clusterContrasts: Create contrasts for testing DE of a cluster

In clusterExperiment: Compare Clusterings for Single-Cell Sequencing

Description

Uses clustering to create different types of contrasts to be tested that can then be fed into DE testing programs.

Usage

## S4 method for signature 'ClusterExperiment'
clusterContrasts(cluster, contrastType, ...)

## S4 method for signature 'vector'
clusterContrasts(
  cluster,
  contrastType = c("Dendro", "Pairs", "OneAgainstAll"),
  dendro = NULL,
  pairMat = NULL,
  outputType = c("limma", "MAST"),
  removeUnassigned = TRUE
)

Arguments

cluster	Either a vector giving contrasts assignments or a ClusterExperiment object
contrastType	What type of contrast to create. ‘Dendro’ traverses the given dendrogram and does contrasts of the samples in each side, ‘Pairs’ does pair-wise contrasts based on the pairs given in pairMat (if pairMat=NULL, does all pairwise), and ‘OneAgainstAll’ compares each cluster to the average of all others.
...	arguments that are passed to from the ClusterExperiment version to the most basic numeric version.
dendro	The dendrogram to traverse if contrastType="Dendro". Note that this should be the dendrogram of the clusters, not of the individual samples, either of class "dendrogram" or "phylo4"
pairMat	matrix giving the pairs of clusters for which to do pair-wise contrasts (must match to elements of cl). If NULL, will do all pairwise of the clusters in cluster (excluding "-1" categories). Each row is a pair to be compared and must match the names of the clusters in the vector cluster.
outputType	character string. Gives format for the resulting contrast matrix. Currently the two options are the format appropriate for limma and MAST package.
removeUnassigned	logical, whether to remove negative valued clusters from the design matrix. Appropriate to pick TRUE (default) if design will be input into linear model on samples that excludes -1.

Details

The input vector must be numeric clusters, but the external commands that make the contrast matrix (e.g. makeContrasts) require syntatically valid R names. For this reason, the names of the levels will be "X1" instead of "1". And negative values (if removeUnassigned=FALSE) will be "X.1","X.2", etc.

Value

List with components:

• contrastMatrix Contrast matrix, the form of which depends on outputType. If outputType=="limma", the result of running makeContrasts: a matrix with number of columns equal to the number of contrasts, and rows equal to the number of levels of the factor that will be fit in a linear model.

• contrastNamesA vector of names for each of the contrasts. NULL if no such additional names.

Author(s)

Elizabeth Purdom

References

Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, and Smyth, GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47. http://nar.oxfordjournals.org/content/43/7/e47

Finak, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biology (2015).

Examples

data(simData)
cl <- clusterMany(simData,nReducedDims=c(5,10,50),  
reduceMethod="PCA", makeMissingDiss=TRUE,
clusterFunction="pam", ks=2:4, findBestK=c(FALSE), removeSil=TRUE,
subsample=FALSE)
#Pairs:
clusterContrasts(cl,contrastType="Pairs")
#Dendrogram
cl<-makeDendrogram(cl)
clusterContrasts(cl,contrastType="Pairs")

clusterExperiment documentation built on Feb. 11, 2021, 2 a.m.