SignacX: SignacBoot – R documentation

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SignacBoot

Generates bootstrapped single cell data

Description

SignacBoot uses a Seurat object or an expression matrix and performs feature selection, normalization and bootstrapping to generate a training data set to be used for cell type or cluster classification.

Usage

SignacBoot(
  E,
  L,
  labels,
  size = 1000,
  impute = TRUE,
  spring.dir = NULL,
  logfc.threshold = 0.25,
  p.val.adj = 0.05,
  verbose = TRUE
)

Arguments

`E`	a gene (rows) by cell (column) matrix, sparse matrix or a Seurat object. Rows are HUGO symbols.
`L`	cell type categories for learning.
`labels`	cell type labels corresponding to the columns of E.
`size`	Number of bootstrapped samples for machine learning. Default is 1,000.
`impute`	if TRUE, performs imputation prior to bootstrapping (see `KSoftImpute`). Default is TRUE.
`spring.dir`	if using SPRING, directory to categorical_coloring_data.json. Default is NULL.
`logfc.threshold`	Cutoff for feature selection. Default is 0.25.
`p.val.adj`	Cutoff for feature selection. Default is 0.05.
`verbose`	if TRUE, code speaks. Default is TRUE.

Value

Training data set (data.frame) to be used for building new models=.

Examples

## Not run: 
# load Seurat object from SignacFast example
P <- readRDS("pbmcs.rds")

# run feature selection + bootstrapping to generate 2,000 bootstrapped cells
x = P@meta.data$celltypes
R_learned = SignacBoot(P, L = c("B.naive", "B.memory"), labels = x)

## End(Not run)

SignacX

Cell Type Identification and Discovery from Single Cell Gene Expression Data

v2.2.0

GPL-3

Authors

Mathew Chamberlain [aut, cre], Virginia Savova [aut], Richa Hanamsagar [aut], Frank Nestle [aut], Emanuele de Rinaldis [aut], Sanofi US [fnd]

Initial release

2021-02-24