chrX.26103_chrX_137060385_137068743_+_2.R 

fitVsDatCorrelation=0.870905268385998
cont.fitVsDatCorrelation=0.258180443042919

fstatistic=13278.2196073809,52,692
cont.fstatistic=3426.18186778408,52,692

residuals=-0.41656690566066,-0.0739832169623648,-0.00439544542784139,0.0797262977053977,0.627486016414729
cont.residuals=-0.562443172011601,-0.161535200940216,-0.0400969694651339,0.119695016776750,1.37103844399656

predictedValues:
Include	Exclude	Both
chrX.26103_chrX_137060385_137068743_+_2.R.tl.Lung	52.9711876540254	43.1302636788569	91.5435613286184
chrX.26103_chrX_137060385_137068743_+_2.R.tl.cerebhem	54.3374719796197	56.6842910100957	63.521204776134
chrX.26103_chrX_137060385_137068743_+_2.R.tl.cortex	55.7694923760941	42.1808166643959	100.764142479995
chrX.26103_chrX_137060385_137068743_+_2.R.tl.heart	68.3763739296155	45.8324973218118	174.899464698918
chrX.26103_chrX_137060385_137068743_+_2.R.tl.kidney	48.4341532269786	42.2176867778747	62.228446317926
chrX.26103_chrX_137060385_137068743_+_2.R.tl.liver	51.4690112770949	44.4270392473373	60.6841310459484
chrX.26103_chrX_137060385_137068743_+_2.R.tl.stomach	51.0200979582278	42.6163920299651	66.9647512239263
chrX.26103_chrX_137060385_137068743_+_2.R.tl.testicle	51.7186144021374	46.4245739543285	66.5689992337388


diffExp=9.84092397516854,-2.34681903047591,13.5886757116983,22.5438766078038,6.21646644910388,7.0419720297576,8.40370592826268,5.29404044780889
diffExpScore=1.05159948881053
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=0,0,1,1,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,1,1,0,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	54.0106003815679	56.7245227311093	58.7673319469423
cerebhem	55.5855496921395	54.4878370577395	49.6403466538315
cortex	50.5574662887	54.873367579176	63.2888801120761
heart	53.6216383338736	56.5268328719305	52.1743535751097
kidney	54.5983436000549	57.3381887645767	54.6257929659157
liver	57.1118653562157	54.4835383316646	53.1040894367081
stomach	54.0233915484519	51.1283338630048	56.290489104146
testicle	56.5246467748896	60.6642005362391	52.2258648387331
cont.diffExp=-2.71392234954143,1.09771263439998,-4.31590129047608,-2.90519453805695,-2.73984516452175,2.62832702455111,2.89505768544709,-4.13955376134946
cont.diffExpScore=2.09370543786657

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=0.158386104036118
cont.tran.correlation=0.252381830154833

tran.covariance=0.00197208295654156
cont.tran.covariance=0.000453046772079619

tran.mean=49.8506227180287
cont.tran.mean=55.1412702319584

weightedLogRatios:
wLogRatio
Lung	0.794755493795464
cerebhem	-0.169823713249723
cortex	1.08398020823166
heart	1.61014086425157
kidney	0.5235734059335
liver	0.569018929860686
stomach	0.691526980818499
testicle	0.420273461656168

cont.weightedLogRatios:
wLogRatio
Lung	-0.196776485116582
cerebhem	0.0799416419313009
cortex	-0.32472660320549
heart	-0.211490754777938
kidney	-0.197052185359532
liver	0.189463977704832
stomach	0.218213532731675
testicle	-0.287656378625826

varWeightedLogRatios=0.266748232792268
cont.varWeightedLogRatios=0.0476784518824457

