Comparison of Transformations for Single-Cell RNA-Seq Data
Open accessAbstract
The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-seq data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe four transformation approaches based on the delta method, model residuals, inferred latent expression state, and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties. However, in benchmarks using simulated and real-world data, it turns out that a rather simple approach, namely, the logarithm with a pseudo-count followed by principal component analysis, performs as well or better than the more sophisticated alternatives. Software: The R package transformGamPoi implementing the delta method- and residuals-based variance-stabilizing transformations is available via Bioconductor. We provide an interactive website to explore the benchmark results at shiny-portal.embl.de/shinyapps/app/08_single-cell_transformation_benchmark . Contact: constantin.ahlmann@embl.de
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The shifted logarithm transformation with size factor s performs well in a benchmark of 22 transformations.
“The shifted logarithm transformation with size factor s performs well but should not be used with counts per million as an input, as it reflects an unrealistically large overdispersion.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [29]
The analytical approximation of Pearson residuals performed similarly well to the shifted logarithm in the benchmark.
“An approach that was shown to perform similarly well in the aforementioned benchmark29 is the analytical approximation of Pearson residuals”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [29]
Using counts per million as input with the shifted logarithm transformation reflects unrealistically large overdispersion.
“The shifted logarithm transformation with size factor s performs well but should not be used with counts per million as an input, as it reflects an unrealistically large overdispersion.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Self-citationRELATIONS BETWEEN TWO SETS OF VARIATES
Hotelling, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 5,429 · see citing works
Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice
Moore, G. W. K., Howell, S. E. L., Brady, M., Xu, X., & McNeil, K. (2021) · Nature Communications · doi:10.1038/s41467-020-20314-w · Cited by 5,155 · see citing works
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · cited 3× · see citing works
“One of the motivations stated by Hafemeister and Satija (2019) for the Pearson residuals-based variance-stabilizing transformation is that the delta method-based transformations fail to stabilize the variance of lowly expressed genes.”
BackgroundPerformance of neural network basecalling tools for Oxford Nanopore sequencing
Wick, R. R., Judd, L. M., & Holt, K. E. (2019) · Genome Biology · doi:10.1186/s13059-019-1727-y · Cited by 3,221 · see citing works
Opportunities and challenges in long-read sequencing data analysis
Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020) · Genome Biology · doi:10.1186/s13059-020-1935-5 · Cited by 2,530 · see citing works
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · see citing works
Ancient DNA and the rewriting of human history: be sparing with Occam’s razor
Haber, M., Mezzavilla, M., Xue, Y., & Tyler-Smith, C. (2016) · Genome Biology · doi:10.1186/s13059-015-0866-z · Cited by 1,924 · see citing works
Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508 · see citing works
Bartlett, M. S. (1947) · Biometrics · doi:10.2307/3001536 · Cited by 1,253 · see citing works
Dunn, P. K., & Smyth, G. K. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474708 · Cited by 1,112 · see citing works
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
L. Lun, A. T., Bach, K., & Marioni, J. C. (2016) · Genome Biology · doi:10.1186/s13059-016-0947-7 · Cited by 1,108 · see citing works
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Self-citationscMC learns biological variation through the alignment of multiple single-cell genomics datasets
Zhang, L., & Nie, Q. H. (2021) · Genome biology · doi:10.1186/s13059-020-02238-2 · Cited by 848 · see citing works
Validation of noise models for single-cell transcriptomics
Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works
Single-cell RNA counting at allele and isoform resolution using Smart-seq3
Hagemann-Jensen, M., Ziegenhain, C., Chen, P., Ramsköld, D., Hendriks, G.-J., Larsson, A. J. M., Faridani, O. R., & Sandberg, R. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0497-0 · Cited by 700 · see citing works
An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637 · see citing works
Comparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634 · see citing works
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Jiang, R., Sun, T., Song, D., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02601-5 · Cited by 614 · see citing works
Power analysis of single-cell RNA-sequencing experiments
Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568 · see citing works
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515 · see citing works
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019) · Genome Biology · doi:10.1186/s13059-019-1861-6 · Cited by 471 · see citing works
Droplet scRNA-seq is not zero-inflated
Svensson, V. (2019) · doi:10.1101/582064 · Cited by 356 · see citing works
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14457-z · Cited by 331 · see citing works
glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data
Ahlmann-Eltze, C., & Huber, W. K. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1009 · Cited by 330 · see citing works
Self-citationThe triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321 · see citing works
Human alveolar type 2 epithelium transdifferentiates into metaplastic KRT5+ basal cells
Kathiriya, J. J., Wang, C., Zhou, M., Brumwell, A. N., Cassandras, M., Saux, C. J. L., Cohen, M., Alysandratos, K., Wang, B., Wolters, P. J., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00809-4 · Cited by 263 · see citing works
Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq
Bagnoli, J., Ziegenhain, C., Janjic, A., Wange, L. E., Vieth, B., Parekh, S., Geuder, J., Hellmann, I., & Enard, W. (2018) · Nature Communications · doi:10.1038/s41467-018-05347-6 · Cited by 242 · see citing works
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Lause, J., Berens, P., & Kobak, D. (2021) · Genome Biology · doi:10.1186/s13059-021-02451-7 · Cited by 195 · see citing works
Cousminer, D. L., Wagley, Y., Pippin, J. A., Elhakeem, A., Way, G. P., Pahl, M. C., McCormack, S. E., Chesi, A., Mitchell, J. A., Kindler, J. M., et al. (2021) · Genome biology · doi:10.1186/s13059-020-02207-9 · Cited by 144 · see citing works
Germain, P., Sonrel, A., & Robinson, M. D. (2020) · Genome biology · doi:10.1186/s13059-020-02136-7 · Cited by 139 · see citing works
Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells
Cannoodt, R., Saelens, W., Deconinck, L., & Saeys, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-24152-2 · Cited by 124 · see citing works
A comparison of marker gene selection methods for single-cell RNA sequencing data
Pullin, J. M., & McCarthy, D. J. (2022) · doi:10.1101/2022.05.09.490241 · Cited by 122 · see citing works
Sun, T., Song, D., Li, W. V., & Li, J. J. (2021) · Genome biology · doi:10.1186/s13059-021-02367-2 · Cited by 110 · see citing works
Hawkey, J., Paranagama, K., Baker, K. S., Bengtsson, R. J., Weill, F., Thomson, N. R., Baker, S. G., Cerdeira, L., Iqbal, Z., Hunt, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22700-4 · Cited by 106 · see citing works
Bayesian inference of gene expression states from single-cell RNA-seq data
Breda, J., Zavolan, M., & van Nimwegen, E. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00875-x · Cited by 87 · see citing works
NLRP3 Inflammasome Mediates Immune-Stromal Interactions in Vasculitis
Porritt, R. A., Zemmour, D., Abe, M., Lee, Y., Narayanan, M., de Carvalho, T. T., Gomez, A. C., Martinon, D., Santiskulvong, C., Fishbein, M. C., et al. (2021) · Circulation Research · doi:10.1161/circresaha.121.319153 · Cited by 75 · see citing works
Sanchez, K. G., Kim, I., Chun, B., Pucilowska, J., Redmond, W. L., Urba, W. J., Martel, M., Wu, Y., Campbell, M. B., Sun, Z., et al. (2021) · Breast Cancer Research · doi:10.1186/s13058-020-01378-4 · Cited by 72 · see citing works
Extreme inbreeding in a European ancestry sample from the contemporary UK population
Yengo, L., Wray, N. R., & Visscher, P. M. (2019) · Nature Communications · doi:10.1038/s41467-019-11724-6 · Cited by 69 · see citing works
Single cell transcriptome atlas of mouse mammary epithelial cells across development
Pal, B., Chen, Y., Milevskiy, M. J. G., Vaillant, F., Prokopuk, L., Dawson, C. A., Capaldo, B. D., Song, X., Jackling, F. C., Timpson, P., et al. (2021) · Breast Cancer Research · doi:10.1186/s13058-021-01445-4 · Cited by 69 · see citing works
Transcriptional kinetics and molecular functions of long noncoding RNAs
Johnsson, P., Ziegenhain, C., Hartmanis, L., Hendriks, G., Hagemann-Jensen, M., Reinius, B., & Sandberg, R. (2022) · Nature Genetics · doi:10.1038/s41588-022-01014-1 · Cited by 61 · see citing works
Why You Cannot Transform Your Way out of Trouble for Small Counts
Warton, D. I. (2017) · Biometrics · doi:10.1111/biom.12728 · Cited by 57 · see citing works
