iGEM 2024 — Team ustc-software

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{% block title %}Notebook{% endblock %}
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[Notebook](#Earlier)

## Earlier

## 2023.11.2~2023.11.28

Within a week of completing the 2023 iGEM competition, we began preparing for the recruitment of new team members for the upcoming year. We created recruitment posters and PowerPoint presentations, and had thorough discussions with our mentors regarding the direction of our project and team structure. We then completed the recruitment process, forming a team of 22 students, including 2 advisors and 1 team leader.

## 2023.11.29~2024.1.7

During this period, the 2023 iGEM team members communicated and interacted with the new team members. Together with the new members, we discussed and determined the project direction, and further established subsequent work arrangements. We also shared our experience in coding with the new members.

## 2024.1.8~2024.2.11

This period coincided with the Chinese winter holiday. During this time, we encouraged team members to explore and learn from the project designs and frontend development of outstanding iGEM teams from previous years.

## Mid-term

## 2024.2.12~2024.6.29

We divided the team into three groups, each responsible for different tasks: the coding group focused on frontend design and researching deep learning models; the biology group was responsible for investigating the latest developments in bioinformatics tools and assessing the biological safety of our models; the HP group was in charge of promoting our work and assisting the biology group in exploring the societal benefits of our project. During this phase, we mainly focused on learning and tracking scientific frontiers. We also designed our team flag, logo, and uniforms.

## 2024.6.30~2024.9.1

During this summer vacation period, we completed the construction of our platform and deployed deep learning models and bioinformatics workflows. We attempted to deploy pipelines for transcriptomics and protein interactions, as well as deep learning models for medical image segmentation and batch correction. We also interviewed professors related to our project direction and incorporated their suggestions. Additionally, we created a promotion video to introduce our project.

## Later

## 2024.9.2~2024.10.1

During this period, we further debugged our platform and conducted internal testing, continuously iterating and enhancing its interactivity. We also created a presentation video based on the platform's performance and submitted materials such as the attribution form.

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{% block title %}Engineering Success{% endblock %}
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[Engineering](#Design)

## Preliminary Design

After recognizing the diversity and complexity of platforms in the scRNA-seq workflow,
along with the lack of a one-stop service and the unfriendliness to beginners,
we embarked on the integration of various platforms. Our initial idea is as follows:
Our initial idea is as follows:

* Step 1: Users upload raw scRNA-seq data(we also provide sample data).
* Step 2: The backend processes the raw data using Cell Ranger, including quality control, normalization, and initial clustering.
* Step 3: Display a summary of the processed data, highlighting key metrics and identified cell types.
* Step 4: Provide an interactive visualization of the scRNA-seq data, allowing users to explore gene expression patterns across different cell types using tools like Plotly and Bokeh.
* Step 5: Perform differential expression analysis and identify marker genes for specific cell clusters.
* Step 6: Conduct gene set enrichment analysis using GSEA or Enrichr to identify overrepresented biological pathways and processes.
* Step 7: Use PAGA for trajectory inference and pseudotime analysis to understand the developmental trajectories and transitions between different cell states.
* Step 8: Generate comprehensive reports that include all analyses, visualizations, and customizable options, ready for download or further exploration.

The advantages of this design compared to other software are:

1. Higher integration and user-friendliness.
   Unlike other bioinformatics tools that focus on singular aspects of scRNA-seq analysis,
   this design covers the entire workflow from data processing to final reporting, making it accessible even to those with limited bioinformatics experience.
2. More valuable information is provided to users.
   The platform not only processes the scRNA-seq data but also offers in-depth analysis and interactive visualizations.
   This enables users to gain deeper insights into their data and make informed decisions based on the biological context

## Build

### Tools and Software Selection

### Tools and Software for scRNA-seq Analysis

#### Scanpy

Scanpy is our primary toolkit for analyzing single-cell RNA sequencing data. It provides efficient algorithms for large-scale data processing, including neighborhood graph construction, clustering, and embedding. Scanpy's integration with other Python libraries and its scalability make it an ideal choice for handling the high-dimensional nature of scRNA-seq datasets.