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.27465945113683	0.0701718307612576	46.6662963700926	6.37480933242091e-216	***
df.mm.trans1	0.615324624541018	0.0630245658834064	9.76325050265877	3.49899616637443e-21	***
df.mm.trans2	0.451392119092868	0.0579596478507567	7.7880410911947	2.49185861470513e-14	***
df.mm.exp2	0.6641792931535	0.079390363807296	8.36599382219309	3.2846965703323e-16	***
df.mm.exp3	-0.0667481601474802	0.079390363807296	-0.840758965527577	0.400773391045229	   
df.mm.exp4	-0.331348700513943	0.079390363807296	-4.17366396403252	3.37956676397954e-05	***
df.mm.exp5	0.275074108051491	0.079390363807296	3.46482992217012	0.000563296535556783	***
df.mm.exp6	0.411987826855412	0.079390363807296	5.18939336082939	2.77541460886757e-07	***
df.mm.exp7	0.263134099889364	0.079390363807296	3.31443373314259	0.000966157389141284	***
df.mm.exp8	0.368249579389256	0.079390363807296	4.63846695907713	4.19763351817141e-06	***
df.mm.trans1:exp2	-0.638713349074575	0.0760104864037428	-8.40296358165554	2.46946696268008e-16	***
df.mm.trans2:exp2	-0.390907099526724	0.06615863650608	-5.90863294909047	5.41207162089568e-09	***
df.mm.trans1:exp3	0.118227011848426	0.0760104864037429	1.55540396387471	0.120307092095593	   
df.mm.trans2:exp3	0.0444887721989972	0.06615863650608	0.67245600194479	0.501517909138254	   
df.mm.trans1:exp4	0.586627918525925	0.0760104864037428	7.71772351790975	4.14826333782309e-14	***
df.mm.trans2:exp4	0.392117164310148	0.06615863650608	5.92692330160269	4.86866998210874e-09	***
df.mm.trans1:exp5	-0.364617034556779	0.0760104864037429	-4.79693068427496	1.974251743359e-06	***
df.mm.trans2:exp5	-0.296459781080383	0.06615863650608	-4.48104430104358	8.69014746366241e-06	***
df.mm.trans1:exp6	-0.44075605963126	0.0760104864037429	-5.7986217492427	1.01671041277221e-08	***
df.mm.trans2:exp6	-0.382364474945074	0.06615863650608	-5.7795096020447	1.13323860802242e-08	***
df.mm.trans1:exp7	-0.300662603743796	0.0760104864037428	-3.95554110977233	8.42302377672105e-05	***
df.mm.trans2:exp7	-0.275120055415284	0.06615863650608	-4.15849040948721	3.60613222497546e-05	***
df.mm.trans1:exp8	-0.392179952775919	0.0760104864037429	-5.15955062690675	3.23702413772148e-07	***
df.mm.trans2:exp8	-0.294645573491410	0.06615863650608	-4.45362221853427	9.84266269872264e-06	***
df.mm.trans1:probe2	0.0295725406703062	0.0380052432018714	0.778117390624933	0.436765770986063	   
df.mm.trans1:probe3	0.172994701969415	0.0380052432018714	4.55186409544926	6.28091809023396e-06	***
df.mm.trans1:probe4	-0.0163977729316304	0.0380052432018714	-0.431460807776725	0.666267726373317	   
df.mm.trans1:probe5	0.378851534229353	0.0380052432018714	9.96840178648555	5.82903434282131e-22	***
df.mm.trans1:probe6	0.110305342615589	0.0380052432018714	2.90237170775840	0.00382125125596597	** 
df.mm.trans1:probe7	0.18742430776916	0.0380052432018714	4.93153817681479	1.02252187328593e-06	***
df.mm.trans1:probe8	0.163750948946571	0.0380052432018714	4.30864099663249	1.88060891550993e-05	***
df.mm.trans1:probe9	0.125728472791597	0.0380052432018714	3.30818756043129	0.00098762013686737	***
df.mm.trans1:probe10	0.200196938878368	0.0380052432018714	5.26761367674948	1.84764017092875e-07	***
df.mm.trans1:probe11	-0.091593003075348	0.0380052432018714	-2.41000965547927	0.0162120068200553	*  
df.mm.trans1:probe12	0.0446572083780808	0.0380052432018714	1.1750275650356	0.240387838460223	   
