name: bio-epitranscriptomics-m6a-differential description: Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio). origin: openai4s category: bioskills/epitranscriptomics metadata: tool_type: r primary_tool: exomePeak2 third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a license: MIT
Reference examples tested with: exomePeak2 1.14+ (Bioconductor 3.18+), QNB 1.1.11 (GitHub lzcyzm/QNB), MeTDiff (GitHub, bundled with MeTPeak), RADAR 0.2.4+ (GitHub scottzijiezhang/RADAR), DESeq2 1.42+, edgeR 4.0+, GenomicFeatures 1.54+, ggplot2 3.5+, GenomicAlignments 1.38+, Rsubread 2.16+.
Before using code patterns, verify installed versions match. If versions differ:
- R: packageVersion('exomePeak2') then ?exomePeak2 to verify parameters
- R: packageVersion('QNB') then ?qnbtest to confirm argument signature
If R throws unused argument or argument is missing, the API moved between Bioconductor minor releases; consult ?exomePeak2 directly. QNB function signature has been stable since 2017 but the GitHub source has occasional changes; pin the commit SHA.
exomePeak2's differential interface is NOT a mode= argument — populate the bam_treated_ip= and bam_treated_input= arguments alongside the standard bam_ip= / bam_input= (control arm) to trigger paired differential calling. peak_calling_mode is a separate argument controlling locus scope (exon | full_transcript | whole_genome). Verify against ?exomePeak2 and Bioconductor 3.20+ release notes. QNB is on GitHub only; install via devtools::install_github('lzcyzm/QNB'). RADAR is on GitHub only; install via devtools::install_github('scottzijiezhang/RADAR'); its differential workflow is countReads -> normalizeLibrary -> adjustExprLevel -> filterBins -> diffIP -> reportResult.
"Compare m6A methylation between my conditions" -> Quantify how much each m6A peak's IP/input enrichment shifts between conditions, after normalising for transcript-abundance changes (which all show up in input) and for IP-efficiency drift (which the design matrix and within-run replicates control for). Then apply effect-size filtering to distinguish real biology from the technical noise floor that all MeRIP differential methods inherit (McIntyre 2020 Sci Rep 10:6590: between-study m6A peak overlap is ~45% median; differential calls within that noise envelope routinely fail to replicate). Critically: a higher MeRIP signal in condition A vs B can mean (1) more transcripts of the peak-bearing gene, (2) more methylation per transcript, OR (3) higher IP efficiency in batch A — distinguishing requires careful normalisation or an orthogonal absolute-stoichiometry method.
exomePeak2::exomePeak2(bam_ip, bam_input, bam_treated_ip, bam_treated_input, ...) -- integrated peak + differential GLM (modern default)QNB::qnbtest(control_ip, treated_ip, control_input, treated_input) -- beta-binomial for small N (Liu 2017 BMC Bioinformatics 18:387)RADAR::diffIP() then reportResult() -- Poisson-NB on peak windows with TMM normalisation (Zhang 2019 Genome Biol 20:294)MeTDiff::metdiff() -- HMM-based differential paired with MeTPeakA higher m6A peak signal in condition A vs B can mean ANY of: (1) more transcripts of the peak-bearing gene (expression up; the input increases proportionally, so the ratio should not change — but residual normalisation noise leaks through), (2) more methylation per transcript (stoichiometry up; the real biology of interest), (3) higher IP efficiency in batch A (technical; antibody lot, IP day, RNA prep). Differential MeRIP WITHOUT per-window input-normalisation OR an orthogonal stoichiometry-aware method (GLORI Liu 2023 Nat Biotechnol 41:355; SAC-seq Hu 2022 Nat Biotechnol 40:1210; MAZTER-seq Garcia-Campos 2019 Cell 178:731; m6Anet per-read modification rate) cannot separate the three. exomePeak2 differential mode, QNB, RADAR, and MeTDiff all implement per-window IP/input ratio modelling, but each has different default normalisations and the choice matters. Equally critical: McIntyre 2020 Sci Rep 10:6590 showed that "differential" m6A peaks from MeRIP-seq routinely do not replicate between independent studies in nominally identical conditions. The empirical noise floor is high; effect-size filtering (|log2FC| >= 0.5 minimum, often >= 1) AND replicate-direction concordance (the change is consistent in direction across replicates) AND minimum N=3 per condition are needed for differential calls to survive replication. For any absolute stoichiometry claim ("this peak is 80% methylated in tumour vs 20% in normal"), require an orthogonal stoichiometry method, not MeRIP alone.
