Description Usage Arguments Value Empirical Bayes variance regularization See Also Examples
For each gene in sca, fits the hurdle model in formula
(linear for et>0), logistic for et==0 vs et>0.
Return an object of class ZlmFit
containing slots giving the coefficients, variance-covariance matrices, etc.
After each gene, optionally run the function on the fit named by 'hook'
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
formula |
a formula with the measurement variable on the LHS and predictors present in colData on the RHS |
sca |
SingleCellAssay object |
method |
character vector, either 'glm', 'glmer' or 'bayesglm' |
silent |
Silence common problems with fitting some genes |
ebayes |
if TRUE, regularize variance using empirical bayes method |
ebayesControl |
list with parameters for empirical bayes procedure. See ebayes. |
force |
Should we continue testing genes even after many errors have occurred? |
hook |
a function called on the |
parallel |
If TRUE and |
LMlike |
if provided, then the model defined in this object will be used, rather than following the formulas. This is intended for internal use. |
onlyCoef |
If TRUE then only an array of model coefficients will be returned (probably only useful for bootstrapping). |
exprs_values |
character or integer passed to 'assay' specifying which assay to use for testing |
... |
arguments passed to the S4 model object upon construction. For example, |
a object of class ZlmFit
with methods to extract coefficients, etc.
OR, if data is a data.frame
just a list of the discrete and continuous fits.
The empirical bayes regularization of the gene variance assumes that the precision (1/variance) is drawn from a
gamma distribution with unknown parameters.
These parameters are estimated by considering the distribution of sample variances over all genes.
The procedure used for this is determined from
ebayesControl
, a named list with components 'method' (one of 'MOM' or 'MLE') and 'model' (one of 'H0' or 'H1')
method MOM uses a method-of-moments estimator, while MLE using the marginal likelihood.
H0 model estimates the precisions using the intercept alone in each gene, while H1 fits the full model specified by formula
ZlmFit-class, ebayes, GLMlike-class, BayesGLMlike-class
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(vbetaFA)
zlmVbeta <- zlm(~ Stim.Condition, subset(vbetaFA, ncells==1)[1:10,])
slotNames(zlmVbeta)
#A matrix of coefficients
coef(zlmVbeta, 'D')['CCL2',]
#An array of covariance matrices
vcov(zlmVbeta, 'D')[,,'CCL2']
waldTest(zlmVbeta, CoefficientHypothesis('Stim.ConditionUnstim'))
## Can also provide just a \code{data.frame} instead
data<- data.frame(x=rnorm(500), z=rbinom(500, 1, .3))
logit.y <- with(data, x*2 + z*2); mu.y <- with(data, 10+10*x+10*z + rnorm(500))
y <- (runif(500)<exp(logit.y)/(1+exp(logit.y)))*1
y[y>0] <- mu.y[y>0]
data$y <- y
fit <- zlm(y ~ x+z, data)
summary.glm(fit$disc)
summary.glm(fit$cont)
|
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!
[1] "coefC" "coefD" "vcovC"
[4] "vcovD" "LMlike" "sca"
[7] "deviance" "loglik" "df.null"
[10] "df.resid" "dispersion" "dispersionNoshrink"
[13] "priorDOF" "priorVar" "converged"
[16] "hookOut"
(Intercept) Stim.ConditionUnstim
-3.8329217 -0.5108005
(Intercept) Stim.ConditionUnstim
(Intercept) 0.1439196 -0.1254838
Stim.ConditionUnstim -0.1254838 0.9182853
, , metric = lambda
test.type
primerid cont disc hurdle
B3GAT1 0.9617702324 0.038250068 1.000020
BAX 7.2211565188 3.645901878 10.867058
BCL2 0.3766814067 2.202291748 2.578973
CCL2 0.8414775522 0.284135226 1.125613
CCL3 NA 3.548463195 NA
CCL4 NA 2.012308210 NA
CCL5 0.1746468538 0.862093478 1.036740
CCR2 5.3383734489 2.308408187 7.646782
CCR4 2.0437612666 0.003737042 2.047498
CCR5 0.0005534473 2.811952306 2.812506
, , 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 0.326741272 0.84494185 0.606524503
BAX 0.007204927 0.05620735 0.004367654
BCL2 0.539384664 0.13780573 0.275412150
CCL2 0.358974532 0.59400356 0.569608276
CCL3 NA 0.05960063 NA
CCL4 NA 0.15602777 NA
CCL5 0.676014596 0.35315351 0.595490308
CCR2 0.020860929 0.12867576 0.021853574
CCR4 0.152831343 0.95125460 0.359245545
CCR5 0.981231129 0.09356445 0.245059834
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-35.152 -1.172 1.019 1.201 10.449
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2448 0.1436 -1.705 0.0881 .
x 2.1279 0.1902 11.190 < 2e-16 ***
z 2.6130 0.3515 7.434 1.05e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 680.93 on 499 degrees of freedom
Residual deviance: 383.28 on 208 degrees of freedom
AIC: 389.28
Number of Fisher Scoring iterations: 6
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-3.7737 -0.6076 0.0398 0.6539 3.2698
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.02431 0.09429 106.31 <2e-16 ***
x 10.02232 0.07557 132.63 <2e-16 ***
z 9.96644 0.12435 80.15 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 1.017034)
Null deviance: 20909.09 on 288 degrees of freedom
Residual deviance: 290.87 on 286 degrees of freedom
AIC: 830.01
Number of Fisher Scoring iterations: 2
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