dataset_seurat: Build SummarizedExperiment using a Seurat object

View source: R/dataset.R

dataset_seuratR Documentation

Build SummarizedExperiment using a Seurat object

Description

Build SummarizedExperiment using a Seurat object

Usage

dataset_seurat(
  seurat_obj,
  counts_layer,
  cell_id_col,
  cell_type_col,
  assay = NULL,
  tpm_layer = NULL,
  name = "SimBu_dataset",
  spike_in_col = NULL,
  additional_cols = NULL,
  filter_genes = TRUE,
  variance_cutoff = 0,
  type_abundance_cutoff = 0,
  scale_tpm = TRUE
)

Arguments

seurat_obj

(mandatory) Seurat object with TPM counts

counts_layer

(mandatory) name of assay in Seurat object which contains count data in 'counts' slot

cell_id_col

(mandatory) name of column in Seurat meta.data with unique cell ids

cell_type_col

(mandatory) name of column in Seurat meta.data with cell type name

assay

name of the Seurat objecy assay that should be used. If NULL (default), the currently active assay is used

tpm_layer

name of assay in Seurat object which contains TPM data in 'counts' slot

name

name of the dataset; will be used for new unique IDs of cells

spike_in_col

which column in annotation contains information on spike_in counts, which can be used to re-scale counts; mandatory for spike_in scaling factor in simulation

additional_cols

list of column names in annotation, that should be stored as well in dataset object

filter_genes

boolean, if TRUE, removes all genes with 0 expression over all samples & genes with variance below variance_cutoff

variance_cutoff

numeric, is only applied if filter_genes is TRUE: removes all genes with variance below the chosen cutoff

type_abundance_cutoff

numeric, remove all cells, whose cell-type appears less then the given value. This removes low abundant cell-types

scale_tpm

boolean, if TRUE (default) the cells in tpm_matrix will be scaled to sum up to 1e6

Value

Return a SummarizedExperiment object

Examples

counts <- Matrix::Matrix(matrix(stats::rpois(3e5, 5), ncol = 300), sparse = TRUE)
tpm <- Matrix::Matrix(matrix(stats::rpois(3e5, 5), ncol = 300), sparse = TRUE)
tpm <- Matrix::t(1e6 * Matrix::t(tpm) / Matrix::colSums(tpm))

colnames(counts) <- paste0("cell-", rep(1:300))
colnames(tpm) <- paste0("cell-", rep(1:300))
rownames(counts) <- paste0("gene-", rep(1:1000))
rownames(tpm) <- paste0("gene-", rep(1:1000))

annotation <- data.frame(
  "ID" = paste0("cell-", rep(1:300)),
  "cell_type" = c(
    rep("T cells CD4", 50),
    rep("T cells CD8", 50),
    rep("Macrophages", 100),
    rep("NK cells", 10),
    rep("B cells", 70),
    rep("Monocytes", 20)
  ),
  row.names = paste0("cell-", rep(1:300))
)

seurat_obj <- Seurat::CreateSeuratObject(counts = counts, assay = "gene_expression", meta.data = annotation)
SeuratObject::LayerData(seurat_obj, assay = "gene_expression", layer = "data") <- tpm

ds_seurat <- SimBu::dataset_seurat(
  seurat_obj = seurat_obj,
  counts_layer = "counts",
  cell_id_col = "ID",
  cell_type_col = "cell_type",
  tpm_layer = "data",
  name = "seurat_dataset"
)

omnideconv/SimBu documentation built on May 5, 2024, 12:33 p.m.