Statistical test with theoretical modeling is one potential solution to solve "the reproducibility crisis". Currents statistical tests applied to part characterization are not fully in a comprehensive way to assess the reliability of dataset and provide guideline for further experiments.

In Measurement work, we introduced two statistics (i.e. K-function and TNE statistics) in order to quantify the first-order treatment effect and help to redesign our experiment. We also developed a framework of statistical analyses for part characterization.

The aim of Expmeasure is to

  • Provide comprehensive statistical tools for part characterization in iGEM, especially for those who find it challenging to understand principles of statistical or code in the computer.

  • Provide novel statistics developed by ZJU-China 2021 that is used for improvement of wet experiments.

Many iGEMers get trouble with choices of an appropriate statistics, mathematical calculation, and data visualization. Software like Origin, SPSS, and SAS can help them to make several basic plots. Unfortunately, advanced methods that is important for redesign the experiment is not available in those tools. Coding is an alternative strategy, but also labor-demanding. Expmeasure simply provides a GUI interface based on R shiny, which enables all iGEMers to perform advanced statistical methods (e.g. detect outliers, assess errors, evaluate effects of each explanatory variables, make predictions, and examine variance patterns)

All iGEMers can make robust conclusions and get suggestions of experimental design by simply clicking a couple of icons in Expmeasure.

How to install

Github is the best route to install Expmeasure. You can download and install the R package using the following code in R/Rstudio:

 install.packages("devtools") # only required if you do not have this package

If you fail to install Expmeasure due to unexpected problem, an R script version is also provided on this page. See Appendix 1 in the user manual of Expmeasure about how to use this software in the R script version. However, you cannot see our logo in the HOME page of Expmeasure GUI if you run the software in this way.

User manual

A detailed user manual is provided. Use this link to download it. The user manual is divided into seven chapters. Chapter 1 provides a quick-start example to all iGEMers to get start with Expmeasure using our test data.

Chapter 2 introduces statistical theory for each module in Expmeasure while Chapter 3 introduces the motivation of designing Expmeasure. General users can simply skip these two chapters.

Chapter 4 introduces the data input and help the users to solve error messages in "Data" page in Expmeasure. Chapter 5 gives a briefly overview of each module, including the input information, output, and tips to avoid wrong statistical analyses and error messages.

Chapter 6 provides a suggested workflow to use Expmeasure and gives two detailed examples that demonstrates how Expmeasure can help iGEMers to detect outliers, "junk data", and potential trend in their dataset and to redesign their experiment in a sensible way. You can also see these examples in a brief version on the Measurement page.

The overall structure of the user manual is shown in Fig. 1. To get access to our test data and the R script version of software, click this link to download.



In cooperation with our hardware device, we developed a smart-phone based software to facilitate its users. Not only will our software convey the result sent by the hardware device, but it also provides tracking of patients’ history detection data and will give instant access to the data to authorized medical partitioners. Once the patient is detected to have abnormal blood AFP concentration, the doctor will instantly receive a notification of the potential morbidity of its patients and giving advice at the first time.

App design

Our app has two versions, the patients’ and doctors’ versions. The patient’s version provides a Bluetooth connection with the hardware device and portrays detection results afterward. Users are also able to see their own detection histories and keep track of their health conditions. (See Fig. 2 for patients’ version’s interface)

Fig.2 App interface (Patients’ version)

The second one is designed for medical partitioners who are in charge of keeping track of his or her patients’ health conditions and giving advice on further examination if needed. Doctors’ version provides access to a patient list and each patient’s detection histories. The app will also send notifications to its user (the doctor) if one of his or her clients was examined to have an abnormal blood AFP concentration. (See Fig. 3 for doctors’ version’s interface)

Fig.3 App interface (Doctors’ version)

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