chr5.18217_chr5_32427293_32429497_+_2.R 

fitVsDatCorrelation=0.863181413529289
cont.fitVsDatCorrelation=0.250486179160726

fstatistic=6364.02510265944,44,508
cont.fstatistic=1722.50111667773,44,508

residuals=-0.583939937710963,-0.117122171517098,0.00489060868426162,0.111382492354311,0.602321427331577
cont.residuals=-0.84363690682532,-0.298090135689306,0.0255414679984406,0.292135821769863,0.847357209511625

predictedValues:
Include	Exclude	Both
chr5.18217_chr5_32427293_32429497_+_2.R.tl.Lung	82.251441282295	113.226175012987	95.5694214873097
chr5.18217_chr5_32427293_32429497_+_2.R.tl.cerebhem	68.4001959830287	93.531377288873	77.0907677301205
chr5.18217_chr5_32427293_32429497_+_2.R.tl.cortex	83.8870706892036	100.247569953802	85.5305921474855
chr5.18217_chr5_32427293_32429497_+_2.R.tl.heart	65.9552756442234	96.844157804332	65.8363181273653
chr5.18217_chr5_32427293_32429497_+_2.R.tl.kidney	76.8069683376269	132.769939279826	75.2101330707715
chr5.18217_chr5_32427293_32429497_+_2.R.tl.liver	71.1105570355854	116.111277233827	72.7981532451084
chr5.18217_chr5_32427293_32429497_+_2.R.tl.stomach	67.5085860728474	98.2308829277163	69.1070236176138
chr5.18217_chr5_32427293_32429497_+_2.R.tl.testicle	64.1144013025091	96.8422903199146	62.1355074109814


diffExp=-30.9747337306918,-25.1311813058442,-16.360499264598,-30.8888821601086,-55.9629709421989,-45.0007201982419,-30.7222968548689,-32.7278890174055
diffExpScore=0.996279335211421
diffExp1.5=0,0,0,0,-1,-1,0,-1
diffExp1.5Score=0.75
diffExp1.4=0,0,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.833333333333333
diffExp1.3=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	77.9417042864785	76.8294333564113	89.5599977057063
cerebhem	89.0358362960717	80.979543971474	78.466940199129
cortex	86.9346766055739	87.659214852032	78.2528651753108
heart	85.0211445378774	80.007741579787	87.5446165821653
kidney	86.1334744982454	78.7297151300662	93.7341993031772
liver	86.4948725017537	95.5405699034043	88.1293306022494
stomach	83.4987661281697	86.1736284696162	78.7866467630044
testicle	76.0859575695991	87.1188108651819	92.1716854606603
cont.diffExp=1.11227093006721,8.05629232459765,-0.724538246458081,5.0134029580904,7.40375936817921,-9.04569740165059,-2.67486234144647,-11.0328532955828
cont.diffExpScore=15.5809682489780

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=0.463681363611163
cont.tran.correlation=0.13644456735191

tran.covariance=0.00621817709176002
cont.tran.covariance=0.000541414973153908

tran.mean=89.2398853855373
cont.tran.mean=84.0115681594839

weightedLogRatios:
wLogRatio
Lung	-1.46046858497766
cerebhem	-1.37116981540489
cortex	-0.805077331337596
heart	-1.68287221472985
kidney	-2.52586950141909
liver	-2.21101467112361
stomach	-1.65021018896680
testicle	-1.800964343124

cont.weightedLogRatios:
wLogRatio
Lung	0.0625064698047516
cerebhem	0.421253530633095
cortex	-0.0370941081726314
heart	0.268177392303086
kidney	0.396445840630987
liver	-0.448572833201632
stomach	-0.140022454724197
testicle	-0.595741612798248

varWeightedLogRatios=0.274498234529789
cont.varWeightedLogRatios=0.140984419077123

