run.anchor {iCellR}

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.