Package 'cpam' reference manual

Title:	Changepoint Additive Models for Time Series Omics Data
Description:	Provides a comprehensive framework for time series omics analysis, integrating changepoint detection, smooth and shape-constrained trends, and uncertainty quantification. It supports gene- and transcript-level inferences, p-value aggregation for improved power, and both case-only and case-control designs. It includes an interactive 'shiny' interface. The methods are described in Yates et al. (2024) <doi:10.1101/2024.12.22.630003>.
Authors:	Luke Yates [aut, cre, cph] , Michael Charleston [aut], Jazmine Humphreys [aut], Steven Smith [aut]
Maintainer:	Luke Yates <[email protected]>
License:	GPL (>= 3)
Version:	0.1.3.9000
Built:	2025-03-14 04:49:49 UTC
Source:	https://github.com/l-a-yates/cpam

Compute p-values for each target ID

Description

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
)
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)

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.

Value

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

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

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

The cpam class

Description

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.

Methods

Objects of class cpam have print and summary methods available.

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)

# Print the object structure
cpo

# Get a summary of the cpam object
summary(cpo)

# 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)

# Print the object structure
cpo

# Get a summary of the cpam object
summary(cpo)

Use model selection to estimate changepoints

Description

Use model selection to estimate changepoints

Usage

estimate_changepoint(
  cpo,
  cps = NULL,
  degs_only = TRUE,
  deg_threshold = 0.05,
  subset = NULL,
  sp = NULL,
  bss = "tp",
  family = c("nb", "gaussian"),
  score = "aic",
  compute_mvn = TRUE
)
estimate_changepoint(
  cpo,
  cps = NULL,
  degs_only = TRUE,
  deg_threshold = 0.05,
  subset = NULL,
  sp = NULL,
  bss = "tp",
  family = c("nb", "gaussian"),
  score = "aic",
  compute_mvn = TRUE
)

Arguments

`cpo`	a cpam object
`cps`	vector of candidate changepoints. Defaults to the set of observed timepoints
`degs_only`	logical; should changepoints only be estimated for differentially expressed genes
`deg_threshold`	logical; the threshold value for DEGs (ignored of `degs_only = F`)
`subset`	character vector; names of targets or genes (if `cpo$gene_level = T`) for which changepoints will be estimated
`sp`	numerical >= 0; supply a fixed smoothing parameter. This can decrease the fitting time but it is not recommended as changepoints estimation is sensitive to smoothness.
`bss`	character vector; names of candidate spline bases (i.e., candidate shape types). Default is thin plate ("tp") splines.
`family`	character; negative binomial ("nb", default) or Gaussian ("gaussian", not currently supported)
`score`	character; model selection score, either Generalised Cross Validation ("gcv") or Akaike Information Criterion ("aic")
`compute_mvn`	Use simulation to compute p-value under multivariate normal model of the model scores

Details

This function estimates changepoints for each target_id. The assumed trajectory type for this modelling stage is initially constant followed by a changepoint into thin-plate smoothing spline.

By default, candidate time points are limited to the discrete observed values in the series, since, despite the use of smoothing constraints, there is generally insufficient information to infer the timing of changepoints beyond the temporal resolution of the data. In any case, the candidate points can be set manually using the cps argument.

To estimate changepoints, a model is fit for each candidate changepoint and generalised cross-validation (GCV, default) or the Akaike Information Criterion (AIC) are used to select among them. Model-selection uncertainty is dealt with by computing the one-standard-error rule, which identifies the least complex model within one standard error of the best scoring model.

Both the minimum and the one-standard-error (default) models are stored in the returned slot "changepoints" so that either can be used. In addition to these, this function also computes the probability (denoted p_mvn) that the null model is the best scoring model, using a simulation based approach based on the multivariate normal model of the pointwise model scores.

Given the computational cost of fitting a separate model for each candidate changepoint, cpam only estimates changepoints for targets associated with 'significant' genes at the chosen threshold deg_threshold.

