Skip to main content
Glama

Jean — Molecular Biology & Genomics

simulate_rnaseq_power

Read-only

Power and FDR for a two-group RNA-seq experiment by SIMULATION (PROPER 1.44.0): whole experiments drawn from a real pilot (Cheung, Gilad, Bottomly, MAQC, or a sequencing depth), each run through edgeR's exact test at every sample size.

Input Schema

NameRequiredDescriptionDefault
seedNothe seed; round i uses seed + i, as PROPER does. Advanced.
genesNogenes in the simulated experiment.
pilotNowhere each gene's baseline expression comes from: one of PROPER's pilots, a sequencing depth applied to aggregated human expression, or one constant. Values: cheung (Cheung (unrelated people, large dispersion)), gilad (Gilad (human liver, moderate)), bottomly (Bottomly (inbred mice, small)), maqc (MAQC (technical replicates)), depth (Sequencing depth), constant (One constant).cheung
_filesNoThe project: record references shaped like _subject. Their records become library documents a tool names in sequences/parts/document; a trace or alignment is found by file name.
cutoffNothe cut-off a gene is called at.
roundsNosimulated experiments. PROPER's own default is 100; each is a full edgeR run at every sample size, so time grows with it.
strataNostratum break points; unset, PROPER's: 0, 10, 20, 40 … 1280, ∞ for expression. Advanced.
targetNowhich DE genes count as worth finding: by |log fold change|, or by |log fold change| / √dispersion. Values: lfc (Log fold change), effectsize (Effect size).lfc
call_byNocall a gene by its FDR or its raw p-value. Values: fdr (FDR), pval (raw p-value).fdr
_subjectNoThe record to act on: exactly one of sequence, accession (+source), sample (an id from list_samples) or file ({name, text} or {name, base64}); record_index picks one record of several.
log_meanNothe natural-log mean count every gene shares. Only applies when pilot is constant.
filter_byNodrop the lowest expression strata before counting, and recompute BH over what is left. Values: none (Keep every gene), expr (Drop low expression). Advanced.none
thresholdNothe threshold a DE gene's size must exceed to count.
dispersionNoone dispersion (BCV²) for every gene, instead of the pilot's; left unset, the pilot's own. Advanced.
replicatesNoreplicates per group at each sample size to evaluate.
stratify_byNostratify the power by expression (mean normalized count) or by dispersion. Values: expr (Expression), dispersion (Dispersion). Advanced.expr
second_groupNoreplicates in the second group at each size, when the groups differ. Advanced.
depth_factorsNosequencing depth multipliers to re-weight power to. Advanced.
dropped_strataNowhich strata (1-based) to drop. Only applies when filter_by is expr. Advanced.
log_fold_changeNoone log fold change for every DE gene; left unset, PROPER's normal(0, 1.5) draws. Advanced.
changed_fractionNothe proportion of genes that are differentially expressed.
dispersion_pilotNotake dispersions from this pilot instead (with a depth or a constant baseline, Cheung's unless set). Advanced.
reads_per_sampleNototal reads per sample. Only applies when pilot is depth.

Output Schema

NameRequiredDescriptionDefault
powerYesmarginal power at each sample size
strataYesthe stratum intervals the stratified readouts are over
powerSEYesthe Monte-Carlo standard error of each marginal power
rScriptYesthe R that reproduces the run
summaryYesper sample size: nominal and achieved FDR (or type I error), marginal power, mean true and false discoveries, false discovery cost
seqDepthYesmarginal power re-weighted to each sequencing depth factor, per sample size
stratifiedFDRYesachieved FDR by stratum and sample size
stratifiedPowerYespower by expression (or dispersion) stratum, one line per sample size

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds genuine behavioral context by disclosing that results come from simulated whole experiments run through edgeR's exact test at every sample size, but it never warns that runtime scales with rounds and sample sizes, nor discusses determinism from the seed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler: the purpose leads and the method follows. It is dense, with nested parentheses listing pilot sources, but every clause carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a 23-parameter, nested-object, simulation-heavy tool, and an output schema exists so return values need no explanation. The description is nevertheless thin for that complexity: no cost/runtime expectation, no guidance on which pilot or replication settings matter, and no differentiation from the closely related power-calculation siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% across 23 parameters, including pilot enumerations, defaults and 'Advanced' markers, so the schema carries the parameter load entirely. The description only echoes the pilot list already present in the schema and adds no new meaning for any parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb+resource: power and FDR for a two-group RNA-seq experiment, computed by simulation. The capitalized 'SIMULATION' implicitly separates it from analytic power siblings, but it never names calculate_rnaseq_power or calculate_power, so the distinction is inferred rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'by SIMULATION (PROPER 1.44.0)' and 'whole experiments drawn from a real pilot' imply the usage context: pick this when you want resampling-based rather than closed-form power estimates. Beyond that there are no explicit when/when-not statements, no prerequisites, and no named alternatives among the many overlapping siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources