pagoda.varnorm: Normalize gene expression variance relative to...

In hms-dbmi/scde: Single Cell Differential Expression

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)

hms-dbmi/scde documentation built on April 19, 2023, 10:21 p.m.