Wrapper function to run all feature types in scFeatures

The scFeatures function generates a variety of features from a Seurat object containing single cell RNA-sequencing data. By default, all feature types will be generated and returned in a single list containing multiple data frames.

Usage

scFeatures(
  data = NULL,
  sample = NULL,
  celltype = NULL,
  spatialCoords = NULL,
  spotProbability = NULL,
  feature_types = NULL,
  type = "scrna",
  ncores = 1,
  species = "Homo sapiens",
  celltype_genes = NULL,
  aggregated_genes = NULL,
  geneset = NULL
)

Arguments

data

input data, a matrix of genes by cells

sample

a vector of sample information

celltype

a vector of cell type information

spatialCoords

a list of two vectors containing the x and y coordinates of each cell

spotProbability

a matrix of spot probability, each row represents a celltype and each column represents a spot

feature_types

vector containing the name of the feature types to generate, options are "proportion_raw", "proportion_logit" , "proportion_ratio", "gene_mean_celltype", "gene_prop_celltype", "gene_cor_celltype", "pathway_gsva" , "pathway_mean", "pathway_prop", "CCI", "gene_mean_aggregated", "gene_prop_aggregated", 'gene_cor_aggregated', "L_stats" , "celltype_interaction" , "morans_I", "nn_correlation". If no value is provided, all the above feature types will be generated.

type

input data type, either "scrna" (stands for single-cell RNA-sequencing data), "spatial_p" (stands for spatial proteomics data), or "spatial_t" (stands for single cell spatial data )

ncores

number of cores , default to 1

species

either "Homo sapiens" or "Mus musculus". Defaults to "Homo sapiens" if no value provided

celltype_genes

the genes of interest for celltype specific gene expression feature category If no value is provided, the top variable genes will be used

aggregated_genes

the genes of interest for overall aggregated gene expression feature category If no value is provided, the top variable genes will be used

geneset

the geneset of interest for celltype specific pathway feature category If no value is provided, the 50 hallmark pathways will be used

Value

a list of dataframes containing the generated feature matrix in the form of sample x features

Examples

utils::data("example_scrnaseq" , package = "scFeatures") 
data <- example_scrnaseq
celltype <- data$celltype
sample <- data$sample
data <- data@assays$RNA@data
scfeatures_result <- scFeatures(data, celltype = celltype, sample = sample, type = "scrna", feature_types = "proportion_raw")
#> generating proportion raw features