ZlmFit-class: An S4 class to hold the output of a call to zlm
In MAST: Model-based Analysis of Single Cell Transcriptomics
Description
Usage
Arguments
Value
Methods (by generic)
Slots
See Also
Examples
Description
This holds output from a call to zlm. Many methods are defined to operate on it. See below.
Usage
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## S4 method for signature 'ZlmFit,CoefficientHypothesis'
lrTest(object, hypothesis)
## S4 method for signature 'ZlmFit,Hypothesis'
lrTest(object, hypothesis)
## S4 method for signature 'ZlmFit,matrix'
lrTest(object, hypothesis)
## S4 method for signature 'ZlmFit,CoefficientHypothesis'
waldTest(object, hypothesis)
## S4 method for signature 'ZlmFit,Hypothesis'
waldTest(object, hypothesis)
## S4 method for signature 'ZlmFit'
coef(object, which, ...)
## S4 method for signature 'ZlmFit'
vcov(object, which, ...)
## S4 method for signature 'ZlmFit'
se.coef(object, which, ...)
Arguments
object
ZlmFit
hypothesis
call to Hypothesis or CoefficientHypothesis or a matrix giving such contrasts.
which
character vector, one of "C" (continuous) or "D" (discrete) specifying which component should be returned
...
ignored
Value
see "Methods (by generic)"
Methods (by generic)
-
lrTest: Returns an array with likelihood-ratio tests on contrasts defined using CoefficientHypothesis().
-
lrTest: Returns an array with likelihood-ratio tests specified by Hypothesis, which is a Hypothesis.
-
lrTest: Returns an array with likelihood-ratio tests specified by Hypothesis, which is a contrast matrix.
-
waldTest: Returns an array with Wald Tests on contrasts defined using CoefficientHypothesis().
-
waldTest: Returns an array with Wald Tests on contrasts defined in Hypothesis()
-
coef: Returns the matrix of coefficients for component which.
-
vcov: Returns an array of variance/covariance matrices for component which.
-
se.coef: Returns a matrix of standard error estimates for coefficients on component which.
Slots
coefCmatrix of continuous coefficients
coefDmatrix of discrete coefficients
vcovCarray of variance/covariance matrices for coefficients
vcovDarray of variance/covariance matrices for coefficients
LMlikethe LmWrapper object used
scathe SingleCellAssay object used
deviancematrix of deviances
loglikmatrix of loglikelihoods
df.nullmatrix of null (intercept only) degrees of freedom
df.residmatrix of residual DOF
dispersionmatrix of dispersions (after shrinkage)
dispersionNoShrinkmatrix of dispersion (before shrinkage)
priorDOFshrinkage weight in terms of number of psuedo-obs
priorVarshrinkage target
convergedoutput that may optionally be set by the underlying modeling function
hookOuta list of length ngenes containing output from a hook function, if zlm was called with one
See Also
zlm summary,ZlmFit-method
Examples
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data(vbetaFA)
zlmVbeta <- zlm(~ Stim.Condition+Population, subset(vbetaFA, ncells==1)[1:10,])
#Coefficients and standard errors
coef(zlmVbeta, 'D')
coef(zlmVbeta, 'C')
se.coef(zlmVbeta, 'C')
#Test for a Population effect by dropping the whole term (a 5 degree of freedom test)
lrTest(zlmVbeta, 'Population')
#Test only if the VbetaResponsive cells differ from the baseline group
lrTest(zlmVbeta, CoefficientHypothesis('PopulationVbetaResponsive'))
# Test if there is a difference between CD154+/Unresponsive and CD154-/Unresponsive.
# Note that because we parse the expression
# the columns must be enclosed in backquotes
# to protect the \quote{+} and \quote{-} characters.
lrTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
`PopulationCD154-VbetaUnresponsive`'))
waldTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
`PopulationCD154-VbetaUnresponsive`'))
Example output
Loading required package: SummarizedExperiment
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: DelayedArray
Loading required package: matrixStats
Attaching package: 'matrixStats'
The following objects are masked from 'package:Biobase':
anyMissing, rowMedians
Attaching package: 'DelayedArray'
The following objects are masked from 'package:matrixStats':
colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
The following object is masked from 'package:base':
apply
Attaching package: 'MAST'
The following object is masked from 'package:stats':
filter
Warning messages:
1: no function found corresponding to methods exports from 'DelayedArray' for: 'acbind', 'arbind'
2: no function found corresponding to methods exports from 'SummarizedExperiment' for: 'acbind', 'arbind'
Done!
(Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
B3GAT1 -4.1795641 0.63282096 -1.162182250
BAX -0.9330077 0.02794714 -0.162052054
BCL2 -3.9226304 -2.20112377 1.332228054
CCL2 -3.8309742 0.54760417 0.147212758
CCL3 -2.3287704 -1.94930881 -0.567152079
CCL4 -2.9386008 -1.06755978 -0.003368973
CCL5 -1.2350667 0.09455937 -0.484094746
CCR2 -4.1050742 1.37877860 1.504033462
CCR4 -0.1126440 -0.21758890 -0.839917265
CCR5 -2.7417205 -1.20694083 0.521925468
PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
B3GAT1 -1.14875333 -0.16659777
BAX 0.45518519 -0.46291603
BCL2 -1.29400053 -0.39116499
CCL2 0.84204812 0.14680392
CCL3 -0.14648827 -0.60189062
CCL4 0.02118041 -1.25132594
CCL5 -0.65755684 -0.41313077
CCR2 0.40289360 0.39925195
CCR4 -0.24604817 -0.68742757
CCR5 -0.16103855 0.02099718
PopulationVbetaResponsive PopulationVbetaUnresponsive
B3GAT1 -1.8211123 -0.53512412
BAX 0.5582425 0.53846239
BCL2 2.0919143 1.91118512
CCL2 -0.7797822 -2.02874891
CCL3 -0.7669146 -2.46844725
CCL4 -2.1491640 -2.15309456
CCL5 -0.8123545 -0.94068157
CCR2 -0.4731219 0.18547502
CCR4 -0.1364576 -0.01272681
CCR5 -0.5077805 -0.12742084
(Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
B3GAT1 18.19441 -1.7969063 NA
BAX 17.67008 -0.6534516 -0.2356154
BCL2 18.73554 1.3286670 -1.3753715
CCL2 23.94679 -7.3355989 -3.6985974
CCL3 19.86182 NA 3.2750181
CCL4 19.54785 NA 1.2579223
CCL5 20.07363 0.4504418 -0.2026483
CCR2 15.27429 -1.2269270 4.1759840
CCR4 18.03446 -0.3702826 -0.1011834
CCR5 16.28993 0.9461661 1.1537964
PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
B3GAT1 NA -0.5500307
BAX -0.19649762 -0.2783088
BCL2 NA -3.1850947
CCL2 -7.04188443 -3.9220153
CCL3 -0.04812137 -0.6043503
CCL4 2.77190815 -2.5532715
CCL5 -0.84757938 -1.5303864
CCR2 3.19304135 4.0151403
CCR4 -0.57583680 -0.7602052
CCR5 -0.51211945 -0.6646556
PopulationVbetaResponsive PopulationVbetaUnresponsive
B3GAT1 NA NA
BAX -0.04213554 -0.01140701
BCL2 -1.58527659 -2.32150932
CCL2 NA NA
CCL3 0.45944273 NA
CCL4 NA NA
CCL5 -1.61445447 -1.33608326
CCR2 3.31403666 2.07650762
CCR4 -0.26166401 -0.75331686
CCR5 -1.26236131 -1.52747203
X1 (Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
B3GAT1 1.1195139 1.9390550 NA
BAX 0.2289293 0.2625570 0.4158706
BCL2 1.1852624 1.2983899 1.3686231
CCL2 2.5224874 4.3690763 4.3690763
CCL3 1.4009323 NA 3.1325799
CCL4 1.7441805 NA 3.2630629
CCL5 0.4951370 1.0038683 1.0106941
CCR2 1.1047933 1.2757054 1.2757054
CCR4 0.2983985 0.4372988 0.6361878
CCR5 0.6656093 2.1048414 0.9984140
X1 PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
B3GAT1 NA 1.9390550
BAX 0.3643741 0.3574157
BCL2 NA 1.6762142
CCL2 3.5673358 3.5673358
