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Bulk RNA-seq data reader. Provides BulkRNAData — a lightweight wrapper around a CSV/TSV of gene expression counts for a single patient sample. Wide format (default) — one row per sample, genes as columns:
The first column is treated as the sample identifier (index). All remaining columns are gene expression values. Only the first row is used; additional rows are ignored. Long format — one row per gene, activated by setting gene_column and value_column:
The separator is inferred from the file extension (.tsv → tab, otherwise comma) but can be overridden via separator.

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BulkRNAData

Single-sample bulk RNA-seq counts for PyTorch pipelines. Reads a CSV or TSV with gene expression counts and stores the values as a contiguous np.float32 array for zero-copy tensor creation via counts. Supports two layouts:
  • Wide (default): one row per sample, genes as columns. The first column is the sample identifier (index).
  • Long: one row per gene, activated by setting gene_column and value_column.
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Path to the CSV or TSV file.
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Optional gene list to filter/align columns to. Missing genes are zero-filled, extras are dropped. When None all columns are kept.
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Optional preprocessing transform applied when accessing counts.
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Column delimiter. When None the separator is inferred from the file extension (.tsv → tab, otherwise comma).
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Column name that contains gene identifiers. Required for long-format files.
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Column name that contains expression values to read. Required for long-format files.
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Sample identifier (first row index in wide format, or the file stem in long format).
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Ordered list of gene column names.

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num_genes

Number of gene columns.

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counts

Gene counts as a list of floats.

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get_counts

Return gene counts as a flat sequence. When no omic_transform is configured, returns a plain list[float]. With a transform, returns the transform output (typically an np.ndarray).
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str | None
Ignored (kept for backward compatibility).
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list[float]
Gene expression values in column order.