Value

a cpam object with the estimated changepoint table added to the slot "changepoints"

References

Yates, L. A., S. A. Richards, and B. W. Brook. 2021. Parsimonious model selection using information theory: a modified selection rule. Ecology 102(10):e03475. 10.1002/ecy.3475

Examples

library(cpam)
library(dplyr)

# load 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[1:20,],
                    gene_level = TRUE,
                    num_cores = 1)
cpo <- compute_p_values(cpo)
cpo <- estimate_changepoint(cpo)
cpo$changepoints

library(cpam)
library(dplyr)

# load 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[1:20,],
                    gene_level = TRUE,
                    num_cores = 1)
cpo <- compute_p_values(cpo)
cpo <- estimate_changepoint(cpo)
cpo$changepoints

Plot clustered targets

Description

Plot clustered targets

Usage

plot_cluster(cpo, res, changepoints, shapes, alpha = 0.1)
plot_cluster(cpo, res, changepoints, shapes, alpha = 0.1)

Arguments

`cpo`	a cpam object
`res`	a tibble, output from `results()` containing columns target_id, cp, and shape
`changepoints`	numerical or character; one or more changepoints (these should be the same as the ones used in `estimate_changepoint()`
`shapes`	character; one or more shapes (these should be the same as the ones used in `select_shape()`
`alpha`	numeric between 0 and 1; controls line transparency in plot (default: 0.1)

Details

Plots the fitted trends for a set of targets whose estimated changepoints and shapes are given by the arguments changepoints and shapes, respectively.

Creates a combined plot showing fitted expression trends for all targets that share specified changepoint times and shape patterns. Each line represents one target's fitted trajectory, with transparency controlled by alpha.

Value

A ggplot object showing overlaid fitted trends, or NULL if no matching targets are found

Examples

library(cpam)

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

# Generate results table
res_example <- results(cpo_example)

# plot all targets with changepoint at timepoint 0 and shape "ilin" (increasing linear)
plot_cluster(cpo_example, res_example, changepoints = 2, shapes = "ilin")

library(cpam)

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

# Generate results table
res_example <- results(cpo_example)

# plot all targets with changepoint at timepoint 0 and shape "ilin" (increasing linear)
plot_cluster(cpo_example, res_example, changepoints = 2, shapes = "ilin")

Plot fitted changepoint additive models

Description

Plot fitted changepoint additive models

Usage

plot_cpam(
  cpo,
  gene_id = NULL,
  target_id = NULL,
  cp_type = c("cp_1se", "cp_min"),
  shape_type = "shape1",
  bs = "auto",
  cp_fix = -1,
  facet = FALSE,
  sp = NULL,
  show_fit = TRUE,
  show_data = TRUE,
  show_fit_ci = TRUE,
  show_data_ci = TRUE,
  ci_prob = "se",
  remove_null = FALSE,
  null_threshold = 0.05,
  null_threshold_adj = TRUE,
  k_mult = 1.2,
  return_fits_only = FALSE,
  family = "nb",
  common_y_scale = TRUE,
  scaled = FALSE,
  base_size = 12
)
plot_cpam(
  cpo,
  gene_id = NULL,
  target_id = NULL,
  cp_type = c("cp_1se", "cp_min"),
  shape_type = "shape1",
  bs = "auto",
  cp_fix = -1,
  facet = FALSE,
  sp = NULL,
  show_fit = TRUE,
  show_data = TRUE,
  show_fit_ci = TRUE,
  show_data_ci = TRUE,
  ci_prob = "se",
  remove_null = FALSE,
  null_threshold = 0.05,
  null_threshold_adj = TRUE,
  k_mult = 1.2,
  return_fits_only = FALSE,
  family = "nb",
  common_y_scale = TRUE,
  scaled = FALSE,
  base_size = 12
)

