Zlm: Zero-inflated Regression For SingleCellAssay In MAST

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MAST Model-based Analysis of Single Cell Transcriptomics

Package index Search the MAST package Vignettes
  • Package overview
  • README.md
  • Interoptability between MAST and SingleCellExperiment-derived packages.
  • MAST Intro
  • Using MAST with RNASeq: MAIT Analysis.
Functions 257 Source code 56 Man pages 74
  • applyFlat: Apply a vectorized binary operation recycling over last...
  • BayesGLMlike-class: Wrapper for bayesian GLM
  • bootVcov1: Bootstrap a zlmfit
  • calcZ: Get Z or T statistics and P values after running...
  • colData-set-SingleCellAssay-DataFrame-method: Replace 'colData'
  • collectResiduals: Residual hooks and collection methods
  • computeEtFromCt: Compute the Et from the Ct
  • convertMASTClassicToSingleCellAssay: Convert a MASTClassic SingleCellAssay
  • CovFromBoots: Extract the inter-gene covariance matrices for continuous and...
  • defaultAssay: Default 'assay' returned
  • defaultPrior: Initialize a prior to be used a prior for...
  • dof: Degrees of freedom of Zero inflated model
  • Drop: Drop specified dimension from an array
  • ebayes: Estimate hyperparameters for hierarchical variance model for...
  • expavg: Exponential average
  • filterLowExpressedGenes: Filter low-expressing genes
  • fit: fit a zero-inflated regression
  • freq: Summary statistics for genes in an experiment
  • FromFlatDF: Construct a SingleCellAssay (or derived subclass) from a...
  • FromMatrix: Construct a SingleCellAssay from a matrix or array of...
  • getConcordance: Get the concordance between two experiments
  • getwellKey: Accessor for wellKey
  • GLMlike-class: Wrapper for regular glm/lm
  • gseaAfterBoot: Gene set analysis for hurdle model
  • GSEATests-class: An S4 class for Gene Set Enrichment output
  • hushWarning: Selectively muffle warnings based on output
  • Hypothesis: Describe a linear model hypothesis to be tested
  • impute: impute missing continuous expression for plotting
  • influence.bayesglm: Influence bayesglm object
  • invlogit: Inverse of logistic transformation
  • LMERlike-class: Wrapper for lmer/glmer
  • LMlike-class: Linear Model-like Class
  • logFC: Calculate log-fold changes from hurdle model components
  • logmean: Log mean
  • LRT: Likelihood Ratio Tests for SingleCellAssays
  • lrTest: Run a likelihood-ratio test
  • lrTest-ZlmFit-character-method: Likelihood ratio test
  • maits-dataset: MAITs data set, RNASeq
  • MAST-defunct: Defunct functions in package 'MAST'
  • mast_filter: Filter a SingleCellAssay
  • MAST-package: MAST: Model-based Analysis of Single- cell Transcriptomics
  • melt.SingleCellAssay: "Melt" a 'SingleCellAssay' matrix
  • model.matrix: Model matrix accessor
  • model.matrix-set: Replace model matrix
  • myBiplot: Makes a nice BiPlot
  • pbootVcov1: Bootstrap a zlmfit
  • plotlrt: Plot a likelihood ratio test object
  • plotSCAConcordance: Concordance plots of filtered single vs n-cell assays
  • plot.thresholdSCRNACountMatrix: Plot cutpoints and densities for thresholding
  • predicted_sig-dataset: Predicted signatures
  • Browse all...
Home / Bioconductor / MAST / zlm: Zero-inflated regression for SingleCellAssay zlm: Zero-inflated regression for SingleCellAssay In MAST: Model-based Analysis of Single Cell Transcriptomics

Description Usage Arguments Value Empirical Bayes variance regularization See Also Examples

View source: R/zeroinf.R

Description

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'

Usage

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15zlm( formula, sca, method = "bayesglm", silent = TRUE, ebayes = TRUE, ebayesControl = NULL, force = FALSE, hook = NULL, parallel = TRUE, LMlike, onlyCoef = FALSE, exprs_values = assay_idx(sca)$aidx, ... )

Arguments

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 fit after each gene.

parallel

If TRUE and option(mc.cores)>1 then multiple cores will be used in fitting.

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, fitArgsC and fitArgsD, or coefPrior.

Value

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.

Empirical Bayes variance regularization

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

See Also

ZlmFit-class, ebayes, GLMlike-class, BayesGLMlike-class

Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18data(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)

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! [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 MAST documentation built on Nov. 8, 2020, 8:19 p.m.

Related to zlm in MAST...

MAST index Package overview README.md Interoptability between MAST and SingleCellExperiment-derived packages. MAST Intro Using MAST with RNASeq: MAIT Analysis.

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