df.mm.trans1:probe13	-0.00314292579584261	0.0380052432018714	-0.0826971631032175	0.934116255121694	   
df.mm.trans1:probe14	0.171729652282289	0.0380052432018714	4.51857790700397	7.320392663766e-06	***
df.mm.trans1:probe15	-0.0172881544800398	0.0380052432018714	-0.454888668603192	0.649332005344889	   
df.mm.trans1:probe16	0.195751655429917	0.0380052432018714	5.15064867208317	3.3885440064807e-07	***
df.mm.trans1:probe17	-0.0282046854444869	0.0380052432018714	-0.742126166504784	0.458262708741374	   
df.mm.trans1:probe18	0.140306034331590	0.0380052432018714	3.69175467675158	0.000240241990045115	***
df.mm.trans1:probe19	0.079472092247112	0.0380052432018714	2.09108232316742	0.036884829867974	*  
df.mm.trans1:probe20	0.118363648188548	0.0380052432018714	3.11440312484882	0.00191924589962423	** 
df.mm.trans1:probe21	0.0783530096250932	0.0380052432018714	2.06163684334047	0.0396152453237952	*  
df.mm.trans1:probe22	-0.0467300244998922	0.0380052432018714	-1.22956783230350	0.219276868292096	   
df.mm.trans2:probe2	0.155181034839240	0.0380052432018714	4.08314805446629	4.96205088422756e-05	***
df.mm.trans2:probe3	-0.00430180351576461	0.0380052432018714	-0.113189737871557	0.909912959679128	   
df.mm.trans2:probe4	0.154037468378429	0.0380052432018714	4.0530583519814	5.6287681107404e-05	***
df.mm.trans2:probe5	0.0232002007582987	0.0380052432018714	0.610447369986998	0.541765922679389	   
df.mm.trans2:probe6	0.0154432854937121	0.0380052432018714	0.406346182595975	0.684613887148407	   
df.mm.trans3:probe2	0.342447916978676	0.0380052432018714	9.01054402308924	1.97530878665922e-18	***
df.mm.trans3:probe3	0.183868315965656	0.0380052432018714	4.83797235526182	1.61810739548790e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94337151990517	0.137944139158172	28.5867275258686	2.78374438843333e-119	***
df.mm.trans1	0.0227434264315128	0.123894009779833	0.183571638951143	0.854403288202154	   
df.mm.trans2	0.101093481692752	0.113937368342079	0.887272395034034	0.3752405284962	   
df.mm.exp2	0.157296003513443	0.156065977957895	1.00788144585798	0.313863665509574	   
df.mm.exp3	-0.173371711677222	0.156065977957895	-1.11088729232194	0.267002699274062	   
df.mm.exp4	0.108276240856594	0.156065977957895	0.693785040617925	0.48804984177624	   
df.mm.exp5	0.0946634215787428	0.156065977957895	0.606560269043917	0.544341707304722	   
df.mm.exp6	0.116855736403075	0.156065977957895	0.748758556683005	0.454257163118072	   
df.mm.exp7	-0.0605704627609821	0.156065977957895	-0.388108052463065	0.698055434623674	   
df.mm.exp8	0.2306518228707	0.156065977957895	1.47791226434327	0.139886247022828	   
df.mm.trans1:exp2	-0.128553064321194	0.149421797895392	-0.86033675228023	0.389901406418071	   
df.mm.trans2:exp2	-0.1975251169031	0.130054981631579	-1.51878162931621	0.129274193857131	   
df.mm.trans1:exp3	0.107302016637630	0.149421797895392	0.718114881155094	0.47292882384318	   
df.mm.trans2:exp3	0.140193217742883	0.130054981631579	1.07795346232890	0.281430138173200	   
df.mm.trans1:exp4	-0.115503884647686	0.149421797895392	-0.773005587367842	0.439783026281606	   
df.mm.trans2:exp4	-0.111767414254350	0.130054981631579	-0.859385875513523	0.390425261200330	   
df.mm.trans1:exp5	-0.0838402069450484	0.149421797895392	-0.561097564919837	0.574912669351724	   
df.mm.trans2:exp5	-0.0839031661644755	0.130054981631579	-0.645136119446444	0.519052805049515	   
