seurat-de-testing

name	seurat-de-testing
description	Differential expression testing in R/Seurat (v5) — the practical method-choice recipe. Three paths — Path A FindAllMarkers (cluster markers, Wilcoxon), Path B FindMarkers (two-group test, optional MAST/LR with covariates), Path C AggregateExpression + DESeq2 (PSEUDOBULK for multi-sample condition effects). Includes the explicit ban on running bulk DE tools (DESeq2, edgeR, limma, pydeseq2) directly on per-cell scRNA matrices — those are bulk + pseudobulk tools only.
when_to_use	A processed Seurat object with clusters / cell-type labels and (a) the user wants cluster markers (Wilcoxon FindAllMarkers); OR (b) the user wants to compare two cell groups (FindMarkers); OR (c) the user wants to test a CONDITION effect (stim vs ctrl, treated vs untreated, disease vs healthy) across MULTIPLE samples / donors — that's the pseudobulk path, not per-cell DE. Use THIS when the session is R/Seurat. For Python/scanpy use the scanpy DE recipes; for full bulk RNA-seq DESeq2 see deseq2-r.
avoid_when	The session is Python/scanpy (use the scanpy DE recipes), the input is bulk RNA-seq with one column per sample (use `deseq2-r` / `bulk_rnaseq_de`), or you have only one biological sample for a condition effect (no replication ⇒ no robust DE).
invocation	interactive+batch
requires_tools	["run_r"]
capabilities_needed	["Seurat"]
keywords	["Seurat","differential expression","DE","FindMarkers","FindAllMarkers","Wilcoxon","MAST","DESeq2","edgeR","pseudobulk","AggregateExpression","condition effect","stim vs ctrl","latent.vars","presto","marker genes","cluster markers","v5","R","p_val_adj","avg_log2FC","pct.1","pct.2"]
produces	["cluster_markers.csv","de_volcano.png","de_topgenes_dotplot.png","pseudobulk_de.csv"]
domain	genomics
source	Seurat v5 DE vignette - satijalab.org/seurat/articles/de_vignette (Seurat 5.5.0). Pseudobulk methodology grounded in Squair et al. 2021 (Nat Commun, DOI 10.1038/s41467-021-25960-2) and Soneson & Robinson 2018 (Nat Methods). Per-cell test references: Finak et al. 2015 (MAST, Genome Biol). DESeq2 / edgeR vignettes for pseudobulk design.

Differential expression with R/Seurat (v5)

DE in scRNA-seq is a method-choice problem before it is a code problem. This recipe picks one of three paths up front and runs it. Misclassifying the question is the single biggest source of wrong DE results in scRNA — more costly than any parameter choice.

This recipe expects a processed Seurat object with clusters / seurat_annotations already set — i.e. the output of seurat-scrna-v2 or seurat-integration (or the joint-cluster output of seurat-rna-atac-integration). For Path C (condition effect across samples), the object must also carry a sample / donor_id column AND a condition column in meta.data — one row per cell.

The three paths

You want…	Path	Function	When
Markers for every cluster (one vs rest)	A	FindAllMarkers(obj, only.pos = TRUE)	"What marks each cluster?" — the standard tutorial call.
Compare TWO cell groups within one object	B	FindMarkers(obj, ident.1, ident.2)	"Cluster A vs B", "CD8 vs CD4". Wilcoxon (default) or MAST/LR for per-cell covariates.
Condition effect across MULTIPLE samples	C	AggregateExpression(return.seurat=T) → FindMarkers(test.use="DESeq2")	"stim vs ctrl", "responders vs non-responders". PSEUDOBULK is required — never run bulk DE tools directly on per-cell counts.

Hard rule — bulk DE tools on per-cell counts is WRONG. DESeq2, edgeR, limma, and pydeseq2 model bulk count distributions and assume each column is an independent biological replicate. A single cell is NOT a replicate of its sample. Running these tools directly on the per-cell matrix inflates the false-positive rate 10–100× (Squair et al. 2021). Always pseudobulk first for condition effects. For the long form of this argument, citations, and the full anti-pattern catalogue, read references/anti_patterns.md.