Arguments

`cpo`	A cpam object containing count data, model fits, and optional changepoint/shape estimates
`gene_id`	character; gene_id (mutually exclusive with target_id)
`target_id`	character; target_id (mutually exclusive with gene_id)
`cp_type`	One of "cp_1se" or "cp_min"; rule for selecting changepoint from fitted models. See `estimate_changepoint()` for details.
`shape_type`	One of "shape1" or "shape2"; which set of fitted shape patterns to use. See `select_shape()` for details.
`bs`	Shape pattern to fit ("null", "lin", "ilin", "dlin", or from cpo$bss). Use "auto" (default) to use estimated shapes as per `shape_type`.
`cp_fix`	Numeric; fixed changepoint time. Set to -1 (default) to use estimated changepoints
`facet`	Logical; for multiple transcripts, plot in separate facets?
`sp`	numerical; set the smooth parameter. NULL (default) for automatic selection
`show_fit`	logical; show the fitted trend?
`show_data`	logical; show (possibly normalized and scaled) data points?
`show_fit_ci`	logical; show credible interval for the fitted trend?
`show_data_ci`	logical; show bootstrapped quantile for data points?
`ci_prob`	"se" for standard error bands (see `mgcv::predict.gam()`), or numeric for simulation-based intervals. If numerical, sets the probability for the simulation-based estimates of credible interval.
`remove_null`	logical; only plot differentially expressed transcripts (not applicable for gene-only analyses)
`null_threshold`	numeric; P value threshold for filtering out NULL transcripts
`null_threshold_adj`	logical; use adjusted (default) or non-adjusted p-values for filtering targets
`k_mult`	numerical; multiplier for the number of knots in the spline. Not recommended to change this value.
`return_fits_only`	logical; return the model fits. Does not plot the function
`family`	character; negative binomial ("nb", default) or Gaussian ("gaussian")
`common_y_scale`	logical; for faceted plots of multiple transcripts, should the scale of the y-axis be common or free.
`scaled`	logical; scaled data by overdispersions (for bootstrapped data only)
`base_size`	numeric; base font size for the plot

Details

Plots the fitted trend and data points for a given gene or target. If a gene ID is supplied, the function will plot all transcripts for that gene. The function can also be used to return the model fit(s) only, which are gamObject objects from the mgcv package.

Value

a ggplot object

Examples

library(cpam)

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

# example gene
plot_cpam(cpo_example, gene_id = "g003")

# gene with estimated changepoint at timepoint 3
plot_cpam(cpo_example, gene_id = "g013")

# manually set the changepoint
plot_cpam(cpo_example, gene_id = "g013", cp_fix = 2)

library(cpam)

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

# example gene
plot_cpam(cpo_example, gene_id = "g003")

# gene with estimated changepoint at timepoint 3
plot_cpam(cpo_example, gene_id = "g013")

# manually set the changepoint
plot_cpam(cpo_example, gene_id = "g013", cp_fix = 2)

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)
)
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)
)

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.

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.

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

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

Create a results table from a cpam object

Description

Create a results table from a cpam object

Usage

results(
  cpo,
  p_threshold = 0.05,
  p_type = c("p_gam", "p_mvn"),
  min_lfc = 0,
  min_count = 0,
  aggregate_to_gene = cpo$aggregate_to_gene,
  add_lfc = TRUE,
  add_counts = TRUE,
  cp_type = c("cp_1se", "cp_min"),
  shape_type = c("shape1", "shape2"),
  summarise_to_gene = FALSE,
  remove_null_targets = TRUE
)
results(
  cpo,
  p_threshold = 0.05,
  p_type = c("p_gam", "p_mvn"),
  min_lfc = 0,
  min_count = 0,
  aggregate_to_gene = cpo$aggregate_to_gene,
  add_lfc = TRUE,
  add_counts = TRUE,
  cp_type = c("cp_1se", "cp_min"),
  shape_type = c("shape1", "shape2"),
  summarise_to_gene = FALSE,
  remove_null_targets = TRUE
)

Arguments

`cpo`	a cpam object
`p_threshold`	numerical; threshold for adjusted p-values; default is 0.05
`p_type`	character; choose the type of p-value. Options are "p_gam" (default) or "p_mvn" (see `compute_p_values()` for details).
`min_lfc`	numerical; maximum absolute log (base 2) fold change must exceed this minimum value; default is 0
`min_count`	numerical; maximum of the modelled counts evaluated at the set of observed time points must exceed this minimum value for
`aggregate_to_gene`	logical; filter by gene-aggregated p-values
`add_lfc`	logical; add log (base 2) fold changes for each time point
`add_counts`	logical; add modelled counts for each time point
`cp_type`	character; model-selection rule used to select the changepoint
`shape_type`	character; "shape1" to include unconstrained or otherwise "shape2"
`summarise_to_gene`	logical; return gene-level results only
`remove_null_targets`	logical; remove targets with null shapes (default is T). If F, targets with null shapes will be included if the aggregated p-value for the corresponding gene passes the specified filtering thresholds.

