chr9.25126_chr9_64526760_64580507_-_2.R 

fitVsDatCorrelation=0.910230670841555
cont.fitVsDatCorrelation=0.254039269218763

fstatistic=10943.1045224396,62,922
cont.fstatistic=1993.83798994904,62,922

residuals=-0.618424119109796,-0.0898426802689818,-0.00108168192708697,0.0877119539629783,1.09533438327568
cont.residuals=-0.724675191043852,-0.271623270774569,-0.0603876084718718,0.209256992105133,1.48034815849729

predictedValues:
Include	Exclude	Both
chr9.25126_chr9_64526760_64580507_-_2.R.tl.Lung	74.6098536417811	132.854078592435	140.621914687712
chr9.25126_chr9_64526760_64580507_-_2.R.tl.cerebhem	66.0183198522399	45.8440700393491	58.8456517433676
chr9.25126_chr9_64526760_64580507_-_2.R.tl.cortex	72.2616774169836	51.946890621856	56.8838609218928
chr9.25126_chr9_64526760_64580507_-_2.R.tl.heart	75.5887392493704	68.4238698938506	71.5209031814849
chr9.25126_chr9_64526760_64580507_-_2.R.tl.kidney	73.8008089486602	51.7857056256507	63.1683261631042
chr9.25126_chr9_64526760_64580507_-_2.R.tl.liver	77.5385172117189	50.4503347478271	61.7240414863664
chr9.25126_chr9_64526760_64580507_-_2.R.tl.stomach	78.2174146077486	69.5904599428577	70.6727722338213
chr9.25126_chr9_64526760_64580507_-_2.R.tl.testicle	71.9475340904984	53.6279706149361	60.3889673406214


diffExp=-58.244224950654,20.1742498128908,20.3147867951275,7.1648693555198,22.0151033230095,27.0881824638918,8.62695466489093,18.3195634755623
diffExpScore=2.73772712811658
diffExp1.5=-1,0,0,0,0,1,0,0
diffExp1.5Score=2
diffExp1.4=-1,1,0,0,1,1,0,0
diffExp1.4Score=1.33333333333333
diffExp1.3=-1,1,1,0,1,1,0,1
diffExp1.3Score=1.2
diffExp1.2=-1,1,1,0,1,1,0,1
diffExp1.2Score=1.2

cont.predictedValues:
Include	Exclude	Both
Lung	65.2937617819728	72.4887858041906	59.9036572590829
cerebhem	67.1322669505171	66.6082329056873	60.8785994680267
cortex	61.4505851370196	73.846538458813	60.2658014411532
heart	63.0057696134188	69.373519596953	67.6125793994749
kidney	66.8642977940202	65.7736436901406	73.4430104617769
liver	64.1264854611517	62.2504174680948	64.5893056362016
stomach	63.859877998248	74.3608614543184	76.4357019941553
testicle	62.6293336516689	70.4074065283742	68.3766498082202
cont.diffExp=-7.19502402221788,0.524034044829804,-12.3959533217934,-6.36774998353417,1.09065410387964,1.87606799305685,-10.5009834560705,-7.77807287670527
cont.diffExpScore=1.14327995642023

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=0.283111174416252
cont.tran.correlation=-0.489571666855669

tran.covariance=0.00688443860611376
cont.tran.covariance=-0.000939287004637512

tran.mean=69.6566403186102
cont.tran.mean=66.8419865184118

weightedLogRatios:
wLogRatio
Lung	-2.65454206861719
cerebhem	1.46151331205686
cortex	1.35833172110273
heart	0.425779600879886
kidney	1.46103552935985
liver	1.77754185314306
stomach	0.502642624783737
testicle	1.21337548320238

cont.weightedLogRatios:
wLogRatio
Lung	-0.442306319116818
cerebhem	0.0329353265429194
cortex	-0.773632264034317
heart	-0.403540208585891
kidney	0.0689815996526151
liver	0.123104308935328
stomach	-0.64439659988387
testicle	-0.491176130207571

varWeightedLogRatios=2.05483706859905
cont.varWeightedLogRatios=0.119193377130189

