pagoda.varnorm {scde} | R Documentation

## Normalize gene expression variance relative to transcriptome-wide expectations

### Description

Normalizes gene expression magnitudes to ensure that the variance follows chi-squared statistics with respect to its ratio to the transcriptome-wide expectation as determined by local regression on expression magnitude (and optionally gene length). Corrects for batch effects.

### Usage

    pagoda.varnorm(models, counts, batch = NULL, trim = 0, prior = NULL,
      fit.genes = NULL, plot = TRUE, minimize.underdispersion = FALSE,
      n.cores = detectCores(), n.randomizations = 100, weight.k = 0.9,
      verbose = 0, weight.df.power = 1, smooth.df = -1, max.adj.var = 10,
      theta.range = c(0.01, 100), gene.length = NULL)

### Arguments

`models` | model matrix (select a subset of rows to normalize variance within a subset of cells)
`counts` | read count matrix
`batch` | measurement batch (optional)
`trim` | trim value for Winsorization (optional, can be set to 1-3 to reduce the impact of outliers, can be as large as 5 or 10 for datasets with several thousand cells)
`prior` | expression magnitude prior
`fit.genes` | a vector of gene names which should be used to establish the variance fit (default is NULL: use all genes). This can be used to specify, for instance, a set spike-in control transcripts such as ERCC.
`plot` | whether to plot the results
`minimize.underdispersion` | whether underdispersion should be minimized (can increase sensitivity in datasets with high complexity of population, however cannot be effectively used in datasets where multiple batches are present)
`n.cores` | number of cores to use
`n.randomizations` | number of bootstrap sampling rounds to use in estimating average expression magnitude for each gene within the given set of cells
`weight.k` | k value to use in the final weight matrix
`verbose` | verbosity level
`weight.df.power` | power factor to use in determining effective number of degrees of freedom (can be increased for datasets exhibiting particularly high levels of noise at low expression magnitudes)
`smooth.df` | degrees of freedom to be used in calculating smoothed local regression between coefficient of variation and expression magnitude (and gene length, if provided). Leave at -1 for automated guess.
`max.adj.var` | maximum value allowed for the estimated adjusted variance (capping of adjusted variance is recommended when scoring pathway overdispersion relative to randomly sampled gene sets)
`theta.range` | valid theta range (should be the same as was set in knn.error.models() call
`gene.length` | optional vector of gene lengths (corresponding to the rows of counts matrix)

### Value

a list containing the following fields:

- mat adjusted expression magnitude values

- matw weight matrix corresponding to the expression matrix

- arv a vector giving adjusted variance values for each gene

- avmodes a vector estimated average expression magnitudes for each gene

- modes a list of batch-specific average expression magnitudes for each gene

- prior estimated (or supplied) expression magnitude prior

- edf estimated effective degrees of freedom

- fit.genes fit.genes parameter

### Examples

    data(pollen)
    cd <- clean.counts(pollen)

    knn <- knn.error.models(cd, k=ncol(cd)/4, n.cores=10, min.count.threshold=2, min.nonfailed=5, max.model.plots=10)
    varinfo <- pagoda.varnorm(knn, counts = cd, trim = 3/ncol(cd), max.adj.var = 5, n.cores = 1, plot = FALSE)

[Package scde version 2.18.0 Index]