filter_counts: Filter gene expression matrix

In kdzimm/hierarchicell: Simulating Cell-Type Specific and Hierarchical Single-Cell RNA-Seq Data

Description

This function loads and filters the input data for the subsequent steps. The data loading and cleaning function is very basic, but data input is critical to the package working correctly. If no input data is given, the package defaults to using normal mucosal cells data for the simulation and power calculations (see alpha_cells).

Usage

filter_counts(expr = alpha_cells, gene_thresh = 0, cell_thresh = 0)

Arguments

expr	a data.frame where the unique cell identifier is in column one and the sample identifier is in column two with the remaining columns all being genes.
gene_thresh	the mean expression threshold for retaining genes. Defaults to 0.
cell_thresh	the mean expression threshold for retaining cells. Defaults to 0.

Details

Input data should be formatted as follows:

Cell_ID	Individual_ID	Gene1	Gene2	Gene3	...
Cell1_Ind1	Ind1	12	24	0	...
Cell2_Ind1	Ind1	11	2	0	...
Cell3_Ind1	Ind1	10	0	0	...
Cell4_Ind1	Ind1	0	124	10	...
Cell1_Ind2	Ind2	9	37	18	...
Cell2_Ind2	Ind2	0	29	0	...

Where the unique cell identifier is in column one and the sample identifier is in column two with the remaining columns all being genes.

Value

a data.frame that has filtered out cells with mean count = 0 and genes with mean count = 0

Note

Data should be only for cells of the specific cell-type you are interested in simulating or computing power for. Data should also contain as many unique sample identifiers as possible. If you are inputing data that has less than 5 unique values for sample identifier (i.e., independent experimental units), then the empirical estimation of the inter-individual heterogeneity is going to be very unstable. Finding such a dataset will be difficult at this time, but, over time (as experiments grow in sample size and the numbers of publically available single-cell RNAseq datasets increase), this should improve dramatically.

Examples

n_genes <- 10
n_cells <- 10

make_data <- function(x){
 mu_random <- round(rgamma(n=1, shape=1, rate=0.001),0)
 size_random <- runif(n=1, min=0, max=3)
 rnbinom(n_cells, size=size_random, mu=mu_random)
}

expr_dat <- as.data.frame(replicate(n_genes,make_data()))
expr_dat$CellID <- paste0("Cell",1:n_cells)
expr_dat$IND <- "IND1"
expr_dat <- expr_dat[,c(11,12,1:10)]
clean_expr_data <- filter_counts(expr_dat)

kdzimm/hierarchicell documentation built on Dec. 21, 2021, 5:23 a.m.