scrna-normalization-decision

name	scrna-normalization-decision
description	Decision guide for picking a scRNA-seq normalization — LogNormalize (shifted-log), SCTransform v2, analytic Pearson residuals, and where CLR fits (ADT only). Use when the user asks "which normalization should I use" or is choosing between scanpy's `normalize_total`+`log1p`, Seurat's `NormalizeData`, `SCTransform`, or `sc.experimental.pp.normalize_pearson_residuals`.
when_to_use	User is preparing scRNA-seq data for clustering/UMAP/DE/integration and has to commit to a normalization method, or is debugging a downstream artifact (over-mixed clusters, depth-correlated PCs, drop-out of rare cells) that points back to the normalization step. Trigger phrases include "which normalization", "LogNormalize vs SCTransform", "should I use Pearson residuals", "do I need SCTransform".
avoid_when	User wants the end-to-end runnable pipeline (use scrna-qc-clustering-v2 or seurat-scrna-v2); user is on CITE-seq / ADT (use bp-cite-seq — different statistics, CLR/DSB, not the methods here); user has bulk RNA-seq (size-factor / VST / rlog territory, not this doc).
invocation	interactive
kind	knowhow_draft
requires_tools	["WebFetch","Read"]
keywords	["normalization","LogNormalize","SCTransform","SCTransform v2","Pearson residuals","analytic Pearson residuals","sctransform","log1p","normalize_total","scran","size factor","CLR","shifted log","depth normalization","glmGamPoi","NB regression","scRNA-seq"]
domain	genomics
source	Hafemeister & Satija 2019 (SCT v1); Choudhary & Satija 2022 (SCT v2 / Pearson residuals); Lause, Berens & Kobak 2021 (analytic Pearson residuals); Heumos et al. 2023 (Best Practices); Booeshaghi et al. 2022 (depth normalization dominance); Cole et al. 2019 (SCnorm comparison); recipe behavior of scrna-qc-clustering-v2, seurat-scrna-v2, bp-normalization.
audience	both
last_reviewed	null
reviewed_by	null

scRNA-seq normalization — which method for which dataset?

The question this knowhow answers: "Which normalization should I use for my scRNA-seq data — LogNormalize, SCTransform, or Pearson residuals?"

Audience: both naive (first-time biologist on a clustering pipeline) and experienced (deciding whether to leave the default for a particular dataset). The decision is multi-axis (median UMI per cell × cell-type-depth heterogeneity × downstream task × speed budget). The executable recipes this knowhow points at are scrna-qc-clustering-v2 (scanpy + shifted-log), seurat-scrna-v2 (Seurat + LogNormalize, with an SCTransform alternative), and bp-normalization (method-choice stage in the best-practices flow — implements both shifted-log and analytic Pearson residuals; routes scran size factors too).

The most defensible empirical claim in the field is not "method X wins" but: depth normalization is the single largest determinant of downstream geometry; the choice among shifted-log, SCT, and Pearson residuals is a second-order refinement of that (Booeshaghi et al. 2022). Read §2 with that calibration.

Quick decision

For a typical 10x-style droplet dataset (3k–500k cells, median UMI 1k–10k, human/mouse tissue):

Your situation	Use	Recipe
First pass / unsure / fast iteration	Shifted-log (normalize_total + log1p, or Seurat LogNormalize)	scrna-qc-clustering-v2 (scanpy) / seurat-scrna-v2 (R)
Low median UMI (≲2k) and strongly heterogeneous cell-type depth (e.g. tissue with neurons + immune cells)	SCTransform v2	seurat-scrna-v2 (uncomment the SCT alternative)
Feature selection / rare-cell detection is the goal	Analytic Pearson residuals	bp-normalization (path 3)
Downstream is scVI / scANVI / totalVI / generative model	Skip normalization — feed raw counts	scvi-integration, scvi-totalvi-citeseq, scvi-de
Downstream is pseudobulk DE (DESeq2/edgeR/limma)	Skip normalization — aggregate raw counts per sample, let the DE tool model depth	bulk_rnaseq_de, deseq2_r, limma_voom (after pseudobulking)
ADT / CITE-seq surface-protein modality	CLR or DSB (NOT the RNA methods on this row)	bp-cite-seq, scvi-totalvi-citeseq

If you're not in the table: default to shifted-log. It is the workhorse, underdetermined choices rarely hurt clustering at typical depths, and you can swap to SCT or Pearson residuals later without re-running QC.

