Description： The R package CGManalyzer contains functions for analyzing data from a continuous glucose monitoring (CGM) study. It covers a wide and comprehensive range of data analysis methods including reading a series of datasets, obtaining summary statistics of glucose levels, plotting data, transform-ing the time stamp format, fixing missing values, calculating multiscale sample entropy (MSE), conducting pairwise com-parison, displaying results using various plots including a new type of plot called an antenna plot, etc.. This package has been developed from our work in directly analyzing data from various CGM devices such as the FreeStyle Libre, Glutalor, Dexcom, and Medtronic CGM. Thus, this package should greatly facilitate the analysis of various CGM studies.
Package Source Version 1.0： CGManalyzer_1.0.tar.gz
Vignette Version 1.0： how to download, install and use CGManalyzer_1.0.pdf
Package Source Version 1.1： CGManalyzer_1.1.tar.gz
Vignette Version 1.1： how to download, install and use CGManalyzer_1.1.pdf
Package Source Version 1.2： CGManalyzer_1.2.tar.gz
Vignette Version 1.2： how to download, install and use CGManalyzer_1.2.pdf
Reference： Zhang XD, Zhang Z and Wang D. CGManalyzer:an R package for analyzing continuous glucose monitoring studies. Bioinformatics 2018; 34(9): 1609-1611.
Description： The R package displayHTS implements recently developed methods and figures for displaying data and hit selection re-sults in high-throughput screening (HTS) experiments. It gen-erates not only certain useful distinctive graphics such as the plate-well series plot, plate image and dual-flashlight plot but also other commonly used figures such as volcano plot and plate correlation plot. These figures are critical for visualizing the data and displaying important features of HTS data and hit selection results.
Package Source： displayHTS_1.0.tar.gz
Vignette： how to download displayHTS.pdf
3. A C program for entropy calculation with missing value
Description： The codes are built on “mse.c” from https://www.physionet.org/physiotools/mse/mse.c. The improvement is in handling with missing value. “mse.c” does not allow the input data to have missing values whereas “SampEn_keepNA.c” does not only allow the input data to have missing values but also contains codes to handle with missing values in the calculation process. This code only contains the part of sample entropy calculation. The Air flow data (in .rar file) is used to test the performance of the program. This data is from Beijing Chaoyang Hospital, the relevant research has been published.
C Codes： SampEn_keepNA.c
Vignette： how to use SampEn_keepNA
Reference： Dong X#, Chen C#, Geng Q, Cao Z, Chen X, Lin J, Yu J, Zhang Z, Shi Y, Zhang XD*. 2019 An improved method of handling missing values in the analysis of sample entropy for continuous monitoring of physiological signals. Entropy 21(3), 274 (doi: 3390/e21030274)
4. A Comprehensive Comparison and Overview of R packages for calculating sample entropy
Description： we have explored the functions of five existing R packages for calculating sample entropy (mousetrap, pracma, nonlinearTseries, MSMVSampEn, CGManalyzer) and have compared their computing capability in several dimensions. We used four published datasets (in Test Data below) on respiratory and heart rate to study their input parameters, types of entropy and program running time. The complete R code is below, readers can download the R code and test data to learn how to use these packages to calculate the sample entropy.
R demo Codes：complete R codes
Reference： Chang Chen, Shixue Sun, Zhixin Cao, Yan Shi, Baoqing Sun, Xiaohua Douglas Zhang, A Comprehensive Comparison and Overview of R packages for calculating sample entropy, Biology Methods and Protocols, , bpz016, https://doi.org/10.1093/biomethods/bpz016