Bundled references — load on demand

This recipe carries the load-bearing code for all three paths. For the depth behind any method, load the matching reference file with read_file ONLY when the task needs it — don't pre-load everything:

• references/per_cell_tests.md — every test.use value in FindMarkers (wilcox / wilcox_limma / MAST / LR / ROC / t / bimod / negbinom / poisson), what each is, when it wins, when it fails, source citations.
• references/pseudobulk_workflow.md — AggregateExpression internals, donor/sample/cell-type structure, the limitation of the FindMarkers(test.use="DESeq2") wrapper (only ~group design), how to drop to native DESeq2 / edgeR for multi-factor / paired / interaction designs.
• references/anti_patterns.md — the per-cell-DESeq2 ban with Squair 2021 + Soneson & Robinson 2018 references, the pydeseq2-on-AnnData mistake, the MAST-with-donor-as-latent.vars mistake, why FPR inflation is the failure mode, and the right answer for each.
• references/output_interpretation.md — what every column means (p_val, p_val_adj, avg_log2FC, pct.1, pct.2, myAUC), how Bonferroni adjustment plays out on the scRNA gene-set, biological-coherence sanity checks (lineage markers in their own cluster, IFN signature UP in STIM).
• references/figure_style.md — palette, theme, dpi, alpha-poke, and the divergent-scale convention shared across the Seurat recipes.

Install

Seurat is a CRAN binary (already provisioned). presto is a GitHub package that Seurat v5 picks up automatically when present — install it once for a 10–100× speed-up on the Wilcoxon default with identical p-values.

suppressPackageStartupMessages({
  library(Seurat); library(ggplot2); library(dplyr); library(cowplot)
})

# Speed-up for the Wilcoxon default — Seurat v5 auto-uses presto when installed.
if (!requireNamespace("presto", quietly = TRUE)) {
  if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
  remotes::install_github("immunogenomics/presto")
}

# Bioconductor packages — install ONLY for the path you'll use.
# Path B with MAST covariates:
# if (!requireNamespace("MAST",   quietly = TRUE)) BiocManager::install("MAST")
# Path C pseudobulk DESeq2:
# if (!requireNamespace("DESeq2", quietly = TRUE)) BiocManager::install("DESeq2")
# Path C native edgeR (multi-factor designs):
# if (!requireNamespace("edgeR",  quietly = TRUE)) BiocManager::install("edgeR")

Decisions to surface up front

Surface these in present_plan BEFORE picking a test.

1. The QUESTION — markers (one cluster vs rest), two-group test (cluster A vs cluster B), or condition effect (stim vs ctrl across multiple samples)? Each maps to a different path below. See the "Three paths" table at the top.
2. Sample replication — for a CONDITION effect, how many biological samples do you have per condition? If n_per_condition < 3, you cannot do robust pseudobulk DE — fall back to a description, not a test. Pseudobulk design detail: references/pseudobulk_workflow.md.
3. Covariates — donor, sex, batch, cell-cycle score. For per-cell tests covariates go into latent.vars (MAST or LR test); for pseudobulk they go into the DESeq2 design formula (requires native DESeq2, not the Seurat wrapper — see references/pseudobulk_workflow.md §"Multi-factor designs").
4. Adjusted p-value cutoff — p_val_adj < 0.05 is the Seurat default; reporting any "gene of interest" with p_val_adj > 0.05 is reporting noise. Column semantics: references/output_interpretation.md.
5. Effect-size cutoff — avg_log2FC magnitude. 0.25 is permissive — use ≥ 0.5 for a stricter "biologically meaningful" set.
6. Mode — INTERACTIVE (default) when this is the primary analysis; BATCH when invoked as one of many DE comparisons in a larger plan. The orchestrator passes args="batch"; the agent declares mode in its present_plan.

Show the user these figures / tables as the analysis proceeds:

• cluster_markers.csv — full one-vs-rest markers, all clusters (Path A)
• de_volcano.png — volcano for the highlighted comparison (Path B / C)
• de_topgenes_dotplot.png — top hits per cluster / group (Path A / B)
• pseudobulk_de.csv — condition-effect table from the pseudobulk path (Path C)

For figure conventions (palette, theme, dpi), see references/figure_style.md.

---

Path A — Cluster markers (one vs rest) with FindAllMarkers

The standard "what marks each cluster?" call. Wilcoxon, one cluster vs all others, per cluster.