#### Cell Ranger

Cell Ranger, developed by 10x Genomics, is used for the initial processing of raw scRNA-seq data. It aligns reads, generates feature-barcode matrices, and performs preliminary clustering and gene expression analysis, providing a solid foundation for downstream analysis.

#### STAR (Spliced Transcripts Alignment to a Reference)

STAR is a highly accurate and fast tool for aligning RNA-seq reads to a reference genome. It is crucial for ensuring the quality of the input data, especially when dealing with spliced transcripts.

#### UMAP (Uniform Manifold Approximation and Projection)

UMAP is a powerful dimension reduction technique that preserves both global and local structure in the data. In Scanpy, UMAP is frequently used for visualizing clusters and identifying cell types, making it easier to interpret high-dimensional data.

#### Gene Set Enrichment Analysis (GSEA) and Enrichr

For gene set enrichment analysis, we use tools like GSEA and Enrichr. These tools help identify overrepresented biological pathways and processes in differentially expressed genes, providing deeper insights into the functional significance of the observed changes.

#### PAGA (Partition-based Graph Abstraction)

PAGA is a method for trajectory inference and pseudotime analysis. It helps to understand the developmental trajectories and transitions between different cell states, which is particularly useful in studies of cellular differentiation and development.

#### Interactive Visualization Tools: Plotly, Bokeh, and Dash

To enhance user-friendliness and provide interactive visualizations, we integrate tools such as Plotly, Bokeh, and Dash. These tools allow users to explore their data interactively, zoom in on specific clusters, and customize visualizations to suit their needs.

#### Customization and Flexibility

Our platform is designed to be highly customizable. Users can choose from a variety of parameters and settings to tailor the analysis to their specific research questions. This flexibility ensures that the platform can adapt to a wide range of experimental designs and biological contexts.

### Tools for Website Development

#### Back-end

![](https://static.igem.wiki/teams/5305/engineering/aliyun.webp)

Alibaba Cloud

Alibaba Cloud is a comprehensive cloud computing platform that provides a wide range of services, including cloud servers, databases, and machine learning. We use Alibaba Cloud to host our back-end applications, ensuring high availability and scalability.

[![](https://static.igem.wiki/teams/5305/engineering/django.webp)](https://www.djangoproject.com/)

Django

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. It is built by experienced developers and takes care of much of the hassle of web development, so you can focus on writing your app without needing to reinvent the wheel. We use Django to build and manage our local website, providing a robust and scalable solution for our web application.

[![](https://static.igem.wiki/teams/5305/engineering/fastapi.webp)](https://fastapi.tiangolo.com/)

FastAPI

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. The key features are speed, fast to code, and fewer bugs. We use FastAPI to create and manage our API endpoints, which interact with our deep learning models and provide real-time results to the front-end.

[![](https://static.igem.wiki/teams/5305/engineering/gunicorn.webp)](https://gunicorn.org/)

Gunicorn

Gunicorn (Green Unicorn) is a Python WSGI HTTP Server for UNIX. It's a pre-fork worker model, which means it can handle multiple requests simultaneously by spawning multiple worker processes. We use Gunicorn to run both our Django and FastAPI applications, ensuring efficient and reliable handling of incoming requests.

[![](https://static.igem.wiki/teams/5305/engineering/nginx.webp)](https://icons8.com/icon/t2x6DtCn5Zzx/nginx)

Nginx

Nginx is an open-source reverse proxy server for HTTP, HTTPS, SMTP, POP3, and IMAP protocols, as well as a load balancer, HTTP cache, and a web server (origin server). It is known for its high concurrency, high performance, and low memory usage. We use Nginx as a reverse proxy to handle incoming requests and forward them to the Gunicorn server, providing an additional layer of security and performance optimization.