CCL3 2.6825812 2.4264860
CCL4 3.2630629 4.2723522
CCL5 1.0847908 0.7768348
CCR2 1.5624136 1.3530899
CCR4 0.5532457 0.4774376
CCR5 1.2452410 0.9413137
X1 PopulationVbetaResponsive PopulationVbetaUnresponsive
B3GAT1 NA NA
BAX 0.3211103 0.3241627
BCL2 1.2670988 1.2983899
CCL2 NA NA
CCL3 3.1325799 NA
CCL4 NA NA
CCL5 1.0362222 0.9807059
CCR2 1.8596442 1.5624136
CCR4 0.4777267 0.4725825
CCR5 1.6304032 1.0869354
Refitting on reduced model...
Done!
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 0.000000 3.293892 3.293892
BAX 1.072472 10.728081 11.800553
BCL2 5.443797 20.567934 26.011731
CCL2 4.103765 4.775242 8.879007
CCL3 1.523584 7.006491 8.530076
CCL4 1.516648 8.444875 9.961523
CCL5 5.572644 5.389674 10.962318
CCR2 12.540757 4.640800 17.181557
CCR4 4.607460 8.728194 13.335653
CCR5 7.136405 1.526627 8.663031
, , metric = df
test.type
primerid cont disc hurdle
B3GAT1 0 5 5
BAX 5 5 10
BCL2 4 5 9
CCL2 3 5 8
CCL3 4 5 9
CCL4 3 5 8
CCL5 5 5 10
CCR2 5 5 10
CCR4 5 5 10
CCR5 5 5 10
, , metric = Pr(>Chisq)
test.type
primerid cont disc hurdle
B3GAT1 1.00000000 0.6547769674 0.65477697
BAX 0.95651117 0.0570459966 0.29862654
BCL2 0.24471407 0.0009773063 0.00203398
CCL2 0.25047521 0.4439211826 0.35260548
CCL3 0.82245576 0.2201579879 0.48173135
CCL4 0.67843337 0.1333623010 0.26773693
CCL5 0.35004597 0.3701952302 0.36046135
CCR2 0.02808429 0.4612686366 0.07044188
CCR4 0.46563569 0.1204092082 0.20550576
CCR5 0.21069179 0.9099767975 0.56435354
attr(,"test")
[1] "Population"
Refitting on reduced model...
Done!
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 0.00000000 2.2974635 2.2974635
BAX 0.01776885 2.5081546 2.5259234
BCL2 1.80508046 9.4881861 11.2932665
CCL2 0.00000000 0.6678630 0.6678630
CCL3 0.02425584 1.1530015 1.1772574
CCL4 0.00000000 3.8914009 3.8914009
CCL5 2.54893181 3.3702326 5.9191644
CCR2 3.99053248 0.1406114 4.1311439
CCR4 0.30752656 0.1884803 0.4960069
CCR5 0.73461509 0.3702349 1.1048500
, , metric = df
test.type
primerid cont disc hurdle
B3GAT1 0 1 1
BAX 1 1 2
BCL2 1 1 2
CCL2 0 1 1
CCL3 1 1 2
CCL4 0 1 1
CCL5 1 1 2
CCR2 1 1 2
CCR4 1 1 2
CCR5 1 1 2
, , metric = Pr(>Chisq)
test.type
primerid cont disc hurdle
B3GAT1 1.0000000 0.129585466 0.129585466
BAX 0.8939563 0.113258489 0.282815165
BCL2 0.1790995 0.002067992 0.003529379
CCL2 1.0000000 0.413797670 0.413797670
CCL3 0.8762357 0.282921705 0.555087964
CCL4 1.0000000 0.048533925 0.048533925
CCL5 0.1103689 0.066384383 0.051840572
CCR2 0.0457566 0.707673921 0.126745777
CCR4 0.5792020 0.664184477 0.780357269
CCR5 0.3913913 0.542876237 0.575552395
attr(,"test")
[1] "PopulationVbetaResponsive"
Refitting on reduced model...