| Tool / mode | Mechanism | Inputs | Output | Strength | Fails when |
|---|---|---|---|---|---|
| exomePeak2 differential (Liu 2022) | Transcript-windowed Poisson GLM with GC-bias correction; integrated peak + differential | control IP/input BAM vectors + treated IP/input BAM vectors + TxDb + BSgenome | Differential peaks with log2FC + FDR per peak | Most use cases; integrates with peak calling; modern default | Small-N (<=2) overdispersion poorly estimated; top-level API does NOT accept arbitrary covariates (use DESeq2/edgeR for batch-aware designs) |
| QNB (Liu 2017 BMC Bioinformatics 18:387) | Quad-negative-binomial joint model of IP, input, condition | per-peak count matrices (4 matrices: ip1, ip2, input1, input2) | Differential peaks with p-value + log2 RR | Designed for small N (2-3 per group); handles overdispersion explicitly | Requires pre-computed count matrices; not integrated with peak calling |
| MeTDiff (bundled with MeTPeak) | HMM + Beta-binomial differential paired with MeTPeak | paired IP/input BAM + GTF + condition factor | Differential peaks per window | Pairs naturally with MeTPeak output; HMM smoothing helps low-coverage | GitHub-only; less benchmarked than exomePeak2 / QNB |
| RADAR (Zhang 2019 Genome Biol 20:294) | Poisson-NB with TMM normalisation; reproducibility-aware | paired IP/input BAM + condition factor | Differential peaks with logFC + FDR | Explicit replicate-variance modeling; reproducibility-aware framework | GitHub-only; slower than exomePeak2 |
| DRME (Liu 2016 Anal Biochem 499:15) | Count-based small-sample alternative | per-peak count matrices | Differential peaks | Sister to QNB from same group; small-N alternative | Less benchmarked than QNB / exomePeak2 |
| edgeR / DESeq2 on peak counts | Generic RNA-seq differential framework applied to featureCounts-on-peaks | peak count matrix + sample sheet | Differential peaks with log2FC + FDR | Familiar; flexible designs; well-tested in RNA-seq | Treats peak counts as RNA counts; loses IP/input pairing structure; defensible only for paired symmetric designs with strong input normalisation |
| Ratio-of-ratios (heuristic) | Compute per-peak log2 (IP_A / Input_A) - log2 (IP_B / Input_B) per sample, then t-test | per-peak count matrix | per-peak t-test | Transparent; no model assumptions | No multiple-testing correction; ignores overdispersion; not recommended for primary analysis |
| Scenario | Recommended | Why wrong choices fail |
|---|---|---|
| Standard 3-vs-3 paired-design MeRIP differential | exomePeak2 with bam_ip (ctrl) + bam_treated_ip (treat) and matching inputs |
QNB usable but designed for smaller N; edgeR/DESeq2 loses IP/input pairing |
| Very small N (2 vs 2) | QNB (designed for small-sample overdispersion); supplement with exomePeak2 if possible | edgeR / DESeq2 dispersion estimation collapses; exomePeak2 GLM also struggles at N=2 |
| Paired design (patient as blocking factor) | QNB per-pair then aggregate; OR featureCounts-on-peaks -> DESeq2 with ~patient + condition design; exomePeak2 cannot encode patient blocking via its top-level API |
Unpaired analysis inflates within-group variance |
| Interaction design (genotype × treatment) | featureCounts-on-peaks -> DESeq2 / edgeR with interaction term; exomePeak2 top-level API is two-group only | QNB pairwise only; build interaction model from pairwise contrasts manually |