Details

This function is usually called after compute_p_values(), estimate_changepoint, and select_shape have been run. The function has several useful filters such as adjusted p-value thresholds, minimum log-fold changes, and minimum counts.

Value

a tibble

Examples

library(cpam)

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

results(cpo_example)

# Add filters
results(cpo_example, p_threshold = 0.01, min_lfc = 1)

library(cpam)

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

results(cpo_example)

# Add filters
results(cpo_example, p_threshold = 0.01, min_lfc = 1)

Use model selection to select a shape for each target

Description

Use model selection to select a shape for each target

Usage

select_shape(
  cpo,
  subset = NULL,
  sp = NULL,
  bss = c("micv", "mdcx", "cv", "cx", "lin", "tp", "null"),
  family = c("nb", "gaussian"),
  score = "gcv",
  cp_type = c("cp_1se", "cp_min")
)
select_shape(
  cpo,
  subset = NULL,
  sp = NULL,
  bss = c("micv", "mdcx", "cv", "cx", "lin", "tp", "null"),
  family = c("nb", "gaussian"),
  score = "gcv",
  cp_type = c("cp_1se", "cp_min")
)

Arguments

`cpo`	a cpam object
`subset`	character vector; names of targets or genes (if `cpo$gene_level = T`) for which changepoints will be estimated
`sp`	numerical >= 0; supply a fixed smoothing parameter. If NULL (default), the smoothing parameter is estimated. Note, this fixed value is in any case applied only to shape constrained bases (i.e., not `bs = 'tp'`).
`bss`	character vector; names of candidate spline bases (i.e., candidate shape types).
`family`	character; negative binomial ("nb", default) or Gaussian ("gaussian")
`score`	character; model selection score, either Generalised Cross Validation ("gcv") or Akaike Information Criterion ("aic")
`cp_type`	character; if changepoints have been estimated using `estimate_changepoint()`, which selection rule should be used. See `estimate_changepoint()` for details.

Details

The function selects the best shape from a list of candidate shapes for each target. It is typically the last step in the analysis, called after p-values have been estimated using compute_p_values() and changepoints have been estimated using estimate_changepoint().

Two shape selections are generated. The first selecting among linear, convex and concave shape classes and their monotonic variants (or the shape set given by bss), and the second selecting among the first options plus an 'unconstrained' smooth. The inclusion of the 'unconstrained' type provides the flexibility to detect targets beyond simpler trends. For computational reasons, as per the changepoint estimation, shapes are selected only for those genes, or their isoforms, identified as significant at the chosen FDR threshold. This is overridden by providing a subset of target names to the subset argument, provided these targets have estimated changepoints.

Value

a cpam object with the selected shapes added to the slot "shapes"

Examples

library(cpam)

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

# Using a small subset of the example data
cpo <- prepare_cpam(exp_design = exp_design_example,
                    count_matrix = count_matrix_example[1:20,],
                    gene_level = TRUE,
                    num_cores = 1)
cpo <- compute_p_values(cpo)
cpo <- estimate_changepoint(cpo)
cpo <- select_shape(cpo)
cpo$shapes


library(cpam)

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

# Using a small subset of the example data
cpo <- prepare_cpam(exp_design = exp_design_example,
                    count_matrix = count_matrix_example[1:20,],
                    gene_level = TRUE,
                    num_cores = 1)
cpo <- compute_p_values(cpo)
cpo <- estimate_changepoint(cpo)
cpo <- select_shape(cpo)
cpo$shapes

Removes lowly expressed genes

Description

Removes lowly expressed genes

Usage

ts_filter(data, min_reads = 5, min_prop = 3/5)
ts_filter(data, min_reads = 5, min_prop = 3/5)

Arguments

data

A tibble or data.frame containing columns:

target_id (character): Transcript identifiers
time (numeric): Time point of measurement
counts (numeric): Read counts

min_reads

minimum reads per transcript per sample

min_prop

minimum proportion of samples that exceed min_read at a given time point (default: 3/5)

Details

Identifies targets that show strong and consistent expression in at least one timepoint. For each timepoint, the function calculates the proportion of samples where a targets exceeds min_reads. Targets are retained if they meet the minimum proportion (min_prop) at any timepoint in the experiment.