Done!
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 0.000000000 0.19720610 0.1972061
BAX 0.009603320 0.43127916 0.4408825
BCL2 2.004979393 2.88482572 4.8898051
CCL2 0.003581828 -0.10852740 -0.1049456
CCL3 1.389919527 -0.59684167 0.7930779
CCL4 0.695474388 1.22446841 1.9199428
CCL5 1.643474587 0.01090422 1.6543788
CCR2 0.037354159 1.44946742 1.4868216
CCR4 0.977322744 0.13931993 1.1166427
CCR5 3.776168456 0.50849269 4.2846611
, , metric = df
test.type
primerid cont disc hurdle
B3GAT1 0 1 1
BAX 1 1 2
BCL2 1 1 2
CCL2 1 1 2
CCL3 1 1 2
CCL4 1 1 2
CCL5 1 1 2
CCR2 1 1 2
CCR4 1 1 2
CCR5 1 1 2
, , metric = Pr(>Chisq)
test.type
primerid cont disc hurdle
B3GAT1 1.00000000 0.65698552 0.65698552
BAX 0.92193505 0.51136195 0.80216477
BCL2 0.15678342 0.08941768 0.08673459
CCL2 0.95227640 1.00000000 1.00000000
CCL3 0.23841869 1.00000000 0.67264409
CCL4 0.40430856 0.26848550 0.38290384
CCL5 0.19984941 0.91683346 0.43727657
CCR2 0.84674577 0.22861343 0.47548935
CCR4 0.32286067 0.70895803 0.57216874
CCR5 0.05198758 0.47579209 0.11738096
attr(,"test")
[1] "Contrast Matrix"
Warning message:
In makeContrasts2(contrasts = h@.Data, levels = terms) :
Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 NA 2.632691e-01 NA
BAX 0.009305455 4.373274e-01 0.446632837
BCL2 1.748463371 2.500540e+00 4.249003565
CCL2 0.002614910 1.215898e-07 0.002615031
CCL3 1.278016527 1.692421e-03 1.279708948
CCL4 0.636616606 1.271218e+00 1.907834528
CCL5 1.553679693 1.810976e-02 1.571789452
CCR2 0.025434721 1.431864e+00 1.457299039
CCR4 0.955370495 1.297495e-01 1.085120034
CCR5 3.317281808 5.264420e-01 3.843723848
, , metric = df
test.type
primerid cont disc hurdle
B3GAT1 1 1 2
BAX 1 1 2
BCL2 1 1 2
CCL2 1 1 2
CCL3 1 1 2
CCL4 1 1 2
CCL5 1 1 2
CCR2 1 1 2
CCR4 1 1 2
CCR5 1 1 2
, , metric = Pr(>Chisq)
test.type
primerid cont disc hurdle
B3GAT1 NA 0.6078831 NA
BAX 0.92315144 0.5084152 0.7998617
BCL2 0.18607002 0.1138073 0.1194925
CCL2 0.95921700 0.9997218 0.9986933
CCL3 0.25826815 0.9671850 0.5273692
CCL4 0.42493864 0.2595383 0.3852290
CCL5 0.21259304 0.8929499 0.4557118
CCR2 0.87328861 0.2314604 0.4825602
CCR4 0.32835604 0.7186919 0.5812583
CCR5 0.06855509 0.4681066 0.1463342
Warning message:
In makeContrasts2(contrasts = h@.Data, levels = terms) :
Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
MAST documentation built on Nov. 8, 2020, 8:19 p.m.
