getTransformedProps: Calculates and transforms cell type proportions

In Oshlack/speckle: Statistical methods for analysing single cell RNA-seq data

Calculates and transforms cell type proportions

Description

Calculates cell types proportions based on clusters/cell types and sample information and performs a variance stabilising transformation on the proportions.

Usage

getTransformedProps(clusters = clusters, sample = sample, transform = NULL)

Arguments

clusters	a factor specifying the cluster or cell type for every cell.
sample	a factor specifying the biological replicate for every cell.
transform	a character scalar specifying which transformation of the proportions to perform. Possible values include "asin" or "logit". Defaults to "asin".

Details

This function is called by the propeller function and calculates cell type proportions and performs an arcsin-square root transformation.

Value

outputs a list object with the following components

Counts	A matrix of cell type counts with the rows corresponding to the clusters/cell types and the columns corresponding to the biological replicates/samples.
TransformedProps	A matrix of transformed cell type proportions with the rows corresponding to the clusters/cell types and the columns corresponding to the biological replicates/samples.
Proportions	A matrix of cell type proportions with the rows corresponding to the clusters/cell types and the columns corresponding to the biological replicates/samples.

Author(s)

Belinda Phipson

See Also

propeller

Examples

  library(speckle)
  library(ggplot2)
  library(limma)

  # Make up some data

  # True cell type proportions for 4 samples
  p_s1 <- c(0.5,0.3,0.2)
  p_s2 <- c(0.6,0.3,0.1)
  p_s3 <- c(0.3,0.4,0.3)
  p_s4 <- c(0.4,0.3,0.3)

  # Total numbers of cells per sample
  numcells <- c(1000,1500,900,1200)

  # Generate cell-level vector for sample info
  biorep <- rep(c("s1","s2","s3","s4"),numcells)
  length(biorep)

  # Numbers of cells for each of 3 clusters per sample
  n_s1 <- p_s1*numcells[1]
  n_s2 <- p_s2*numcells[2]
  n_s3 <- p_s3*numcells[3]
  n_s4 <- p_s4*numcells[4]

  cl_s1 <- rep(c("c0","c1","c2"),n_s1)
  cl_s2 <- rep(c("c0","c1","c2"),n_s2)
  cl_s3 <- rep(c("c0","c1","c2"),n_s3)
  cl_s4 <- rep(c("c0","c1","c2"),n_s4)

  # Generate cell-level vector for cluster info
  clust <- c(cl_s1,cl_s2,cl_s3,cl_s4)
  length(clust)

  getTransformedProps(clusters = clust, sample = biorep)

Oshlack/speckle documentation built on Oct. 16, 2022, 9:39 a.m.