# obj is your processed Seurat object with Idents() set to the cluster labels
# (set explicitly if it's not already the active identity):
#   Idents(obj) <- "seurat_clusters"      # or "wnn_clusters" / "seurat_annotations"

markers <- FindAllMarkers(
  obj,
  only.pos        = TRUE,    # one-sided: genes UP in each cluster vs rest
  min.pct         = 0.25,    # gene expressed in >=25% of either group
  logfc.threshold = 0.25,    # |avg_log2FC| >= 0.25 prefilter
  test.use        = "wilcox",
  verbose         = FALSE
)
# NOTE: Seurat 5.5.0's default is `logfc.threshold = 0.1, min.pct = 0.01`
# (loosened from earlier versions). We pin the historical PBMC3k-tutorial
# defaults (0.25 / 0.25) above for stability across Seurat patch releases.

# Top 5 markers per cluster by avg_log2FC
top5 <- markers %>%
        filter(p_val_adj < 0.05) %>%
        group_by(cluster) %>%
        slice_max(order_by = avg_log2FC, n = 5) %>%
        ungroup()

write.csv(markers, "cluster_markers.csv", row.names = FALSE)
cat(sprintf("Total significant markers (p_adj<0.05): %d\n",
            sum(markers$p_val_adj < 0.05)))
print(top5 %>% select(cluster, gene, avg_log2FC, pct.1, pct.2, p_val_adj),
      n = Inf)

min.pct and logfc.threshold are SPEED prefilters, not significance tests. They reduce the gene set Seurat tests; the actual significance is p_val_adj. Set them low (e.g. min.pct = 0.1, logfc.threshold = 0.1) if you're looking for SUBTLE differences and don't mind a longer runtime.

Dotplot of top markers

genes_to_show <- unique(top5$gene)

p_dot <- DotPlot(obj, features = genes_to_show, cluster.idents = FALSE) +
  RotatedAxis() +
  scale_colour_gradient2(low = "#2166ac", mid = "grey90", high = "#b2182b",
                         midpoint = 0, name = "avg expr") +
  ggtitle(sprintf("Top 5 markers per cluster (Wilcoxon, only.pos, n=%d genes)",
                  length(genes_to_show))) +
  theme_cowplot() +
  theme(plot.title  = element_text(size = 12, face = "bold"),
        axis.text.x = element_text(angle = 60, hjust = 1, size = 8),
        axis.text.y = element_text(size = 10),
        panel.grid.major = element_line(colour = "grey92", linewidth = 0.3),
        panel.grid.minor = element_blank())

suppressMessages(
  ggsave("de_topgenes_dotplot.png", p_dot,
         width = max(12, 0.18 * length(genes_to_show)),
         height = 6.5, dpi = 120, bg = "white")
)

Report: number of significant markers per cluster, the top 3 markers for each cluster (by avg_log2FC), and any cluster with <5 markers (weakly distinguished — may merge with a neighbor). For column semantics and biological-coherence checks, see references/output_interpretation.md.

---

Path B — Two-group FindMarkers (with optional covariates via MAST)

When the user names a specific comparison ("cluster 3 vs cluster 5", "CD8 T-cells vs CD4 T-cells", "this cluster vs everything except cluster 7").

# Pick the comparison — pass cluster IDs (or annotation labels) as ident.1 / ident.2.
# ident.2 = NULL means "compare ident.1 to ALL OTHER cells".
de <- FindMarkers(
  obj,
  ident.1         = "3",          # or "CD8 T cell"
  ident.2         = "5",          # or NULL for vs rest
  test.use        = "wilcox",
  min.pct         = 0.25,
  logfc.threshold = 0.25,
  verbose         = FALSE
)
de$gene <- rownames(de)
head(de[order(de$p_val_adj), ], 20)

When to switch to MAST (per-cell, with covariates)

If the comparison is confounded by a per-cell covariate — donor effect within a single-sample object, cell-cycle phase, percent.mt — adjust with MAST. MAST is a zero-inflated two-part regression (Finak et al. 2015) that takes latent.vars to condition out per-cell effects.