Strong default rationale. Shifted-log is what scrna-qc-clustering-v2, seurat-scrna-v2, and the Heumos best-practices doc reach for first. It is fast, deterministic, well-understood, and downstream tools (scanpy/Seurat plotting, marker DE on log-normalized data) all expect it. SCT and Pearson residuals are better in known regimes (see §4); they are not blanket upgrades.

REVIEW(scrna-normalization): is "shifted-log is the safe default at typical

depths" still the field consensus in 2025, or has the analytic-Pearson-residuals path (Lause 2021 + Heumos 2023) overtaken it for first-pass clustering on modern droplet data? Cite the most recent benchmark known.

When NOT to normalize (or: when this decision doesn't apply)

• You're feeding a count-model downstream. scVI/scANVI/totalVI, pydeseq2 on pseudobulk, glmGamPoi for marker DE — these consume raw counts and model depth internally. Normalizing first throws away the count statistic and can degrade the model. Keep raw counts in adata.layers["counts"] / the RNA assay's counts slot, and hand those to the count-model tool. (Heumos et al. 2023 — keep-raw-counts is a section heading, not a tip.)
• You're doing pseudobulk DE. Aggregate raw counts per sample × cell-type first; the bulk DE method (DESeq2/edgeR/limma-voom) handles depth. Running scRNA normalization and then pseudobulking double-corrects and can invert the variance structure.
• You only have ADT / surface-protein counts. Use bp-cite-seq. The methods in §4 below were developed against negative-binomial-like RNA count distributions; ADT counts are bimodal (background + specific) and need CLR or DSB. CLR on RNA is mathematically defined but loses information and is not the right tool — do not transplant it.
• You're combining RNA across multiple samples for joint analysis. Normalize per-sample (or jointly inside the integration recipe), then run Harmony / scVI / RPCA — the integration step is what aligns samples, not the normalization. See scrna_pipeline.md for the don't-sc.concat guardrail.

Alternatives matrix

Method	Characterization	When it wins	When it fails	Cost	Recipe
Shifted-log (LogNormalize / normalize_total+log1p)	Divide each cell by its size factor (default: median library size, or fixed 10k for "CP10k"), add 1, take log. The single-cell field's default for ~7 years.	Typical droplet data at moderate-to-high depth (≳3k UMI/cell), homogeneous-enough cell-type depth, downstream is PCA/UMAP/clustering or per-cluster marker DE. Fast, deterministic, every tool consumes log-norm input. (Heumos 2023)	Strongly depth-heterogeneous data (e.g. neurons at 50k UMI mixed with PBMCs at 2k) — the constant pseudo-count of 1 distorts low-count genes more in low-depth cells than high (Lause 2021); residual depth-correlation in PC1 is a common smell.	Seconds even on 1M cells; in-place on .X.	scrna-qc-clustering-v2, seurat-scrna-v2, bp-normalization path 1
SCTransform v2 (regularized NB + glmGamPoi)	Per-gene regularized negative-binomial GLM on raw counts, with sequencing depth as the predictor; returns Pearson residuals as the "normalized" values and an adjusted count matrix. v2 (Choudhary & Satija 2022) re-parameterizes for stability and uses glmGamPoi for speed.	Heterogeneous depth across cell types and low median UMI (≲2k). Choudhary & Satija 2022 specifically report improved clustering separability + better marker stability vs v1 on low-depth + heterogeneous data; their headline benchmark vs LogNormalize is sharpest in that regime.	Atlas-scale data where the per-gene fit cost matters; data where SCT's regularization "smooths away" rare-cell signal you wanted to keep (less common since v2's reparameterization, but documented for v1). The "residual" output is unfamiliar to people expecting log-normalized counts; downstream marker DE has to be done correctly (see anti-patterns).	~minutes on 50k cells with glmGamPoi; substantially slower without. R-only as a turnkey package (Seurat::SCTransform).	seurat-scrna-v2 (the SCTransform alternative block)
Analytic Pearson residuals (sc.experimental.pp.normalize_pearson_residuals)	Closed-form Pearson residuals against a null NB(μ_ij, θ) where μ_ij = (gene_total × cell_total) / grand_total. No GLM fit per gene — analytic. (Lause, Berens & Kobak 2021)	Feature selection (HVG identification) — Lause 2021 shows analytic Pearson residuals select more informative genes than mean-variance-based HVG on shifted-log data; rare-cell-type detection benefits. Heumos 2023 recommends it specifically for HVG selection. Fast.	As the input to downstream, residuals can have negative values and are not log-counts — many tools and plots assume log-normalized expression and behave oddly on residuals. Best used in the HVG/PCA step, with shifted-log retained as the expression layer for plotting and DE.	Seconds even on 1M cells; cheaper than SCT because no per-gene optimization.	bp-normalization path 3 (scanpy sc.experimental.pp.normalize_pearson_residuals)
CLR (centered log ratio)	Compositional normalization: log(x / geometric_mean(x)). Mathematically appropriate for compositional data where only ratios are meaningful.	ADT / surface-protein counts (and similar bimodal bounded counts). Seurat uses CLR as the default ADT normalization.	RNA counts. Do not use on RNA. The compositional assumption is weaker for the RNA modality (you care about absolute expression for marker genes), and the field does not use CLR-on-RNA in practice.	Seconds.	bp-cite-seq (CLR / DSB); scvi-totalvi-citeseq (totalVI handles both modalities)

