View source: R/propeller.anova.R
propeller.anova  R Documentation 
This function is called by propeller
and performs Ftests between
multiple experimental groups or conditions (> 2) on transformed cell type
proportions.
propeller.anova( prop.list = prop.list, design = design, coef = coef, 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 
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 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.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 groupspecific
columns.
The propeller.anova
function uses the limma
functions
lmFit
and eBayes
to extract F statistics and pvalues.
This has the additional advantage that empirical Bayes shrinkage of the
variances are performed.
produces a dataframe of results
Belinda Phipson
propeller
, getTransformedProps
,
lmFit
, 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) 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 celllevel 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 celllevel 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)
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