propeller.anova: Performs F-tests for transformed cell type proportions

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

Performs F-tests for transformed cell type proportions

Description

This function is called by propeller and performs F-tests between multiple experimental groups or conditions (> 2) on transformed cell type proportions.

Usage

propeller.anova(
  prop.list = prop.list,
  design = design,
  coef = coef,
  robust = robust,
  trend = trend,
  sort = sort
)

Arguments

prop.list	a list object containing two matrices: TransformedProps and Proportions
design	a design matrix with rows corresponding to samples and columns to coefficients to be estimated
coef	a vector specifying which the columns of the design matrix corresponding to the groups to test
robust	logical, should robust variance estimation be used. Defaults to TRUE.
trend	logical, should a trend between means and variances be accounted for. Defaults to FALSE.
sort	logical, should the output be sorted by P-value.

Details

In order to run this function, the user needs to run the getTransformedProps function first. The output from getTransformedProps is used as input. The propeller.anova function expects that the design matrix is not in the intercept format. This coef vector will identify the columns in the design matrix that correspond to the groups being tested. Note that additional confounding covariates can be accounted for as extra columns in the design matrix, but need to come after the group-specific columns.

The propeller.anova function uses the limma functions lmFit and eBayes to extract F statistics and p-values. This has the additional advantage that empirical Bayes shrinkage of the variances are performed.

Value

produces a dataframe of results

Author(s)

Belinda Phipson

See Also

propeller, getTransformedProps, lmFit, eBayes

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)
  p_s5 <- c(0.8,0.1,0.1)
  p_s6 <- c(0.75,0.2,0.05)

  # Total numbers of cells per sample
  numcells <- c(1000,1500,900,1200,1000,800)

  # Generate cell-level vector for sample info
  biorep <- rep(c("s1","s2","s3","s4","s5","s6"),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]
  n_s5 <- p_s5*numcells[5]
  n_s6 <- p_s6*numcells[6]

  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)
  cl_s5 <- rep(c("c0","c1","c2"),n_s5)
  cl_s6 <- rep(c("c0","c1","c2"),n_s6)

  # Generate cell-level vector for cluster info
  clust <- c(cl_s1,cl_s2,cl_s3,cl_s4,cl_s5,cl_s6)
  length(clust)

  prop.list <- getTransformedProps(clusters = clust, sample = biorep)

  # Assume s1 and s2 belong to group A, s3 and s4 belong to group B, s5 and
  # s6 belong to group C
  grp <- rep(c("A","B","C"), each=2)

  # Make sure design matrix does not have an intercept term
  design <- model.matrix(~0+grp)
  design

  propeller.anova(prop.list, design=design, coef=c(1,2,3), robust=TRUE,
  trend=FALSE, sort=TRUE)

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