| Batch confounding (antibody lot, sequencing run, IP day) | Include batch as fixed effect in DESeq2 / edgeR model on featureCounts-on-peaks matrix; OR run exomePeak2 per-batch and meta-analyse | Pooling cross-batch counts without batch term attributes lot-effect to condition |
| Time-course differential | featureCounts-on-peaks -> DESeq2 / limma with time as numeric covariate; OR pairwise time-point exomePeak2 contrasts | Naive group-vs-group ignores time structure |
| Stoichiometry claims (not just enrichment) | NOT MeRIP differential -- orthogonal GLORI / SAC-seq / m6Anet per-read | MeRIP IP fold-change is relative; cannot give per-molecule stoichiometry |
| Cross-batch differential (different antibody lots) | featureCounts-on-peaks -> DESeq2 / edgeR with lot in design; OR run exomePeak2 per-lot then meta-analyse; ideally avoid confounding lot with condition at the experimental-design stage |
Lot effect inflates false positives; exomePeak2 top-level API cannot encode lot |
| Validation of differential calls | Run >=2 differential methods; require concordant direction across replicates AND | log2FC |
| Visualising differential peaks | Volcano plot with | log2FC |
| Wanting to test a single gene / locus | Targeted: per-peak boxplot across replicates with condition factor; manual t-test or Wilcoxon at high-coverage peak | Whole-transcriptome differential testing wastes multiple-testing budget for single-locus questions |
Methodology evolves; before any high-stakes differential analysis, web-search "exomePeak2 differential mode Bioconductor 3.20" and "MeRIP differential benchmark McIntyre" for current consensus parameters.
Goal: Identify m6A peaks that differ in methylation level between conditions, controlling for transcript-abundance differences (via input normalisation) and GC bias (via internal correction), with an integrated peak-calling + differential pipeline.
Approach: Build TxDb from the matched GTF; pass control IP/input BAM vectors via bam_ip and bam_input AND treatment IP/input BAM vectors via bam_treated_ip and bam_treated_input — populating the treated arms triggers differential mode (there is no separate mode= argument). Output is per-peak log2FC + FDR.
library(exomePeak2)
library(GenomicFeatures)
library(BSgenome.Hsapiens.UCSC.hg38)
txdb <- makeTxDbFromGFF('refs/annotation.gtf', format='gtf')
ctrl_ip <- c('aligned/ctrl_IP1.bam', 'aligned/ctrl_IP2.bam', 'aligned/ctrl_IP3.bam')
ctrl_input <- c('aligned/ctrl_Input1.bam', 'aligned/ctrl_Input2.bam', 'aligned/ctrl_Input3.bam')
treat_ip <- c('aligned/treat_IP1.bam', 'aligned/treat_IP2.bam', 'aligned/treat_IP3.bam')
treat_input <- c('aligned/treat_Input1.bam', 'aligned/treat_Input2.bam', 'aligned/treat_Input3.bam')
result <- exomePeak2(
bam_ip = ctrl_ip,
bam_input = ctrl_input,
bam_treated_ip = treat_ip,
bam_treated_input = treat_input,
txdb = txdb,
genome = BSgenome.Hsapiens.UCSC.hg38,
paired_end = TRUE,
library_type = 'unstranded',
peak_calling_mode = 'exon',
save_dir = 'exomepeak2_diff_output',
experiment_name = 'ctrl_vs_treat'
)
diff_table <- as.data.frame(result)
nrow(diff_table)
head(diff_table[, c('seqnames', 'start', 'end', 'log2FC', 'pvalue', 'padj')])
peak_calling_mode accepts 'exon' (transcript-aware, default), 'full_transcript', or 'whole_genome'; the meaning is locus scope, NOT differential-vs-non-differential. For arbitrary covariate adjustment (batch, antibody lot, patient blocking), the exomePeak2 top-level API is insufficient — move counts into DESeq2 / edgeR via the featureCounts-on-peaks route below.
