normalizeVariance {veloviz} | R Documentation

## Identify overdispersed genes by normalizing counts per million (CPM) gene expression variance relative to transcriptome-wide expectations (Modified from SCDE/PAGODA2 code)

### Description

Normalizes gene expression magnitudes to with respect to its ratio to the transcriptome-wide expectation as determined by local regression on expression magnitude

### Usage

    normalizeVariance(
      cpm,
      gam.k = 5,
      alpha = 0.05,
      max.adjusted.variance = 1000,
      min.adjusted.variance = 0.001,
      verbose = TRUE,
      plot = FALSE,
      details = FALSE
    )

### Arguments

`cpm` | Counts per million (CPM) matrix. Rows are genes, columns are cells.
`gam.k` | Generalized additive model parameter; the dimension of the basis used to represent the smooth term (default: 5)
`alpha` | Significance threshold for overdispersed genes (default: 0.05)
`max.adjusted.variance` | Ceiling on maximum variance after normalization to prevent infinites (default: 1e3)
`min.adjusted.variance` | Floor on minimum variance after normalization (default: 1e-3)
`verbose` | Boolean for verbosity setting (default: TRUE)
`plot` | Boolean to plot mean variance plots before and after correction
`details` | Boolean to return detailed data frame or names of genes (default: FALSE)

### Value

A list with two items: (1) an adjusted CPM matrix with the same dimensions as the input and (2) a dataframe with the summary statistics for each gene.

### Examples

    data(vel)
    curr <- vel$current

    normalizeDepth(curr)

[Package veloviz version 1.0.0 Index]