# Install once: BiocManager::install("MAST")
de_mast <- FindMarkers(
  obj,
  ident.1     = "3",
  ident.2     = "5",
  test.use    = "MAST",
  latent.vars = c("percent.mt", "S.Score", "G2M.Score"),    # the per-cell confounds
  min.pct     = 0.25,
  logfc.threshold = 0.25,
  verbose     = FALSE
)
de_mast$gene <- rownames(de_mast)

latent.vars MUST be column names that exist in [e-mail] AND be numeric (Seurat's MAST wrapper coerces them into the zlm design as-is — categorical covariates need one-hot encoding first). For the full test.use catalogue (wilcox, wilcox_limma, MAST, LR, ROC, t, bimod, negbinom, poisson) with one-line trade-offs per test and source citations, see references/per_cell_tests.md.

MAST is still per-cell. It treats cells as units of analysis. For a CONDITION effect across samples, go to Path C (pseudobulk) — MAST does NOT account for sample-level biological variance, just cell-level covariates. See references/anti_patterns.md §"MAST with donor as latent.vars" for the common confusion.

Volcano plot

de$nlp <- -log10(pmax(de$p_val_adj, .Machine$double.xmin))
de$sig <- with(de, ifelse(p_val_adj < 0.05 & abs(avg_log2FC) >= 0.5,
                          "sig", "ns"))

p_volc <- ggplot(de, aes(x = avg_log2FC, y = nlp, colour = avg_log2FC)) +
  geom_point(alpha = 0.5, size = 0.9) +
  scale_colour_gradient2(low = "#2166ac", mid = "grey90", high = "#b2182b",
                         midpoint = 0, name = "log2FC") +
  geom_hline(yintercept = -log10(0.05), colour = "red",
             linetype = "dashed", linewidth = 0.5) +
  geom_vline(xintercept = c(-0.5, 0.5), colour = "red",
             linetype = "dashed", linewidth = 0.5) +
  labs(title = "DE volcano (FindMarkers, Wilcoxon)",
       x = "avg_log2FC", y = "-log10(p_adj)") +
  theme_cowplot() +
  theme(plot.title = element_text(size = 12, face = "bold"),
        panel.grid.major = element_line(colour = "grey92", linewidth = 0.3),
        panel.grid.minor = element_blank())

ggsave("de_volcano.png", p_volc,
       width = 7, height = 5.5, dpi = 120, bg = "white")

Report: total tested genes, significant up / down, the top 10 by avg_log2FC (filtered by p_val_adj < 0.05), and any covariates passed via latent.vars if MAST was used.

---

Path C — Condition effect across samples (PSEUDOBULK, the right way)

This is the bioinformatically correct path for "stim vs ctrl", "responders vs non-responders", "disease vs healthy". The procedure:

1. Aggregate per-cell counts up to sample × cell-type pseudobulks with AggregateExpression.
2. Test with DESeq2 (or edgeR) on the sample-level pseudobulk matrix — each pseudobulk column IS a biological replicate now.

This is the Seurat v5 vignette pattern (satijalab.org/seurat/articles/de_vignette). Methodological grounding: Squair et al. 2021 (Nat Commun) shows pseudobulk methods match bulk-DE behavior on scRNA data; per-cell DE tools without sample-level aggregation give inflated false-positive rates.

Prerequisites

[e-mail] must carry, per cell:

• a sample / donor identifier (e.g. donor_id),
• a condition label (e.g. stim with levels "STIM" / "CTRL"),
• a cell-type / cluster label (e.g. seurat_annotations).

Each donor × cell-type combination becomes ONE pseudobulk sample. You need at least 3 donors per condition for robust DE — fewer is descriptive at best. If your fixture lacks a real donor_id (e.g. ifnb has only one sample per condition), the Seurat v5 DE vignette mocks it for demonstration: set.seed(42); obj$donor_id <- sample(c('d1','d2','d3'), ncol(obj), replace=TRUE). That exercises the code path but is NOT a biological claim — note it explicitly in your report.

Aggregate to pseudobulk

# AggregateExpression is the canonical Seurat v5 pseudobulk function.
# return.seurat = TRUE wraps the aggregated counts back into a Seurat object
# whose columns are the (sample x celltype) pseudobulks (sum-of-counts by
# default; one column per group.by combination).
pseudo <- AggregateExpression(
  obj,
  assays      = "RNA",
  return.seurat = TRUE,
  group.by    = c("stim", "donor_id", "seurat_annotations")
)
# Joined cell-state ident, e.g. "CD14 Mono_STIM" / "CD14 Mono_CTRL":
pseudo$celltype.stim <- paste(pseudo$seurat_annotations, pseudo$stim, sep = "_")
Idents(pseudo) <- "celltype.stim"

cat(sprintf("Pseudobulk samples: %d (donors x celltypes x conditions)\n",
            ncol(pseudo)))
print(table(pseudo$stim, pseudo$seurat_annotations))

Verify replication. Print table(donor_id, stim) — you need at least 3 donors per condition. If any cell-type has <3 donors in either condition after aggregation, EXCLUDE that cell-type from the test (the table call above shows you which to drop).