## scRNA-seq Testing

1. During the initial testing of our scRNA-seq pipeline, we found that the data preprocessing step using Cell Ranger was robust and efficient. However, the large size of the raw sequencing data required significant computational resources, which posed a challenge for users with limited hardware. To address this, we optimized the pipeline to use more efficient parameters and provided detailed documentation on how to handle large datasets.
2. When performing quality control (QC) checks, we noticed that some users were having difficulty interpreting the QC metrics and deciding on appropriate filtering criteria. To improve user experience, we integrated an interactive QC dashboard using Plotly, which allows users to visually explore the distribution of QC metrics and make informed decisions about data filtering.
3. In the clustering and dimensionality reduction steps, we initially used UMAP and t-SNE. While these methods worked well, some users reported that the visualization results were not always intuitive, especially for complex datasets. To enhance interpretability, we added support for additional visualization techniques such as PHATE and TriMap, which provide alternative perspectives on the data.
4. During gene set enrichment analysis, we observed that the output from GSEA and Enrichr was sometimes difficult to interpret, particularly for users without a strong background in bioinformatics. To make the results more accessible, we developed a custom visualization tool that generates interactive plots and tables, highlighting the most significant pathways and providing context for the enriched gene sets.
5. For trajectory inference, we tested several tools including PAGA and Monocle. Users reported that while PAGA provided good results, the learning curve was steep for new users. To simplify the process, we created a user-friendly interface that guides users through the steps of trajectory inference, with pre-defined settings and clear explanations of each parameter.
6. When integrating multiple datasets, we encountered issues with batch effects, which can confound downstream analyses. To mitigate this, we implemented a batch correction step using tools like Harmony and MNN (Mutual Nearest Neighbors). We also provided detailed guidelines on how to assess and correct for batch effects, ensuring that the integrated data is reliable and consistent.
7. Finally, we conducted a usability test with a group of wet lab researchers. They provided valuable feedback on the overall user experience, suggesting improvements in the documentation, error messages, and the clarity of the visualizations. Based on their input, we made several enhancements, including more detailed tutorials, clearer error messages, and improved visualizations that are more intuitive and informative.

## Learn

### Iterations

1. **Iterations in scRNA-seq Data Processing: Enhance Efficiency and Accuracy**

   Initially, our scRNA-seq pipeline relied heavily on Cell Ranger for data preprocessing. While robust, this approach was resource-intensive and not always user-friendly for those with limited computational resources. To address these issues, we implemented several iterations:
   * **Optimized Parameters:** We fine-tuned the parameters of Cell Ranger to balance between computational efficiency and data quality, making it more accessible for users with varying hardware capabilities.
   * **Alternative Preprocessing Tools:** We integrated support for alternative tools like Alevin and STARsolo, which offer more flexibility and can handle different types of sequencing data more efficiently.
   * **Automated QC Checks:** We developed an automated quality control (QC) module that provides detailed reports and recommendations, simplifying the process of identifying and filtering low-quality cells and genes.
2. **Iterations in Integration: Enhance Data Integration and Accessibility**

   To improve the integration of multiple datasets and make the platform more user-friendly, we introduced the following enhancements:
   * **Batch Effect Correction:** We incorporated advanced batch effect correction methods such as Harmony and MNN (Mutual Nearest Neighbors), ensuring that integrated datasets are consistent and reliable.
   * **Public Dataset Integration:** We added a feature to directly import and integrate public datasets from repositories like GEO and ArrayExpress, streamlining the process of combining external data with user-generated data.
   * **Interactive Data Exploration:** We developed an interactive data exploration dashboard using Plotly and Dash, allowing users to visualize and explore their data in real-time, without the need for extensive programming knowledge.
3. **Iterations in User-Friendliness: Enhance Output Information and Visualization**

   To provide users with more intuitive and informative outputs, we made the following improvements:
   * **Enhanced Visualizations:** We introduced a variety of visualization techniques, including UMAP, t-SNE, PHATE, and TriMap, each with customizable settings, to help users better understand the structure and relationships within their data.
   * **Interactive Heatmaps and Plots:** We implemented interactive heatmaps and plots for gene expression, differential expression, and pathway enrichment analysis, allowing users to easily identify and interpret key biological insights.
   * **Customizable Reports:** We developed a feature to generate customizable reports, enabling users to export their results in various formats (PDF, HTML, etc.) with tailored content and visualizations.