Goal: Test differential m6A at pre-called peaks using a quad-negative-binomial model that handles small-N overdispersion better than generic GLM frameworks.
Approach: Count reads in each IP and Input BAM at each peak using featureCounts or summarizeOverlaps; pass the four count matrices (ip1, ip2, input1, input2 — ip/input per group) to qnbtest().
library(QNB)
library(Rsubread)
library(rtracklayer)
peaks <- import('exomepeak2_output/m6a_run1/peaks.bed')
peak_saf <- data.frame(
GeneID = paste0('peak_', seq_along(peaks)),
Chr = as.character(seqnames(peaks)),
Start = start(peaks),
End = end(peaks),
Strand = as.character(strand(peaks))
)
count_matrix <- function(bam_paths, peak_saf) {
fc <- featureCounts(
files = bam_paths,
annot.ext = peak_saf,
isPairedEnd = TRUE,
nthreads = 8,
allowMultiOverlap = TRUE
)
fc$counts
}
ip_ctrl <- count_matrix(c('aligned/ctrl_IP1.bam', 'aligned/ctrl_IP2.bam', 'aligned/ctrl_IP3.bam'), peak_saf)
ip_treat <- count_matrix(c('aligned/treat_IP1.bam', 'aligned/treat_IP2.bam', 'aligned/treat_IP3.bam'), peak_saf)
in_ctrl <- count_matrix(c('aligned/ctrl_Input1.bam', 'aligned/ctrl_Input2.bam', 'aligned/ctrl_Input3.bam'), peak_saf)
in_treat <- count_matrix(c('aligned/treat_Input1.bam', 'aligned/treat_Input2.bam', 'aligned/treat_Input3.bam'), peak_saf)
qnb_result <- qnbtest(
control_ip = ip_ctrl,
treated_ip = ip_treat,
control_input = in_ctrl,
treated_input = in_treat,
mode = 'per-condition'
)
head(qnb_result)
sig <- qnb_result[qnb_result$padj < 0.05 & abs(qnb_result$log2.RR) > 0.5, ]
nrow(sig)
Verify QNB argument names against ?qnbtest for the installed version; older tutorials may show different signatures.
Goal: Test differential m6A using a Poisson-NB framework with TMM normalisation and explicit replicate-variance modeling; useful when replicate variability is a known issue.
Approach: RADAR's documented workflow is countReads -> normalizeLibrary -> adjustExprLevel -> filterBins -> diffIP -> reportResult. CRITICAL: RADAR expects matched BAMs in bamFolder named <sample>.input.bam and <sample>.m6A.bam per replicate; the generic IP / Input naming used elsewhere must be re-conformed or symlinked. variable() is set with a data.frame, NOT a bare factor.
library(RADAR)
radar <- countReads(
samplenames = c('ctrl_rep1', 'ctrl_rep2', 'ctrl_rep3', 'treat_rep1', 'treat_rep2', 'treat_rep3'),
gtf = 'refs/annotation.gtf',
bamFolder = 'aligned_radar/',
modification = 'm6A',
strandToKeep = 'opposite',
threads = 8
)
radar <- normalizeLibrary(radar)
radar <- adjustExprLevel(radar)
variable(radar) <- data.frame(group = c('ctrl', 'ctrl', 'ctrl', 'treat', 'treat', 'treat'))
radar <- filterBins(radar, minCountsCutOff = 15)
radar <- diffIP(radar)
result <- reportResult(radar, cutoff = 0.1, Beta_cutoff = 0.5)
sig <- result[result$padj < 0.05 & abs(result$logFC) > 0.5, ]
nrow(sig)
reportResult thresholds (cutoff = p-value cutoff; Beta_cutoff = effect-size cutoff in beta units) are RADAR-specific; convert to the project's standard reporting thresholds downstream. The aligned_radar/ directory should contain BAMs with RADAR's expected naming (<sample>.input.bam, <sample>.m6A.bam).
