MapCODEXtoCITE.internal: Run all methods for anchor correction to map the CODEX...

In CamaraLab/STvEA: Spatially-resolved Transcriptomics via Epitope Anchoring

Run all methods for anchor correction to map the CODEX dataset to the CITE-seq dataset Since the CODEX dataset is usually much bigger, allows to subsample equal-sized chunks of the CODEX dataset to map individually Takes matrices and data frames instead of STvEA.data class

Description

Run all methods for anchor correction to map the CODEX dataset to the CITE-seq dataset Since the CODEX dataset is usually much bigger, allows to subsample equal-sized chunks of the CODEX dataset to map individually Takes matrices and data frames instead of STvEA.data class

Usage

MapCODEXtoCITE.internal(
  cite_protein,
  codex_protein,
  cite_latent,
  num_chunks,
  seed = NULL,
  num_cores = 1,
  num.cc = NULL,
  k.anchor = 20,
  k.filter = 100,
  k.score = 80,
  k.weight = 100
)

Arguments

cite_protein	a (n cell x f feature) protein expression matrix
codex_protein	a (m cell x f feature) protein expression matrix to be corrected
cite_latent	a (cell x feature) embedding of the mRNA expression matrix from CITE-seq
num_chunks	number of equal sized chunks to split CODEX dataset into for correction
seed	set.seed before randomly sampling chunks of CODEX dataset
num_cores	number of cores to use in parallelized correction of CODEX dataset. On Windows, this must be set to 1.
num.cc	number of canonical vectors to calculate. Defaults to number of proteins - 1
k.anchor	number of nn used to find anchors via mutual nearest neighbors
k.filter	number of nn in original feature space to use for filtering
k.score	number of nn to use in shared nearest neighbor scoring
k.weight	number of nn in original query feature space to make correction vectors

Value

a (m cell x f feature) expression matrix of the CODEX data corrected into the CITE-seq space

CamaraLab/STvEA documentation built on April 2, 2024, 6:07 a.m.