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.71680463938638	0.0741245502484053	63.6335009599314	0	***
df.mm.trans1	-0.0340690470047395	0.0642491546559018	-0.530264517676576	0.596056223157662	   
df.mm.trans2	0.093017717891407	0.0564036465817516	1.64914369067587	0.0994588441973962	.  
df.mm.exp2	-0.315188935011666	0.0723756251984493	-4.35490448818146	1.48040954828706e-05	***
df.mm.exp3	-0.0659449846011565	0.0723756251984493	-0.911149083967698	0.362454977630233	   
df.mm.exp4	0.0255901754218409	0.0723756251984493	0.353574499034368	0.723738618950715	   
df.mm.exp5	-0.152768280886299	0.0723756251984493	-2.11076975801478	0.035060831754438	*  
df.mm.exp6	-0.106358439727442	0.0723756251984493	-1.46953396859529	0.142029141009140	   
df.mm.exp7	0.0886102977180619	0.0723756251984493	1.22431132684655	0.221147542878531	   
df.mm.exp8	-0.0982476209202334	0.0723756251984493	-1.35746835555264	0.174964605557693	   
df.mm.trans1:exp2	0.192848627202441	0.0674906184969528	2.85741383761579	0.00436717471976286	** 
df.mm.trans2:exp2	-0.748816581047336	0.0489015989736342	-15.3127218079529	2.50332995463891e-47	***
df.mm.trans1:exp3	0.0339663388442881	0.0674906184969528	0.503274967702684	0.614891170789862	   
df.mm.trans2:exp3	-0.873084525397646	0.0489015989736342	-17.8539054698064	1.83565815543665e-61	***
df.mm.trans1:exp4	-0.0125554398967026	0.0674906184969528	-0.18603237274036	0.85246029488401	   
df.mm.trans2:exp4	-0.689119809082659	0.0489015989736342	-14.0919688424545	5.49799104692199e-41	***
df.mm.trans1:exp5	0.141865388958576	0.0674906184969528	2.10200161323135	0.0358238782895949	*  
df.mm.trans2:exp5	-0.789368933571098	0.0489015989736342	-16.1419861546183	8.22435123038811e-52	***
df.mm.trans1:exp6	0.144860664067207	0.0674906184969528	2.14638222753504	0.0321028772361867	*  
df.mm.trans2:exp6	-0.861903550723551	0.0489015989736342	-17.6252631573102	3.81231816062639e-60	***
df.mm.trans1:exp7	-0.0413905666035632	0.0674906184969528	-0.613278816009547	0.539843252330462	   
df.mm.trans2:exp7	-0.735234182027123	0.0489015989736342	-15.0349722188743	7.40277909778356e-46	***
df.mm.trans1:exp8	0.0619121962848036	0.0674906184969528	0.91734522017455	0.359201659418634	   
df.mm.trans2:exp8	-0.808932979751154	0.0489015989736342	-16.5420558167699	5.07360728924283e-54	***
df.mm.trans1:probe2	-0.0686537317710137	0.0462076677134456	-1.48576492102493	0.137683146640945	   
df.mm.trans1:probe3	-0.1976551576319	0.0462076677134456	-4.27754023115055	2.08684013551821e-05	***
df.mm.trans1:probe4	-0.0783388761245117	0.0462076677134456	-1.69536529327397	0.090343776992105	.  
df.mm.trans1:probe5	-0.618095944166063	0.0462076677134456	-13.3764800248987	2.00122610780757e-37	***
df.mm.trans1:probe6	-0.498232029207729	0.0462076677134456	-10.7824535161023	1.30263391493909e-25	***
df.mm.trans1:probe7	-0.532028874182899	0.0462076677134456	-11.5138655662573	9.33090470364192e-29	***
df.mm.trans1:probe8	-0.444568755043358	0.0462076677134456	-9.62110353200096	6.03416018211849e-21	***