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Description Usage Arguments Value Methods (by generic) Slots See Also Examples
Description
This holds output from a call to zlm. Many methods are defined to operate on it. See below.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## S4 method for signature 'ZlmFit,CoefficientHypothesis'
lrTest(object, hypothesis)
## S4 method for signature 'ZlmFit,Hypothesis'
lrTest(object, hypothesis)
## S4 method for signature 'ZlmFit,matrix'
lrTest(object, hypothesis)
## S4 method for signature 'ZlmFit,CoefficientHypothesis'
waldTest(object, hypothesis)
## S4 method for signature 'ZlmFit,Hypothesis'
waldTest(object, hypothesis)
## S4 method for signature 'ZlmFit'
coef(object, which, ...)
## S4 method for signature 'ZlmFit'
vcov(object, which, ...)
## S4 method for signature 'ZlmFit'
se.coef(object, which, ...)
|
Arguments
object |
|
hypothesis |
call to Hypothesis or CoefficientHypothesis or a matrix giving such contrasts. |
which |
character vector, one of "C" (continuous) or "D" (discrete) specifying which component should be returned |
... |
ignored |
Value
see "Methods (by generic)"
Methods (by generic)
-
lrTest: Returns an array with likelihood-ratio tests on contrasts defined usingCoefficientHypothesis(). -
lrTest: Returns an array with likelihood-ratio tests specified byHypothesis, which is a Hypothesis. -
lrTest: Returns an array with likelihood-ratio tests specified byHypothesis, which is a contrastmatrix. -
waldTest: Returns an array with Wald Tests on contrasts defined usingCoefficientHypothesis(). -
waldTest: Returns an array with Wald Tests on contrasts defined inHypothesis() -
coef: Returns the matrix of coefficients for componentwhich. -
vcov: Returns an array of variance/covariance matrices for componentwhich. -
se.coef: Returns a matrix of standard error estimates for coefficients on componentwhich.
Slots
coefCmatrix of continuous coefficients
coefDmatrix of discrete coefficients
vcovCarray of variance/covariance matrices for coefficients
vcovDarray of variance/covariance matrices for coefficients
LMlikethe LmWrapper object used
scathe
SingleCellAssayobject useddeviancematrix of deviances
loglikmatrix of loglikelihoods
df.nullmatrix of null (intercept only) degrees of freedom
df.residmatrix of residual DOF
dispersionmatrix of dispersions (after shrinkage)
dispersionNoShrinkmatrix of dispersion (before shrinkage)
priorDOFshrinkage weight in terms of number of psuedo-obs
priorVarshrinkage target
convergedoutput that may optionally be set by the underlying modeling function
hookOuta list of length ngenes containing output from a hook function, if
zlmwas called with one
See Also
zlm summary,ZlmFit-method
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(vbetaFA)
zlmVbeta <- zlm(~ Stim.Condition+Population, subset(vbetaFA, ncells==1)[1:10,])
#Coefficients and standard errors
coef(zlmVbeta, 'D')
coef(zlmVbeta, 'C')
se.coef(zlmVbeta, 'C')
#Test for a Population effect by dropping the whole term (a 5 degree of freedom test)
lrTest(zlmVbeta, 'Population')
#Test only if the VbetaResponsive cells differ from the baseline group
lrTest(zlmVbeta, CoefficientHypothesis('PopulationVbetaResponsive'))
# Test if there is a difference between CD154+/Unresponsive and CD154-/Unresponsive.
# Note that because we parse the expression
# the columns must be enclosed in backquotes
# to protect the \quote{+} and \quote{-} characters.
lrTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
`PopulationCD154-VbetaUnresponsive`'))
waldTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
`PopulationCD154-VbetaUnresponsive`'))
|
Example output
Loading required package: SummarizedExperiment
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: DelayedArray
Loading required package: matrixStats
Attaching package: 'matrixStats'
The following objects are masked from 'package:Biobase':
anyMissing, rowMedians
Attaching package: 'DelayedArray'
The following objects are masked from 'package:matrixStats':
colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
The following object is masked from 'package:base':
apply
Attaching package: 'MAST'
The following object is masked from 'package:stats':
filter
Warning messages:
1: no function found corresponding to methods exports from 'DelayedArray' for: 'acbind', 'arbind'
2: no function found corresponding to methods exports from 'SummarizedExperiment' for: 'acbind', 'arbind'
Done!
(Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
B3GAT1 -4.1795641 0.63282096 -1.162182250
BAX -0.9330077 0.02794714 -0.162052054
BCL2 -3.9226304 -2.20112377 1.332228054
CCL2 -3.8309742 0.54760417 0.147212758
CCL3 -2.3287704 -1.94930881 -0.567152079
CCL4 -2.9386008 -1.06755978 -0.003368973
CCL5 -1.2350667 0.09455937 -0.484094746
CCR2 -4.1050742 1.37877860 1.504033462
CCR4 -0.1126440 -0.21758890 -0.839917265
CCR5 -2.7417205 -1.20694083 0.521925468
PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
B3GAT1 -1.14875333 -0.16659777
BAX 0.45518519 -0.46291603
BCL2 -1.29400053 -0.39116499
CCL2 0.84204812 0.14680392
CCL3 -0.14648827 -0.60189062
CCL4 0.02118041 -1.25132594
CCL5 -0.65755684 -0.41313077
CCR2 0.40289360 0.39925195
CCR4 -0.24604817 -0.68742757
CCR5 -0.16103855 0.02099718
PopulationVbetaResponsive PopulationVbetaUnresponsive
B3GAT1 -1.8211123 -0.53512412
BAX 0.5582425 0.53846239
BCL2 2.0919143 1.91118512
CCL2 -0.7797822 -2.02874891
CCL3 -0.7669146 -2.46844725
CCL4 -2.1491640 -2.15309456
CCL5 -0.8123545 -0.94068157
CCR2 -0.4731219 0.18547502
CCR4 -0.1364576 -0.01272681
CCR5 -0.5077805 -0.12742084
(Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
B3GAT1 18.19441 -1.7969063 NA
BAX 17.67008 -0.6534516 -0.2356154
BCL2 18.73554 1.3286670 -1.3753715
CCL2 23.94679 -7.3355989 -3.6985974
CCL3 19.86182 NA 3.2750181
CCL4 19.54785 NA 1.2579223
CCL5 20.07363 0.4504418 -0.2026483
CCR2 15.27429 -1.2269270 4.1759840
CCR4 18.03446 -0.3702826 -0.1011834
CCR5 16.28993 0.9461661 1.1537964
PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
B3GAT1 NA -0.5500307
BAX -0.19649762 -0.2783088
BCL2 NA -3.1850947
CCL2 -7.04188443 -3.9220153
CCL3 -0.04812137 -0.6043503
CCL4 2.77190815 -2.5532715
CCL5 -0.84757938 -1.5303864
CCR2 3.19304135 4.0151403
CCR4 -0.57583680 -0.7602052
CCR5 -0.51211945 -0.6646556
PopulationVbetaResponsive PopulationVbetaUnresponsive
B3GAT1 NA NA
BAX -0.04213554 -0.01140701
BCL2 -1.58527659 -2.32150932
CCL2 NA NA
CCL3 0.45944273 NA
CCL4 NA NA
CCL5 -1.61445447 -1.33608326
CCR2 3.31403666 2.07650762
CCR4 -0.26166401 -0.75331686
CCR5 -1.26236131 -1.52747203
X1 (Intercept) Stim.ConditionUnstim PopulationCD154+VbetaUnresponsive
B3GAT1 1.1195139 1.9390550 NA
BAX 0.2289293 0.2625570 0.4158706
BCL2 1.1852624 1.2983899 1.3686231
CCL2 2.5224874 4.3690763 4.3690763
CCL3 1.4009323 NA 3.1325799
CCL4 1.7441805 NA 3.2630629
CCL5 0.4951370 1.0038683 1.0106941
CCR2 1.1047933 1.2757054 1.2757054
CCR4 0.2983985 0.4372988 0.6361878
CCR5 0.6656093 2.1048414 0.9984140
X1 PopulationCD154-VbetaResponsive PopulationCD154-VbetaUnresponsive
B3GAT1 NA 1.9390550
BAX 0.3643741 0.3574157
BCL2 NA 1.6762142
