Deep validation of VISTA differential-expression fidelity

This validator combines validate_vista() with backend-to-backend numerical equivalence checks against standalone DESeq2, edgeR, and limma runs.

Usage

validate_vista_deep(
  counts,
  sample_info,
  column_geneid,
  group_column,
  group_numerator,
  group_denominator,
  methods = c("deseq2", "edger", "limma"),
  min_counts = 10,
  min_replicates = 1,
  log2fc_cutoff = 1,
  pval_cutoff = 0.05,
  p_value_type = "padj",
  covariates = NULL,
  design_formula = NULL,
  tolerance = 1e-08,
  return_plots = FALSE,
  error = TRUE
)

Arguments

counts

Raw counts (matrix/data.frame) with a gene-id column and sample columns.

sample_info

Data frame with sample metadata.

column_geneid

Column name in counts that contains gene identifiers.

group_column

Column in sample_info used to group samples.

group_numerator

Character vector of numerator groups for pairwise comparisons.

group_denominator

Character vector of denominator groups.

methods

Character vector of backends to benchmark. Any subset of c("deseq2", "edger", "limma").

min_counts

Minimum total counts per gene to retain.

min_replicates

Minimum samples per group meeting filtering criteria.

log2fc_cutoff

Absolute log2 fold-change threshold for DEG calling.

pval_cutoff

P-value (or adjusted p-value) threshold.

p_value_type

Either "padj" or "pvalue".

covariates

Optional character vector of additional sample_info columns.

design_formula

Optional model formula (or formula string) including group_column.

tolerance

Numeric tolerance used for floating-point comparisons.

return_plots

Logical; if TRUE, return paired VISTA/reference plots for MA, volcano, DEG count, and PCA views.

error

Logical; if TRUE, abort when any discrepancy is detected.

Value

Invisibly returns the full benchmark report.

Examples

v <- example_vista()
si <- as.data.frame(sample_info(v))
data("count_data", package = "VISTA")
count_subset <- count_data[seq_len(500), c("gene_id", si$sample_names), drop = FALSE]

report <- validate_vista_deep(
  counts = count_subset,
  sample_info = si,
  column_geneid = "gene_id",
  group_column = "cond_long",
  group_numerator = "treatment1",
  group_denominator = "control",
  methods = "limma",
  min_counts = 5,
  min_replicates = 1,
  error = FALSE
)

report$valid
#> [1] TRUE

# \donttest{
data("count_data", package = "VISTA")
data("sample_metadata", package = "VISTA")

target_groups <- c("control", "treatment1")
sample_subset <- sample_metadata[sample_metadata$cond_long %in% target_groups, ]
count_subset <- count_data[1:150, c("gene_id", sample_subset$sample_names)]

validate_vista_deep(
  counts = count_subset,
  sample_info = sample_subset,
  column_geneid = "gene_id",
  group_column = "cond_long",
  group_numerator = "treatment1",
  group_denominator = "control",
  methods = c("deseq2", "edger"),
  min_counts = 5,
  min_replicates = 1
)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
# }