tasks/task_019/instruction.md

Complete robust topic-to-cistrome binarization

The included pycisTopic source can turn topic distributions attached to a full cisTopic model into selected cell or region sets. A downstream workflow now needs to consume probability matrices exported independently from topic-model training and produce the discrete topic sets used for motif enrichment and cistrome construction.

The current source does not complete that workflow reliably. The public reproduction exercises both region-topic and cell-topic inputs, including zero-heavy probabilities at the precision used by exported matrices. It reports that the required topic sets or portable output artifacts cannot be produced consistently.

Inspect the source snapshot and fixtures, and complete the scientific capability so that independent topic-probability matrices can be validated, optionally smoothed, thresholded, ranked and exported without a full in-memory cisTopic object.

Requirements:

• support matrices whose rows are named cells or genomic regions and whose columns are topics;
• preserve name-to-score alignment and deterministic descending ranking;
• support top-n selection and the histogram-based thresholding methods already documented by the source;
• remain numerically finite for zero-heavy float32 topic probabilities;
• treat constant topics as having no separable evidence rather than selecting every row because of a histogram artifact;
• reject duplicate names, non-finite values, invalid ntop bounds and malformed genomic intervals;
• produce the required portable downstream artifacts, including deterministic threshold tables, cell tables and BED-compatible region tables;
• retain the existing object-based entry point where practical;
• do not hard-code the public names, topic count, matrix shape or expected selected sets.

Run the public workflow with:

python reproduce.py

The benchmark runs offline. Generated files belong under outputs/.