REVIEW(scrna-normalization): the "SCT v2 wins on low-depth + heterogeneous"

claim is sourced to Choudhary & Satija 2022 — confirm the specific median-UMI threshold and the heterogeneity definition the paper uses; we're paraphrasing as "≲2k median UMI and visibly different per-cell-type depth distributions", which may understate or overstate. Pull the paper's Figure 2 / Table 1.

REVIEW(scrna-normalization): for atlas-scale data (>1M cells, many tissues,

deep heterogeneity), is there a community consensus on normalization as of 2024-2025? Heumos 2023 names trade-offs without a winner; some atlas papers have moved to feeding raw counts into scVI and skipping normalization entirely for the embedding step. Confirm whether that pattern has hardened into consensus.

Anti-patterns and common mistakes

• Using shifted-log output as input to a count model. scVI/scANVI, pydeseq2 (after pseudobulking), glmGamPoi marker DE — all expect raw counts. Passing log-normalized values silently degrades the model. Fix: keep raw counts in adata.layers["counts"] (scanpy) or the counts slot of the RNA assay (Seurat) and pass those.

• Running SCTransform and then NormalizeData again on the same assay, or running both with DefaultAssay set to "SCT" while doing marker DE on the SCT residuals. The seurat-scrna-v2 recipe's SCT block is explicit: for marker DE switch DefaultAssay back to "RNA" (with NormalizeData run once), or use PrepSCTFindMarkers. Doing DE on raw SCT residuals without that step produces uninterpretable fold changes.

• Normalizing per-sample and then sc.concat-ing, calling it "integration". Normalization is not batch correction. Concatenation after per-sample shifted-log still gives you a joint embedding dominated by batch. Use a real integration recipe (harmony-integration-scanpy, scvi-integration, seurat-integration); see scrna_pipeline.md.

• Switching to SCT because the dataset is "big". SCT's advantages are about depth + heterogeneity, not size. On a large but uniformly-deep-and-typed dataset, shifted-log is the right choice; SCT just adds runtime without improving the clustering. The 1M-cell argument for SCT is not in the literature; the 1M-cell argument for scVI (skip normalization, model counts directly) is.

• Pearson residuals as the expression layer for plotting / DE. Residuals contain negative values and don't read like log-expression. Use Pearson residuals for HVG selection and as the PCA input; keep shifted-log values as the layer you display (sc.pl.umap(adata, color="GENE")) and as the input to marker DE. The bp-normalization recipe enforces this by computing both and storing them in named layers.

• Picking SCTransform because "newer is better". Choudhary & Satija 2022 is real evidence, not a recency claim. The evidence is regime-specific (§4 row 2). On a typical PBMC-like dataset at ~5k median UMI, shifted-log and SCT v2 give similar clusters; SCT's cost only pays off when the preconditions hold.

• Forgetting to re-run normalization after filtering. If you filter cells (QC) after calling NormalizeData / normalize_total, the size factors were computed on the pre-filter set. Some downstream tools care; most don't. The Seurat / scanpy recipes both filter first, normalize second, which is the right order.

REVIEW(scrna-normalization): "Pearson residuals are unfamiliar for

plotting" is a UX claim, not a methods claim — is there a benchmark where plotting on residuals vs log-norm leads to different interpretive outcomes, or is it purely an idiom mismatch? If purely idiom, soften the anti-pattern.

Sanity checks — how to know your choice was right

After normalizing, before you commit downstream:

• PC1 vs total counts per cell. Plot total UMI (adata.obs["n_counts"] or nCount_RNA) against PC1 (and PC2). Strong correlation (|r| > ~0.4) is a residual depth artifact — your normalization didn't fully remove sequencing depth. With shifted-log on heterogeneous-depth data this is the canonical warning; switching to SCT v2 or analytic Pearson residuals usually drops the correlation.

