scDist: scDist: Identify perturbed cell types in single-cell RNA-seq...

In phillipnicol/pcDiffPop: Robust Identification of Perturbed Cell Types in Single-cell RNA-seq

scDist: Identify perturbed cell types in single-cell RNA-seq data

Description

Estimate the distance between condition means in gene expression space.

DESCRIPTION

Usage

scDist(
  normalized_counts,
  meta.data,
  fixed.effects,
  random.effects = c(),
  clusters,
  d = 20,
  truncate = FALSE,
  min.counts.per.cell = 20,
  weights = NULL
)

Arguments

normalized_counts	A matrix containing normalized data with genes on rows and cells on columns
meta.data	A data frame containing meta data for each cell.
fixed.effects	The columns in meta.data corresponding to the fixed effects. In a typical case, this would be the condition of interest.
random.effects	The columns in meta.data corresponding to the random effects. In a typical use case this would be the column containing patient ID.
clusters	The column containing the cell-type annotation.
d	The number of PCs to use.
truncate	Whether or not to round negative distances to 0.
min.counts.per.cell	The minimum number of cells per cluster to perform the estimation.
weights	An optional vector of length equal to the number of genes specifying the weight to place on each gene in the distance estimate.

Value

A list with components

• results - A data frame containing the cell type, estimated distance, and other statistics such as p-value.

• vals For each cell type a list of more detailed information (such as raw data) and coefficients for each PC are included.

Author(s)

Phillip B. Nicol <[e-mail]>

phillipnicol/pcDiffPop documentation built on Nov. 29, 2024, 6:06 p.m.