Goal: Visualise the differential peak set with effect size on the x-axis and statistical significance on the y-axis; flag peaks passing |log2FC| and FDR thresholds.
Approach: Standard ggplot2 volcano with colour-coded significance and threshold lines.
library(ggplot2)
diff_table$significance <- with(diff_table,
ifelse(padj < 0.05 & abs(log2FC) > 0.5, 'differential', 'not_sig'))
ggplot(diff_table, aes(x=log2FC, y=-log10(padj), colour=significance)) +
geom_point(alpha=0.5, size=0.8) +
geom_vline(xintercept=c(-0.5, 0.5), linetype='dashed') +
geom_hline(yintercept=-log10(0.05), linetype='dashed') +
scale_colour_manual(values=c(differential='red', not_sig='grey60')) +
labs(x='log2 (treat / ctrl) MeRIP enrichment ratio',
y='-log10 (FDR)',
title='Differential m6A peaks: ctrl vs treat',
caption='Per-window IP/input ratio normalised; not absolute stoichiometry') +
theme_minimal()
The caption is intentional: MeRIP differential reports CHANGES IN ENRICHMENT RATIO, NOT changes in absolute stoichiometry. For stoichiometry claims, cross-validate with GLORI / SAC-seq / m6Anet.
Trigger: "Peak X shows hyper-methylation in treatment" inferred from MeRIP IP fold-change alone.
Mechanism: MeRIP IP fold-change conflates per-molecule methylation stoichiometry, transcript abundance, and IP efficiency variation between libraries. An IP fold-change increase can reflect any or all of these.
Symptom: Reported m6A "hyper-methylation" tracks RNA-seq expression changes between conditions; reverse-direction effects when properly normalised against input.
Fix: Use "increased / decreased enrichment" terminology for MeRIP-only studies. Reserve "hyper- / hypo-methylated" for studies with absolute quantification orthogonal validation (GLORI, SAC-seq, MAZTER-seq, m6Anet per-read). For high-stakes claims at named loci, run GLORI on a subset of conditions.
Trigger: Reporting "1,500 differential m6A peaks" with FDR < 0.05 (uncorrected p-value or naive multiple testing) and no effect-size filter.
Mechanism: With sufficient sequencing depth, MeRIP-seq has high statistical power to detect very small (~1.1-1.2x) IP-ratio changes that lie within antibody / technical noise. McIntyre 2020 Sci Rep 10:6590 showed these changes do not replicate.
Symptom: Differential peak set has many peaks with small effect sizes; replication in an independent study recovers <30% of original calls.
Fix: Apply effect-size filter (|log2FC| >= 0.5 minimum, often >= 1) AND adjusted p-value (FDR < 0.05) AND replicate-direction concordance. Differential peaks should be reported with effect size, not just p-value. Report effect-size distribution alongside peak count.
Trigger: Differential m6A study with N=2 IP and N=2 input per condition.
Mechanism: Per McIntyre 2020 and many subsequent benchmarks, MeRIP replicate variance is high; N=2 estimates of dispersion are unreliable; differential calls are unstable.
Symptom: Many "differential" peaks; volcano plot dense; small fraction replicates in held-out replicate.
Fix: Minimum N=3 per condition (per condition per IP/input arm = 12 BAMs for a 2-condition study); N=4-5 preferred for high-stakes claims. Underpowered studies should report effect-size-only filtered subsets (the most extreme peaks) and acknowledge the noise floor explicitly.
Trigger: Control samples processed in batch 1 with antibody lot A; treatment samples processed in batch 2 with antibody lot B.
Mechanism: Anti-m6A antibody lots have batch-to-batch variability in pulldown efficiency and m6A-vs-m6Am cross-reactivity. Pooling cross-batch counts in a differential model attributes batch-effect to condition.
Symptom: "Differential" peaks at high-abundance transcripts; effect sizes track batch rather than condition; reanalysis with batch in the model removes most differential peaks.
