cpam (version 0.1.3)

prepare_cpam: Prepare a cpam object

Description

Prepare a cpam object

Usage

prepare_cpam(
  exp_design,
  count_matrix = NULL,
  t2g = NULL,
  import_type = NULL,
  model_type = c("case-only", "case-control"),
  bootstrap = TRUE,
  filter_fun = "ts_filter",
  filter_fun_args = list(min_reads = 5, min_prop = 3/5),
  regularize = TRUE,
  gene_level = FALSE,
  aggregate_to_gene = !gene_level,
  condition_var = "condition",
  case_value = "treatment",
  num_cores = 1,
  normalize = TRUE,
  fixed_effects = NULL,
  intercept_cc = c("1", condition_var)
)

Value

an object of class cpam-class. The returned object has methods print and summary for displaying information. See cpam-class for details on the structure of the returned object.

Arguments

exp_design

a dataframe or tibble with the experimental design, containing at least a 'time' and a 'sample' column

count_matrix

a matrix of counts. Column names must be in 'sample' column of exp_design,

t2g

a transcript to gene dataframe or tibble with columns target_id and gene_id

import_type

software used for quantification, one of "kallisto", "salmon" ,.... Ignored if count_matrix is supplied.

model_type

"case-only" (default) or "case-control"

bootstrap

logical; load bootstrap samples, also called inferential replicates, if available, and rescale counts.

filter_fun

filter function to remove lowly expressed genes (default is filter_fun())

filter_fun_args

arguments for filter function

regularize

logical; use empirical Bayes regularization of dispersions (default is TRUE)

gene_level

logical; aggregate counts to gene level before data preparation and modelling (default is FALSE)

aggregate_to_gene

logical; aggregate p values from transcript- to gene-level

condition_var

string; column name in exp_design for the condition variable (for model_type = "case_control" only)

case_value

value of condition_var that indicates the "case". All other values are deemed to be control

num_cores

integer; number of cores to use for parallel computation

normalize

logical; use model offsets based on sampling depth and gene length

fixed_effects

a model formula of the form ~ effect1 + effect2

intercept_cc

string; intercept for case-control model: "1" (default) for common intercept or "condition"

Details

This function prepares a cpam object for analysis. The function loads count data from files or a matrix, filters lowly expressed genes, computes normalisation factors, and estimates dispersions. Many of these steps can be customised or turned off.

When bootstrap samples (inferential replicates) are available, it loads and summarises these using means, standard errors, and estimated overdispersions. The latter are a measure of quantification uncertainty and they are used to rescale the counts which accounts for this uncertainty during the modelling steps.

References

Pedro L Baldoni, Yunshun Chen, Soroor Hediyeh-zadeh, Yang Liao, Xueyi Dong, Matthew E Ritchie, Wei Shi, Gordon K Smyth, Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR, Nucleic Acids Research, Volume 52, Issue 3, 9 February 2024, Page e13, https://doi.org/10.1093/nar/gkad1167

Yunshun Chen, Lizhong Chen, Aaron T L Lun, Pedro L Baldoni, Gordon K Smyth, edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets, Nucleic Acids Research, Volume 53, Issue 2, 27 January 2025, https://doi.org/10.1093/nar/gkaf018

Examples

library(cpam)

# load gene-only example data
load(system.file("extdata", "exp_design_example.rda", package = "cpam"))
load(system.file("extdata", "count_matrix_example.rda", package = "cpam"))

cpo <- prepare_cpam(exp_design = exp_design_example,
                    count_matrix = count_matrix_example,
                    gene_level = TRUE)
cpo