countsplit | R Documentation |
Takes one matrix of counts and splits it into a specified number of folds. Each fold is a matrix of counts with the same dimension as the original matrix. Summing element-wise across the folds yields the original data matrix.
countsplit(X, folds = 2, epsilon = rep(1/folds, folds), overdisps = NULL)
X |
A cell-by-gene matrix of integer counts |
folds |
An integer specifying how many folds you would like to split your data into. |
epsilon |
A vector, which has length |
overdisps |
If NULL, then Poisson count splitting will be performed. Otherwise, this parameter should be a vector of non-negative numbers whose length is equal to the number of columns of |
When the argument overdisps
is set to NULL, this function performs the Poisson count splitting methodology outlined in
Neufeld et al. (2022). With this setting, the folds of data are independent only if the original data were drawn from a Poisson distribution.
If the data are thought to be overdispersed relative to the Poisson, then we may instead model them as coming from a negative binomial distribution,
If we assume that X_{ij} \sim NB(\mu_{ij}, b_j)
, where this parameterization means that E[X_{ij}] = \mu_{ij}
and Var[X_{ij}] = \mu_{ij} + \mu_{ij}^2/b_j
, then
we should pass in overdisps
= c(b_1, \ldots, b_j)
. If this is the correct assumption, then the resulting folds of data will be independent.
This is the negative binomial count splitting method of Neufeld et al. (2023).
Please see our tutorials and vignettes for more details.
A list of length folds
. Each element in the list stores a sparse matrix with the same dimensions as the data X
. Each list element is a fold of data.
reference
library(countsplit)
library(Matrix)
library(Rcpp)
# A Poisson count splitting example.
n=400
p=2
X <- matrix(rpois(n*p, 7), nrow=n, ncol=p)
split <- countsplit(X, folds=2)
Xtrain <- split[[1]]
Xtest <- split[[2]]
cor(Xtrain[,1], Xtest[,1])
cor(Xtrain[,2], Xtest[,2])
# A negative binomial count splitting example.
X <- matrix(rnbinom(n*p, mu=7, size=7), nrow=n, ncol=p)
split <- countsplit(X, folds=2, overdisps=c(7,7))
Xtrain <- split[[1]]
Xtest <- split[[2]]
cor(Xtrain[,1], Xtest[,1])
cor(Xtrain[,2], Xtest[,2])
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