Upstream Regulator Analysis

Which upstream regulators have an unexpectedly large number of known targets in my molecular signature? Upstream Regulator Analysis (URA) moves from a list of changed genes or molecular features to the regulators that may best explain the observed pattern — highlighting transcription factors, signaling regulators, drugs, and other regulatory entities connected to the submitted features.

How it works

1. Provide the signal. Upload the features you want to investigate and the background your assay could have detected. If the upload includes significance values, choose the cutoff for this run.

2. Test regulators. The inference compares each regulator's known targets with your signature, relative to the measured background.

3. Review evidence. Explore ranked regulators, overlapping targets, statistical values, direction fields where supported, knowledge sources, publications, and a network view.

Example input and output

Feature table

entity,effect,adj_p_value
IL1B,1.42,0.003
CXCL8,1.18,0.006
NFKBIA,0.91,0.012
JUN,-0.76,0.018
HMOX1,0.72,0.021

Background and run settings

Assayed background: 18,432 genes
Significance column: adj_p_value
Significance cutoff: ≤ 0.05
Effect column: effect

Result

Regulator	Result group	Overlapping targets	q-value	Direction
RELA	gene	IL1B, CXCL8, NFKBIA, JUN	0.008	Activated
MAPK14	gene	IL1B, JUN, HMOX1	0.031	Inconclusive
Dexamethasone	chemical	IL1B, CXCL8, NFKBIA	0.044	not_computable

RELA has four known targets in the submitted signature and is significant within the gene-regulator family. The chemical row belongs to a separately corrected family and should not be globally ranked against the gene rows.

Illustrative only — names and values above are simplified and fabricated to demonstrate the input and output shape. They are not a biological result.

How the result is organized

Regulators are reported in separate gene, chemical, and other groups. Each group receives its own multiple-testing correction. This makes gene results easier to interpret and prevents growth in a chemical knowledge source from silently changing gene q-values.

• p-value: how surprising the observed overlap is under the run's measured background.

• q-value: the p-value adjusted for the regulator family in that result group.

• Overlap: the submitted features that are known targets of the regulator.

• Direction: an activation-oriented call — Activated, Inhibited, or Inconclusive — when the submitted effects and regulatory evidence support it; Biased when dataset skew rules out a reliable call; not_computable when there is not enough signed evidence. A regulator without a direction call keeps its enrichment result.

• Evidence: the knowledge sources and available publications behind the regulator–target relationships.

What makes the statistics trustworthy

• The query is explicit. The inference tests the narrowed signature, not an entire measurement table by accident.

• The background is experiment-aware. Only entities that the assay could observe and the tested regulatory network could contain contribute to the statistical universe.

• Incomplete families are visible. If the system cannot establish the full hypothesis family, it withholds q-values instead of presenting an overconfident correction.

Reading the result responsibly

• Compare q-values within a regulator group. Do not merge the gene, chemical, and other tables and treat the union as one jointly controlled 5% result.

• A regulator can rank even when the regulator itself was not measured. URA is designed to find upstream explanations, including regulators activated after transcription.

• Results describe consistency with curated regulatory knowledge; they do not prove causality.

• p-values and q-values are conditioned on this run's measured background and build. They should not be compared directly across different datasets or knowledge-graph builds.

In short: URA provides a ranked, evidence-linked set of upstream explanations for a molecular signature, while making the tested population, background, multiple-testing scope, and any statistical limitations visible in the result.

Next: Toxicity Endpoint Analysis.