feat: Added FDR threshold as a user defined option in prediction pipeline

[Rive-001]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @Rive-001, 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 enhances the prediction pipeline by providing users with the ability to define the False Discovery Rate (FDR) threshold. This new feature allows for greater control over the statistical significance criteria applied during cell evaluation metrics, making the model's output more customizable and adaptable to specific analytical needs. The change primarily involves adding a new command-line argument and integrating its value into the evaluation process.

Highlights

• New User-Defined Option: Introduced a new command-line argument --fdr-threshold for the prediction pipeline, allowing users to specify the False Discovery Rate threshold.
• Configurable FDR Threshold: The specified FDR threshold is now passed to the evaluation component, enabling dynamic control over significance criteria in cell-eval metrics.
• Version Update: The project version in pyproject.toml has been incremented from 0.9.32 to 0.9.33.

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[gemini-code-assist]

Code Review

This pull request adds a user-configurable --fdr-threshold option to the prediction pipeline for cell-eval metrics. The implementation correctly adds the command-line argument and passes it to the MetricsEvaluator. My review includes one suggestion to add input validation for the new threshold to make the CLI more robust by ensuring the value is within a valid range (e.g., between 0 and 1).

[gemini-code-assist]

The new --fdr-threshold argument accepts any float value, which could lead to errors or unexpected behavior in cell-eval if an invalid value (e.g., negative or > 1) is provided. It would be more robust to add validation to ensure the input is within a sensible range, like (0, 1).

You can achieve this by using a custom type function with argparse.

For example:

import argparse

def fdr_threshold_range(value):
    try:
        f_value = float(value)
    except ValueError:
        raise argparse.ArgumentTypeError(f"{value} is not a floating-point number")
    if not (0.0 < f_value < 1.0):
        raise argparse.ArgumentTypeError(f"{value} is not a valid FDR threshold, it must be between 0 and 1.")
    return f_value

# ... in add_arguments_predict ...
parser.add_argument(
    "--fdr-threshold",
    type=fdr_threshold_range,
    default=1e-3,
    help="FDR threshold for DE significance in cell-eval metrics. Default is 1e-3.",
)