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    {
      "page": "computeDevianceResiduals",
      "title": "Deviance residuals of the zero-inflated negative binomial model",
      "topics": [
        "computeDevianceResiduals"
      ]
    },
    {
      "page": "computeObservationalWeights",
      "title": "Observational weights of the zero-inflated negative binomial model for each entry in the matrix of counts",
      "topics": [
        "computeObservationalWeights"
      ]
    },
    {
      "page": "getAlpha_mu",
      "title": "Returns the matrix of paramters alpha_mu",
      "topics": [
        "getAlpha_mu"
      ]
    },
    {
      "page": "getAlpha_pi",
      "title": "Returns the matrix of paramters alpha_pi",
      "topics": [
        "getAlpha_pi"
      ]
    },
    {
      "page": "getBeta_mu",
      "title": "Returns the matrix of paramters beta_mu",
      "topics": [
        "getBeta_mu"
      ]
    },
    {
      "page": "getBeta_pi",
      "title": "Returns the matrix of paramters beta_pi",
      "topics": [
        "getBeta_pi"
      ]
    },
    {
      "page": "getEpsilon_alpha",
      "title": "Returns the vector of regularization parameter for alpha",
      "topics": [
        "getEpsilon_alpha"
      ]
    },
    {
      "page": "getEpsilon_beta_mu",
      "title": "Returns the vector of regularization parameter for beta_mu",
      "topics": [
        "getEpsilon_beta_mu"
      ]
    },
    {
      "page": "getEpsilon_beta_pi",
      "title": "Returns the vector of regularization parameter for beta_pi",
      "topics": [
        "getEpsilon_beta_pi"
      ]
    },
    {
      "page": "getEpsilon_gamma_mu",
      "title": "Returns the vector of regularization parameter for gamma_mu",
      "topics": [
        "getEpsilon_gamma_mu"
      ]
    },
    {
      "page": "getEpsilon_gamma_pi",
      "title": "Returns the vector of regularization parameter for gamma_pi",
      "topics": [
        "getEpsilon_gamma_pi"
      ]
    },
    {
      "page": "getEpsilon_W",
      "title": "Returns the vector of regularization parameter for W",
      "topics": [
        "getEpsilon_W"
      ]
    },
    {
      "page": "getEpsilon_zeta",
      "title": "Returns the regularization parameter for the dispersion parameter",
      "topics": [
        "getEpsilon_zeta"
      ]
    },
    {
      "page": "getGamma_mu",
      "title": "Returns the matrix of paramters gamma_mu",
      "topics": [
        "getGamma_mu"
      ]
    },
    {
      "page": "getGamma_pi",
      "title": "Returns the matrix of paramters gamma_pi",
      "topics": [
        "getGamma_pi"
      ]
    },
    {
      "page": "getLogitPi",
      "title": "Returns the matrix of logit of probabilities of zero",
      "topics": [
        "getLogitPi"
      ]
    },
    {
      "page": "getLogMu",
      "title": "Returns the matrix of logarithm of mean parameters",
      "topics": [
        "getLogMu"
      ]
    },
    {
      "page": "getMu",
      "title": "Returns the matrix of mean parameters",
      "topics": [
        "getMu"
      ]
    },
    {
      "page": "getPhi",
      "title": "Returns the vector of dispersion parameters",
      "topics": [
        "getPhi"
      ]
    },
    {
      "page": "getPi",
      "title": "Returns the matrix of probabilities of zero",
      "topics": [
        "getPi"
      ]
    },
    {
      "page": "getTheta",
      "title": "Returns the vector of inverse dispersion parameters",
      "topics": [
        "getTheta"
      ]
    },
    {
      "page": "getV_mu",
      "title": "Returns the gene-level design matrix for mu",
      "topics": [
        "getV_mu"
      ]
    },
    {
      "page": "getV_pi",
      "title": "Returns the gene-level design matrix for pi",
      "topics": [
        "getV_pi"
      ]
    },
    {
      "page": "getW",
      "title": "Returns the low-dimensional matrix of inferred sample-level covariates W",
      "topics": [
        "getW"
      ]
    },
    {
      "page": "getX_mu",
      "title": "Returns the sample-level design matrix for mu",
      "topics": [
        "getX_mu"
      ]
    },
    {
      "page": "getX_pi",
      "title": "Returns the sample-level design matrix for pi",
      "topics": [
        "getX_pi"
      ]
    },
    {
      "page": "getZeta",
      "title": "Returns the vector of log of inverse dispersion parameters",
      "topics": [
        "getZeta"
      ]
    },