Value

a character vector of transcript IDs to keep

Examples

data <- dplyr::tibble(
  target_id = rep(paste0("t", 1:3), each = 6),
  time = rep(c(0, 4, 8), 6),
  counts = c(6,6,6, 0,0,0, 6,0,6, 0,6,6, 6,6,6, 6,0,0)
)
ts_filter(data)

data <- dplyr::tibble(
  target_id = rep(paste0("t", 1:3), each = 6),
  time = rep(c(0, 4, 8), 6),
  counts = c(6,6,6, 0,0,0, 6,0,6, 0,6,6, 6,6,6, 6,0,0)
)
ts_filter(data)

Launches a Shiny app to visualise the data and fitted models of a cpam object

Description

Launches a Shiny app to visualise the data and fitted models of a cpam object

Usage

visualise(
  cpo,
  subset = NULL,
  degs_only = TRUE,
  deg_threshold = 0.05,
  p_type = c("p_gam", "p_mvn"),
  shape_type = c("shape1", "shape2")
)
visualise(
  cpo,
  subset = NULL,
  degs_only = TRUE,
  deg_threshold = 0.05,
  p_type = c("p_gam", "p_mvn"),
  shape_type = c("shape1", "shape2")
)

Arguments

`cpo`	A cpam object containing count data, model fits, and optional changepoint/shape estimates
`subset`	Character vector; names of targets or genes (if `cpo$gene_level = TRUE`) to load into the Shiny app. If NULL, all genes/targets are included based on `degs_only`.
`degs_only`	Logical; if TRUE, display only differentially expressed genes/targets with adjusted p-value below `deg_threshold`. Default is TRUE.
`deg_threshold`	Numeric; significance threshold for differentially expressed genes/targets. Only used when `degs_only = TRUE`. Default is 0.05.
`p_type`	character; choose the type of p-value. Options are "p_gam" (default) or "p_mvn" (see `compute_p_values()` for details).
`shape_type`	character; "shape1" to include unconstrained or otherwise "shape2". Default is "shape1". In some instances, all of the transcripts for a gene may be "null" shaped, but the p-value for the gene may still be significant. This is due to the different methods of determining significance for the changepoints and the gene-level p-values. Here, conservatively, we remove these null-shaped genes from the DEG list.

Value

None (launches Shiny app in browser)

Examples

if (interactive()){

# A simple gene-level example
cpo <- cpo_example

# Launch visualization with all genes
visualise(cpo, degs_only = FALSE)

# Launch with only significant genes
visualise(cpo, deg_threshold = 0.05)

# Launch with specific genes
visualise(cpo, subset = c("g001","g002","g003"))
}
if (interactive()){

# A simple gene-level example
cpo <- cpo_example

# Launch visualization with all genes
visualise(cpo, degs_only = FALSE)

# Launch with only significant genes
visualise(cpo, deg_threshold = 0.05)

# Launch with specific genes
visualise(cpo, subset = c("g001","g002","g003"))
}

Package 'cpam'

Help Index

Compute p-values for each target ID

Description

Usage

Arguments

Details

Value

References

Examples

The cpam class

Description

Details

Methods

See Also

Examples

Use model selection to estimate changepoints

Description

Usage

Arguments

Details

Value

References

Examples

Plot clustered targets

Description

Usage

Arguments

Details

Value

See Also

Examples

Plot fitted changepoint additive models

Description

Usage

Arguments

Details

Value

Examples

Prepare a cpam object

Description

Usage

Arguments

Details

Value

References

Examples

Create a results table from a cpam object

Description

Usage

Arguments

Details

Value

Examples

Use model selection to select a shape for each target

Description

Usage

Arguments

Details

Value

Examples

Removes lowly expressed genes

Description

Usage

Arguments

Details

Value

Examples

Launches a Shiny app to visualise the data and fitted models of a cpam object

Description

Usage

Arguments

Value

Examples