Screen genes via univariate regression

Step 1 of the SEED pipeline: runs univariate regression for each gene against one or more clinical indicators. Supports Cox regression (survival), logistic regression (binary), and Spearman correlation (ordinal). When multiple indicators are provided, genes significant across all indicators are retained (intersection).

Usage

br_seed_screen(
  data,
  y,
  y_type,
  genes = NULL,
  x2 = NULL,
  p_threshold = 0.05,
  n_workers = 1L,
  ...
)

Arguments

data: A data.frame containing clinical variables and gene expression columns side by side. Gene columns must be numeric.
y: Character vector specifying clinical indicators. For survival: a length-2 vector c("time", "status"). For binary/ordinal: a single column name. When screening against multiple indicators, pass a named list, e.g., list(OS = c("time", "status"), ORR = "response").
y_type: Character vector of indicator types: "survival", "binary", or "ordinal". Must match y in length.
genes: Character vector of gene column names. If NULL, all numeric columns not in y or x2 are used.
x2: Optional character vector of adjustment covariates included in all models.
p_threshold: Numeric significance threshold for gene filtering (0 to 1). Default is 0.05.
n_workers: Integer number of parallel workers for batch regression. Passed to br_pipeline().
...: Additional arguments passed to br_pipeline().

Value

A list with class breg_seed_screen containing:

results: data.frame of screening results (gene, indicator, p.value, etc.)
genes: character vector of significant genes (intersection across indicators)
breg: the underlying breg object (for regression-based screening)
n_genes_in, n_genes_out: counts for progress reporting

Examples

# \donttest{
set.seed(123)
n <- 100
# Create gene with real survival signal
test_data <- data.frame(
  time = rexp(n, 0.1),
  status = sample(0:1, n, replace = TRUE),
  GENE1 = rnorm(n),
  GENE2 = rnorm(n, mean = 0.8 * (1:n) / n),
  GENE3 = rnorm(n),
  age = rnorm(n, 60, 10)
)

# Single survival indicator; use relaxed p_threshold for example data
res <- br_seed_screen(test_data,
  y = c("time", "status"),
  y_type = "survival",
  genes = c("GENE1", "GENE2", "GENE3"),
  x2 = "age",
  p_threshold = 0.5
)
#> screening 3 genes against "survival" indicator
#> screening survival (survival)...
#> pre-filtering: no variables were filtered out
#> exponentiate estimates of model(s) constructed from coxph method at default
#> 2 significant genes found
print(res)
#> $results
#> # A tibble: 3 × 9
#>   Focal_variable term  estimate std.error statistic p.value conf.low conf.high
#>   <chr>          <chr>    <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 GENE1          GENE1    1.14      0.135     0.965   0.335    0.874      1.48
#> 2 GENE2          GENE2    0.976     0.141    -0.170   0.865    0.740      1.29
#> 3 GENE3          GENE3    1.18      0.145     1.14    0.256    0.888      1.56
#> # ℹ 1 more variable: indicator <chr>
#> 
#> $genes
#> [1] "GENE1" "GENE3"
#> 
#> $breg
#> an object of <breg> class with slots:
#> • y (response variable): time and status
#> • x (focal terms): GENE1, GENE2, and GENE3
#> • x2 (control term): age
#> • group_by:
#> • data: <tibble[,7]>
#> • config: <list: method = "coxph", extra = "">
#> • models: <list: GENE1 = <coxph>, GENE2 = <coxph>, GENE3 = <coxph>>
#> • results: <brm.hlpr[,21]> with colnames Focal_variable, term, variable,
#> var_label, var_class, var_type, var_nlevels, contrasts, contrasts_type,
#> reference_row, label, n_obs, n_ind, n_event, exposure, estimate, std.error,
#> statistic, …, conf.low, and conf.high
#> • results_tidy: <tibble[,8]> with colnames Focal_variable, term, estimate,
#> std.error, statistic, p.value, conf.low, and conf.high
#> 
#> Focal term(s) are injected into the model one by one,
#> while control term(s) remain constant across all models in the batch.
#> 
#> $screen_results
#> $screen_results$survival
#> $screen_results$survival$breg
#> an object of <breg> class with slots:
#> • y (response variable): time and status
#> • x (focal terms): GENE1, GENE2, and GENE3
#> • x2 (control term): age
#> • group_by:
#> • data: <tibble[,7]>
#> • config: <list: method = "coxph", extra = "">
#> • models: <list: GENE1 = <coxph>, GENE2 = <coxph>, GENE3 = <coxph>>
#> • results: <brm.hlpr[,21]> with colnames Focal_variable, term, variable,
#> var_label, var_class, var_type, var_nlevels, contrasts, contrasts_type,
#> reference_row, label, n_obs, n_ind, n_event, exposure, estimate, std.error,
#> statistic, …, conf.low, and conf.high
#> • results_tidy: <tibble[,8]> with colnames Focal_variable, term, estimate,
#> std.error, statistic, p.value, conf.low, and conf.high
#> 
#> Focal term(s) are injected into the model one by one,
#> while control term(s) remain constant across all models in the batch.
#> 
#> $screen_results$survival$genes
#> [1] "GENE1" "GENE3"
#> 
#> $screen_results$survival$results
#> # A tibble: 3 × 8
#>   Focal_variable term  estimate std.error statistic p.value conf.low conf.high
#>   <chr>          <chr>    <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 GENE1          GENE1    1.14      0.135     0.965   0.335    0.874      1.48
#> 2 GENE2          GENE2    0.976     0.141    -0.170   0.865    0.740      1.29
#> 3 GENE3          GENE3    1.18      0.145     1.14    0.256    0.888      1.56
#> 
#> 
#> 
#> $p_threshold
#> [1] 0.5
#> 
#> $n_genes_in
#> [1] 3
#> 
#> $n_genes_out
#> [1] 2
#> 
#> attr(,"class")
#> [1] "breg_seed_screen" "list"            
# }

Usage

Arguments

Value

See also

Examples