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29820112185925	0.100089204774441	42.9437033848514	3.45236621103161e-171	***
df.mm.trans1	-0.152202743883270	0.0862480603763228	-1.76470917976786	0.0782135102079224	.  
df.mm.trans2	0.436003826655359	0.0805513838944674	5.41274160139296	9.58434042220355e-08	***
df.mm.exp2	-0.160626243232150	0.108296876043749	-1.48320292422158	0.13864062225262	   
df.mm.exp3	0.00892483918521574	0.108296876043749	0.082410864572034	0.93435245746858	   
df.mm.exp4	-0.00440706656755365	0.108296876043749	-0.0406943092778902	0.967555586659378	   
df.mm.exp5	0.330311880236363	0.108296876043749	3.05005917347908	0.00240742566245924	** 
df.mm.exp6	0.151778867936414	0.108296876043749	1.40150735165343	0.161673059342463	   
df.mm.exp7	-0.0153962927499050	0.108296876043749	-0.142167468835254	0.8870040918316	   
df.mm.exp8	0.0251198170751387	0.108296876043749	0.231953293509510	0.816667698314423	   
df.mm.trans1:exp2	-0.0237789795754415	0.0977564422312795	-0.243247186913609	0.807912104913904	   
df.mm.trans2:exp2	-0.0304641578622571	0.0859327099223566	-0.354511778923097	0.723102491617349	   
df.mm.trans1:exp3	0.0107657459082255	0.0977564422312795	0.110128249990472	0.912351156126834	   
df.mm.trans2:exp3	-0.130669380224575	0.0859327099223566	-1.52060118135039	0.128982103429516	   
df.mm.trans1:exp4	-0.216396975544111	0.0977564422312796	-2.2136339110229	0.0272974501214212	*  
df.mm.trans2:exp4	-0.151877234571736	0.0859327099223566	-1.76739724266769	0.077762031303591	.  
df.mm.trans1:exp5	-0.398797423316876	0.0977564422312796	-4.07950017629908	5.23939484097414e-05	***
df.mm.trans2:exp5	-0.171081396732118	0.0859327099223566	-1.99087631341658	0.0470297394549152	*  
df.mm.trans1:exp6	-0.2973239733173	0.0977564422312795	-3.0414770273029	0.00247584277215236	** 
df.mm.trans2:exp6	-0.126617217222708	0.0859327099223566	-1.47344611076634	0.141250132958908	   
df.mm.trans1:exp7	-0.182129829062711	0.0977564422312795	-1.86309796986898	0.063025364797836	.  
df.mm.trans2:exp7	-0.126670418302297	0.0859327099223566	-1.47406521238243	0.141083433949623	   
df.mm.trans1:exp8	-0.274231721757919	0.0977564422312795	-2.80525472796075	0.00522043031756775	** 
df.mm.trans2:exp8	-0.181423401798132	0.0859327099223566	-2.11122635329498	0.0352407244433640	*  
df.mm.trans1:probe2	0.0269609925095992	0.0570774652965285	0.472357915151481	0.63687418949754	   
df.mm.trans1:probe3	0.146090247398666	0.0570774652965285	2.55950832153633	0.0107704442410140	*  
df.mm.trans1:probe4	0.037787656632973	0.0570774652965285	0.6620416032257	0.50824476553792	   
df.mm.trans1:probe5	-0.0892615093544723	0.0570774652965285	-1.56386603523372	0.118471735999521	   
df.mm.trans1:probe6	0.046448696679668	0.0570774652965285	0.813783450935637	0.416150287542128	   
df.mm.trans1:probe7	0.648271168464299	0.0570774652965285	11.3577427640909	8.5219340244274e-27	***
df.mm.trans1:probe8	0.773629446438493	0.0570774652965285	13.5540259613726	6.06428698950904e-36	***
df.mm.trans1:probe9	0.527481497690617	0.0570774652965285	9.24150178972119	6.65140607981551e-19	***
df.mm.trans1:probe10	0.777776343968912	0.0570774652965285	13.6266798101179	2.92833934084945e-36	***
df.mm.trans1:probe11	0.669586454581481	0.0570774652965285	11.7311876254990	2.7318279819518e-28	***
df.mm.trans1:probe12	0.919532094974298	0.0570774652965285	16.1102475416025	1.80428507639660e-47	***
df.mm.trans2:probe2	-0.00660689996333864	0.0570774652965285	-0.115753212393271	0.907893873949672	   
df.mm.trans2:probe3	-0.086899869992658	0.0570774652965285	-1.52248999743062	0.128508567439873	   
df.mm.trans2:probe4	0.0691586076856377	0.0570774652965285	1.21166220900570	0.226205005527256	   
df.mm.trans2:probe5	-0.0115601477400097	0.0570774652965285	-0.202534357122420	0.839580089790427	   
df.mm.trans2:probe6	-0.0170850858811915	0.0570774652965285	-0.299331545162897	0.76480951124253	   
df.mm.trans3:probe2	0.355602412444593	0.0570774652965285	6.2301717603816	9.78618867365141e-10	***
df.mm.trans3:probe3	-0.18642732640446	0.0570774652965285	-3.26621593015621	0.00116356384329726	** 
df.mm.trans3:probe4	-0.149164970845634	0.0570774652965285	-2.61337762759249	0.00923103469968828	** 
