waldTest: Run a Wald test
In MAST: Model-based Analysis of Single Cell Transcriptomics
Description
Usage
Arguments
Value
See Also
Examples
Description
Run a Wald tests on discrete and continuous components
hypothesis can be one of a character giving complete factors or terms to be dropped from the model, CoefficientHypothesis giving names of coefficients to be dropped, Hypothesis giving contrasts using the symbolically, or a contrast matrix, with one row for each coefficient in the full model, and one column for each contrast being tested.
Usage
1
waldTest(object, hypothesis)
Arguments
object
LMlike or subclass
hypothesis
the hypothesis to be tested. See details.
Value
array giving test statistics
See Also
fit
lrTest
lht
Examples
1
2
#see ZlmFit-class for examples
example('ZlmFit-class')
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, cbind, colMeans, colSums, colnames, 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
ZlmFt-> data(vbetaFA)
ZlmFt-> zlmVbeta <- zlm(~ Stim.Condition+Population, subset(vbetaFA, ncells==1)[1:10,])
Done!
ZlmFt-> #Coefficients and standard errors
ZlmFt-> coef(zlmVbeta, 'D')
(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
ZlmFt-> coef(zlmVbeta, 'C')
(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
ZlmFt-> se.coef(zlmVbeta, 'C')
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
ZlmFt-> #Test for a Population effect by dropping the whole term (a 5 degree of freedom test)
ZlmFt-> lrTest(zlmVbeta, 'Population')
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"
ZlmFt-> #Test only if the VbetaResponsive cells differ from the baseline group
ZlmFt-> lrTest(zlmVbeta, CoefficientHypothesis('PopulationVbetaResponsive'))
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"
ZlmFt-> # Test if there is a difference between CD154+/Unresponsive and CD154-/Unresponsive.
ZlmFt-> # Note that because we parse the expression
ZlmFt-> # the columns must be enclosed in backquotes
ZlmFt-> # to protect the \quote{+} and \quote{-} characters.
ZlmFt-> lrTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
ZlmFt-+ `PopulationCD154-VbetaUnresponsive`'))
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"
ZlmFt-> waldTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
ZlmFt-+ `PopulationCD154-VbetaUnresponsive`'))
, , 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 messages:
1: In makeContrasts2(contrasts = h@.Data, levels = terms) :
Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
2: In makeContrasts2(contrasts = h@.Data, levels = terms) :
Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
Warning message:
system call failed: Cannot allocate memory
MAST documentation built on Nov. 8, 2020, 8:19 p.m.
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Description Usage Arguments Value See Also Examples
Description
Run a Wald tests on discrete and continuous components
hypothesis can be one of a character giving complete factors or terms to be dropped from the model, CoefficientHypothesis giving names of coefficients to be dropped, Hypothesis giving contrasts using the symbolically, or a contrast matrix, with one row for each coefficient in the full model, and one column for each contrast being tested.
Usage
1 | waldTest(object, hypothesis)
|
Arguments
object |
LMlike or subclass |
hypothesis |
the hypothesis to be tested. See details. |
Value
array giving test statistics
See Also
fit
lrTest
lht
Examples
1 2 | #see ZlmFit-class for examples
example('ZlmFit-class')
|
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, cbind, colMeans, colSums, colnames, 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
ZlmFt-> data(vbetaFA)
ZlmFt-> zlmVbeta <- zlm(~ Stim.Condition+Population, subset(vbetaFA, ncells==1)[1:10,])
Done!
ZlmFt-> #Coefficients and standard errors
ZlmFt-> coef(zlmVbeta, 'D')
(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
ZlmFt-> coef(zlmVbeta, 'C')
(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
ZlmFt-> se.coef(zlmVbeta, 'C')
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
ZlmFt-> #Test for a Population effect by dropping the whole term (a 5 degree of freedom test)
ZlmFt-> lrTest(zlmVbeta, 'Population')
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"
ZlmFt-> #Test only if the VbetaResponsive cells differ from the baseline group
ZlmFt-> lrTest(zlmVbeta, CoefficientHypothesis('PopulationVbetaResponsive'))
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"
ZlmFt-> # Test if there is a difference between CD154+/Unresponsive and CD154-/Unresponsive.
ZlmFt-> # Note that because we parse the expression
ZlmFt-> # the columns must be enclosed in backquotes
ZlmFt-> # to protect the \quote{+} and \quote{-} characters.
ZlmFt-> lrTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
ZlmFt-+ `PopulationCD154-VbetaUnresponsive`'))
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"
ZlmFt-> waldTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
ZlmFt-+ `PopulationCD154-VbetaUnresponsive`'))
, , 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 messages:
1: In makeContrasts2(contrasts = h@.Data, levels = terms) :
Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
2: In makeContrasts2(contrasts = h@.Data, levels = terms) :
Some levels contain symbols. Be careful to escape these names with backticks ('`') when specifying contrasts.
Warning message:
system call failed: Cannot allocate memory
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