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Concrete omics preprocessing transforms.

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Log1pTransform

Element-wise log(1 + x) ↔ expm1(x). Entries equal to NON_MEASURED_GENE (the missing-gene sentinel) are preserved verbatim rather than transformed, since log1p(-1) = -inf. The model is trained to receive the sentinel unchanged in its input space, so it must survive the transform.

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process

Applies element-wise log1p, preserving the missing-gene sentinel. Entries equal to NON_MEASURED_GENE are left unchanged; all other entries are log1p-transformed.
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Input counts array.
Returns: Log1p-transformed array with sentinels preserved.

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reverse

Applies element-wise expm1 (inverse of log1p). Entries equal to NON_MEASURED_GENE are preserved unchanged so the transform remains a true inverse of process.
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Log1p-transformed array.
Returns: Array in original counts space.

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Expm1Transform

Element-wise expm1(x) ↔ log1p(x). Inverse of Log1pTransform. Useful as a standalone post-processor to convert log1p-space model output back to raw counts.

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process

Applies element-wise expm1.
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Log-space array.
Returns: Array in counts space.

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reverse

Applies element-wise log1p (inverse of expm1).
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Counts-space array.
Returns: Log1p-transformed array.

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TPMNormaliser

Transcripts Per Million normalisation (row-wise). Forward: counts → (counts / row_sum) * 1e6 Not reversible without storing original totals. Entries equal to NON_MEASURED_GENE (the missing-gene sentinel) are excluded from the row total and preserved verbatim in the output, so a partially or fully unmeasured sample is not scaled by a meaningless negative sum and the sentinel survives for downstream transforms.

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process

Normalises each row to sum to factor (default 1e6). All-zero rows remain all zeros (division is skipped). Entries equal to NON_MEASURED_GENE are excluded from the row sum and left unchanged in the output.
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Raw counts array.
Returns: TPM-normalised array with missing-gene sentinels preserved.

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ZScoreNormaliser

Gene-wise z-score normalisation. Forward: (x - mean) / std Reverse: x * std + mean

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process

Applies gene-wise z-score normalisation.
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Raw expression array.
Returns: Z-scored array.

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reverse

Reverses z-score normalisation to recover original scale.
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Z-scored array.
Returns: Array in original expression scale.

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SubsetColumns

Selects and reorders columns by index. Not reversible (information is discarded).

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process

Selects columns at the stored indices.
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Input array with columns to subset.
Returns: Array with only the selected columns. Raises:
  • ValueError - If any index is out of range for the matrix’s last-axis size.