    {
      "page": "glmWeightedF",
      "title": "Zero-inflation adjusted statistical tests for assessing differential expression.",
      "topics": [
        "glmWeightedF"
      ]
    },
    {
      "page": "imputeZeros",
      "title": "Impute the zeros using the estimated parameters from the ZINB model.",
      "topics": [
        "imputeZeros"
      ]
    },
    {
      "page": "independentFiltering",
      "title": "Perform independent filtering in differential expression analysis.",
      "topics": [
        "independentFiltering"
      ]
    },
    {
      "page": "loglik",
      "title": "Compute the log-likelihood of a model given some data",
      "topics": [
        "loglik",
        "loglik,ZinbModel,matrix-method"
      ]
    },
    {
      "page": "nFactors",
      "title": "Generic function that returns the number of latent factors",
      "topics": [
        "nFactors"
      ]
    },
    {
      "page": "nFeatures",
      "title": "Generic function that returns the number of features",
      "topics": [
        "nFeatures"
      ]
    },
    {
      "page": "nParams",
      "title": "Generic function that returns the total number of parameters of the model",
      "topics": [
        "nParams",
        "nParams,ZinbModel-method"
      ]
    },
    {
      "page": "nSamples",
      "title": "Generic function that returns the number of samples",
      "topics": [
        "nSamples"
      ]
    },
    {
      "page": "orthogonalizeTraceNorm",
      "title": "Orthogonalize matrices to minimize trace norm of their product",
      "topics": [
        "orthogonalizeTraceNorm"
      ]
    },
    {
      "page": "penalty",
      "title": "Compute the penalty of a model",
      "topics": [
        "penalty",
        "penalty,ZinbModel-method"
      ]
    },
    {
      "page": "pvalueAdjustment",
      "title": "Perform independent filtering in differential expression analysis.",
      "topics": [
        "pvalueAdjustment"
      ]
    },
    {
      "page": "solveRidgeRegression",
      "title": "Solve ridge regression or logistic regression problems",
      "topics": [
        "solveRidgeRegression"
      ]
    },
    {
      "page": "toydata",
      "title": "Toy dataset to check the model",
      "topics": [
        "toydata"
      ]
    },
    {
      "page": "zinb.loglik",
      "title": "Log-likelihood of the zero-inflated negative binomial model",
      "topics": [
        "zinb.loglik"
      ]
    },
    {
      "page": "zinb.loglik.dispersion",
      "title": "Log-likelihood of the zero-inflated negative binomial model, for a fixed dispersion parameter",
      "topics": [
        "zinb.loglik.dispersion"
      ]
    },
    {
      "page": "zinb.loglik.dispersion.gradient",
      "title": "Derivative of the log-likelihood of the zero-inflated negative binomial model with respect to the log of the inverse dispersion parameter",
      "topics": [
        "zinb.loglik.dispersion.gradient"
      ]
    },
    {
      "page": "zinb.loglik.matrix",
      "title": "Log-likelihood of the zero-inflated negative binomial model for each entry in the matrix of counts",
      "topics": [
        "zinb.loglik.matrix"
      ]
    },
    {
      "page": "zinb.loglik.regression",
      "title": "Penalized log-likelihood of the ZINB regression model",
      "topics": [
        "zinb.loglik.regression"
      ]
    },
    {
      "page": "zinb.loglik.regression.gradient",
      "title": "Gradient of the penalized log-likelihood of the ZINB regression model",
      "topics": [
        "zinb.loglik.regression.gradient"
      ]
    },
    {
      "page": "zinb.regression.parseModel",
      "title": "Parse ZINB regression model",
      "topics": [
        "zinb.regression.parseModel"
      ]
    },
    {
      "page": "zinbAIC",
      "title": "Compute the AIC or BIC of a model given some data",
      "topics": [
        "zinbAIC",
        "zinbAIC,ZinbModel,matrix-method",
        "zinbBIC",
        "zinbBIC,ZinbModel,matrix-method"
      ]
    },
    {
      "page": "zinbFit",
      "title": "Fit a ZINB regression model",
      "topics": [
        "zinbFit",
        "zinbFit,dgCMatrix-method",
        "zinbFit,matrix-method",
        "zinbFit,SummarizedExperiment-method"
      ]
    },
    {
      "page": "zinbInitialize",
      "title": "Initialize the parameters of a ZINB regression model",
      "topics": [
        "zinbInitialize"
      ]
