robustSingleCell (version 0.1.1)

read.data: Read and Preprocess Data

Description

Read 10X data files or a raw data matrix and perform normalization, QC filtering and duplicates removal.

Usage

read.data(environment, genome = "mm10", min.genes.per.cell = 500,
  max.genes.per.cell.quantile = 0.98,
  max.UMIs.per.cell.quantile = 0.98, min.cells.per.gene = 1,
  max.mitochondrial.frac = 0.1, max.ribosomal.frac = NA,
  cell.filters = NA, raw.data.matrices = NA, rerun = F,
  subsample = NULL, seed = 0)

Arguments

environment

environment object

genome

genome annotation

min.genes.per.cell

minimum required number of genes per cell

max.genes.per.cell.quantile

upper quantile for number of genes per cell

max.UMIs.per.cell.quantile

upper quantile for number of UMIs per cell

min.cells.per.gene

minimum required number of cells per gene

max.mitochondrial.frac

maximum fraction of reads mapped to mitochondrial genes per cell

max.ribosomal.frac

maximum fraction of reads mapped to ribosomal genes per cell

cell.filters

filtering option for cells based on marker genes

raw.data.matrices

logical indicating if data matrices is provided instead of 10X dataset

rerun

whether to rerun loading the dataset or load from cache

subsample

number of cells to subsample

seed

seed for subsampling of cells

Examples

# NOT RUN {
LCMV1 <- setup_LCMV_example()
data.path <- system.file("extdata/LCMV1_small.txt", package = "robustSingleCell")
# name of list should be the same as LCMV1$datasets
raw_LCMV1 <- as.matrix(read.table(data.path, check.names = FALSE))
LCMV1 <- read.data(LCMV1,
raw.data.matrices = list(LCMV1 = raw_LCMV1),
min.genes.per.cell = 100,
max.genes.per.cell.quantile = 1,
max.UMIs.per.cell.quantile = 1,
min.cells.per.gene = 1)
# }