• HVG list reasonableness. Look at the top 20–50 highly-variable genes. They should be biology (cell-type markers, immediate-early genes, stress-response, cell-cycle if you didn't regress it) — not dominated by ribosomal RPS/RPL genes or mitochondrial MT-* genes. If RPS/RPL/MT dominate, the normalization isn't pulling out biological variance well; this is often a Pearson-residuals-fixable failure for the HVG step (Lause 2021).

• UMAP coloured by total counts per cell. If cells gradient smoothly by depth across the UMAP rather than clustering by biology, the embedding is still depth-driven. Most often a QC problem (too-wide count distribution let through), but normalization choice contributes.

• Marker genes match canonical biology. Top marker per cluster should be a known marker for the cell type you'd guess from the UMAP geography (or at least a sensible candidate). If all clusters' top markers are ribosomal, you have a normalization + DE-method mismatch.

• Switching method changes < ~10% of cluster assignments. Run shifted-log; then run SCT v2 or Pearson residuals on the same upstream; cross-tab the cluster assignments (pd.crosstab / table(seurat$shifted, seurat$sct)). On a dataset where the preconditions for SCT/Pearson don't hold, the clusters should be ~the same — that's evidence the default was fine. Large reshuffles mean the dataset is in a regime where normalization matters; pick the method whose marker biology you trust more.

REVIEW(scrna-normalization): the |r| > 0.4 threshold for PC1-vs-depth is

a rule-of-thumb I've seen used in practice but I don't have a cite for the specific cutoff. Either find a citation or soften to "any visibly strong correlation".

See also

Method papers (cited above; cite formats simplified):

• Hafemeister & Satija 2019, Genome Biology — Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (SCT v1) (https://doi.org/10.1186/s13059-019-1874-1)
• Choudhary & Satija 2022, Genome Biology — Comparison and evaluation of statistical error models for scRNA-seq (SCT v2 + Pearson residuals discussion) (https://doi.org/10.1186/s13059-021-02584-9)

  REVIEW(citations): confirm this is the SCT v2 paper and not a sibling

  paper from the Satija lab; the title above is paraphrased.
• Lause, Berens & Kobak 2021, Genome Biology — Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data (https://doi.org/10.1186/s13059-021-02451-7)
• Booeshaghi et al. 2022 — Depth normalization for single-cell genomics count data (preprint / paper — the "depth normalization is the most important step" line). # REVIEW(citations): confirm the venue (bioRxiv vs journal) and final DOI.

Best-practices / consensus:

• Heumos et al. 2023, Nat Rev Genet — Best practices for single-cell analysis across modalities — the sc-best-practices.org book (https://doi.org/10.1038/s41576-023-00586-w; https://www.sc-best-practices.org/preprocessing_visualization/normalization.html)
• Cole et al. 2019, Cell Systems — Performance assessment and selection of normalization procedures for single-cell RNA-seq (SCnorm benchmark era; older but still cited) (https://doi.org/10.1016/j.cels.2019.03.010)

Recipes that execute the paths above:

• scrna-qc-clustering-v2 — scanpy single-sample QC + clustering; uses shifted-log (sc.pp.normalize_total + sc.pp.log1p).
• seurat-scrna-v2 — Seurat single-sample; default path uses NormalizeData(method="LogNormalize"); carries an explicit SCTransform alternative block.
• bp-normalization — best-practices normalization stage; implements shifted-log, scran pooling size factors, and analytic Pearson residuals with a downstream-task → method map.
• bp-cite-seq — CITE-seq / ADT normalization (CLR / DSB) — the CLR pointer from §2.
• scvi-integration, scvi-de, scvi-totalvi-citeseq — recipes that consume raw counts directly; do not pre-normalize.

Adjacent knowhow:

• scrna_pipeline.md — upstream framing (one sample vs many; do not sc.concat).
• bulk_rnaseq_de.md — pseudobulk DE path (skip scRNA normalization, aggregate raw counts, let DESeq2 handle depth).

Recipe-coverage gaps (for the expert reviewer):

• # TODO(recipe): seurat-sctransform — the assignment named a standalone seurat-sctransform recipe but the catalogue carries SCT only as the alternative block inside seurat-scrna-v2. Decide whether to split SCT into its own recipe (cleaner pointer) or keep it inlined (current state). Both are defensible; current state is what the matrix points at.
• # TODO(recipe): scanpy-pearson-residuals-clustering — analytic Pearson residuals are accessible only through bp-normalization (a stage, not a full pipeline). A user who wants "scrna-qc-clustering but with Pearson residuals for HVG" has to compose two recipes. A combined recipe would close this gap.