Nomaru, H., Liu, Y., Bono, C. D., Righelli, D., Cirino, A., Wang, W., Song, H., Racedo, S. E., Dantas, A. G., Zhang, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26966-6 · Cited by 52 · see citing works
ZEB1 promotes pathogenic Th1 and Th17 cell differentiation in multiple sclerosis
Qian, Y., Arellano, G., Ifergan, I., Lin, J., Snowden, C. K., Kim, T., Thomas, J. J., Law, C., Guan, T., Balabanov, R. D., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109602 · Cited by 46 · see citing works
Panebianco, C. J., Dave, A., Charytonowicz, D., Sebra, R., & Iatridis, J. C. (2021) · The FASEB Journal · doi:10.1096/fj.202101149r · Cited by 45 · see citing works
Lu, X., Hosono, Y., Nagae, M., Ishizuka, S., Ishikawa, E., Motooka, D., Ozaki, Y., Sax, N., Maeda, Y., Kato, Y., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20211327 · Cited by 44 · see citing works
Self-organization of human dorsal-ventral forebrain structures by light induced SHH
Santis, R. D., Etoc, F., Rosado-Olivieri, E. A., & Brivanlou, A. H. (2021) · Nature Communications · doi:10.1038/s41467-021-26881-w · Cited by 39 · see citing works
Normalization for sampled count data
Booeshaghi, A. S., Hallgrímsdóttir, I. B., Gálvez-Merchán, Á., & Pachter, L. (2022) · doi:10.1101/2022.05.06.490859 · Cited by 39 · see citing works
Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance
Larsson, A. J. M., Ziegenhain, C., Hagemann-Jensen, M., Reinius, B., Jacob, T., Dalessandri, T., Hendriks, G., Kasper, M., & Sandberg, R. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008772 · Cited by 35 · see citing works
Wang, L. (2021) · Nature Communications · doi:10.1038/s41467-021-26682-1 · Cited by 33 · see citing works
PsiNorm: a scalable normalization for single-cell RNA-seq data
Borella, M., Martello, G., Risso, D., & Romualdi, C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab641 · Cited by 19 · see citing works
MYC-induced human acute myeloid leukemia requires a continuing IL3/GM-CSF co-stimulus.
Bulaeva, E., Pellacani, D., Nakamichi, N., Hammond, C., Beer, P., Lorzadeh, A., Moksa, M., Carles, A., Bilenky, M., Lefort, S., et al. (2020) · Blood · doi:10.1182/blood.2020006374 · Cited by 17 · see citing works
Normalization by distributional resampling of high throughput single-cell RNA-sequencing data
Brown, J., Ni, Z., Mohanty, C., Bacher, R. L., & Kendziorski, C. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab450 · Cited by 17 · see citing works
Agostinis, F., Romualdi, C., Sales, G., & Risso, D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac149 · Cited by 6 · see citing works
const-ae/transformGamPoi-Paper: Preprint v3
C. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7504123 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [4]Townes, F. W. Generalized principal component analysis. Preprint at arXiv10.48550/arXiv.1907.02647 (2019).
- [45]Ahlmann-Eltze, C. Online supplement of comparison of transformations for single-cell RNA-seq data. Zenodo10.5281/zenodo.7504146 (2022). PMC10172138 37037999
Cited by
Sorted by citations
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
“(KNN graph) overlap with the ground truth 29 .”
BackgroundMethodComparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634 · see citing works
The Specious Art of Single-Cell Genomics
Chari, T., & Pachter, L. (2021) · doi:10.1101/2021.08.25.457696 · Cited by 379 · see citing works
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Lause, J., Berens, P., & Kobak, D. (2021) · Genome Biology · doi:10.1186/s13059-021-02451-7 · Cited by 195 · see citing works
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186 · see citing works
Normalization for sampled count data
Booeshaghi, A. S., Hallgrímsdóttir, I. B., Gálvez-Merchán, Á., & Pachter, L. (2022) · doi:10.1101/2022.05.06.490859 · Cited by 39 · see citing works
- Authors
- Ahlmann-Eltze, C., & Huber, W.
- Affiliations
- Heidelberg University (DE)
- European Molecular Biology Laboratory (DE)
- Corresponding author
- Ahlmann-Eltze, Constantin
- Venue
- Nature Methods
- Publisher
- openRxiv
- Published
- 2021-06-25
- Volume
- 20
- Issue
- 5
- Pages
- 665-672
- ISSN
- 1548-7091 (ISSN-L)
- PMC
- PMC10172138
- OpenAlex
- W4363673883
- Cited by
- 148
- References
- 55
- Citations by year
- 2026: 40 · 2025: 37 · 2024: 41 · 2023: 19 · 2022: 8 · 2021: 3
- FWCI
- 12.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European Molecular Biology Laboratory
- Bundesministerium für Bildung und Forschung (031L0263A)
- H2020 European Research Council (810296)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, delta method, model residuals, factor analysis, principal component analysis, benchmarking
- MeSH terms
- Single-Cell Gene Expression Analysis, Software, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
