Statistical Analysis
Prospective RNA-seq or omics power memos use simulation or voom models to size replicates before you sequence, with curves and commented R [1].
| Category | Details |
|---|---|
| Supported platforms / instruments | Illumina NovaSeq, NextSeq, and HiSeq bulk RNA-seq (poly(A)+ and rRNA-depleted); 10x Genomics Chromium scRNA-seq; 10x Visium and Xenium spatial transcriptomics scoped separately; proteomics and metagenomics count matrices scoped separately |
| Input requirements | Pilot gene-level count matrix (≥2 biological replicates per ENCODE minimum; ≥3 preferred for dispersion estimates) or documented public reference dataset; target FDR; minimum detectable fold change or effect size; budget or per-sample cost constraints; sample manifest with biological vs technical replicate labels |
| Reference builds supported | Human GRCh38 (GENCODE v44 / Ensembl 110); mouse GRCm39 (GENCODE vM33 / Ensembl 110); custom references on request for pilot annotation alignment |
| Primary tools (with versions) | PROPER 1.44+; RNASeqDesign (GitHub); scPower 1.0+ (GitHub); RNASeqPower 1.52+; DESeq2 1.52+; ssizeRNA 1.3.3 (scoped on request). Pinned per project |
| Typical turnaround time | 1–2 weeks (standard design memo from pilot or public priors); 2–3 weeks (multi-contrast designs, scRNA-seq cell-type power, or budget optimization across modalities). Confirmed at kickoff |
| Deliverable formats | Power-vs-sample-size and power-vs-depth curves (PDF/SVG); recommended replicate and depth tables (.csv, .xlsx); simulation parameter log; grant-ready design memo; commented R scripts; Methods draft |
| Key cited best-practice reference | Jeon et al. [1], Biomolecules; Ching et al. [2], RNA; Schurch et al. [3], RNA |
| Custom / bespoke analysis | Non-standard contrasts, proteomics or spatial power models, multi-omics replicate planning, client-specified FDR or effect-size targets, and analyses beyond standard bulk or scRNA-seq design workflows. Scoped at kickoff |
Experimental design consulting applies negative-binomial or cell-level models to estimate statistical power, sample size, and sequencing depth before data collection, not after differential expression on finished datasets [1]. With three biological replicates, Schurch et al. [3] found that common DE tools recover only 20–40% of significantly differentially expressed genes compared with a 48-replicate reference set. Each project is scoped to the modality and estimand at kickoff. Custom designs beyond standard bulk or scRNA-seq workflows are supported when documented in advance. See the experimental design glossary.
Use experimental design consulting when you have not yet locked sample counts or sequencing depth and need a power justification for a grant, protocol, or sequencing core quote.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| Simulation-based experimental design consulting | Grant power sections, pilot-to-production scaling, budget allocation between replicates and depth | Requires effect-size and dispersion assumptions; spatial and proteomics frameworks less standardized than bulk RNA-seq | Lower than full downstream analysis; 1–2 week design memo typical |
| Rule-of-thumb guidelines only (e.g., ENCODE n≥2, ~20–30M reads) | Quick feasibility screening | Ignores study-specific dispersion and effect size; often underpowered for comprehensive DE [3] | No consulting fee; higher risk of a failed or inconclusive study |
| Post-hoc analysis after sequencing | When data already exist and the question is inference, not planning | Cannot recover missing biological replication; sequencing cost is already spent if the study is underpowered | Full analysis cost; may require additional samples |
Kickoff confirms modality, primary contrast, target FDR, minimum detectable effect size, and unit of inference (sample, donor, or cell type). Paired, blocked, or multi-factor designs are documented before simulation [1]. Confounded batch-by-condition layouts are flagged.
Tools used: Custom scoping template
Output: design_scope.md with primary estimand, contrast definition, and assumption log
Client pilot count matrices, public reference datasets, sample manifests, and budget ceilings are catalogued. Technical replicates from the same library are not treated as independent biological units [6].
Tools used: Custom validation scripts
Output: input_manifest.csv with sample IDs, condition, batch, replicate type, and budget fields
When a pilot matrix is provided, library sizes, gene detection rates, and outlier libraries are assessed before parameter estimation. Matrices with fewer than three biological replicates per condition are accepted with documented uncertainty [3, 6].
Tools used: DESeq2 1.52+ (diagnostic size factors and dispersion previews)
Output: pilot_qc_summary.csv with library size, detected genes, and outlier flags per sample
Mean expression, dispersion, and fold-change distributions are estimated from pilot counts using PROPER estParam() or DESeq2 dispersion fitting. Public reference datasets (for example, Bottomly, Cheung) are used when no pilot exists [7].
Tools used: PROPER 1.44+; DESeq2 1.52+
Output: simulation_priors.rds and simulation_priors.csv with mean, dispersion, and DE-gene fraction parameters
Bulk RNA-seq uses PROPER 1.44+ or RNASeqPower 1.52+. Budget-constrained studies add RNASeqDesign. scRNA-seq cell-type DE uses scPower 1.0+ [1, 5]. ssizeRNA 1.3.3 provides faster voom-based estimates when scoped [8].