    },
    {
      "page": "zinbModel",
      "title": "Initialize an object of class ZinbModel",
      "topics": [
        "zinbModel"
      ]
    },
    {
      "page": "ZinbModel-class",
      "title": "Class ZinbModel",
      "topics": [
        "getAlpha_mu,ZinbModel-method",
        "getAlpha_pi,ZinbModel-method",
        "getBeta_mu,ZinbModel-method",
        "getBeta_pi,ZinbModel-method",
        "getEpsilon_alpha,ZinbModel-method",
        "getEpsilon_beta_mu,ZinbModel-method",
        "getEpsilon_beta_pi,ZinbModel-method",
        "getEpsilon_gamma_mu,ZinbModel-method",
        "getEpsilon_gamma_pi,ZinbModel-method",
        "getEpsilon_W,ZinbModel-method",
        "getEpsilon_zeta,ZinbModel-method",
        "getGamma_mu,ZinbModel-method",
        "getGamma_pi,ZinbModel-method",
        "getLogitPi,ZinbModel-method",
        "getLogMu,ZinbModel-method",
        "getMu,ZinbModel-method",
        "getPhi,ZinbModel-method",
        "getPi,ZinbModel-method",
        "getTheta,ZinbModel-method",
        "getV_mu,ZinbModel-method",
        "getV_pi,ZinbModel-method",
        "getW,ZinbModel-method",
        "getX_mu,ZinbModel-method",
        "getX_pi,ZinbModel-method",
        "getZeta,ZinbModel-method",
        "nFactors,ZinbModel-method",
        "nFeatures,ZinbModel-method",
        "nSamples,ZinbModel-method",
        "show,ZinbModel-method",
        "ZinbModel",
        "ZinbModel-class"
      ]
    },
    {
      "page": "zinbOptimize",
      "title": "Optimize the parameters of a ZINB regression model",
      "topics": [
        "zinbOptimize"
      ]
    },
    {
      "page": "zinbOptimizeDispersion",
      "title": "Optimize the dispersion parameters of a ZINB regression model",
      "topics": [
        "zinbOptimizeDispersion"
      ]
    },
    {
      "page": "zinbSim",
      "title": "Simulate counts from a zero-inflated negative binomial model",
      "topics": [
        "zinbSim",
        "zinbSim,ZinbModel-method"
      ]
    },
    {
      "page": "zinbsurf",
      "title": "Perform dimensionality reduction using a ZINB regression model for large datasets.",
      "topics": [
        "zinbsurf",
        "zinbsurf,SummarizedExperiment-method"
      ]
    },
    {
      "page": "zinbwave",
      "title": "Perform dimensionality reduction using a ZINB regression model with gene and cell-level covariates.",
      "topics": [
        "zinbwave",
        "zinbwave,SummarizedExperiment-method"
      ]
    }
  ],
  "_readme": "https://github.com/bioc/zinbwave/raw/HEAD/README.md",
  "_rundeps": [
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    "askpass",
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    "Matrix",
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    "matrixStats",
    "memoise",
    "mime",
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    "pkgconfig",
    "png",
    "R6",
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    "RSQLite",
    "S4Arrays",
    "S4Vectors",
    "Seqinfo",
    "SingleCellExperiment",
    "snow",
    "softImpute",
    "SparseArray",
    "statmod",
    "SummarizedExperiment",
    "survival",
    "sys",
    "vctrs",
    "XML",
    "xtable",
    "XVector"
  ],
  "_vignettes": [
    {
      "source": "intro.Rmd",
      "filename": "intro.html",
      "title": "An introduction to ZINB-WaVE",
      "author": "Davide Risso",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Installation",
        "Introduction",
        "The ZINB-WaVE model",
        "Example dataset",
        "Gene filtering",
        "ZINB-WaVE",
        "Adding covariates",
        "Sample-level covariates",
        "Gene-level covariates",
        "t-SNE representation",
        "Normalized values and deviance residuals",
        "The zinbFit function",
        "Differential Expression",
        "Differential expression with edgeR",
        "Differential expression with DESeq2",
        "Using zinbwave with Seurat",
        "Working with large datasets",
        "A note on performance and parallel computing",
        "Session Info",
        "References"
      ],
      "created": "2017-06-22 19:42:39",
      "modified": "2024-03-07 23:09:24",
      "commits": 22
    }
  ],
  "_score": 10.231008749188025,
  "_indexed": true,
  "_nocasepkg": "zinbwave",
  "_universes": [
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    "drisso"
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