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Interactive notebook workspace mock with code cells, outputs, and links to datasets/runs. Business and scientific purpose: scale computational research throughput while preserving reproducibility, observability, and cost control. Notebooks is connected to linked entities, metrics, and workflow actions in this workspace. Use these panels to review evidence, identify risks, and decide the next best scientific action. The view uses deterministic demo data shaped to mirror realistic drug discovery operations.
Notebook: RNA-seq QC and Differential Expression
This notebook computes QC summaries and exports a report-ready table.
import pandas as pd
from numpy.random import default_rng
rng = default_rng(20260101)
qc = pd.DataFrame({
"sample": [f"S{i:02d}" for i in range(1, 9)],
"mapped_reads_pct": [round(88 + rng.random()*10, 2) for _ in range(8)],
"batch": ["A","A","A","A","B","B","B","B"]
})
qc| sample | mapped_reads_pct | batch |
|---|---|---|
| S01 | 95.42 | A |
| S02 | 90.11 | A |
| S03 | 93.86 | A |
| S04 | 91.02 | A |
# Export analysis artifact
qc.to_csv("analysis_qc_summary.csv", index=False)
print("artifact written: analysis_qc_summary.csv")artifact written: analysis_qc_summary.csv