Fix: Include antibody_lot / batch / prep_day as a fixed effect in a DESeq2 / edgeR model on featureCounts-on-peaks counts; OR run exomePeak2 separately per lot and meta-analyse the per-lot differential peak sets; OR re-design the experiment to avoid lot-condition confounding. exomePeak2's top-level API does NOT accept arbitrary covariates — DESeq2 / edgeR is the route when covariate handling is required.
mode= argumentTrigger: exomePeak2(..., mode='differential') OR exomePeak2(..., mode='diff_peak') returns "unused argument" error.
Mechanism: exomePeak2 has NO mode= argument. Differential is triggered by populating bam_treated_ip and bam_treated_input alongside the standard bam_ip and bam_input (control arm). The peak_calling_mode argument is unrelated — it accepts 'exon' | 'full_transcript' | 'whole_genome' and controls locus scope, not differential-vs-non-differential.
Fix: Populate the four BAM-vector arguments (bam_ip, bam_input, bam_treated_ip, bam_treated_input); drop any mode= reference; consult ?exomePeak2 for the authoritative signature in the installed version.
Trigger: Compute featureCounts at peaks, build a count matrix, run edgeR / DESeq2 on the matrix as if peaks were genes.
Mechanism: Peak counts reflect both IP enrichment AND transcript abundance. Generic RNA-seq DE on peak counts mixes the two; size-factor normalisation on IP-only counts loses the input-pair information.
Fix: edgeR / DESeq2 on peak counts is defensible ONLY for paired symmetric designs where input is modelled as an offset (per-sample size factor on input counts AND per-sample size factor on IP counts, then differential on the ratio). For most uses, exomePeak2 / QNB / RADAR's purpose-built models are more appropriate.
Trigger: featureCounts(...) invoked without strandSpecific= flag; or with wrong strand setting.
Mechanism: Strand-aware counting matters when the protocol is stranded (most modern MeRIP is unstranded; some are reverse-stranded). Wrong strand counts include antisense reads as if they were sense.
Fix: Verify protocol strandedness from sequencing-core notes or by inspecting featureCounts summary at a few transcripts. Pass strandSpecific=0 (unstranded), 1 (forward), or 2 (reverse) explicitly.
| Pattern | Likely cause | Action |
|---|---|---|
| exomePeak2 calls a differential peak; QNB does not | Different dispersion estimates; QNB more conservative at low coverage | Trust intersection; report concordant set as high-confidence |
| Most "differential" peaks fall at high-abundance housekeeping transcripts | Expression / IP-efficiency confound | Check log2FC vs input log2FC; if correlated, batch effect or expression-driven |
| RADAR vs exomePeak2 disagree on direction | Different normalisation (TMM vs internal) | Inspect normalisation diagnostic; choose method aligned with experimental design |
| Differential peaks anti-correlated with RNA-seq DE | Expression conflated with methylation in the differential model | Re-run with stronger input normalisation; consider per-peak ratio normalisation |
| Single differential peak survives across all methods | High-confidence call | Orthogonally validate (GLORI / SAC-seq) at the named locus |
| Differential calls scatter randomly across genome | Underpowered; technical noise dominates | Increase N; apply stricter effect-size filter; report null result honestly |
| Cross-condition peak overlap < 50% before differential | Conditions are biologically very different; OR antibody lot effect | Inspect cross-replicate concordance; check antibody lot metadata |
| Volcano shows extreme outliers at low-coverage peaks | Per-peak variance dominated by Poisson sampling | Filter peaks by minimum coverage (>=30 reads in IP AND input) before differential |
| Quantity | Threshold | Source / rationale |
|---|---|---|
| Minimum biological replicates per condition | 3 (4-5 preferred) | McIntyre 2020 Sci Rep 10:6590 — N=2 routinely under-powered |
| FDR threshold | 0.05 | Standard convention |
| Effect-size threshold ( | log2FC | ) |
| Minimum coverage per peak window (each IP AND input) | 30 reads | Standard convention; below this, statistical calls noisy |
