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Compute p-values for each target ID

Source: R/compute_p_values.R
compute_p_values.Rd

Compute p-values for each target ID

Usage

compute_p_values(
  cpo,
  subset = NULL,
  p_adj_method = "BH",
  gam_method = "REML",
  gam_optimizer = "efs",
  silent = TRUE
)

Arguments

cpo

a cpam object

subset

a character vector of target_id names

p_adj_method

method for p-value adjustment

gam_method

fitting method for mgcv::gam (default is "REML")

gam_optimizer

optimization method for mgcv::gam (default is "efs")

silent

logical; silences warnings from model fitting (default is TRUE)

Value

an updated cpam object with raw, adjusted, and possibly aggregated p-values stored in the new slot "p_table"

Details

This function computes p-values for each target_id in the supplied cpam object. The p-values are computed from a negative binomial GAM model with a thin-plate spline basis function(s) for time using the mgcv package.

The p-values are stored in the new slot p_table in the cpam object. If aggregate_to_gene is set to TRUE (default), the target p-values are aggregated to the gene level using the lancaster method. The columns p_val_target and p_val_gene store the raw p-values for target- and gene-level, respectively. The function also computes adjusted p-values using the p_adj_method. The default method is "BH" (Benjamini-Hochberg), but any methods supported by the function p.adjust can be used. The adjusted p-values are stored in the columns q_val_target and q_val_gene.

References

Wood, S.N. (2013a) On p-values for smooth components of an extended generalized additive model. Biometrika 100:221-228 doi:10.1093/biomet/ass048

Yi L, Pachter L (2018). aggregation: p-Value Aggregation Methods. R package version 1.0.1, https://CRAN.R-project.org/package=aggregation.

Examples

library(cpam)

# load gene-only example cpam object
load(system.file("extdata", "cpo_example.rda", package = "cpam"))

# run on a small subset of the example data
cpo <- compute_p_values(cpo_example, subset = paste0("g00",1:9))
cpo$p_table
#> # A tibble: 9 × 4
#>   target_id counts_mean p_val_target q_val_target
#>   <chr>           <dbl>        <dbl>        <dbl>
#> 1 g001           92078.    4.65e-  1    8.37e-  1
#> 2 g002            8589.    9.00e-  1    9.09e-  1
#> 3 g003            2147.    1   e-319    9   e-319
#> 4 g004           25248.    8.93e-  1    9.09e-  1
#> 5 g005          116963.    9.09e-  1    9.09e-  1
#> 6 g006            2122.    6.62e-  1    9.09e-  1
#> 7 g007            3337.    3.74e-  1    8.37e-  1
#> 8 g008            4646.    2.51e-  1    7.54e-  1
#> 9 g009           37448.    2.37e-  1    7.54e-  1