### Improvement of Build

1. **Build a User-Friendly GUI Interface**

   Our interface design aims to minimize cognitive load for the user. Through a clear and straightforward layout, consistent color coding, and intuitive icons, users can quickly become familiar with the platform upon their first visit. We also provide detailed hints and documentation to ensure users never feel lost or confused during their experience.
2. **Suitable for a Wide Range of Users, from Beginners to Seasoned Researchers**

   Recognizing the diversity of user backgrounds, skills, and experiences, our interface was rebuilt to offer a high degree of customization. Whether a beginner or an expert, everyone can adjust the tool's parameters and display according to their needs and preferences. For beginners, we provide guided workflows and default settings, while advanced users have access to a wide range of configurable options.
3. **Integrated Documentation and Tutorials**

   We have integrated comprehensive documentation and step-by-step tutorials directly into the platform. These resources cover everything from basic data upload and processing to advanced analysis and interpretation, ensuring that users can maximize the utility of the platform regardless of their prior experience.
4. **Community and Support**

   To foster a community of users and provide ongoing support, we have established a forum and a dedicated support team. Users can share their experiences, ask questions, and receive timely assistance, creating a collaborative environment that enhances the overall user experience.

## Reference

[1] Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., ... & Satija, R. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573-3587.

[2] Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., Satija, R. (2022). Multimodal single-cell analysis. Annual Review of Biomedical Data Science, 5, 199-218.

[3] Korsunsky, I., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., ... & Raychaudhuri, S. (2021). Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods, 18(4), 471-478.

[4] Haghverdi, L., Lun, A. T., Morgan, M. D., & Marioni, J. C. (2018). Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nature Biotechnology, 36(5), 421-427.

[5] Packer, J. S., Zhu, Q., Hu, Q., Lin, D., Ma, Y. A., Liu, J. Z., ... & Trapnell, C. (2020). A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science, 367(6482), eaax3198.

[6] Svensson, V., da Veiga Beltrame, E., & Pachter, L. (2020). A curated database reveals trends in single-cell transcriptomics. Database, 2020, baaa073.

[7] Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020). CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nature Protocols, 15(4), 1484-1506.

[8] Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053-1058.

[9] Tran, H. T., Ang, C. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Chen, J., ... & Newell, E. W. (2020). A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biology, 21(1), 1-16.

[10] Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Buettner, F. (2021). Computational principles and challenges in single-cell data integration. Nature Biotechnology, 39(4), 421-430.

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{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
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[Experiments](#Overview)

##

In this section, we will demonstrate how to use existing h5ad files for single-cell RNA sequencing (scRNA-seq) workflows in our platform, Mo-BASE, including batch correction, data processing, analysis, and visualization. Our workflow aims to help users efficiently process and interpret single-cell data.

##

## Data Import

Import single-cell RNA sequencing data from h5ad files. You can easily load the data using the `anndata.read\_h5ad` function.

![Data Import Demonstration](https://static.igem.wiki/teams/5305/experiments/upload.webp)

Upload Data

## Quality Control (QC)

Use quality control metrics (such as gene counts and mitochondrial gene expression) to filter cells and ensure data quality.
We conduct detailed analysis by plotting the high-expressed genes and violin plots, which help identify expression characteristics and potential issues in the samples.
QC metrics can be calculated using `scanpy.pp.calculate\_qc\_metrics`.

![High-Expressed Genes Plot](https://static.igem.wiki/teams/5305/experiments/highest-expr-genes.webp)

High-Expressed Genes Plot

![Violin Plot](https://static.igem.wiki/teams/5305/experiments/violin.webp)

Violin Plot

## Data Normalization

After quality control, we perform data normalization to adjust for differences in sequencing depth and to make the data comparable across cells.
This step ensures that the expression values reflect true biological variations rather than technical artifacts. We typically use methods like `scanpy.pp.normalize\_total` to normalize the data to a common scale.

![Data Normalization Demonstration](https://static.igem.wiki/teams/5305/experiments/filter-genes-dispersion.webp)

Contrast before and after normalization

## Dimensionality Reduction

Perform dimensionality reduction using PCA, then use UMAP, or t-SNE to visualize the distribution and populations of cells.

![PCA](https://static.igem.wiki/teams/5305/experiments/pca-cst3.webp)

PCA (colored with CST3)

## Clustering Analysis

Apply clustering algorithms such as Leiden or Louvain to identify cell subpopulations. This helps to understand functional differences among different cell types.

