fixes an issue where spearman lfc calculation expects f64

[abhinadduri]

seeing strange errors failing on spearman lfc due to polars expecting f64 but receiving f32

[gemini-code-assist]

Summary of Changes

Hello @abhinadduri, 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 addresses a critical bug in the Spearman LFC calculation within the cell-eval library. The core problem stemmed from a type mismatch where the Polars DataFrame library expected f64 (double-precision float) for correlation computations, but the input data was f32 (single-precision float). The fix involves explicitly casting the relevant columns to Float64 before performing the Spearman correlation, thereby preventing errors and ensuring accurate results. A minor version bump accompanies this fix.

Highlights

• Bug Fix: Resolved an issue where the Spearman LFC calculation failed due to Polars expecting f64 data types but receiving f32.
• Data Type Coercion: Explicitly cast the fold change columns to Float64 within the Spearman correlation function to ensure compatibility with Polars' requirements.
• Version Update: Incremented the project version from 0.6.6 to 0.6.7 in pyproject.toml.

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[gemini-code-assist]

Code Review

This pull request addresses a TypeError in the Spearman LFC calculation by casting the f32 columns to f64 as expected by polars. The fix is correct and targeted, and the package version has been bumped accordingly. While reviewing, I noticed a potential logic issue in the DESpearmanLFC class. The class name and docstrings indicate that the correlation should be computed on log fold changes, but the implementation uses the raw fold change column. I've left a specific comment with a suggestion to correct this to ensure the metric is calculated as intended.

[gemini-code-assist]

The class DESpearmanLFC and its docstrings state that it computes the Spearman correlation on log fold changes. However, the code is using data.real.fold_change_col. This seems to be a discrepancy. To align with the documented intent and for correctness of the metric, you should use data.real.log2_fold_change_col instead.

Suggested change

	pl.col(data.real.fold_change_col).cast(pl.Float64),
	pl.col(f"{data.real.fold_change_col}_pred").cast(pl.Float64),
	pl.col(data.real.log2_fold_change_col).cast(pl.Float64),
	pl.col(f"{data.real.log2_fold_change_col}_pred").cast(pl.Float64),