Tools used: PROPER 1.44+; RNASeqPower 1.52+; scPower 1.0+; RNASeqDesign
Output: framework_selection_log.txt documenting tool choice and rationale
Negative-binomial counts are simulated under proposed replicate counts and depths. DE detection runs within each iteration. PROPER recommends at least 20 iterations for stable estimates [7].
Tools used: PROPER 1.44+ (runSims(), comparePower()); scPower 1.0+ (scRNA-seq)
Output: power_simulation_results.rds; per-iteration DE detection summaries
Power is evaluated across replicate count and reads-per-sample grids. Ching et al. [2] showed sample size is more potent than depth beyond ~20 million reads. RNASeqDesign finds the local budget optimum when a fixed budget is supplied [4].
Tools used: PROPER 1.44+ (power.seqDepth()); RNASeqDesign
Output: replicate_depth_grid.csv with columns: n_replicates_per_group, sequencing_depth_m_reads, marginal_power, estimated_cost
Randomized block designs and balanced batch allocation are evaluated for effective power [6]. Blocking is recommended at the design stage rather than relying on post-hoc correction after confounded library prep.
Tools used: Custom design scripts; PROPER 1.44+ (paired and multi-factor extensions when scoped)
Output: blocking_recommendations.md with batch layout diagram and confounding audit
For each design point, the memo reports minimum detectable log₂ fold change (or cell-type DE effect) at 80% power and target FDR. Underpowered designs are flagged with alternatives [2].
Tools used: PROPER 1.44+; RNASeqPower 1.52+; scPower 1.0+
Output: power_summary_table.csv with columns: modality, n_replicates_per_group, sequencing_depth_m_reads, target_fdr, power, detectable_log2fc
Power curves, assumption tables, and recommended replicate counts are compiled into a grant-ready design memo. Commented R scripts reproduce simulations. A Methods draft cites software versions and reference datasets.
Tools used: PROPER 1.44+; RNASeqDesign; scPower 1.0+; ggplot2
Output: design_memo.pdf; power curve figures (PDF/SVG); commented R scripts; README; Methods draft
A pilot gene-level count matrix with metadata is preferred. ENCODE requires ≥2 biological replicates, and ≥3 per condition improve dispersion estimates [6]. Without a pilot, Pepkio uses published reference datasets plus your target FDR, effect size, and budget. A sample manifest and contrast definition are required at kickoff.
Yes. Dispersion is estimated from published reference matrices matched to tissue and library type [4, 7]. The design memo documents literature-derived priors and recommends a confirmatory pilot when budget allows.
Outlier pilot samples are flagged in pilot_qc_summary.csv before parameter estimation. Very low-yield libraries may not represent production data. Options then include re-sequencing or using public priors with inflated dispersion [10].
Yes, for formats we can parameterize after kickoff. Bulk RNA-seq uses PROPER 1.44+ or RNASeqDesign. scRNA-seq cell-type DE uses scPower 1.0+ for 10x Chromium. Spatial (Visium, Xenium) and proteomics models are scoped separately; those frameworks are less mature [1].
Standard memos typically complete in 1–2 weeks. Multi-contrast, multi-population scRNA-seq, or cross-modality budget optimization may take 2–3 weeks. Timelines are confirmed at kickoff.
Randomized, balanced blocking at the library-prep and sequencing stage is recommended so batch is not confounded with condition [6]. The design memo includes a batch layout diagram and flags fully confounded designs. Post-hoc ComBat or surrogate variable correction is not a substitute for proper blocking and is out of scope for this service.
Yes. You retain full ownership of all scripts and outputs. Pepkio delivers commented R scripts with renv.lock or equivalent environment locks via private Git or agreed file transfer. Simulation objects use standard .rds and .csv formats reproducible on R ≥4.3 installations matching the locked environment.
Yes. When scoped at kickoff, checkpoint reviews occur after pilot QC and parameter estimation, after the replicate-vs-depth sweep, and before final memo delivery. You can review target FDR, effect-size assumptions, budget constraints, and blocking layout within agreed scope.
Support covers clarification of simulation methods, assumption tables, and minor figure or table revisions within agreed scope (typically ≤20% of project scope). Requests to re-run with a different modality, new contrasts, or updated pilot data after sequencing begins are scoped and priced separately.
No. Pepkio operates as a fee-for-service provider unless co-authorship is discussed in advance.
Increasing biological replicate count yields more power than increasing sequencing depth once depth reaches approximately 20 million reads per sample [2]. Schurch et al. [3] suggest at least six biological replicates per condition, rising to at least 12 when identifying SDE genes across all fold changes. Pepkio quantifies the tradeoff for your pilot dispersion and budget using PROPER and RNASeqDesign sweeps.
Individual cells are not independent biological replicates. Donor-level variation drives inferential power [5]. scPower 1.0+ models power as a function of donor count, cells per donor, cell-type prevalence, and sequencing depth. Shallow sequencing of more cells often outperforms deep sequencing of fewer cells for cell-type-specific DE when donor count is fixed [5].
The design memo includes power curves, effect-size assumptions, FDR targets, replicate and depth recommendations, and cited tool versions suitable for grant Power sections [4, 7]. Final grant prose remains the client's responsibility.
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