Refactor Excel Imports For CohortDto Creation

by RICHARD 46 views
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Hey guys! Today, we're diving deep into a significant refactoring effort focused on improving how we handle legacy Excel files and create CohortDto objects. This is a crucial step in solidifying our data model and ensuring a smoother, more efficient workflow. Think of it as the final polish on a masterpiece – everything else should just magically work after this!

The Goal: Direct CohortDto Creation

Our primary objective is to streamline the import process. Currently, we might be using intermediate data types, such as PtTemplateDiscussion, before finally arriving at the desired CohortDto structure. The goal is to cut out the middleman and directly create CohortDto objects from the imported Excel data. This will not only simplify our code but also make it more robust and easier to maintain.

Why is this important? Efficiency, clarity, and maintainability are key in any software project. By eliminating unnecessary steps, we reduce the chances of errors and make the codebase easier to understand and modify in the future. This is especially crucial as our project matures and evolves.

To achieve this, we're making some strategic decisions about how we classify and handle genetic variants, which leads us to our next key point.

Strategy: HGVS vs. Structural Variants

Here's the game plan: We're simplifying our variant classification by categorizing them into two main groups: HGVS variants and Structural Variants (SVs). Anything that follows the c. or n. HGVS nomenclature will be classified as HGVS. Everything else will be considered a structural variant.

Why this approach? This approach provides a clear, consistent framework for variant classification. By having this foundational distinction, we set the stage for a more organized and manageable data structure.

We understand that SV types can be quite detailed. To address this, we'll allow users to refine the SV type within the GUI. This flexibility ensures that we capture the necessary granularity without complicating the initial import process. We've realized that reliably extracting detailed SV types directly from the Excel sheets is challenging, as this information was often added during the Jupyter notebook analysis phase.

Think of it this way: We're building a solid foundation with the broad HGVS/SV classification, and then providing the tools for users to add the specific details as needed. It's like building a house – you start with the frame and then add the interior design.

This brings us to the anticipated final major rewrite of our variant handling.

The Last Major Rewrite (Hopefully!)

This refactoring effort is significant, and we're approaching it as potentially the last major rewrite in this area. Our aim is to establish a solid, long-term solution for variant management. This means carefully considering the design, implementation, and potential future needs of the system.

What's involved in this rewrite? We're primarily focused on restructuring how we store and access variant information. Instead of using multiple HashMaps, we'll be using two lists: one for HGVS variants and one for SVs. This change will significantly simplify our processing logic.

Why lists instead of HashMaps? With separate lists, we eliminate the need to constantly check which HashMap a variant key belongs to. This simplifies our code and improves performance. It's like having two clearly labeled drawers instead of one overflowing box – much easier to find what you need!

Now, let's delve into the specific code changes that will bring this vision to life.

Code Transformation: From geneVarDtoList to Allele Maps

Let's get into the nitty-gritty of the code changes. We're moving away from the geneVarDtoList structure and adopting a new approach using hgvsAlleleMap and svAlleleMap. This shift is at the heart of our refactoring effort.

The Old Way (geneVarDtoList):

"geneVarDtoList": [
 {
 "hgncId": "HGNC:29915",
 "geneSymbol": "NUP210L",
 "transcript": "NM_207308.3",
 "allele1": "c.718-1G>A",
 "allele2": "na",
 "variantComment": ""
 }
],

This structure represented variants as a list of objects, each containing detailed information like HGNC ID, gene symbol, transcript, and alleles. While comprehensive, this structure could lead to more complex processing logic when dealing with different variant types.

The New Way (hgvsAlleleMap and svAlleleMap):

"hgvsAlleleMap": {
 "c8242GtoT_FBN1_NM_000138v5": 1,
 },
"svAlleleMap": {
 "FBN1_SV_DEL_Ex_4": 2
}

Here, we're using maps (dictionaries) to store variants. The keys represent the variant identifiers, and the values could represent the count or other relevant information. We have separate maps for HGVS alleles and SV alleles, which aligns perfectly with our classification strategy.

Why this change? This new structure offers several advantages:

  • Simplified Processing: By separating HGVS and SV variants into distinct maps, we can process them more efficiently. We no longer need to constantly check the variant type.
  • Improved Organization: The maps provide a clear and organized way to store and access variant information.
  • Enhanced Readability: The code becomes more readable and easier to understand.

Let's break down the structure further:

  • hgvsAlleleMap: This map stores HGVS variants. The key (e.g., c8242GtoT_FBN1_NM_000138v5) is a unique identifier for the variant, and the value (e.g., 1) could represent the number of times this variant appears in the cohort.
  • svAlleleMap: Similarly, this map stores structural variants. The key (e.g., FBN1_SV_DEL_Ex_4) identifies the SV, and the value (e.g., 2) could represent its frequency.

This shift to allele maps is a fundamental change that will have a ripple effect throughout our codebase, simplifying variant processing and management.

Benefits of the Refactoring

Okay, so we've talked about the what and the how, but let's zoom out and consider the why. What are the overall benefits of this refactoring effort?

  • Improved Code Clarity and Maintainability: The simplified data structures and processing logic make the code easier to understand, modify, and debug. This is crucial for long-term project health.
  • Increased Efficiency: Direct CohortDto creation and streamlined variant processing reduce the computational overhead, leading to faster import times and improved overall performance.
  • Enhanced Data Integrity: By establishing clear classification rules and data structures, we minimize the risk of errors and inconsistencies in our data.
  • Greater Flexibility: The ability to refine SV types in the GUI provides users with the necessary control over the data without complicating the initial import process.
  • Future-Proofing: This refactoring lays a solid foundation for future enhancements and expansions. By addressing the core data structures and processing logic, we make it easier to adapt to evolving requirements.

In a nutshell: This refactoring isn't just about making the code look better; it's about making it work better, both now and in the future.

Conclusion

This refactoring effort is a significant step forward in our project's evolution. By streamlining the import process, simplifying variant classification, and restructuring our data storage, we're creating a more robust, efficient, and maintainable system. This direct CohortDto creation from legacy Excel files will help to reduce intermediate datatypes. We're excited about the benefits this will bring and confident that this will be the last major rewrite in this area. Stay tuned for more updates as we continue to refine and improve our tools!