df.mm.trans1:probe9	-0.129619576054915	0.0462076677134456	-2.80515296419511	0.00513505994059064	** 
df.mm.trans1:probe10	-0.589137912936492	0.0462076677134456	-12.7497868230442	2.08232584890336e-34	***
df.mm.trans1:probe11	-0.757642338301767	0.0462076677134456	-16.3964635263620	3.25755936213472e-53	***
df.mm.trans1:probe12	-0.88920956706656	0.0462076677134456	-19.2437664800774	1.22644866503589e-69	***
df.mm.trans1:probe13	-0.831302556993755	0.0462076677134456	-17.9905759829523	2.96774688642534e-62	***
df.mm.trans1:probe14	-0.884144312207122	0.0462076677134456	-19.1341471222936	5.53636885735079e-69	***
df.mm.trans1:probe15	-0.745704325945351	0.0462076677134456	-16.138107869235	8.63730871345006e-52	***
df.mm.trans1:probe16	-0.881826698699165	0.0462076677134456	-19.0839906521092	1.10203651583261e-68	***
df.mm.trans1:probe17	-0.881436949784973	0.0462076677134456	-19.0755559283182	1.23720145006817e-68	***
df.mm.trans1:probe18	-0.611678593945988	0.0462076677134456	-13.2375993901982	9.5136867287019e-37	***
df.mm.trans1:probe19	0.0707287371037993	0.0462076677134456	1.53067100340186	0.12619373608852	   
df.mm.trans1:probe20	-0.31125038020238	0.0462076677134456	-6.73590327329617	2.86447645164139e-11	***
df.mm.trans1:probe21	-0.8815463682382	0.0462076677134456	-19.0779239000995	1.19766598215390e-68	***
df.mm.trans1:probe22	-0.934161243074324	0.0462076677134456	-20.2165850236691	1.63375898708599e-75	***
df.mm.trans1:probe23	-0.557357094245498	0.0462076677134456	-12.0620044643222	3.26708252174447e-31	***
df.mm.trans1:probe24	-0.134466693273786	0.0462076677134456	-2.91005151152995	0.00370060164739361	** 
df.mm.trans1:probe25	-0.57887602203214	0.0462076677134456	-12.5277048307655	2.31174090653072e-33	***
df.mm.trans2:probe2	0.109756810550749	0.0462076677134456	2.37529431763143	0.0177385986072293	*  
df.mm.trans2:probe3	0.564530008407369	0.0462076677134456	12.2172365830769	6.3653641474347e-32	***
df.mm.trans2:probe4	0.410032914709011	0.0462076677134456	8.87369856561056	3.6184505393629e-18	***
df.mm.trans2:probe5	0.0208918391406571	0.0462076677134456	0.452129271492705	0.651282205808863	   
df.mm.trans2:probe6	0.16565267023542	0.0462076677134456	3.58496064468578	0.000354815460156735	***
df.mm.trans3:probe2	0.378844537087096	0.0462076677134456	8.19873747873362	8.0948285993181e-16	***
df.mm.trans3:probe3	0.350758621860924	0.0462076677134456	7.59091811419991	7.7702660490389e-14	***
df.mm.trans3:probe4	-0.00135958822308982	0.0462076677134456	-0.0294234331739320	0.976533251458563	   
df.mm.trans3:probe5	0.341176720439593	0.0462076677134456	7.38355206662631	3.44359272763546e-13	***
df.mm.trans3:probe6	-0.0731679107723428	0.0462076677134456	-1.58345820927578	0.113659992611286	   
df.mm.trans3:probe7	0.462360476459181	0.0462076677134456	10.0061418231815	1.90431240151689e-22	***
df.mm.trans3:probe8	-0.0548231605959117	0.0462076677134456	-1.18645158495977	0.23574964174724	   
df.mm.trans3:probe9	-0.0344625865396217	0.0462076677134456	-0.745819649529589	0.455966467572572	   