Test with DESeq2 on pseudobulk

# Install once: BiocManager::install("DESeq2")
# FindMarkers on the pseudobulk object routes through DESeq2 because each column
# is a biological replicate (donor x condition). NOTE the Seurat wrapper builds
# a `~group` design only — for multi-factor / paired / interaction designs
# (e.g. ~donor + condition) drop to native DESeq2 (see
# references/pseudobulk_workflow.md §"Multi-factor designs").
de_pb <- FindMarkers(
  object   = pseudo,
  ident.1  = "CD14 Mono_STIM",            # cell-type _ condition (one side)
  ident.2  = "CD14 Mono_CTRL",            # cell-type _ condition (other side)
  test.use = "DESeq2"
)
de_pb$gene <- rownames(de_pb)

write.csv(de_pb, "pseudobulk_de.csv", row.names = FALSE)
cat(sprintf("Pseudobulk DESeq2: %d significant (p_adj<0.05), %d up, %d down\n",
            sum(de_pb$p_val_adj < 0.05, na.rm = TRUE),
            sum(de_pb$p_val_adj < 0.05 & de_pb$avg_log2FC >  0, na.rm = TRUE),
            sum(de_pb$p_val_adj < 0.05 & de_pb$avg_log2FC <  0, na.rm = TRUE)))

Loop over cell-types (one DE per cell-type)

For a thorough condition analysis, run pseudobulk DESeq2 per cell-type and collate. Skip cell-types with <3 donors per condition.

ct_list <- unique(pseudo$seurat_annotations)
de_all  <- list()
for (ct in ct_list) {
  ident_a <- paste0(ct, "_STIM");  ident_b <- paste0(ct, "_CTRL")
  cells_a <- WhichCells(pseudo, idents = ident_a)
  cells_b <- WhichCells(pseudo, idents = ident_b)
  if (length(cells_a) < 3 || length(cells_b) < 3) next  # need ≥3 donors / side
  d <- tryCatch(
    FindMarkers(pseudo, ident.1 = ident_a, ident.2 = ident_b, test.use = "DESeq2"),
    error = function(e) NULL
  )
  if (is.null(d)) next
  d$gene <- rownames(d); d$celltype <- ct
  de_all[[ct]] <- d
}
de_all <- do.call(rbind, de_all)
write.csv(de_all, "pseudobulk_de.csv", row.names = FALSE)
cat(sprintf("Pseudobulk DESeq2 (all cell-types): %d total sig (p_adj<0.05)\n",
            sum(de_all$p_val_adj < 0.05, na.rm = TRUE)))

When the ~group design isn't enough

The Seurat FindMarkers(test.use = "DESeq2") wrapper builds the design ~group (two-level factor; ident.1 vs ident.2). For a richer design (~donor + condition, paired designs, interaction terms), drop to native DESeq2 / edgeR — pull the matrix off the pseudobulk object via GetAssayData(pseudo, assay="RNA", layer="counts") and build the DESeqDataSet / DGEList yourself. The pattern is in references/pseudobulk_workflow.md §"Multi-factor designs".

Report

For the pseudobulk path, report:

• the design: # donors per condition, # cell-types tested, total pseudobulk columns
• which cell-types had insufficient replication and were excluded
• per cell-type: # significant genes (p_val_adj < 0.05), top up / down
• whether the DE direction makes biological sense — stim vs ctrl on ifnb should put IFN-stimulated genes (ISG15, IFIT1/2/3, CXCL10, RSAD2, MX1, OAS1) UP in STIM. Biological-coherence sanity-checks are documented in references/output_interpretation.md.