![Leiden](https://static.igem.wiki/teams/5305/experiments/umap-leiden.webp)

Leiden

![Louvain](https://static.igem.wiki/teams/5305/experiments/umap-louvain.webp)

Louvain

Clustering with Leiden or Louvain

## Batch Correction

If your data comes from multiple experimental batches, use methods like BBKNN, Combat, or Harmony for batch correction to eliminate batch effects.

![clustering without batch correction](https://static.igem.wiki/teams/5305/experiments/umap-pbmc68k-louvain-label-not-batch-correct.webp)

clustering without batch correction

![clustering without batch correction](https://static.igem.wiki/teams/5305/experiments/umap-pbmc68k-louvain-label-bbknn.webp)

clustering with BBKNN

![clustering without batch correction](https://static.igem.wiki/teams/5305/experiments/umap-pbmc68k-louvain-label-combat.webp)

clustering with Combat

![clustering without batch correction](https://static.igem.wiki/teams/5305/experiments/umap-pbmc68k-louvain-label-harmony.webp)

clustering with Harmony

Batch Correction

## Differential Expression Analysis

After identifying cell clusters, we perform differential expression analysis to determine which genes are significantly expressed between different cell types or conditions.
This helps in understanding the functional roles of different cell populations.
We typically use methods like `scanpy.tl.rank\_genes\_groups` to identify marker genes for each cluster.

![Rank Genes Groups with Wilcoxon](https://static.igem.wiki/teams/5305/experiments/rank-genes-groups-leidende-wilcoxon.webp)

Rank Genes Groups with Wilcoxon

![Rank Genes Groups with T-test](https://static.igem.wiki/teams/5305/experiments/rank-genes-groups-leidende-t-test.webp)

Rank Genes Groups with T-test

![Rank Genes Groups with Logreg](https://static.igem.wiki/teams/5305/experiments/rank-genes-groups-leidende-logreg.webp)

Rank Genes Groups with Logreg

Differential Expression Analysis with different methods

## Enrichment Analysis

To understand the biological significance of our differentially expressed genes, we perform enrichment analysis. This process identifies over-represented biological functions, pathways, or gene sets in our gene list using tools like Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Enrichment analysis provides insights into the functional context of our findings.

![](https://static.igem.wiki/teams/5305/experiments/pbmc3k-enrichment-barplot.webp)

Enrichment Barplot

## Gene Annotation

Once we have identified marker genes, we perform gene annotation to assign biological functions and pathways to these genes. This can be done using public databases such as Ensembl or gene ontology resources. Annotating genes allows us to interpret their roles in biological processes and diseases.

![](https://static.igem.wiki/teams/5305/experiments/dotplot-pbmc3k-annotation-dotplot.webp)

Gene Annotation Dotplot

![](https://static.igem.wiki/teams/5305/experiments/dotplot-pbmc3k-annotation-groups-dotplot.webp)

Gene Annotation Groups Dotplot

![](https://static.igem.wiki/teams/5305/experiments/umap-pbmc3k-annotation.webp)

Gene Marker

## Trajectory Inference

Finally, we apply trajectory inference methods to study the dynamic processes of cell differentiation. This approach helps in understanding how cells transition between different states and can reveal developmental pathways. Popular methods for trajectory inference include Monocle and Slingshot, which help to visualize these transitions in a biological context.

![](https://static.igem.wiki/teams/5305/experiments/tracksplot-pbmc3k-annotation-tracksplot.webp)

Gene Annotation Trackplot

![](https://static.igem.wiki/teams/5305/experiments/tracksplot-pbmc3k-annotation-groups-tracksplot.webp)

Gene Annotation Groups Trackplot

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{% block title %}Results{% endblock %}
{% block lead %}You can describe the results of your project and your future plans here.{% endblock %}
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[Results](#Results)

## scRNA-seq Results and Analysis

1. First, we imported the raw scRNA-seq data from the uploaded .h5ad file into our analysis pipeline.

![Data Import Demonstration](https://static.igem.wiki/teams/5305/experiments/upload.webp)

Upload Data

2. Quality control metrics were calculated to assess the quality of the data. We evaluated metrics such as the number of genes detected and the percentage of mitochondrial genes.

![Quality Control Metrics](https://static.igem.wiki/teams/5305/results/1.webp)

3. After filtering out low-quality cells, we proceeded to perform normalization on the data using the appropriate methods to ensure comparability across samples.

