FAQ

1. Is there a way to download all datasets in a batch?

Reply: Unfortunately, we cannot provide the raw data download since we don't own these data. Users could download the related data with the 'analysis' menu.

2. How to download the pictures of high resolution in SkinDB?

Reply: SkinDB offers vector graphics in PDF format, allowing users to download and display them with clarity. Additionally, users can freely adjust the resolution of the images according to their needs.

3. What are the format requirements for entering a gene name?

Reply: When entering a gene name, you must use the human gene symbol. This means that you should provide the official, abbreviated name that is commonly used to refer to a specific human gene in the scientific literature and databases. Please ensure that you do not use full gene names, aliases, or names from other organisms, as this may lead to inaccuracies or errors in your queries or analyses.

4. Is there a user manual available?

Reply: Due to the numerous analysis modules and the wide range of diseases covered, we have provided detailed usage instructions, illustrations, methodological explanations, and result interpretations within each analysis module. This is to facilitate users in learning how to perform analyses and understand the results while they are engaged in the process. We aim to make the analysis process as clear and straightforward as possible for all users.

5. How is the data processed?

Reply: The data processing is facilitated by an R package named "fromto," developed by Liuyuyao et al. This package is hosted on GitHub at https://github.com/grswsci/fromto, and can be installed using the command devtools::install_github("grswsci/fromto").

The "fromto" package is a powerful tool for batch processing GEO datasets. We manually search for dermatology-related datasets within the GEO repository and utilize the geoget or geoget2 functions to download them in bulk. The geogpl function automatically identifies the GPL ID of the platform annotation file required for each dataset, and the geoann function leverages built-in platform annotation files to complete the annotation process.

Notably, we also update old gene names within the datasets using the "fromto" package. The fromtoupdate function is designed to recognize and correct old gene names to their latest counterparts whenever possible. This ensures consistency across sequencing data from different years and platforms, facilitating the robust analysis of multi-dataset intersections.

6. What is the architecture of the website?

Reply: Our website is built on a solid and scalable architecture to ensure smooth and efficient operation. For the backend system, we have chosen the Django framework, which is renowned for its robustness, security, and ease of use. Django provides a comprehensive set of tools and features that enable us to handle various aspects of the website, such as user authentication, database management, and API integration, in a streamlined and efficient manner.

On the frontend side, we have utilized a combination of HTML, CSS, and JavaScript to create an intuitive and user-friendly interface. HTML serves as the backbone of our web pages, defining the structure and content. CSS is used to style the pages, making them visually appealing and consistent across different devices and browsers. JavaScript adds interactivity to the pages, allowing users to perform various actions and receive immediate feedback.

For the analysis operations, we rely on R language version 4.3.3. R is a powerful and versatile programming language that is widely used in data analysis and statistical computing. With its extensive library of functions and packages, R enables us to perform complex data processing tasks and generate accurate results. By integrating R into our website's architecture, we can provide users with advanced analysis capabilities and help them gain deeper insights into their data.

7. What are the future update directions for the website?

In our 1.0 version, we integrated Bulk RNA data and provided analysis functionalities. Moving forward, our 2.0 version and subsequent updates will focus on incorporating single-cell transcriptome data and laying the foundation for spatial transcriptome integration. Our long-term vision, driven by the dedicated efforts of The Sparkle Research Network, is to establish the first comprehensive human pan-disease database. This ambitious initiative aims to significantly reduce the costs associated with scientific exploration and accelerate research progress.

8. How can I cite or acknowledge the use of SkinDB in my research or publications?

Reply: We appreciate your interest in citing SkinDB in your research or publications. To properly acknowledge our platform, please include the following citation in your work: "[Title of SkinDB], available at [website URL], accessed on [date of access]. Developed and maintained by The Sparkle Research Network." Additionally, if you have used specific analysis modules or datasets, mentioning them specifically would be greatly appreciated.

9. Is there a limit to the number of analyses I can perform on SkinDB?

Reply: Currently, there is no hard limit on the number of analyses you can perform on SkinDB. However, we kindly ask users to use the platform responsibly and avoid excessive or unnecessary analysis requests that could strain our server resources. If we notice unusual or excessive usage patterns, we may contact the user to discuss alternative arrangements or impose temporary usage limits.

10. How do you ensure the privacy and security of user data on SkinDB?

Reply: The privacy and security of user data are of utmost importance to us. We have implemented robust security measures to protect user information, including encryption of sensitive data, regular security audits, and strict access controls. We do not share or sell user data to third parties, and all data is stored securely on our servers. Additionally, we encourage users to create strong passwords and to log out of their accounts when not in use to further enhance security.

11. Can I collaborate with The Sparkle Research Network or contribute to SkinDB?

Reply: We welcome collaborations and contributions from the scientific community. If you are interested in collaborating with The Sparkle Research Network or contributing to SkinDB, please reach out to us via our contact form or email. We are always looking for opportunities to partner with researchers, clinicians, and other stakeholders to advance biomedicine research and improve patient outcomes.

12. How often is the data on SkinDB updated?

Reply:We strive to keep the data on SkinDB up-to-date and accurate. Our team regularly monitors and updates the datasets to ensure that they reflect the latest research findings and clinical practices. The frequency of updates may vary depending on the availability of new data and the specific requirements of each dataset. We encourage users to check the website periodically for the latest updates and announcements.