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Perform sample category gene classification (HPA logic, sample-agnostic)

Source: R/hc_classify.R
hc_classify.Rd

hc_classify classifies genes based on their expression patterns across different sample categories using a logic similar to that employed by the Human Protein Atlas (HPA). The function processes each cluster of genes identified in the AnnDatR object and categorizes them into specificity categories such as "Enriched", "Group enriched", "Enhanced", and "Low specificity". It also generates pie charts to visualize the distribution of these categories within each cluster.

Usage

hc_classify(
  AnnDatR,
  sample_categories,
  cutoff_detected = 1,
  lowest_exp_limit = 0.1,
  group_enriched_limit = NULL,
  fold = 4
)

Arguments

AnnDatR

AnnDatR object containing gene expression data and sample annotations

sample_categories

Name of the column in AnnDatR$var containing sample categories

cutoff_detected

Expression value threshold to consider a gene as detected (default: 1)

lowest_exp_limit

Minimum expression value to consider for calculations (default: 0.1)

group_enriched_limit

Maximum number of sample categories for group enriched classification (default: NULL, auto-calculated)

fold

Fold change threshold for classification (default: 4)

Value

A tibble with gene classification results including ENSG, specificity category, specific sample categories, tau value, and distribution category

Examples

# Run clustering pipeline
adata_res <- hc_pca(example_adata, components = 40)
adata_res <- hc_distance(adata_res, components = 20)
adata_res <- hc_snn(adata_res, neighbors = 15)
#> Building SNN based on a provided distance matrix
#> Computing SNN
adata_res <- hc_cluster_consensus(adata_res, resolution = 7)
#> Iteration: 0 *** value: 948.279
#> Iteration: 1 *** value: 70.5063
#> Iteration: 2 *** value: 22.246
#> Iteration: 3 *** value: 22.2447
#> Iteration: 4 *** value: 22.2447
#> Minimum: 22.2447
#> Joining with `by = join_by(cons_cluster)`

# Classify genes based on sample categories
gene_classification <- hc_classify(adata_res, "tissue_name")
head(gene_classification$classification[["1"]])
#> # A tibble: 6 × 5
#>   ENSG            spec_category   spec_sample_categories   tau dist_category   
#>   <chr>           <chr>           <chr>                  <dbl> <chr>           
#> 1 ENSG00000072501 Low specificity NA                      0.35 Detected in all 
#> 2 ENSG00000072736 Enhanced        thymus                  0.51 Detected in all 
#> 3 ENSG00000077147 Low specificity NA                      0.28 Detected in all 
#> 4 ENSG00000078177 Enhanced        thymus                  0.64 Detected in many
#> 5 ENSG00000100629 Enhanced        thymus                  0.74 Detected in many
#> 6 ENSG00000101868 Enhanced        thymus                  0.51 Detected in many
gene_classification$pie_charts[["1"]]