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Slide-level aggregation utilities. Small, dependency-light helpers that reduce a written tile-output file (the (N_tiles, D) outputs array produced by bioptimus.inference.writers) to a single slide-level vector. Two convenience entry points build on the same mean-pool:
  • slide_embedding — averages tile embeddings into one slide embedding.
  • pseudobulk — averages predicted spatial gene expression into a single bulk RNA-seq-like profile, paired with the output gene names.
These operate on the output of SlideInference.predict/.embed (or the Inference facade), so no model or server is needed to aggregate.

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Pseudobulk

A predicted pseudobulk gene-expression profile for one slide. Pairs the aggregated expression vector with the gene names it is indexed by, so callers can align it to a reference panel without a separate lookup. eq=False keeps identity equality: a value-based __eq__ would compare the numpy expression field with == and raise on truthiness. Attributes:
  • expression - The pseudobulk expression vector of shape (G,) as float32.
  • gene_names - Ordered gene identifiers for each entry of expression, or None when the output has no gene names.

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slide_reduce

Reduces per-tile outputs to one slide-level vector by the chosen pooling.
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The (N_tiles, D) per-tile outputs.
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The pooling strategy over the tile axis. One of "mean", "max", "sum", or "median". Defaults to "mean".
Returns: The slide-level vector of shape (D,) as float32. Raises:
  • BioptimusValueError - If pool is not a supported strategy, or outputs is not a 2-D (N_tiles, D) array or has no tiles.

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slide_mean

Mean-pools per-tile outputs into one slide-level vector. Thin alias for slide_reduce(outputs, pool="mean"), kept for convenience and backward compatibility.
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The (N_tiles, D) per-tile outputs.
Returns: The slide-level vector of shape (D,) as float32. Raises:
  • BioptimusValueError - If outputs is not a 2-D (N_tiles, D) array or has no tiles.

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slide_embedding

Reduces a slide’s tile embeddings to one slide-level embedding. Reads the per-tile embeddings from a file written in embedding mode (the embedding is the outputs array) or in the combined PREDICTION_WITH_EMBEDDING mode (the embedding is the separate embeddings array, alongside the outputs prediction). The embeddings array is preferred when present.
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Path to a tile-output file written in embedding or combined mode.
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The on-disk format. Inferred from the extension when omitted.
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The pooling strategy over tiles ("mean", "max", "sum", or "median"). Defaults to "mean".
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When True, saves the vector next to the input as <name>.slide.npy. Defaults to False.
Returns: The slide embedding of shape (D,) as float32.

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pseudobulk

Reduces a slide’s predicted spatial expression to a pseudobulk profile.
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Path to a tile-output file written in predict mode.
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The on-disk format. Inferred from the extension when omitted.
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The pooling strategy over tiles ("mean", "max", "sum", or "median"). Defaults to "mean".
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When True, saves the vector next to the input as <name>.slide.npy. Defaults to False.
Returns: A Pseudobulk pairing the (G,) expression vector with the output gene names (None if the file has none).