View source: R/data_processing.R
CleanCITE | R Documentation |
This mixture model can either be: - a Negative Binomial on the expression counts (with optional weighting/normalization by the total ADT counts per cell after cleaning) - a Gaussian on the log-normalized expression with zeros removed, similar to the method proposed by Trong et al. in SISUA (https://www.biorxiv.org/content/10.1101/631382v1)
CleanCITE(
stvea_object,
model = "nb",
num_cores = 1,
maxit = 500,
factr = 1e-09,
optim_inits = NULL,
normalize = TRUE
)
stvea_object |
STvEA.data class with CITE-seq protein data |
model |
"nb" (Negative Binomial) or "gaussian" model to fit |
num_cores |
number of cores to use for parallelized fits |
maxit |
maximum number of iterations for optim function - only used if model is "nb" |
factr |
accuracy of optim function - only used if model is "nb" |
optim_inits |
a matrix of (proteins x params) with initialization parameters for each protein to input to the optim function. If NULL, starts at two default parameter sets and picks the better one - only used if model is "nb" |
normalize |
divide cleaned CITE-seq expression by total ADT counts per cell - only used if model is "nb" |
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