
Evaluate n_bins and n_genes for bin mapping
evaluate_parameters.Rd
Will use the n_bins and n_genes implied by the sce
and
pseudotime_bins_top_n_genes_df
parameters and return quality metrics and
an optional chart.
Usage
evaluate_parameters(
blase_data,
bootstrap_iterations = 200,
BPPARAM = BiocParallel::SerialParam(),
make_plot = FALSE,
plot_columns = 4
)
Arguments
- blase_data
The BlaseData object to use.
- bootstrap_iterations
Iterations for bootstrapping when calculating confident mappings.
- BPPARAM
The BiocParallel configuration. Defaults to SerialParam.
- make_plot
Whether or not to render the plot showing the correlations for each pseudobulk bin when we try to map the given bin.
- plot_columns
How many columns to use in the plot.
.@import patchwork
Value
A vector of length 3:
"worst top 2 distance" containing the lowest difference between the absolute values of the top 2 most correlated bins for each bin. Higher is better for differentiating.
"mean top 2 distance" containing the mean top 2 distance across the entire set of genes and bins. Higher is better for differentiation, but it should matter less than the worst value.
"confident_mapping_pct" - The percent of mappings for this setup which were annotated as confident by BLASE
Examples
ncells <- 70
ngenes <- 100
counts_matrix <- matrix(
c(seq_len(3500) / 10, seq_len(3500) / 5),
ncol = ncells,
nrow = ngenes
)
sce <- SingleCellExperiment::SingleCellExperiment(assays = list(
normcounts = counts_matrix, logcounts = log(counts_matrix)
))
colnames(sce) <- seq_len(ncells)
rownames(sce) <- as.character(seq_len(ngenes))
sce$cell_type <- c(
rep("celltype_1", ncells / 2),
rep("celltype_2", ncells / 2)
)
sce$pseudotime <- seq_len(ncells) - 1
genelist <- as.character(seq_len(ngenes))
# Evaluating created BlaseData
blase_data <- as.BlaseData(sce, pseudotime_slot = "pseudotime", n_bins = 10)
genes(blase_data) <- genelist[1:20]
# Check convexity of parameters
evaluate_parameters(blase_data, make_plot = TRUE)
#> Warning: Bulk ID matches a gene, if this fails then check you areusing bulk name and not geneIds:1
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#> Warning: Bulk ID matches a gene, if this fails then check you areusing bulk name and not geneIds:10
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