NIRSVascMuscle is an R package designed for analyzing Near-Infrared Spectroscopy (NIRS) data in muscle physiology research. This package provides tools for data smoothing, cleaning, event slicing, aggregation, heterogeneity, and on-off transient analysis across multiple channels. The data processing methods are also applicable to a variety of physiologic data, such as Oxygen consumption, EMG, ECG, and more.
The kinetics analyses implemented functions from the following original papers:
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Bauer, T. A., Brass, E. P., Barstow, T. J., & Hiatt, W. R. (2007). Skeletal muscle StO2 kinetics are slowed during low work rate calf exercise in peripheral arterial disease. European Journal of Applied Physiology, 100(2), 143-151. https://doi.org/10.1007/s00421-007-0412-0
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Grassi, B., Pogliaghi, S., Rampichini, S., Quaresima, V., Ferrari, M., Marconi, C., & Cerretelli, P. (2003). Muscle oxygenation and pulmonary gas exchange kinetics during cycling exercise on-transitions in humans. J Appl Physiol (1985), 95(1), 149-158. https://doi.org/10.1152/japplphysiol.00695.2002
The heterogeneity analyses are based on the paper:
- Vogiatzis, I., Habazettl, H., Louvaris, Z., Andrianopoulos, V., Wagner, H., Zakynthinos, S., & Wagner, P. D. (2015). A method for assessing heterogeneity of blood flow and metabolism in exercising normal human muscle by near-infrared spectroscopy. Journal of applied physiology (Bethesda, Md. : 1985), 118(6), 783-793. https://doi.org/10.1152/japplphysiol.00458.2014
To install the NIRSVascMuscle package from GitHub, you can use the following code:
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
library(devtools)
devtools::install_github("DMegaritis/NIRSVascMuscle")
library(NIRSVascMuscle)
cat("NIRSVascMuscle package loaded successfully.\n")# Loading the package
# Loading the package
library(NIRSVascMuscle)
# Testing functions using the dummy data of NIRSVascMuscle:
# Test function: clean
path <- system.file("extdata", package = "NIRSVascMuscle")
winsize <- 20
vars <- c("TOI_1", "TOI_2")
cleaned_files <- clean(path, winsize, vars)
# Test function slice_event
files <- cleaned_files
period_col <- "Comment"
events <- list("EVNT13 ")
sampling_freq <- 5
data_before <- 10
data_after <- 60
transient_phase <- "kinetics"
time_column <- "elpsec"
cleaned_sliced <- slice_event(files, period_col, events, sampling_freq, data_before, data_after, transient_phase, time_column)
# Aggregate
aggregated <- aggregate_NIRS(cleaned_sliced)
# Heterogeneity calculator
vars <- c("TOI_1", "TOI_2", "TOI_3", "TOI_4", "ID", "Comment")
cv_vars <- c("TOI_1", "TOI_2", "TOI_3", "TOI_4")
ID <- "ID"
time_point <- "Comment"
hetero <- heterogeneity(aggregated, vars, cv_vars, ID, time_point)
# Test function: filter_smooth
# this function works with both paths and lists (error if there are missing data)
path <- system.file("extdata", package = "NIRSVascMuscle")
vars <- c("TOI_1", "TOI_2")
filtered_smoothed <- filter_smooth(path, vars)
# Single_decay
path_or_list <- filtered_smoothed
vars <- "TOI_1"
event_column <- "Comment"
start_event <- "EVNT13 "
time_column <- "elpsec"
transient_phase <- "decrease"
eyeball_data <- "yes"
plot_fitted <- "no"
plot_directory <- "/Users/dimitrismegaritis/Desktop"
decay_start <- 10
y_Bas_user <- 70
A_p_user <- 15
T_Dp_user <- 9
tau_p_user <- 5
decay <- single_decay(path_or_list, vars, event_column, start_event, time_column, transient_phase, eyeball_data, plot_fitted, plot_directory, decay_start, y_Bas_user, A_p_user, T_Dp_user, tau_p_user)
# Double decay
path_or_list <- filtered_smoothed
vars <- "TOI_1"
event_column <- "Comment"
start_event <- "EVNT13 "
time_column <- "elpsec"
transient_phase <- "decrease"
eyeball_data <- "yes"
plot_fitted <- "no"
plot_directory <- "/Users/dimitrismegaritis/Desktop"
decay_start <- 10
y_Bas_user <- 70
A_p_user <- 15
T_Dp_user <- 9
tau_p_user <- 5
A_s_user <- 55
T_Ds_user <- 5
tau_s_user <- 4
decay <- double_decay(path_or_list, vars, event_column, start_event, time_column, transient_phase, eyeball_data, plot_fitted, plot_directory, decay_start, y_Bas_user, A_p_user, T_Dp_user, tau_p_user, A_s_user, T_Ds_user, tau_s_user)