View source: R/propeller.ttest.R
propeller.ttest  R Documentation 
This function is called by propeller
and performs ttests between two
experimental groups or conditions on the transformed cell type proportions.
propeller.ttest( prop.list = prop.list, design = design, contrasts = contrasts, robust = robust, trend = trend, sort = sort )
prop.list 
a list object containing two matrices:

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 Pvalue. 
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 groupspecific 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.
produces a dataframe of results
Belinda Phipson
propeller
, getTransformedProps
,
lmFit
, contrasts.fit
, eBayes
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 celllevel 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 celllevel 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)
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