compute_PB_counts: Discover differentially regulated genes

In SimoneTiberi/DifferentialRegulation: Differentially regulated genes from scRNA-seq data

Discover differentially regulated genes

Description

compute_PB_counts computese the pseudo-bulk (PB) counts, needed to perform differential testing by DifferentialRegulation.

Usage

compute_PB_counts(
  sce,
  EC_list,
  design,
  sample_col_name = "sample",
  group_col_name = "group",
  sce_cluster_name = "cell_type",
  min_cells_per_cluster = 100,
  min_counts_per_gene_per_group = 20,
  min_counts_ECs = 0
)

Arguments

sce	a SingleCellExperiment object, computed via load_USA.
EC_list	a list, computed via load_EC.
design	a data.frame indicating the design of the experiment with one row for each sample; 'design' must contain a column with the sample id and one with the group id.
sample_col_name	a character ("sample" by default), indicating the column name of the 'design' element which stores the sample id.
group_col_name	a character ("group" by default), indicating the column name of the 'design' element which stores the group id.
sce_cluster_name	a character ("cell_type" by default), indicating the name of the 'colData(sce)' element, which stores the cluster id of each cell (i.e., colData(sce)$name_cluster).
min_cells_per_cluster	cell cluster (e.g., cell-type) filter. 'min_cells_per_cluster' is the minimum number of cells, across all samples and groups, for a cell cluster to be considered. Cell clusters with less than 'min_cells_per_cluster' cells will not be analyzed.
min_counts_per_gene_per_group	minimum number of counts per gene, in each cell, across all samples of every group. In each cell cluster, only genes with at least 'min_counts_per_gene_per_group' counts in both groups of samples will be analyzed.
min_counts_ECs	equivalence classes (ECs) filter. 'min_counts_ECs' indicates the minimum number of counts (across all cells in a cell cluster) for each equivalence class; by default all ECs are considered (min_counts_ECs = 0). ECs with less or equal than 'min_counts_ECs' will be discarded. Increasing 'min_counts_ECs' will marginally decrease computational cost computational at the cost of a marginal loss in performance.

Value

A list of objects required perform differential testing by DifferentialRegulation.

Author(s)

Simone Tiberi [e-mail]

See Also

load_EC, load_USA, DifferentialRegulation, plot_pi

Examples

# load internal data to the package:
data_dir = system.file("extdata", package = "DifferentialRegulation")

# specify samples ids:
sample_ids = paste0("organoid", c(1:3, 16:18))
# set directories of each sample input data (obtained via alevin-fry):
base_dir = file.path(data_dir, "alevin-fry", sample_ids)
file.exists(base_dir)

# set paths to USA counts, cell id and gene id:
# Note that alevin-fry needs to be run with '--use-mtx' option
# to store counts in a 'quants_mat.mtx' file.
path_to_counts = file.path(base_dir,"/alevin/quants_mat.mtx")
path_to_cell_id = file.path(base_dir,"/alevin/quants_mat_rows.txt")
path_to_gene_id = file.path(base_dir,"/alevin/quants_mat_cols.txt")

# load USA counts:
sce = load_USA(path_to_counts,
               path_to_cell_id,
               path_to_gene_id,
               sample_ids)
 
# define the design of the study:
design = data.frame(sample = sample_ids,
                    group = c( rep("3 mon", 3), rep("6 mon", 3) ))
design

# cell types should be assigned to each cell;
# here we load pre-computed cell types:
path_to_DF = file.path(data_dir,"DF_cell_types.txt")
DF_cell_types = read.csv(path_to_DF, sep = "\t", header = TRUE)
matches = match(colnames(sce), DF_cell_types$cell_id)
sce$cell_type = DF_cell_types$cell_type[matches]

# set paths to EC counts and ECs:
path_to_EC_counts = file.path(base_dir,"/alevin/geqc_counts.mtx")
path_to_EC = file.path(base_dir,"/alevin/gene_eqclass.txt.gz")

# load EC counts:
EC_list = load_EC(path_to_EC_counts,
                  path_to_EC,
                  path_to_cell_id,
                  path_to_gene_id,
                  sample_ids)

PB_counts = compute_PB_counts(sce = sce,
                              EC_list = EC_list,
                              design =  design,
                              sample_col_name = "sample",
                              group_col_name = "group",
                              sce_cluster_name = "cell_type",
                              min_cells_per_cluster = 100, 
                              min_counts_per_gene_per_group = 20)

SimoneTiberi/DifferentialRegulation documentation built on Feb. 5, 2024, 8:48 a.m.