1. CGManalyzer

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 1.0CGManalyzer_1.0.tar.gz

Vignette Version 1.0how to download, install and use CGManalyzer_1.0.pdf

Package Source Version 1.1CGManalyzer_1.1.tar.gz

Vignette Version 1.1how to download, install and use CGManalyzer_1.1.pdf

Test DataDataset

Reference: Zhang XD, Zhang Z and Wang D. CGManalyzer:an R package for analyzing continuous glucose monitoring studies. Bioinformatics 2018; 34(9): 1609-1611.

2. displayHTS

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 SourcedisplayHTS_1.0.tar.gz

Vignettehow to download displayHTS.pdf

Reference: Zhang XHD*, Zhang ZZ. 2013. displayHTS: a R package for displaying data and results from high-throughput screening experiments. Bioinformatics 29 (6): 794–796

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 CodesSampEn_keepNA.c

Vignette: how to use SampEn_keepNA

Test DataGroup 1: Air flow data part 1.zip                    Group 1: Air flow data part 2.zip                          Group 2: Air flow data.zip

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)