df.mm.trans3:probe10	0.333888114748811	0.0462076677134456	7.22581621776284	1.04370092743643e-12	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30784623784242	0.173128563475337	24.8823541960255	5.80277443153975e-105	***
df.mm.trans1	-0.161540320705155	0.150063154687679	-1.07648223870386	0.281993228652026	   
df.mm.trans2	-0.0250052327544672	0.131738840569618	-0.189809115112511	0.849500491504796	   
df.mm.exp2	-0.072979526491608	0.169043697118507	-0.431719890984438	0.666045942166112	   
df.mm.exp3	-0.0481331212016309	0.169043697118507	-0.284737745459315	0.77590901182302	   
df.mm.exp4	-0.200653327515998	0.169043697118507	-1.18699088422878	0.235536953259572	   
df.mm.exp5	-0.27721618395572	0.169043697118507	-1.63990843007521	0.101365315923389	   
df.mm.exp6	-0.245616951758281	0.169043697118507	-1.45297905775270	0.146569816135568	   
df.mm.exp7	-0.240419678125953	0.169043697118507	-1.42223390888930	0.155296561359505	   
df.mm.exp8	-0.203089980284022	0.169043697118507	-1.20140522093319	0.229902567486464	   
df.mm.trans1:exp2	0.100747833382099	0.157634060365727	0.639124775117472	0.522900509387061	   
df.mm.trans2:exp2	-0.0116241577332929	0.114216727839566	-0.101772813432554	0.91895913359114	   
df.mm.trans1:exp3	-0.0125300207162889	0.157634060365727	-0.0794880287116755	0.93666170183517	   
df.mm.trans2:exp3	0.0666903851510606	0.114216727839566	0.583893326420077	0.559434899696888	   
df.mm.trans1:exp4	0.164983130830301	0.157634060365727	1.04662108206516	0.295548671698355	   
df.mm.trans2:exp4	0.156726688878115	0.114216727839566	1.37218682274160	0.170339026641477	   
df.mm.trans1:exp5	0.300984843329324	0.157634060365727	1.90938964987015	0.056521988982908	.  
df.mm.trans2:exp5	0.180003518889332	0.114216727839566	1.57598210257060	0.115372963692596	   
df.mm.trans1:exp6	0.227577920017785	0.157634060365727	1.44371032180344	0.149160164390770	   
df.mm.trans2:exp6	0.093350322061609	0.114216727839566	0.817308671219624	0.413963195195538	   
df.mm.trans1:exp7	0.218214454904956	0.157634060365727	1.38431030957825	0.166598407732343	   
df.mm.trans2:exp7	0.265917554493837	0.114216727839566	2.32818396677725	0.0201178309291739	*  
df.mm.trans1:exp8	0.161427237272180	0.157634060365727	1.02406318087381	0.306074037611825	   
df.mm.trans2:exp8	0.173956572949390	0.114216727839566	1.52303936769873	0.128091750097273	   
df.mm.trans1:probe2	0.0639307565074857	0.107924663366799	0.592364659875812	0.553751665306112	   
df.mm.trans1:probe3	0.0284338992240339	0.107924663366799	0.263460624634025	0.792254436174132	   
df.mm.trans1:probe4	0.162857852714295	0.107924663366799	1.50899569786746	0.131642470139532	   
df.mm.trans1:probe5	-0.0221525461318946	0.107924663366799	-0.205259348890490	0.83741480967729	   
df.mm.trans1:probe6	0.0883754586398377	0.107924663366799	0.818862490582708	0.413076469820555	   
df.mm.trans1:probe7	0.103938625199278	0.107924663366799	0.963066475788076	0.335766675573853	   
df.mm.trans1:probe8	-0.076801751765768	0.107924663366799	-0.711623732424764	0.476877812799343	   