![Data Normalization](https://static.igem.wiki/teams/5305/results/2.webp)

4. We conducted dimensionality reduction using PCA, followed by UMAP, to visualize the distribution of cells in the reduced space.

![Dimensionality Reduction](https://static.igem.wiki/teams/5305/results/3.webp)

5. Clustering analysis was performed on the UMAP coordinates using the Leiden algorithm, revealing distinct cell populations in the dataset.

![Clustering Results](https://static.igem.wiki/teams/5305/results/4.webp)

6. Differential expression analysis was conducted to identify marker genes for each cluster, which can provide insights into the biological characteristics of the identified cell types.

![Differential Expression Analysis](https://static.igem.wiki/teams/5305/results/5.webp)

7. Finally, we visualized the expression of key marker genes across the identified clusters using violin plots and heatmaps.

![Marker Gene Expression Visualization](https://static.igem.wiki/teams/5305/results/6.webp)

8. Full results, including detailed metrics and analysis outputs, are available for review and further investigation.

## Statistical Evaluation of Users

To better understand users' real needs and make our platform more practical and reliable, we conducted a trial activity at three universities: USTC, HFUT, and AHU. After trying out the platform, users could fill out our questionnaire, provide feedback, and suggest modifications.
Over the three days from Seqtember 20th to Seqtember 24th, we collected a total of 154 questionnaires.
Here is a statistical analysis of the questionnaire results:

In terms of personnel structure, out of the 154 participants, 140 were from USTC, and the remaining 14 were from HFUT and AHU. Most of them were first and second-year students, mainly from biology-related majors.

![Survey Result 1](https://static.igem.wiki/teams/5305/results/question1.webp)
![Survey Result 2](https://static.igem.wiki/teams/5305/results/question2.webp)
![Survey Result 3](https://static.igem.wiki/teams/5305/results/question3.webp)

Personal Information of Participants

Regarding the trial experience, the majority of users found our interface design and feature guidance to be excellent, scoring 4.64 and 4.68, respectively.

![Survey Result 4](https://static.igem.wiki/teams/5305/results/question4.webp)
![Survey Result 5](https://static.igem.wiki/teams/5305/results/question5.webp)

Evaluation of Interface Design and Guidance

In terms of specific functionality, most users believed that our platform could help them in learning synthetic biology
and RNa-seq analysis to some extent. However, many also felt that there is room for improvement in our platform.

![Survey Result 6](https://static.igem.wiki/teams/5305/results/question6.webp)
![Survey Result 7](https://static.igem.wiki/teams/5305/results/question7.webp)

Benefit Evaluation

Of course, we understand that conducting simple online trials may not capture more professional guidance.
Therefore, we also reached out to several peers who conducted in-depth trials and evaluations of our platform.
For more details, please click on the link:
[Website Usage Experience](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%20%7D%7D)

## Challenges in Developing scRNA-seq Workflow

Initially, our goal was to create a comprehensive scRNA-seq analysis platform to streamline the workflow from data preprocessing to downstream analysis. We began by focusing on dimensionality reduction and clustering algorithms, such as PCA, UMAP, and Leiden clustering. However, we quickly encountered challenges when dealing with high-dimensional datasets and the integration of batch effect correction methods, which required complex computational pipelines and substantial computational resources.

Despite successfully implementing basic functions like quality control and dimensionality reduction, we faced difficulties when trying to integrate batch correction methods like BBKNN and Harmony into the platform. These methods were computationally intensive, especially when handling large datasets, resulting in long processing times and system instability. To improve user experience and processing efficiency, we had to simplify the workflow and provide users with pre-set parameters for the most common tasks, reducing the burden on computational resources.

Furthermore, one of the major hurdles was the lack of consistent datasets for validating our models. While several publicly available datasets provided good benchmarks, many of the specific cases we wanted to target required proprietary or unpublished datasets from collaborating labs. Unfortunately, due to the asynchronous nature of academic collaboration and access restrictions to certain datasets, we were unable to proceed with the in-depth validation that the project required. This led to the reconsideration of certain modules, focusing more on making the platform accessible and user-friendly for a broader range of users.

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