---

Anti-pattern quick reference

For the full Squair-2021-grounded catalogue with citations, see references/anti_patterns.md. The three most common live-session mistakes:

Tempting wrong thing	Why it's wrong	Right answer
DESeqDataSetFromMatrix(per_cell_counts, design = ~condition)	Cells aren't replicates; FPR inflated 10–100× (Squair 2021)	Path C: AggregateExpression → DESeq2
FindMarkers(obj, test.use = "DESeq2") directly on per-cell object for condition effect	Same — DESeq2 needs replicates, not cells	Path C: pseudobulk THEN DESeq2
MAST(latent.vars = "donor") as a condition test across donors	MAST treats donor as per-cell covariate, not replicate; still anti-conservative	Path C

---

Batch variant — use INSTEAD of the path-specific code when invoked with args="batch"

Branch on $ARGUMENTS at the top of the body. In batch mode:

• Skip per-comparison volcano / dotplot figures.
• Drop the "Report" narrative blocks; print ONE final summary line per call.
• Still save the canonical CSV(s) — cluster_markers.csv for Path A, pseudobulk_de.csv for Path C.

Path A (cluster markers) batch — one call per Seurat object:

obj <- readRDS("/path/to/seurat_processed.rds")
markers <- FindAllMarkers(obj, only.pos = TRUE,
                          min.pct = 0.25, logfc.threshold = 0.25,
                          test.use = "wilcox", verbose = FALSE)
write.csv(markers, "cluster_markers.csv", row.names = FALSE)
cat(sprintf("batch ok | %d clusters | %d significant markers (p_adj<0.05)\n",
            length(unique(markers$cluster)),
            sum(markers$p_val_adj < 0.05)))

Path C (pseudobulk DE) batch — one call per (cell-type, comparison):

obj    <- readRDS("/path/to/seurat_processed.rds")
pseudo <- AggregateExpression(obj, assays = "RNA", return.seurat = TRUE,
                              group.by = c("stim", "donor_id",
                                           "seurat_annotations"))
pseudo$celltype.stim <- paste(pseudo$seurat_annotations, pseudo$stim, sep = "_")
Idents(pseudo) <- "celltype.stim"

de_pb <- FindMarkers(pseudo,
                     ident.1 = "CD14 Mono_STIM",
                     ident.2 = "CD14 Mono_CTRL",
                     test.use = "DESeq2")
de_pb$gene <- rownames(de_pb)
write.csv(de_pb, "pseudobulk_de.csv", row.names = FALSE)
cat(sprintf("batch ok | pseudobulk | %d sig (p_adj<0.05)\n",
            sum(de_pb$p_val_adj < 0.05, na.rm = TRUE)))

---

Final response checklist

Summarize:

• which PATH you ran (A: cluster markers / B: two-group / C: pseudobulk condition effect) — and WHY that path matched the question
• input object: cells, clusters, sample / condition columns present
• test used (test.use = ...), any latent.vars, prefilter cutoffs
• effect-size threshold and adjusted p-value cutoff applied
• per-comparison: # genes tested, # significant, top 5 by avg_log2FC
• biological sanity: do the top hits make sense for the labelled groups (e.g. interferon-response genes UP in stim, lineage markers UP in their own cluster) — see references/output_interpretation.md
• saved files: cluster_markers.csv / pseudobulk_de.csv / volcano / dotplot
• caveats:
  • per-cell DE tools (Wilcoxon, MAST) treat cells as units — p-values are anti-conservative for cross-sample comparisons; use pseudobulk for condition effects (Squair 2021)
  • min.pct / logfc.threshold are speed prefilters, not significance
  • covariate adjustment via latent.vars only on MAST / LR / negbinom / poisson — Wilcoxon does not accept covariates
  • DESeq2 is for pseudobulk only, never per-cell
  • the Seurat FindMarkers(test.use="DESeq2") wrapper is ~group design only; richer designs need native DESeq2 / edgeR

See also

• seurat-scrna-v2 — the prerequisite QC + clustering + initial FindAllMarkers recipe. This recipe is the deeper dive on the method-choice step that recipe ends on.
• seurat-integration — multi-sample integration with IntegrateLayers; remember to JoinLayers() before any DE on the integrated object.
• seurat-rna-atac-integration — DE on the joint WNN clusters of a 10x Multiome sample. The DE itself uses this recipe; the clusters come from there.
• deseq2-r / bulk_rnaseq_de — true bulk RNA-seq DE (one column per sample, no aggregation needed).
• seurat-index — the framework menu for picking the right Seurat recipe for your task.