df.mm.trans1:probe9	0.0267680040378180	0.107924663366799	0.248024901841414	0.804170366754513	   
df.mm.trans1:probe10	0.0341185125078487	0.107924663366799	0.316132674807532	0.751973358679064	   
df.mm.trans1:probe11	0.0750494932274069	0.107924663366799	0.695387790762331	0.486987532015728	   
df.mm.trans1:probe12	0.00796513620977896	0.107924663366799	0.0738027431478582	0.941183352328475	   
df.mm.trans1:probe13	-0.0085289759139336	0.107924663366799	-0.0790271254768384	0.9370281955347	   
df.mm.trans1:probe14	-0.0159502617913364	0.107924663366799	-0.147790702270961	0.882540260267367	   
df.mm.trans1:probe15	0.0483826405555284	0.107924663366799	0.448300129425395	0.654041934507207	   
df.mm.trans1:probe16	0.0499297376318926	0.107924663366799	0.462635101878412	0.64373510541449	   
df.mm.trans1:probe17	0.102469748767802	0.107924663366799	0.949456274137659	0.342637399728235	   
df.mm.trans1:probe18	-0.0445371804717382	0.107924663366799	-0.412669162750793	0.679944960954231	   
df.mm.trans1:probe19	0.236375499871473	0.107924663366799	2.19018982777007	0.0287603180309646	*  
df.mm.trans1:probe20	0.0580310513772823	0.107924663366799	0.537699628305111	0.5909141912249	   
df.mm.trans1:probe21	0.163184851468868	0.107924663366799	1.51202557764075	0.130870019229209	   
df.mm.trans1:probe22	0.157501694383614	0.107924663366799	1.45936702019926	0.144804722928153	   
df.mm.trans1:probe23	0.0485822295725509	0.107924663366799	0.450149465905085	0.652708491326177	   
df.mm.trans1:probe24	-0.0630199903007381	0.107924663366799	-0.583925752786965	0.559413091610053	   
df.mm.trans1:probe25	-0.0842340835996796	0.107924663366799	-0.780489657988522	0.435303040356178	   
df.mm.trans2:probe2	-0.0274567617724236	0.107924663366799	-0.254406739997023	0.799238072542893	   
df.mm.trans2:probe3	-0.112513418326498	0.107924663366799	-1.04251813085673	0.297444796927681	   
df.mm.trans2:probe4	0.0238426141022771	0.107924663366799	0.22091904999735	0.825204376419636	   
df.mm.trans2:probe5	0.049060014943496	0.107924663366799	0.454576492648002	0.649520951824182	   
df.mm.trans2:probe6	0.0765214109864128	0.107924663366799	0.709026172510193	0.478487475341498	   
df.mm.trans3:probe2	0.065567242929259	0.107924663366799	0.607527889213035	0.543650235848027	   
df.mm.trans3:probe3	-0.138802301749396	0.107924663366799	-1.28610363395486	0.198729772632126	   
df.mm.trans3:probe4	-0.0237850084188217	0.107924663366799	-0.220385291710242	0.8256198882669	   
df.mm.trans3:probe5	0.0418580734180459	0.107924663366799	0.387845299788283	0.698220043017373	   
df.mm.trans3:probe6	-0.00632915174112082	0.107924663366799	-0.0586441647689942	0.95324824026477	   
df.mm.trans3:probe7	0.068658270039676	0.107924663366799	0.636168489183328	0.524824494785048	   
df.mm.trans3:probe8	-0.171520077338282	0.107924663366799	-1.58925746893778	0.112345099354732	   
df.mm.trans3:probe9	-0.0657448575618393	0.107924663366799	-0.609173617140641	0.542559435848203	   
df.mm.trans3:probe10	-0.0555334400481108	0.107924663366799	-0.514557454391787	0.606985620050923