827056678178	-0.459435163690382	0.646027369157929	   
df.mm.exp3	0.0437207431137305	0.117827056678178	0.371058603569683	0.710677049942308	   
df.mm.exp4	-0.0219607517132870	0.117827056678178	-0.186381229680282	0.852185812784602	   
df.mm.exp5	0.158916920048430	0.117827056678178	1.34873028766628	0.177746727650262	   
df.mm.exp6	0.216036530236360	0.117827056678178	1.83350527736955	0.0670418503924045	.  
df.mm.exp7	0.150624987650023	0.117827056678178	1.27835653284141	0.201437624147389	   
df.mm.exp8	-0.0246188411992315	0.117827056678178	-0.208940475076732	0.834539735542553	   
df.mm.trans1:exp2	-0.0214994469862887	0.0995820382737	-0.215896836005683	0.829114754117804	   
df.mm.trans2:exp2	0.120818697208496	0.0771359151630859	1.56630924716525	0.11761101583262	   
df.mm.trans1:exp3	-0.141842753201690	0.0995820382737	-1.42438089901149	0.154666370650460	   
df.mm.trans2:exp3	0.0555844356705715	0.0771359151630859	0.720603827063583	0.471331508776313	   
df.mm.trans1:exp4	-0.0532983856130128	0.0995820382737	-0.53522087453686	0.592623054954297	   
df.mm.trans2:exp4	0.0802191889654123	0.0771359151630859	1.03997196112612	0.298618928638573	   
df.mm.trans1:exp5	-0.229539969938529	0.0995820382736999	-2.30503385869288	0.0213806030528924	*  
df.mm.trans2:exp5	0.0912887992482802	0.0771359151630859	1.18347982331280	0.236916662327852	   
df.mm.trans1:exp6	-0.243276993920694	0.0995820382737	-2.4429806633607	0.0147486938459844	*  
df.mm.trans2:exp6	0.00173760306725803	0.0771359151630859	0.0225265113350153	0.98203271986144	   
df.mm.trans1:exp7	-0.139235140502494	0.0995820382737	-1.39819532634799	0.162382472313687	   
df.mm.trans2:exp7	-0.00321486738631393	0.0771359151630859	-0.0416779574017738	0.966764230005103	   
df.mm.trans1:exp8	0.0299372445302861	0.0995820382737	0.300628959290871	0.763763571615822	   
df.mm.trans2:exp8	0.0551453260170284	0.0771359151630859	0.714911152612068	0.474840516800869	   
df.mm.trans1:probe2	-0.000453393349455896	0.0771359151630859	-0.00587785013631201	0.99531142169035	   
df.mm.trans1:probe3	-0.104719544005350	0.0771359151630859	-1.35759773879580	0.174915456579582	   
df.mm.trans1:probe4	-0.00763210305028996	0.0771359151630859	-0.098943572966674	0.921204056146887	   
df.mm.trans1:probe5	0.0370099166532186	0.0771359151630859	0.479801355503072	0.63147967616611	   
df.mm.trans1:probe6	-0.144009963005138	0.0771359151630859	-1.86696382224366	0.0622161064457623	.  
df.mm.trans1:probe7	-0.108977091415347	0.0771359151630859	-1.41279313514257	0.158045814370711	   
df.mm.trans1:probe8	-0.0985304035112978	0.0771359151630859	-1.27736091939764	0.201788587951287	   
df.mm.trans1:probe9	-0.0710977963092785	0.0771359151630859	-0.921721044716444	0.356909349832308	   
df.mm.trans1:probe10	-0.0914956097601433	0.0771359151630859	-1.18616094158859	0.235856886772916	   
df.mm.trans1:probe11	-0.0545629049741023	0.0771359151630859	-0.707360570737274	0.479516832612358	   
df.mm.trans1:probe12	-0.163994822176462	0.0771359151630859	-2.12605012632226	0.0337577986975464	*  
df.mm.trans2:probe2	-0.0863078041986757	0.0771359151630859	-1.11890555801663	0.263464760454205	   
df.mm.trans2:probe3	-0.0188574760762254	0.0771359151630859	-0.244470763539341	0.806919312180993	   
df.mm.trans2:probe4	-0.0866626966287878	0.0771359151630859	-1.12350642947011	0.261507801672388	   
df.mm.trans2:probe5	-0.163608605508408	0.0771359151630859	-2.12104316338888	0.0341780234576379	*  
df.mm.trans2:probe6	-0.0807001164630925	0.0771359151630859	-1.04620676752808	0.295733101240971	   
df.mm.trans3:probe2	-0.082117542611779	0.0771359151630859	-1.06458246379991	0.287336854932658	   
df.mm.trans3:probe3	0.0418948989218501	0.0771359151630859	0.543130898664689	0.58716776198559	   
df.mm.trans3:probe4	0.00108748428289997	0.0771359151630859	0.0140982871675372	0.988754542739207	   
df.mm.trans3:probe5	-0.0710470420495627	0.0771359151630859	-0.921063059916387	0.357252574705869	   
df.mm.trans3:probe6	-0.105300373325969	0.0771359151630859	-1.36512768537634	0.172537818243379	   
df.mm.trans3:probe7	-0.0740211470139723	0.0771359151630859	-0.95961974208087	0.337492082948251	   
df.mm.trans3:probe8	0.0876103917657313	0.0771359151630859	1.13579247203458	0.256331393254897	   
df.mm.trans3:probe9	0.0269231215797104	0.0771359151630858	0.349034837050779	0.727140910436481	   
df.mm.trans3:probe10	-0.0830717119808181	0.0771359151630859	-1.07695243914825	0.281776360732952	   
df.mm.trans3:probe11	-0.0649349369933144	0.0771359151630859	-0.841824937916724	0.400098932255485	   
df.mm.trans3:probe12	-0.0705600841746032	0.0771359151630859	-0.914750074921913	0.360556193007891	   
df.mm.trans3:probe13	-0.071812711477826	0.0771359151630859	-0.930989297605336	0.352096870527216	   
df.mm.trans3:probe14	-0.0840707990556436	0.0771359151630859	-1.08990473345517	0.276032951125671	   
df.mm.trans3:probe15	-0.109580265482131	0.0771359151630859	-1.42061276190797	0.15575922070039	   
df.mm.trans3:probe16	-0.113685570825091	0.0771359151630859	-1.47383447236906	0.140859186893946	   
df.mm.trans3:probe17	-0.0369854484006886	0.0771359151630859	-0.479484145906502	0.631705187528187	   
df.mm.trans3:probe18	-0.0469881753249198	0.0771359151630859	-0.609160793977414	0.542564279162372	   
df.mm.trans3:probe19	-0.0834613867381819	0.0771359151630859	-1.08200423319957	0.279526667795181	   
df.mm.trans3:probe20	-0.137459710502337	0.0771359151630859	-1.78204549996860	0.0750628331061911	.  
df.mm.trans3:probe21	-0.0878472367219973	0.0771359151630859	-1.13886296073969	0.255048930716586	   
df.mm.trans3:probe22	-0.000944858602820622	0.0771359151630859	-0.0122492693685288	0.990229326927384	   
df.mm.trans3:probe23	-0.0106629894755410	0.0771359151630859	-0.138236377347655	0.890083055984737	   
df.mm.trans3:probe24	-0.116590037875831	0.0771359151630859	-1.51148835959655	0.130998384618088