20251115 update block metadata #13
Conversation
Summary of ChangesHello @aiturbidemil, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request primarily focuses on improving the clarity and completeness of the rarefaction analysis block's metadata and documentation. It introduces a new documentation link and expands the block's description in 'package.json', alongside a substantial rewrite of the 'description.md' file to provide a more thorough explanation of the analysis. Additionally, it includes routine updates to project dependencies, ensuring the block leverages the latest versions of its underlying libraries. Highlights
Using Gemini Code AssistThe full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips. Invoking Gemini You can request assistance from Gemini at any point by creating a comment using either
Customization To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a Limitations & Feedback Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here. You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension. Footnotes
|
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Code Review
This pull request updates the block's metadata, including its description and documentation, to provide a clearer and more detailed explanation of its functionality. It also bumps the version of @platforma-sdk/block-tools. The changes are generally positive, improving the project's documentation. I've added a couple of suggestions to further enhance the readability of the long description markdown file.
| # Overview | ||
|
|
||
| Rarefaction is a technique used to assess species richness from the results of sampling. Specifically, it is used to compare the clonal diversity of immune repertoires of different sizes. | ||
| Performs rarefaction analysis on clonotype data from immune receptor repertoires (TCR or BCR) to assess repertoire diversity and compare clonotype richness across samples with different sequencing depths. Rarefaction is a statistical technique that estimates the expected number of unique clonotypes as a function of sampling depth, enabling fair comparison of diversity between repertoires of different sizes. |
There was a problem hiding this comment.
The first sentence is quite long and dense. For better readability, consider breaking it into two separate sentences. This will help users quickly grasp the block's primary function and its application.
| Performs rarefaction analysis on clonotype data from immune receptor repertoires (TCR or BCR) to assess repertoire diversity and compare clonotype richness across samples with different sequencing depths. Rarefaction is a statistical technique that estimates the expected number of unique clonotypes as a function of sampling depth, enabling fair comparison of diversity between repertoires of different sizes. | |
| This block performs rarefaction analysis on clonotype data from immune receptor repertoires (TCR or BCR). The analysis is used to assess repertoire diversity and compare clonotype richness across samples with different sequencing depths. Rarefaction is a statistical technique that estimates the expected number of unique clonotypes as a function of sampling depth, enabling fair comparison of diversity between repertoires of different sizes. |
| This block performs rarefaction analysis on clonotype data. It calculates the mean number of unique clonotypes for various subsampling depths for each sample, providing insight into the clonal richness of the repertoires. | ||
|
|
||
| The analysis is based on a random subsampling of clonotypes from each repertoire at different depths. At each depth, the number of unique clonotypes is recorded. This process is repeated multiple times to generate a smooth rarefaction curve, which represents the expected number of unique clonotypes as a function of the number of sampled clonotypes. The shape of the curve can be used to compare the diversity of different repertoires. For example, a steeper curve indicates a higher diversity. | ||
| The block processes clonotype count data from VDJ processing blocks and performs random subsampling at multiple depths for each sample. At each subsampling depth, the number of unique clonotypes is recorded, and this process is repeated multiple times to generate smooth rarefaction curves representing the mean expected number of unique clonotypes. The shape and slope of these curves provide insights into repertoire diversity: steeper curves indicate higher diversity and suggest that additional sequencing would reveal more unique clonotypes, while plateauing curves indicate that most diversity has been captured at the current sequencing depth. |
There was a problem hiding this comment.
This paragraph describes a sequence of steps. Using a list format would make the process clearer and easier for users to understand at a glance.
| The block processes clonotype count data from VDJ processing blocks and performs random subsampling at multiple depths for each sample. At each subsampling depth, the number of unique clonotypes is recorded, and this process is repeated multiple times to generate smooth rarefaction curves representing the mean expected number of unique clonotypes. The shape and slope of these curves provide insights into repertoire diversity: steeper curves indicate higher diversity and suggest that additional sequencing would reveal more unique clonotypes, while plateauing curves indicate that most diversity has been captured at the current sequencing depth. | |
| The block processes clonotype count data from VDJ processing blocks and performs the following steps for each sample: | |
| * Performs random subsampling at multiple depths. | |
| * Records the number of unique clonotypes at each depth. | |
| * Repeats this process multiple times to generate smooth rarefaction curves, which represent the mean expected number of unique clonotypes. | |
| The shape and slope of these curves provide insights into repertoire diversity: steeper curves indicate higher diversity and suggest that additional sequencing would reveal more unique clonotypes, while plateauing curves indicate that most diversity has been captured at the current sequencing depth. |
No description provided.