rdi - Calculate RDI dissimilarity matrix

Description¶

Wrapper function for calculating RDIs

Usage¶

rdi(genes, seqAnnot, params = NULL, ...)

Arguments¶

genes

vector (or matrix) containing the gene calls for each sequence. If genes is a matrix, counts will be calculated for each column of ‘genes’, and the resulting count matrices will be concatenated.

seqAnnot

matrix containing repertoire annotations. Must be same length as ‘genes’.

params

list; contains parameters to pass to child functions. Should contain countParams and distParams lists, which contain parameters for calcVDJcounts and calcRDI, respectively. See Details.

…

other parameters to pass to calcVDJcounts and calcRDI.

Value¶

Dissimilarity structure, as calculated by dist. In addition to the standard attributes returned by dist, two additional attributes are defined as follows:

nseq

integer, the number of sequences used after subsampling the repertoires

Details¶

This function is a wrapper for the two core functions of RDI, calcVDJcounts and calcRDI. To control the function of both calcVDJcounts and calcRDI, additional parameters can be specified either directly in the RDI function call, or parameters for the individual functions can be wrapped up into lists of parameters and passed into the params parameter. params should be a list containing at least one of two parameter lists: countParams and distParams, which
will be passed to calcVDJcounts and calcRDI, respectively. An example analysis is included below.

Examples¶

#create genes
genes = sample(letters, 10000, replace=TRUE)

#create sequence annotations
seqAnnot = data.frame(donor = sample(1:4, 10000, replace=TRUE),
visit = sample(c("V1","V2","V3"), 10000, replace=TRUE),
cellType = sample(c("B","T"), 10000, replace=TRUE)
)

#parameters
params = list(
countParams = list(
select = list(
visit = c("V1","V3"),
cellType = "B"
),
combine = list(
visit = "V[13]"
),
simplifyNames = FALSE
),
distParams = list(
constScale=FALSE
)
)

##calculate RDI
d = rdi(genes, seqAnnot, params)

##plot using hierarchical clustering
plot(hclust(d))