run.anchor: Run anchor alignment on the main data.

In rezakj/iCellR: Analyzing High-Throughput Single Cell Sequencing Data

Run anchor alignment on the main data.

Description

This function takes an object of class iCellR and runs anchor alignment. It's a wrapper for Seurat.

Usage

run.anchor(
  x = NULL,
  method = "base.mean.rank",
  top.rank = 500,
  gene.list = "character",
  data.type = "main",
  normalization.method = "LogNormalize",
  scale.factor = 10000,
  margin = 1,
  block.size = NULL,
  selection.method = "vst",
  nfeatures = 2000,
  anchor.features = 2000,
  scale = TRUE,
  sct.clip.range = NULL,
  reduction = c("cca", "rpca"),
  l2.norm = TRUE,
  dims = 1:30,
  k.anchor = 5,
  k.filter = 200,
  k.score = 30,
  max.features = 200,
  nn.method = "rann",
  eps = 0,
  k.weight = 100
)

Arguments

x	An object of class iCellR.
method	Choose from "base.mean.rank" or "gene.model", default is "base.mean.rank". If gene.model is chosen you need to provide gene.list.
top.rank	A number taking the top genes ranked by base mean, default = 500.
gene.list	A charactor vector of genes to be used for PCA. If "clust.method" is set to "gene.model", default = "my_model_genes.txt".
data.type	Choose from "main" and "imputed", default = "main"
normalization.method	Choose from "LogNormalize", "CLR" and "RC". LogNormalize: Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor. This is then natural-log transformed using log1p. CLR: Applies a centered log ratio transformation. RC: Relative counts. Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor. No log-transformation is applied. For counts per million (CPM) set ‘scale.factor = 1e6’
scale.factor	Sets the scale factor for cell-level normalization.
margin	If performing CLR normalization, normalize across features (1) or cells (2)
block.size	How many cells should be run in each chunk, will try to split evenly across threads
selection.method	Choose from "vst","mean.var.plot (mvp)","dispersion (disp)".
nfeatures	Number of features to select as top variable features; only used when ‘selection.method’ is set to ‘'dispersion'’ or ‘'vst'’
anchor.features	A numeric value. This will call ‘SelectIntegrationFeatures’ to select the provided number of features to be used in anchor finding
scale	Whether or not to scale the features provided. Only set to FALSE if you have previously scaled the features you want to use for each object in the object.list
sct.clip.range	Numeric of length two specifying the min and max values the Pearson residual will be clipped to
reduction	cca: Canonical correlation analysis. rpca: Reciprocal PCA
l2.norm	Perform L2 normalization on the CCA cell embeddings after dimensional reduction
dims	Which dimensions to use from the CCA to specify the neighbor search space
k.anchor	How many neighbors (k) to use when picking anchors
k.filter	How many neighbors (k) to use when filtering anchors
k.score	How many neighbors (k) to use when scoring anchors
max.features	The maximum number of features to use when specifying the neighborhood search space in the anchor filtering
nn.method	Method for nearest neighbor finding. Options include: rann, annoy
eps	Error bound on the neighbor finding algorithm (from RANN)
k.weight	Number of neighbors to consider when weighting

Value

An object of class iCellR.

rezakj/iCellR documentation built on March 29, 2024, 6:55 p.m.