df.mm.trans3:probe5	-0.130756588490053	0.0570774652965285	-2.29086186309688	0.0223800613249593	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22143398596114	0.191918300138861	21.9959950817965	7.92121929067562e-76	***
df.mm.trans1	0.125784004265760	0.165378286049932	0.760583552231172	0.4472587167623	   
df.mm.trans2	0.0920096504074017	0.154455065415874	0.595704971926067	0.551637637952849	   
df.mm.exp2	0.317917917027036	0.207656284286642	1.53098143944535	0.126396441328651	   
df.mm.exp3	0.376028073049287	0.207656284286642	1.81081961637255	0.0707593757433829	.  
df.mm.exp4	0.150234603593289	0.207656284286642	0.723477279338729	0.469719701267119	   
df.mm.exp5	0.0788154573785341	0.207656284286642	0.379547662856857	0.704439826558039	   
df.mm.exp6	0.338190492487061	0.207656284286642	1.62860706888231	0.104016181516235	   
df.mm.exp7	0.311812312525409	0.207656284286642	1.50157898469855	0.133827173072688	   
df.mm.exp8	0.0728433529190866	0.207656284286642	0.350788097597547	0.725892746729922	   
df.mm.trans1:exp2	-0.184840139645266	0.187445292056520	-0.986101798649236	0.324552587274414	   
df.mm.trans2:exp2	-0.265309151372732	0.164773610218911	-1.61014346302332	0.107987710584035	   
df.mm.trans1:exp3	-0.266832246351678	0.187445292056520	-1.42352066261136	0.155199150509176	   
df.mm.trans2:exp3	-0.244159148401919	0.164773610218911	-1.48178551211896	0.139017387328373	   
df.mm.trans1:exp4	-0.0632957850879209	0.187445292056520	-0.337676046133158	0.735746815081714	   
df.mm.trans2:exp4	-0.109699017415552	0.164773610218911	-0.665755986470227	0.505869260740833	   
df.mm.trans1:exp5	0.021121498453342	0.187445292056520	0.112680869290509	0.910328081121207	   
df.mm.trans2:exp5	-0.054382612065217	0.164773610218911	-0.330044428795161	0.741502449338555	   
df.mm.trans1:exp6	-0.234066524061214	0.187445292056520	-1.24871914089278	0.212342960263143	   
df.mm.trans2:exp6	-0.120227333028175	0.164773610218911	-0.729651628488604	0.465939455469051	   
df.mm.trans1:exp7	-0.242941624004564	0.187445292056520	-1.29606682216009	0.195540979320537	   
df.mm.trans2:exp7	-0.197035929451966	0.164773610218911	-1.19579785373515	0.232333322796941	   
df.mm.trans1:exp8	-0.0969407974207696	0.187445292056520	-0.517168483439632	0.605263648008264	   
df.mm.trans2:exp8	0.052841662661318	0.164773610218911	0.320692510111994	0.748575301361351	   
df.mm.trans1:probe2	-0.0369527383973207	0.109444471465537	-0.337639150726373	0.735774605504369	   
df.mm.trans1:probe3	0.130616640792295	0.109444471465537	1.19345124557913	0.233249744554433	   
df.mm.trans1:probe4	-0.0722495516958412	0.109444471465537	-0.660148025097748	0.50945804313793	   
df.mm.trans1:probe5	0.0377873513686024	0.109444471465537	0.345265054164944	0.730038030418768	   
df.mm.trans1:probe6	-0.140059799226781	0.109444471465537	-1.27973389017539	0.201222874109307	   
df.mm.trans1:probe7	0.120401956760347	0.109444471465537	1.10011913025923	0.271801257557119	   
df.mm.trans1:probe8	0.0240029598789050	0.109444471465537	0.219316330532632	0.826491733178479	   
df.mm.trans1:probe9	0.103095641057344	0.109444471465537	0.941990396379293	0.346645295504156	   
df.mm.trans1:probe10	-0.0352798907362628	0.109444471465537	-0.322354252013287	0.747316955957966	   
df.mm.trans1:probe11	0.0279811161981425	0.109444471465537	0.255664957977831	0.798313041050066	   
df.mm.trans1:probe12	-0.0107096924399036	0.109444471465537	-0.0978550336667846	0.922086003279922	   
df.mm.trans2:probe2	0.0172289469917153	0.109444471465537	0.157421811819343	0.874974973705256	   
df.mm.trans2:probe3	0.0787657219773731	0.109444471465537	0.7196866221075	0.472048928391052	   
df.mm.trans2:probe4	0.100739418902281	0.109444471465537	0.920461468298127	0.357768545844819	   
df.mm.trans2:probe5	0.128894270023312	0.109444471465537	1.17771385157540	0.239462138877445	   
df.mm.trans2:probe6	-0.0160424087711210	0.109444471465537	-0.146580348521055	0.88352141578213	   
df.mm.trans3:probe2	-0.0105890590251466	0.109444471465537	-0.0967527996924078	0.92296086641375	   
df.mm.trans3:probe3	-0.00307694507392457	0.109444471465537	-0.0281142120083559	0.97758209996423	   
df.mm.trans3:probe4	0.118489459662729	0.109444471465537	1.08264454180347	0.279479845886677	   
df.mm.trans3:probe5	0.173582171830648	0.109444471465537	1.58602960484219	0.113354834813848