CCL2 3.5673358 3.5673358
CCL3 2.6825812 2.4264860
CCL4 3.2630629 4.2723522
CCL5 1.0847908 0.7768348
CCR2 1.5624136 1.3530899
CCR4 0.5532457 0.4774376
CCR5 1.2452410 0.9413137
X1 PopulationVbetaResponsive PopulationVbetaUnresponsive
B3GAT1 NA NA
BAX 0.3211103 0.3241627
BCL2 1.2670988 1.2983899
CCL2 NA NA
CCL3 3.1325799 NA
CCL4 NA NA
CCL5 1.0362222 0.9807059
CCR2 1.8596442 1.5624136
CCR4 0.4777267 0.4725825
CCR5 1.6304032 1.0869354
Refitting on reduced model...
Done!
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 0.000000 3.293892 3.293892
BAX 1.072472 10.728081 11.800553
BCL2 5.443797 20.567934 26.011731
CCL2 4.103765 4.775242 8.879007
CCL3 1.523584 7.006491 8.530076
CCL4 1.516648 8.444875 9.961523
CCL5 5.572644 5.389674 10.962318
CCR2 12.540757 4.640800 17.181557
CCR4 4.607460 8.728194 13.335653
CCR5 7.136405 1.526627 8.663031
, , metric = df
test.type
primerid cont disc hurdle
B3GAT1 0 5 5
BAX 5 5 10
BCL2 4 5 9
CCL2 3 5 8
CCL3 4 5 9
CCL4 3 5 8
CCL5 5 5 10
CCR2 5 5 10
CCR4 5 5 10
CCR5 5 5 10
, , metric = Pr(>Chisq)
test.type
primerid cont disc hurdle
B3GAT1 1.00000000 0.6547769674 0.65477697
BAX 0.95651117 0.0570459966 0.29862654
BCL2 0.24471407 0.0009773063 0.00203398
CCL2 0.25047521 0.4439211826 0.35260548
CCL3 0.82245576 0.2201579879 0.48173135
CCL4 0.67843337 0.1333623010 0.26773693
CCL5 0.35004597 0.3701952302 0.36046135
CCR2 0.02808429 0.4612686366 0.07044188
CCR4 0.46563569 0.1204092082 0.20550576
CCR5 0.21069179 0.9099767975 0.56435354
attr(,"test")
[1] "Population"
Refitting on reduced model...
Done!
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 0.00000000 2.2974635 2.2974635
BAX 0.01776885 2.5081546 2.5259234
BCL2 1.80508046 9.4881861 11.2932665
CCL2 0.00000000 0.6678630 0.6678630
CCL3 0.02425584 1.1530015 1.1772574
CCL4 0.00000000 3.8914009 3.8914009
CCL5 2.54893181 3.3702326 5.9191644
CCR2 3.99053248 0.1406114 4.1311439
CCR4 0.30752656 0.1884803 0.4960069
CCR5 0.73461509 0.3702349 1.1048500
, , metric = df
test.type
primerid cont disc hurdle
B3GAT1 0 1 1
BAX 1 1 2
BCL2 1 1 2
CCL2 0 1 1
CCL3 1 1 2
CCL4 0 1 1
CCL5 1 1 2
CCR2 1 1 2
CCR4 1 1 2
CCR5 1 1 2
, , metric = Pr(>Chisq)
test.type
primerid cont disc hurdle
B3GAT1 1.0000000 0.129585466 0.129585466
BAX 0.8939563 0.113258489 0.282815165
BCL2 0.1790995 0.002067992 0.003529379
CCL2 1.0000000 0.413797670 0.413797670
CCL3 0.8762357 0.282921705 0.555087964
CCL4 1.0000000 0.048533925 0.048533925
CCL5 0.1103689 0.066384383 0.051840572
CCR2 0.0457566 0.707673921 0.126745777
CCR4 0.5792020 0.664184477 0.780357269
CCR5 0.3913913 0.542876237 0.575552395
attr(,"test")
[1] "PopulationVbetaResponsive"
Refitting on reduced model...
