Thanks for using package eCV! This vignette provides a high-level introduction to the functionality and usage of eCV and related functions. Please refer to Gonzalez-Reymundez et al. (2023) for more details about each implemented method.
To install, please do
install.packages("eCV")
Once installed, you can load the package eCV using
library
.
library("eCV")
FALSE Loading required package: idr
FALSE Loading required package: mvtnorm
FALSE Loading required package: future
FALSE Loading required package: future.apply
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FALSE
FALSE Enhanced Coefficient of Variation and IDR Extensions
FALSE for Reproducibility Assessment
FALSE
FALSE This package provides extensions and alternative methods to IDR to
FALSE measure the reproducibility of omic data with an arbitrary number of
FALSE replicates. It introduces an enhanced Coefficient of Variation (eCV)
FALSE metric to assess the likelihood of omic features being reproducible.
The following examples assume you have installed and loaded package
tidyverse
.
library("tidyverse")
FALSE ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
FALSE ✔ ggplot2 3.4.3 ✔ purrr 1.0.2
FALSE ✔ tibble 3.2.1 ✔ dplyr 1.1.2
FALSE ✔ tidyr 1.3.0 ✔ stringr 1.5.0
FALSE ✔ readr 2.1.2 ✔ forcats 0.5.1
FALSE ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
FALSE ✖ dplyr::filter() masks stats::filter()
FALSE ✖ dplyr::lag() masks stats::lag()
simulate_data
The simulate_data
function simulates omic features,
specifically designed to mimic reproducible and irreproducible groups.
Building upon the copula mixture model simulations introduced in the
original publication Li et al. (2011).
This function produces samples of paired omic features for a specified
number of replicates. The reproducibility state of each feature is
determined by a binomial variable, where the mixing probabilities
between two multivariate normal distributions are controlled by a vector
of probabilities.
Users can tailor simulations using the scenario parameter, aligning with predefined scenarios from Li et al. (2011). These scenarios range from highly correlated and abundant reproducible features (Scenario 1) to situations with fewer, less correlated reproducible features (Scenarios 2 and 3), and even introduce a “reproducible noise” component (Scenario 4).
Users can customize the number of replicates (n_reps), the quantity of features (n_features), and the simulation scenario, while the function returns simulated data and associated parameter values in a convenient list format.
Here is an example that simulates 1,000 features across four replicates according to “Scenario 1”.
set.seed(42)
out <- simulate_data(scenario = 1, n_reps = 4, n_features = 1000)
out$sim_data %>% as.data.frame() %>%
mutate(`Features group` = as.character(out$sim_params$feature_group)) %>%
ggplot(aes(x=`Rep 1`,y=`Rep 2`,color=`Features group`)) +
geom_point(size=1, alpha=0.5) +
scale_color_manual(values = c( "#009CA6" , "#F4364C")) +
theme_classic()
mrep_assessment
The mrep_assessment
function is a comprehensive tool
within the package eCV for evaluating the reproducibility of omic
feature values across multiple sample replicates. This function acts as
a wrapper for various methods, allowing users to choose between “IDR,”
“gIDR,” “mIDR,” and “eCV” based on their specific needs.
When employing the traditional “IDR” method, the function utilizes
the idr
package’s implementation
(idr::est.IDR
), considering only the first two replicates
regardless of the total available. For scenarios with more than two
replicates, users can opt for “gIDR” and “mIDR,” both of which reduce to
the traditional IDR when there are only two replicates.
When calling the “eCV” method within mrep_assessment
, an
“enhanced” coefficient of variation (eCV) is calculated. This metric
gauges the likelihood of an omic feature being reproducible by
considering the variability among replicates and the mean intensity. The
eCV is calculated as \(\frac{|\sigma^2 -
\mu^2|} { \mu^2}\), offering insights into the noise (\(\sigma\)) and mean (\(\mu\)) intensity characteristics of the
features.
The “eCV” method further extends its analysis by making inferences based on the probabilities of eCV values originating from the group of reproducible features. It assumes a prior Normal distribution for reproducible features, sampling pseudo replicates using Probabilistic Bootstrap.
The user-friendly interface of mrep_assessment requires a numeric
matrix x
representing omic features across replicates, and
allows customization through the method
and
param
parameters.
The function returns a list with two elements: a numeric vector
rep_index
indicating reproducibility levels, and a string
specifying the method used in this assessment. Additionally, users can
leverage parallel computing via future
and
future.apply
packages. This option is managed by adjusting
the n_threads
parameter for the “mIDR” and “eCV”
methods.
Here is an example where we call function
mrep_assessment
interactively on the previous simulated
data, using multiple methods.
# Define parameters for each method.
params <- list(
eCV = list(max.ite = 100),
gIDR = list(
mu = 2,
sigma = 1.3,
rho = 0.8,
p = 0.7,
eps = 1e-3,
max.ite = 50
),
mIDR = list(
mu = 2,
sigma = 1.3,
rho = 0.8,
p = 0.7,
eps = 1e-3,
max.ite = 50
)
)
# Create a list to store results
results <- NULL
# Loop through methods and calculate reproducibility
for (method in c("eCV", "gIDR", "mIDR")) {
results <- results %>%
bind_rows(data.frame(
value =
mrep_assessment(
x = out$sim_data,
method = method,
param = params[[method]]
)$rep_index,
Method = method,
group = out$sim_params$feature_group
))
}
# Plot results
results %>%
mutate(group = ifelse(group == 1,"FALSE","TRUE")) %>%
ggplot(aes(x=Method, y = value,fill=group)) +
scale_fill_manual(values = c( "#009CA6" , "#F4364C")) +
geom_boxplot() +
theme_classic() +
labs(y="Reproducibility assessment", fill="Reproducible\nfeature")