process_abundance_expression_info: process_abundance_expression_info

In saeyslab/multinichenetr: MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data

process_abundance_expression_info

Description

process_abundance_expression_info Visualize cell type abundances. Calculate the average and fraction of expression of each gene per sample and per group. Calculate relative abundances of cell types as well. Under the hood, the following functions are used: 'get_avg_frac_exprs_abund', 'process_info_to_ic', 'combine_sender_receiver_info_ic'

Usage

process_abundance_expression_info(sce, sample_id, group_id, celltype_id, min_cells = 10, senders_oi, receivers_oi, lr_network, batches = NA, frq_list, abundance_info)

Arguments

sce	SingleCellExperiment object of the scRNAseq data of interest. Contains both sender and receiver cell types.
sample_id	Name of the meta data column that indicates from which sample/patient a cell comes from
group_id	Name of the meta data column that indicates from which group/condition a cell comes from
celltype_id	Name of the column in the meta data of sce that indicates the cell type of a cell.
min_cells	Indicates the minimal number of cells that a sample should have to be considered in the DE analysis. Default: 10. See 'muscat::pbDS'.
senders_oi	Default NULL: all celltypes will be considered as senders. If you want to select specific senders_oi: you can add this here as character vector.
receivers_oi	Default NULL: all celltypes will be considered as receivers. If you want to select specific receivers_oi: you can add this here as character vector.
lr_network	Prior knowledge Ligand-Receptor network (columns: ligand, receptor)
batches	NA if no batches should be corrected for. If there should be corrected for batches during DE analysis and pseudobulk expression calculation, this argument should be the name(s) of the columns in the meta data that indicate the batch(s). Should be categorical. Pseudobulk expression values will be corrected for the first element of this vector.
frq_list	output of 'get_frac_exprs'
rel_abundance_df	'rel_abundance_df' slot of 'get_abundance_info()' output.

Value

List containing data frames with average and fraction of expression per sample and per group, and relative cell type abundances as well.

Examples

## Not run: 
library(dplyr)
lr_network = readRDS(url("https://zenodo.org/record/3260758/files/lr_network.rds"))
lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% dplyr::distinct(ligand, receptor)
sample_id = "tumor"
group_id = "pEMT"
celltype_id = "celltype"
senders_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()  
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique() 
abundance_info = get_abundance_info(sce = sce, sample_id = sample_id, group_id = group_id, celltype_id =  celltype_id, min_cells = 10, senders_oi = senders_oi, receivers_oi = receivers_oi)
frq_list = get_frac_exprs(sce = sce, sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id)
abundance_celltype_info = process_abundance_expression_info(sce = sce, sample_id = sample_id, group_id = group_id, celltype_id =  celltype_id, min_cells = 10, senders_oi = senders_oi, receivers_oi = receivers_oi, lr_network, frq_list = frq_list, abundance_info = abundance_info)

## End(Not run)

saeyslab/multinichenetr documentation built on Jan. 15, 2025, 7:55 p.m.