propeller.ttest: Performs t-tests of transformed cell type proportions

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

Performs t-tests of transformed cell type proportions

Description

This function is called by propeller and performs t-tests between two experimental groups or conditions on the transformed cell type proportions.

Usage

propeller.ttest(
  prop.list = prop.list,
  design = design,
  contrasts = contrasts,
  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
contrasts	a vector specifying which columns of the design matrix correspond to the two 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.ttest function expects that the design matrix is not in the intercept format and a contrast vector needs to be supplied. This contrast vector will identify the two groups to be tested. Note that additional confounding covariates can be accounted for as extra columns in the design matrix after the group-specific columns.

The propeller.ttest function uses the limma functions lmFit, contrasts.fit and eBayes which 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, contrasts.fit, 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)

  # 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)

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

  # Assume s1 and s2 belong to group 1 and s3 and s4 belong to group 2
  grp <- rep(c("A","B"), each=2)

  design <- model.matrix(~0+grp)
  design

  # Compare Grp A to B
  contrasts <- c(1,-1)

  propeller.ttest(prop.list, design=design, contrasts=contrasts, robust=TRUE,
  trend=FALSE, sort=TRUE)

  # Pretend additional sex variable exists and we want to control for it
  # in the linear model
  sex <- rep(c(0,1),2)
  des.sex <- model.matrix(~0+grp+sex)
  des.sex

  # Compare Grp A to B
  cont.sex <- c(1,-1,0)

  propeller.ttest(prop.list, design=des.sex, contrasts=cont.sex, robust=TRUE,
  trend=FALSE, sort=TRUE)

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