The cpam class stores data and analysis results for a time series
omics experiment. This object is generated by the prepare_cpam
function and contains all necessary data and parameters for downstream analysis.
Details
A cpam object is a list with the following components:
- exp_design
A data frame containing the experimental design information, with columns for 'sample', 'time', and potentially other variables.
- count_matrix_raw
The original count matrix before filtering.
- count_matrix_filtered
The count matrix after filtering low-count genes/transcripts.
- target_to_keep
A vector of transcript/gene IDs that passed the filtering criteria.
- data_long
A long-format data frame containing all relevant information for each target and sample.
- t2g
A transcript-to-gene mapping data frame if provided.
- regularize
Logical; whether empirical Bayes regularization of dispersions was used.
- overdispersion.prior
Median overdispersion.
- model_type
String; the type of design used, either "case-only" or "case-control".
- condition_var
String; the column name in exp_design for the condition variable (for case-control models).
- case_value
The value of condition_var that indicates the "case" in case-control models.
- bootstrap
Logical; whether bootstrap samples (inferential replicates) were used.
- nboot
The number of bootstrap samples used, if applicable.
- filter
A list containing the filtering function and its arguments used.
- gene_level
Logical; whether the analysis was performed at the gene level.
- aggregate_to_gene
Logical; whether p-values should be aggregated from transcript to gene level.
- times
An ordered vector of unique time points in the experimental design.
- num_cores
The number of cores used for parallel computation.
- fixed_effects
The formula for fixed effects in the model.
- intercept_cc
String; the intercept type for case-control models.
- bss
A vector of basis function types used for modelling.
See also
prepare_cpam for creating a cpam object.
Examples
# 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"))
# Create a cpam object with the example data
cpo <- prepare_cpam(exp_design = exp_design_example,
count_matrix = count_matrix_example,
gene_level = TRUE)
#> ℹ Processing count matrix
#> ✔ Processing count matrix [28ms]
#>
#> ℹ Filtering low count genes
#> ℹ Estimating dispersions using edgeR
#> ✔ Estimating dispersions using edgeR [478ms]
#>
#> ℹ Filtering low count genes
#> ✔ Filtering low count genes [653ms]
#>
# Print the object structure
cpo
#>
#> ── cpam object ─────────────────────────────────────────────────────────────────
#> • case-only time series
#> • 30 samples
#> • 6 time points
#> • Counts aggregated for gene-level inference
# Get a summary of the cpam object
summary(cpo)
#>
#> ── cpam object ─────────────────────────────────────────────────────────────────
#> • case-only time series
#> • 30 samples
#> • 6 time points
#> • Counts aggregated for gene-level inference
#>
#> → use `compute_p_values()()` to compute p-values
#> → use `estimate_changepoints()()` to estimate changepoints
#> → use `select_shape()()` to select among candidate shapes