df.mm.trans1:exp6	-0.061024172406648	0.149421797895392	-0.408402075642071	0.683104851553527	   
df.mm.trans2:exp6	-0.157163746219647	0.130054981631579	-1.20844080132864	0.227290532213334	   
df.mm.trans1:exp7	0.060807261692787	0.149421797895392	0.406950408503031	0.684170251106434	   
df.mm.trans2:exp7	-0.0432973318673238	0.130054981631579	-0.332915597112434	0.739298828907875	   
df.mm.trans1:exp8	-0.185155384394667	0.149421797895392	-1.2391457404648	0.215711640202736	   
df.mm.trans2:exp8	-0.163504692689337	0.130054981631579	-1.25719669203071	0.209106561613117	   
df.mm.trans1:probe2	-0.00602651603793342	0.0747108989476959	-0.0806644829980229	0.935732106024462	   
df.mm.trans1:probe3	0.00472283654714619	0.0747108989476959	0.0632148269351248	0.949613693062762	   
df.mm.trans1:probe4	-0.030010430060694	0.0747108989476959	-0.4016874443139	0.688038088218126	   
df.mm.trans1:probe5	-0.0078976706602031	0.0747108989476959	-0.105709752813069	0.915843276490602	   
df.mm.trans1:probe6	0.0171603534230076	0.0747108989476959	0.229690094279569	0.818400467275526	   
df.mm.trans1:probe7	0.0757326269963332	0.0747108989476959	1.01367575632242	0.311091784170728	   
df.mm.trans1:probe8	0.009868374947303	0.0747108989476959	0.132087487719987	0.894953487962356	   
df.mm.trans1:probe9	-0.0155342129500205	0.0747108989476959	-0.207924321201058	0.835349236051926	   
df.mm.trans1:probe10	0.123634735828187	0.0747108989476959	1.65484203201385	0.0984099820122717	.  
df.mm.trans1:probe11	0.0544821856460167	0.0747108989476959	0.729240129798986	0.466101530426542	   
df.mm.trans1:probe12	0.0838995549777584	0.0747108989476959	1.12298949898187	0.261831410047561	   
df.mm.trans1:probe13	-0.0091338727857846	0.0747108989476959	-0.122256229203976	0.902731590594115	   
df.mm.trans1:probe14	-0.0619506874861914	0.0747108989476959	-0.829205488874686	0.407274068750634	   
df.mm.trans1:probe15	0.0174013996352870	0.0747108989476959	0.23291648046518	0.81589513487408	   
df.mm.trans1:probe16	0.109126844071140	0.0747108989476959	1.46065494604125	0.144564027263963	   
df.mm.trans1:probe17	-0.000274539537836164	0.0747108989476959	-0.00367469193522040	0.997069085696016	   
df.mm.trans1:probe18	0.0157457908593032	0.0747108989476959	0.21075627627405	0.833139502998834	   
df.mm.trans1:probe19	-0.0112878988115526	0.0747108989476959	-0.151087712375876	0.879950557364161	   
df.mm.trans1:probe20	-0.0157269592107722	0.0747108989476959	-0.210504216015155	0.833336128647724	   
df.mm.trans1:probe21	0.183957428498465	0.0747108989476959	2.46225692756356	0.0140492169185312	*  
df.mm.trans1:probe22	0.0387452642354718	0.0747108989476959	0.518602570457583	0.60420370392072	   
df.mm.trans2:probe2	0.0678359084585235	0.0747108989476959	0.90797874759899	0.364205538021013	   
df.mm.trans2:probe3	-0.0533078435488501	0.0747108989476959	-0.713521645431817	0.475763587295821	   
df.mm.trans2:probe4	0.0132668471780715	0.0747108989476959	0.177575793691888	0.859108101491275	   
df.mm.trans2:probe5	-0.049859877263192	0.0747108989476959	-0.667370865100931	0.504757753492568	   
df.mm.trans2:probe6	-0.0342604971102038	0.0747108989476959	-0.458574285583006	0.64668389035123	   
df.mm.trans3:probe2	0.000491795569103207	0.0747108989476959	0.00658264826190227	0.994749741679504	   
df.mm.trans3:probe3	0.0186412844816204	0.0747108989476959	0.249512249808036	0.803038611231833