Done!
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 0.000000000 0.19720610 0.1972061
BAX 0.009603320 0.43127916 0.4408825
BCL2 2.004979393 2.88482572 4.8898051
CCL2 0.003581828 -0.10852740 -0.1049456
CCL3 1.389919527 -0.59684167 0.7930779
CCL4 0.695474388 1.22446841 1.9199428
CCL5 1.643474587 0.01090422 1.6543788
CCR2 0.037354159 1.44946742 1.4868216
CCR4 0.977322744 0.13931993 1.1166427
CCR5 3.776168456 0.50849269 4.2846611
, , metric = df
test.type
primerid cont disc hurdle
B3GAT1 0 1 1
BAX 1 1 2
BCL2 1 1 2
CCL2 1 1 2
CCL3 1 1 2
CCL4 1 1 2
CCL5 1 1 2
CCR2 1 1 2
CCR4 1 1 2
CCR5 1 1 2
, , metric = Pr(>Chisq)
test.type
primerid cont disc hurdle
B3GAT1 1.00000000 0.65698552 0.65698552
BAX 0.92193505 0.51136195 0.80216477
BCL2 0.15678342 0.08941768 0.08673459
CCL2 0.95227640 1.00000000 1.00000000
CCL3 0.23841869 1.00000000 0.67264409
CCL4 0.40430856 0.26848550 0.38290384
CCL5 0.19984941 0.91683346 0.43727657
CCR2 0.84674577 0.22861343 0.47548935
CCR4 0.32286067 0.70895803 0.57216874
CCR5 0.05198758 0.47579209 0.11738096
attr(,"test")
[1] "Contrast Matrix"
Warning message:
In makeContrasts2(contrasts = h@.Data, levels = terms) :
Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 NA 2.632691e-01 NA
BAX 0.009305455 4.373274e-01 0.446632837
BCL2 1.748463371 2.500540e+00 4.249003565
CCL2 0.002614910 1.215898e-07 0.002615031
CCL3 1.278016527 1.692421e-03 1.279708948
CCL4 0.636616606 1.271218e+00 1.907834528
CCL5 1.553679693 1.810976e-02 1.571789452
CCR2 0.025434721 1.431864e+00 1.457299039
CCR4 0.955370495 1.297495e-01 1.085120034
CCR5 3.317281808 5.264420e-01 3.843723848
, , metric = df
test.type
primerid cont disc hurdle
B3GAT1 1 1 2
BAX 1 1 2
BCL2 1 1 2
CCL2 1 1 2
CCL3 1 1 2
CCL4 1 1 2
CCL5 1 1 2
CCR2 1 1 2
CCR4 1 1 2
CCR5 1 1 2
, , metric = Pr(>Chisq)
test.type
primerid cont disc hurdle
B3GAT1 NA 0.6078831 NA
BAX 0.92315144 0.5084152 0.7998617
BCL2 0.18607002 0.1138073 0.1194925
CCL2 0.95921700 0.9997218 0.9986933
CCL3 0.25826815 0.9671850 0.5273692
CCL4 0.42493864 0.2595383 0.3852290
CCL5 0.21259304 0.8929499 0.4557118
CCR2 0.87328861 0.2314604 0.4825602
CCR4 0.32835604 0.7186919 0.5812583
CCR5 0.06855509 0.4681066 0.1463342
Warning message:
In makeContrasts2(contrasts = h@.Data, levels = terms) :
Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
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