| Replicate-direction concordance | Same direction in >=2/N replicates | Guardrail against single-replicate artifacts |
| Antibody lot tracking | Mandatory in design matrix when lots differ | Lot confounding is a known false-positive source |
| MeRIP per-window IP/input ratio inflation | log2(IP/input) >= 1 for "enriched"; >=2 for "strongly enriched" | Convention |
| Cross-study peak overlap baseline | ~45% median between labs | McIntyre 2020 Sci Rep 10:6590 — bounds inter-study reproducibility |
| Effect size for biological validation | log2FC | |
| GLORI orthogonal validation threshold | Stoichiometry change >= 10% at named site | Liu C 2023 Nat Biotechnol 41:355 calibration |
| Error / symptom | Cause | Solution |
|---|---|---|
exomePeak2 mode='differential' rejected |
exomePeak2 has no mode= arg; differential is via bam_treated_ip + bam_treated_input |
Populate the four BAM-vector args; consult ?exomePeak2 |
| QNB error "unused argument" | Function signature changed between versions | Pin commit SHA; verify against installed ?qnbtest |
| RADAR install fails | GitHub-only; requires devtools and specific Bioconductor deps | devtools::install_github('scottzijiezhang/RADAR'); check Bioconductor requirements |
| featureCounts returns zero counts | Strand setting wrong; OR peak BED has different chromosome naming than BAM | Verify strandSpecific=; reconcile chromosome names |
| Volcano plot all peaks near origin | Low effect sizes; technical noise dominant | Check input normalisation; increase N; report null result if appropriate |
| FDR-significant but small effect size | Large dataset finds small differences with high power | Apply effect-size filter; report effect-size distribution |
| Many differential peaks track expression changes | Input normalisation insufficient | Re-run with stronger input adjustment; OR use ratio-of-ratios |
edgeR estimateDisp() fails at N=2 |
Small-N dispersion estimation collapses | Use QNB instead; designed for this case |
| MeTDiff install fails | GitHub-only, bundled with MeTPeak | devtools::install_github('compgenomics/MeTPeak') |
| exomePeak2 result has no log2FC / padj columns | bam_treated_ip / bam_treated_input were not populated; only ran peak calling on the control arm |
Pass all four BAM-vector arguments (bam_ip, bam_input, bam_treated_ip, bam_treated_input) to trigger differential output |
| Per-peak boxplot shows huge within-condition variance | High biological noise; OR one replicate is an outlier | Inspect plotCorrelation in merip-preprocessing for outlier; consider exclusion |
| Pushback | Response |
|---|---|
| "How many replicates per condition?" | N=3 minimum; N=4-5 preferred; rationale McIntyre 2020 |
| "What's the effect-size threshold?" | |
| "Was batch / antibody lot controlled for?" | Yes — antibody_lot included as fixed effect in DESeq2 model on featureCounts-on-peaks counts (exomePeak2's top-level API does not accept covariates); per-lot exomePeak2 + meta-analysis when DESeq2 path infeasible; lot-condition confounding assessed |
| "Does the differential signal track expression changes?" | Cross-checked log2FC vs input log2FC per peak; only peaks with strong IP/input ratio shift reported as differential |
| "Was orthogonal validation done?" | Top hits orthogonally validated via GLORI / m6Anet / per-locus assay |
| "Why exomePeak2 over QNB?" | exomePeak2 default for standard 3-vs-3; QNB used for small-N sensitivity analyses; both reported as concordance check |
| "What about absolute stoichiometry?" | MeRIP differential reports relative enrichment changes; absolute stoichiometry requires GLORI / SAC-seq |
| "Was the right normalisation used?" | Per-window IP/input ratio normalisation; exomePeak2 internal; alternative TMM (RADAR) compared |
| "How does this replicate in independent studies?" | Cross-study peak overlap reported; differential subset checked against published m6A-Atlas |
| "Why not edgeR / DESeq2?" | Generic RNA-seq DE on peak counts loses IP/input pairing structure; used only for sensitivity analysis with paired symmetric design |